From 7b43f5575399b18c7847efb665a3c1f16d5fcc2c Mon Sep 17 00:00:00 2001
From: Sky <Iflyinskyin2013@gmail.com>
Date: Sat, 6 Dec 2025 19:28:16 +0800
Subject: [PATCH 001/135] ggml : improve error handling for search path
 existence checks (#17653)

* Improve error handling for search path existence checks

Refactor existence checks for search paths using std::error_code to handle potential errors.

* Improve cache file existence check with error code

Update fs::exists to use std::error_code for error handling.

* Simplify existence check for search paths

Simplify existence check for search paths

* Fix logging path in error message for posix_stat

* Update ggml/src/ggml-backend-reg.cpp

Co-authored-by: Aman Gupta <amangupta052@gmail.com>

* Adapt to the coding standard

---------

Co-authored-by: Aman Gupta <amangupta052@gmail.com>
---
 ggml/src/ggml-backend-reg.cpp  | 14 +++++++++++---
 ggml/src/ggml-rpc/ggml-rpc.cpp |  3 ++-
 2 files changed, 13 insertions(+), 4 deletions(-)

diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp
index e96b5c403d..88e6dc45c7 100644
--- a/ggml/src/ggml-backend-reg.cpp
+++ b/ggml/src/ggml-backend-reg.cpp
@@ -534,8 +534,12 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
     fs::path best_path;
 
     for (const auto & search_path : search_paths) {
-        if (!fs::exists(search_path)) {
-            GGML_LOG_DEBUG("%s: search path %s does not exist\n", __func__, path_str(search_path).c_str());
+        if (std::error_code ec; !fs::exists(search_path, ec)) {
+            if (ec) {
+                GGML_LOG_DEBUG("%s: posix_stat(%s) failure, error-message: %s\n", __func__, path_str(search_path).c_str(), ec.message().c_str());
+            } else {
+                GGML_LOG_DEBUG("%s: search path %s does not exist\n", __func__, path_str(search_path).c_str());
+            }
             continue;
         }
         fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied);
@@ -575,8 +579,12 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent,
         for (const auto & search_path : search_paths) {
             fs::path filename = backend_filename_prefix().native() + name_path.native() + backend_filename_extension().native();
             fs::path path = search_path / filename;
-            if (fs::exists(path)) {
+            if (std::error_code ec; fs::exists(path, ec)) {
                 return get_reg().load_backend(path, silent);
+            } else {
+                if (ec) {
+                    GGML_LOG_DEBUG("%s: posix_stat(%s) failure, error-message: %s\n", __func__, path_str(path).c_str(), ec.message().c_str());
+                }
             }
         }
         return nullptr;
diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp
index cf656b6a08..18a45d2d96 100644
--- a/ggml/src/ggml-rpc/ggml-rpc.cpp
+++ b/ggml/src/ggml-rpc/ggml-rpc.cpp
@@ -1257,7 +1257,8 @@ bool rpc_server::get_cached_file(uint64_t hash, std::vector<uint8_t> & data) {
     char hash_str[17];
     snprintf(hash_str, sizeof(hash_str), "%016" PRIx64, hash);
     fs::path cache_file = fs::path(cache_dir) / hash_str;
-    if (!fs::exists(cache_file)) {
+    std::error_code ec;
+    if (!fs::exists(cache_file, ec)) {
         return false;
     }
     std::ifstream ifs(cache_file, std::ios::binary);

From 21f24f27a9bd58e35ba2ba9a3366bfc660b1f095 Mon Sep 17 00:00:00 2001
From: Aleksander Grygier <aleksander.grygier@gmail.com>
Date: Sat, 6 Dec 2025 13:19:05 +0100
Subject: [PATCH 002/135] webui: Per-conversation system message with UI
 displaying, edition & branching (#17275)

* feat: Per-conversation system message with optional display in UI, edition and branching (WIP)

* chore: update webui build output
---
 tools/server/public/index.html.gz             | Bin 1442226 -> 1442523 bytes
 .../app/chat/ChatMessages/ChatMessage.svelte  |  27 ++-
 .../ChatMessages/ChatMessageSystem.svelte     | 216 ++++++++++++++++++
 .../chat/ChatMessages/ChatMessageUser.svelte  |   2 +-
 .../app/chat/ChatMessages/ChatMessages.svelte |   9 +-
 .../app/chat/ChatSettings/ChatSettings.svelte |  12 +-
 .../ChatSettings/ChatSettingsFields.svelte    |  19 +-
 .../webui/src/lib/components/app/index.ts     |   2 +
 .../app/misc/MarkdownContent.svelte           |   8 +-
 .../src/lib/constants/settings-config.ts      |   4 +-
 tools/server/webui/src/lib/services/chat.ts   |  46 +---
 .../server/webui/src/lib/services/database.ts |  43 ++++
 .../webui/src/lib/stores/chat.svelte.ts       |  31 ++-
 tools/server/webui/src/lib/types/chat.d.ts    |   2 +-
 14 files changed, 357 insertions(+), 64 deletions(-)
 create mode 100644 tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageSystem.svelte

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index 4527cb335677f2921182e4a3d5085312b79988e5..38927736683ab580fd9dceb450e6022427d773a7 100644
GIT binary patch
delta 1428061
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diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte
index 2315611361..96ed56a775 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte
@@ -3,6 +3,7 @@
 	import { copyToClipboard, isIMEComposing } from '$lib/utils';
 	import ChatMessageAssistant from './ChatMessageAssistant.svelte';
 	import ChatMessageUser from './ChatMessageUser.svelte';
+	import ChatMessageSystem from './ChatMessageSystem.svelte';
 
 	interface Props {
 		class?: string;
@@ -140,8 +141,7 @@
 	}
 
 	function handleSaveEdit() {
-		if (message.role === 'user') {
-			// For user messages, trim to avoid accidental whitespace
+		if (message.role === 'user' || message.role === 'system') {
 			onEditWithBranching?.(message, editedContent.trim());
 		} else {
 			// For assistant messages, preserve exact content including trailing whitespace
@@ -167,7 +167,28 @@
 	}
 </script>
 
-{#if message.role === 'user'}
+{#if message.role === 'system'}
+	<ChatMessageSystem
+		bind:textareaElement
+		class={className}
+		{deletionInfo}
+		{editedContent}
+		{isEditing}
+		{message}
+		onCancelEdit={handleCancelEdit}
+		onConfirmDelete={handleConfirmDelete}
+		onCopy={handleCopy}
+		onDelete={handleDelete}
+		onEdit={handleEdit}
+		onEditKeydown={handleEditKeydown}
+		onEditedContentChange={handleEditedContentChange}
+		{onNavigateToSibling}
+		onSaveEdit={handleSaveEdit}
+		onShowDeleteDialogChange={handleShowDeleteDialogChange}
+		{showDeleteDialog}
+		{siblingInfo}
+	/>
+{:else if message.role === 'user'}
 	<ChatMessageUser
 		bind:textareaElement
 		class={className}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageSystem.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageSystem.svelte
new file mode 100644
index 0000000000..c203822f60
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageSystem.svelte
@@ -0,0 +1,216 @@
+<script lang="ts">
+	import { Check, X } from '@lucide/svelte';
+	import { Card } from '$lib/components/ui/card';
+	import { Button } from '$lib/components/ui/button';
+	import { MarkdownContent } from '$lib/components/app';
+	import { INPUT_CLASSES } from '$lib/constants/input-classes';
+	import { config } from '$lib/stores/settings.svelte';
+	import ChatMessageActions from './ChatMessageActions.svelte';
+
+	interface Props {
+		class?: string;
+		message: DatabaseMessage;
+		isEditing: boolean;
+		editedContent: string;
+		siblingInfo?: ChatMessageSiblingInfo | null;
+		showDeleteDialog: boolean;
+		deletionInfo: {
+			totalCount: number;
+			userMessages: number;
+			assistantMessages: number;
+			messageTypes: string[];
+		} | null;
+		onCancelEdit: () => void;
+		onSaveEdit: () => void;
+		onEditKeydown: (event: KeyboardEvent) => void;
+		onEditedContentChange: (content: string) => void;
+		onCopy: () => void;
+		onEdit: () => void;
+		onDelete: () => void;
+		onConfirmDelete: () => void;
+		onNavigateToSibling?: (siblingId: string) => void;
+		onShowDeleteDialogChange: (show: boolean) => void;
+		textareaElement?: HTMLTextAreaElement;
+	}
+
+	let {
+		class: className = '',
+		message,
+		isEditing,
+		editedContent,
+		siblingInfo = null,
+		showDeleteDialog,
+		deletionInfo,
+		onCancelEdit,
+		onSaveEdit,
+		onEditKeydown,
+		onEditedContentChange,
+		onCopy,
+		onEdit,
+		onDelete,
+		onConfirmDelete,
+		onNavigateToSibling,
+		onShowDeleteDialogChange,
+		textareaElement = $bindable()
+	}: Props = $props();
+
+	let isMultiline = $state(false);
+	let messageElement: HTMLElement | undefined = $state();
+	let isExpanded = $state(false);
+	let contentHeight = $state(0);
+	const MAX_HEIGHT = 200; // pixels
+	const currentConfig = config();
+
+	let showExpandButton = $derived(contentHeight > MAX_HEIGHT);
+
+	$effect(() => {
+		if (!messageElement || !message.content.trim()) return;
+
+		if (message.content.includes('\n')) {
+			isMultiline = true;
+		}
+
+		const resizeObserver = new ResizeObserver((entries) => {
+			for (const entry of entries) {
+				const element = entry.target as HTMLElement;
+				const estimatedSingleLineHeight = 24;
+
+				isMultiline = element.offsetHeight > estimatedSingleLineHeight * 1.5;
+				contentHeight = element.scrollHeight;
+			}
+		});
+
+		resizeObserver.observe(messageElement);
+
+		return () => {
+			resizeObserver.disconnect();
+		};
+	});
+
+	function toggleExpand() {
+		isExpanded = !isExpanded;
+	}
+</script>
+
+<div
+	aria-label="System message with actions"
+	class="group flex flex-col items-end gap-3 md:gap-2 {className}"
+	role="group"
+>
+	{#if isEditing}
+		<div class="w-full max-w-[80%]">
+			<textarea
+				bind:this={textareaElement}
+				bind:value={editedContent}
+				class="min-h-[60px] w-full resize-none rounded-2xl px-3 py-2 text-sm {INPUT_CLASSES}"
+				onkeydown={onEditKeydown}
+				oninput={(e) => onEditedContentChange(e.currentTarget.value)}
+				placeholder="Edit system message..."
+			></textarea>
+
+			<div class="mt-2 flex justify-end gap-2">
+				<Button class="h-8 px-3" onclick={onCancelEdit} size="sm" variant="outline">
+					<X class="mr-1 h-3 w-3" />
+					Cancel
+				</Button>
+
+				<Button class="h-8 px-3" onclick={onSaveEdit} disabled={!editedContent.trim()} size="sm">
+					<Check class="mr-1 h-3 w-3" />
+					Send
+				</Button>
+			</div>
+		</div>
+	{:else}
+		{#if message.content.trim()}
+			<div class="relative max-w-[80%]">
+				<button
+					class="group/expand w-full text-left {!isExpanded && showExpandButton
+						? 'cursor-pointer'
+						: 'cursor-auto'}"
+					onclick={showExpandButton && !isExpanded ? toggleExpand : undefined}
+					type="button"
+				>
+					<Card
+						class="rounded-[1.125rem] !border-2 !border-dashed !border-border/50 bg-muted px-3.75 py-1.5 data-[multiline]:py-2.5"
+						data-multiline={isMultiline ? '' : undefined}
+						style="border: 2px dashed hsl(var(--border));"
+					>
+						<div
+							class="relative overflow-hidden transition-all duration-300 {isExpanded
+								? 'cursor-text select-text'
+								: 'select-none'}"
+							style={!isExpanded && showExpandButton
+								? `max-height: ${MAX_HEIGHT}px;`
+								: 'max-height: none;'}
+						>
+							{#if currentConfig.renderUserContentAsMarkdown}
+								<div bind:this={messageElement} class="text-md {isExpanded ? 'cursor-text' : ''}">
+									<MarkdownContent class="markdown-system-content" content={message.content} />
+								</div>
+							{:else}
+								<span
+									bind:this={messageElement}
+									class="text-md whitespace-pre-wrap {isExpanded ? 'cursor-text' : ''}"
+								>
+									{message.content}
+								</span>
+							{/if}
+
+							{#if !isExpanded && showExpandButton}
+								<div
+									class="pointer-events-none absolute right-0 bottom-0 left-0 h-48 bg-gradient-to-t from-muted to-transparent"
+								></div>
+								<div
+									class="pointer-events-none absolute right-0 bottom-4 left-0 flex justify-center opacity-0 transition-opacity group-hover/expand:opacity-100"
+								>
+									<Button
+										class="rounded-full px-4 py-1.5 text-xs shadow-md"
+										size="sm"
+										variant="outline"
+									>
+										Show full system message
+									</Button>
+								</div>
+							{/if}
+						</div>
+
+						{#if isExpanded && showExpandButton}
+							<div class="mb-2 flex justify-center">
+								<Button
+									class="rounded-full px-4 py-1.5 text-xs"
+									onclick={(e) => {
+										e.stopPropagation();
+										toggleExpand();
+									}}
+									size="sm"
+									variant="outline"
+								>
+									Collapse System Message
+								</Button>
+							</div>
+						{/if}
+					</Card>
+				</button>
+			</div>
+		{/if}
+
+		{#if message.timestamp}
+			<div class="max-w-[80%]">
+				<ChatMessageActions
+					actionsPosition="right"
+					{deletionInfo}
+					justify="end"
+					{onConfirmDelete}
+					{onCopy}
+					{onDelete}
+					{onEdit}
+					{onNavigateToSibling}
+					{onShowDeleteDialogChange}
+					{siblingInfo}
+					{showDeleteDialog}
+					role="user"
+				/>
+			</div>
+		{/if}
+	{/if}
+</div>
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte
index 8556cbef5b..3d2b8dd35b 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte
@@ -145,7 +145,7 @@
 
 		{#if message.content.trim()}
 			<Card
-				class="max-w-[80%] rounded-[1.125rem] bg-primary px-3.75 py-1.5 text-primary-foreground data-[multiline]:py-2.5"
+				class="max-w-[80%] rounded-[1.125rem] border-none bg-primary px-3.75 py-1.5 text-primary-foreground data-[multiline]:py-2.5"
 				data-multiline={isMultiline ? '' : undefined}
 			>
 				{#if currentConfig.renderUserContentAsMarkdown}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte
index f307f829bc..2e5f57cb61 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte
@@ -2,6 +2,7 @@
 	import { ChatMessage } from '$lib/components/app';
 	import { chatStore } from '$lib/stores/chat.svelte';
 	import { conversationsStore, activeConversation } from '$lib/stores/conversations.svelte';
+	import { config } from '$lib/stores/settings.svelte';
 	import { getMessageSiblings } from '$lib/utils';
 
 	interface Props {
@@ -13,6 +14,7 @@
 	let { class: className, messages = [], onUserAction }: Props = $props();
 
 	let allConversationMessages = $state<DatabaseMessage[]>([]);
+	const currentConfig = config();
 
 	function refreshAllMessages() {
 		const conversation = activeConversation();
@@ -40,7 +42,12 @@
 			return [];
 		}
 
-		return messages.map((message) => {
+		// Filter out system messages if showSystemMessage is false
+		const filteredMessages = currentConfig.showSystemMessage
+			? messages
+			: messages.filter((msg) => msg.type !== 'system');
+
+		return filteredMessages.map((message) => {
 			const siblingInfo = getMessageSiblings(allConversationMessages, message.id);
 
 			return {
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte
index 67df20439c..45640e42a0 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte
@@ -36,12 +36,6 @@
 			title: 'General',
 			icon: Settings,
 			fields: [
-				{ key: 'apiKey', label: 'API Key', type: 'input' },
-				{
-					key: 'systemMessage',
-					label: 'System Message (will be disabled if left empty)',
-					type: 'textarea'
-				},
 				{
 					key: 'theme',
 					label: 'Theme',
@@ -52,6 +46,12 @@
 						{ value: 'dark', label: 'Dark', icon: Moon }
 					]
 				},
+				{ key: 'apiKey', label: 'API Key', type: 'input' },
+				{
+					key: 'systemMessage',
+					label: 'System Message',
+					type: 'textarea'
+				},
 				{
 					key: 'pasteLongTextToFileLen',
 					label: 'Paste long text to file length',
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte
index ef46e2d176..a6f51f47d6 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte
@@ -95,7 +95,7 @@
 			</div>
 			{#if field.help || SETTING_CONFIG_INFO[field.key]}
 				<p class="mt-1 text-xs text-muted-foreground">
-					{field.help || SETTING_CONFIG_INFO[field.key]}
+					{@html field.help || SETTING_CONFIG_INFO[field.key]}
 				</p>
 			{/if}
 		{:else if field.type === 'textarea'}
@@ -112,13 +112,28 @@
 				value={String(localConfig[field.key] ?? '')}
 				onchange={(e) => onConfigChange(field.key, e.currentTarget.value)}
 				placeholder={`Default: ${SETTING_CONFIG_DEFAULT[field.key] ?? 'none'}`}
-				class="min-h-[100px] w-full md:max-w-2xl"
+				class="min-h-[10rem] w-full md:max-w-2xl"
 			/>
+
 			{#if field.help || SETTING_CONFIG_INFO[field.key]}
 				<p class="mt-1 text-xs text-muted-foreground">
 					{field.help || SETTING_CONFIG_INFO[field.key]}
 				</p>
 			{/if}
+
+			{#if field.key === 'systemMessage'}
+				<div class="mt-3 flex items-center gap-2">
+					<Checkbox
+						id="showSystemMessage"
+						checked={Boolean(localConfig.showSystemMessage ?? true)}
+						onCheckedChange={(checked) => onConfigChange('showSystemMessage', Boolean(checked))}
+					/>
+
+					<Label for="showSystemMessage" class="cursor-pointer text-sm font-normal">
+						Show system message in conversations
+					</Label>
+				</div>
+			{/if}
 		{:else if field.type === 'select'}
 			{@const selectedOption = field.options?.find(
 				(opt: { value: string; label: string; icon?: Component }) =>
diff --git a/tools/server/webui/src/lib/components/app/index.ts b/tools/server/webui/src/lib/components/app/index.ts
index cf4d7495e2..87b24598b7 100644
--- a/tools/server/webui/src/lib/components/app/index.ts
+++ b/tools/server/webui/src/lib/components/app/index.ts
@@ -19,8 +19,10 @@ export { default as ChatMessage } from './chat/ChatMessages/ChatMessage.svelte';
 export { default as ChatMessageActions } from './chat/ChatMessages/ChatMessageActions.svelte';
 export { default as ChatMessageBranchingControls } from './chat/ChatMessages/ChatMessageBranchingControls.svelte';
 export { default as ChatMessageStatistics } from './chat/ChatMessages/ChatMessageStatistics.svelte';
+export { default as ChatMessageSystem } from './chat/ChatMessages/ChatMessageSystem.svelte';
 export { default as ChatMessageThinkingBlock } from './chat/ChatMessages/ChatMessageThinkingBlock.svelte';
 export { default as ChatMessages } from './chat/ChatMessages/ChatMessages.svelte';
+export { default as MessageBranchingControls } from './chat/ChatMessages/ChatMessageBranchingControls.svelte';
 
 export { default as ChatScreen } from './chat/ChatScreen/ChatScreen.svelte';
 export { default as ChatScreenHeader } from './chat/ChatScreen/ChatScreenHeader.svelte';
diff --git a/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte b/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte
index 99d6e21e13..9c37bde0d4 100644
--- a/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte
+++ b/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte
@@ -337,19 +337,23 @@
 		line-height: 1.75;
 	}
 
+	div :global(:is(h1, h2, h3, h4, h5, h6):first-child) {
+		margin-top: 0;
+	}
+
 	/* Headers with consistent spacing */
 	div :global(h1) {
 		font-size: 1.875rem;
 		font-weight: 700;
-		margin: 1.5rem 0 0.75rem 0;
 		line-height: 1.2;
+		margin: 1.5rem 0 0.75rem 0;
 	}
 
 	div :global(h2) {
 		font-size: 1.5rem;
 		font-weight: 600;
-		margin: 1.25rem 0 0.5rem 0;
 		line-height: 1.3;
+		margin: 1.25rem 0 0.5rem 0;
 	}
 
 	div :global(h3) {
diff --git a/tools/server/webui/src/lib/constants/settings-config.ts b/tools/server/webui/src/lib/constants/settings-config.ts
index 1fc35b48c4..3764a2856b 100644
--- a/tools/server/webui/src/lib/constants/settings-config.ts
+++ b/tools/server/webui/src/lib/constants/settings-config.ts
@@ -3,6 +3,7 @@ export const SETTING_CONFIG_DEFAULT: Record<string, string | number | boolean> =
 	// Do not use nested objects, keep it single level. Prefix the key if you need to group them.
 	apiKey: '',
 	systemMessage: '',
+	showSystemMessage: true,
 	theme: 'system',
 	showThoughtInProgress: false,
 	showToolCalls: false,
@@ -42,8 +43,9 @@ export const SETTING_CONFIG_DEFAULT: Record<string, string | number | boolean> =
 };
 
 export const SETTING_CONFIG_INFO: Record<string, string> = {
-	apiKey: 'Set the API Key if you are using --api-key option for the server.',
+	apiKey: 'Set the API Key if you are using <code>--api-key</code> option for the server.',
 	systemMessage: 'The starting message that defines how model should behave.',
+	showSystemMessage: 'Display the system message at the top of each conversation.',
 	theme:
 		'Choose the color theme for the interface. You can choose between System (follows your device settings), Light, or Dark.',
 	pasteLongTextToFileLen:
diff --git a/tools/server/webui/src/lib/services/chat.ts b/tools/server/webui/src/lib/services/chat.ts
index a6a6812403..c03b764419 100644
--- a/tools/server/webui/src/lib/services/chat.ts
+++ b/tools/server/webui/src/lib/services/chat.ts
@@ -89,7 +89,6 @@ export class ChatService {
 			custom,
 			timings_per_token,
 			// Config options
-			systemMessage,
 			disableReasoningFormat
 		} = options;
 
@@ -103,6 +102,7 @@ export class ChatService {
 				}
 			})
 			.filter((msg) => {
+				// Filter out empty system messages
 				if (msg.role === 'system') {
 					const content = typeof msg.content === 'string' ? msg.content : '';
 
@@ -112,10 +112,8 @@ export class ChatService {
 				return true;
 			});
 
-		const processedMessages = ChatService.injectSystemMessage(normalizedMessages, systemMessage);
-
 		const requestBody: ApiChatCompletionRequest = {
-			messages: processedMessages.map((msg: ApiChatMessageData) => ({
+			messages: normalizedMessages.map((msg: ApiChatMessageData) => ({
 				role: msg.role,
 				content: msg.content
 			})),
@@ -677,46 +675,6 @@ export class ChatService {
 	// Utilities
 	// ─────────────────────────────────────────────────────────────────────────────
 
-	/**
-	 * Injects a system message at the beginning of the conversation if provided.
-	 * Checks for existing system messages to avoid duplication.
-	 *
-	 * @param messages - Array of chat messages to process
-	 * @param systemMessage - Optional system message to inject
-	 * @returns Array of messages with system message injected at the beginning if provided
-	 * @private
-	 */
-	private static injectSystemMessage(
-		messages: ApiChatMessageData[],
-		systemMessage?: string
-	): ApiChatMessageData[] {
-		const trimmedSystemMessage = systemMessage?.trim();
-
-		if (!trimmedSystemMessage) {
-			return messages;
-		}
-
-		if (messages.length > 0 && messages[0].role === 'system') {
-			if (messages[0].content !== trimmedSystemMessage) {
-				const updatedMessages = [...messages];
-				updatedMessages[0] = {
-					role: 'system',
-					content: trimmedSystemMessage
-				};
-				return updatedMessages;
-			}
-
-			return messages;
-		}
-
-		const systemMsg: ApiChatMessageData = {
-			role: 'system',
-			content: trimmedSystemMessage
-		};
-
-		return [systemMsg, ...messages];
-	}
-
 	/**
 	 * Parses error response and creates appropriate error with context information
 	 * @param response - HTTP response object
diff --git a/tools/server/webui/src/lib/services/database.ts b/tools/server/webui/src/lib/services/database.ts
index 185a598c3b..3b24628cff 100644
--- a/tools/server/webui/src/lib/services/database.ts
+++ b/tools/server/webui/src/lib/services/database.ts
@@ -166,6 +166,49 @@ export class DatabaseService {
 		return rootMessage.id;
 	}
 
+	/**
+	 * Creates a system prompt message for a conversation.
+	 *
+	 * @param convId - Conversation ID
+	 * @param systemPrompt - The system prompt content (must be non-empty)
+	 * @param parentId - Parent message ID (typically the root message)
+	 * @returns The created system message
+	 * @throws Error if systemPrompt is empty
+	 */
+	static async createSystemMessage(
+		convId: string,
+		systemPrompt: string,
+		parentId: string
+	): Promise<DatabaseMessage> {
+		const trimmedPrompt = systemPrompt.trim();
+		if (!trimmedPrompt) {
+			throw new Error('Cannot create system message with empty content');
+		}
+
+		const systemMessage: DatabaseMessage = {
+			id: uuid(),
+			convId,
+			type: 'system',
+			timestamp: Date.now(),
+			role: 'system',
+			content: trimmedPrompt,
+			parent: parentId,
+			thinking: '',
+			children: []
+		};
+
+		await db.messages.add(systemMessage);
+
+		const parentMessage = await db.messages.get(parentId);
+		if (parentMessage) {
+			await db.messages.update(parentId, {
+				children: [...parentMessage.children, systemMessage.id]
+			});
+		}
+
+		return systemMessage;
+	}
+
 	/**
 	 * Deletes a conversation and all its messages.
 	 *
diff --git a/tools/server/webui/src/lib/stores/chat.svelte.ts b/tools/server/webui/src/lib/stores/chat.svelte.ts
index f21e291163..dd6b77e71a 100644
--- a/tools/server/webui/src/lib/stores/chat.svelte.ts
+++ b/tools/server/webui/src/lib/stores/chat.svelte.ts
@@ -624,6 +624,22 @@ class ChatStore {
 		this.clearChatStreaming(currentConv.id);
 
 		try {
+			if (isNewConversation) {
+				const rootId = await DatabaseService.createRootMessage(currentConv.id);
+				const currentConfig = config();
+				const systemPrompt = currentConfig.systemMessage?.toString().trim();
+
+				if (systemPrompt) {
+					const systemMessage = await DatabaseService.createSystemMessage(
+						currentConv.id,
+						systemPrompt,
+						rootId
+					);
+
+					conversationsStore.addMessageToActive(systemMessage);
+				}
+			}
+
 			const userMessage = await this.addMessage('user', content, 'text', '-1', extras);
 			if (!userMessage) throw new Error('Failed to add user message');
 			if (isNewConversation && content)
@@ -999,14 +1015,20 @@ class ChatStore {
 		const activeConv = conversationsStore.activeConversation;
 		if (!activeConv || this.isLoading) return;
 
-		const result = this.getMessageByIdWithRole(messageId, 'user');
+		let result = this.getMessageByIdWithRole(messageId, 'user');
+
+		if (!result) {
+			result = this.getMessageByIdWithRole(messageId, 'system');
+		}
+
 		if (!result) return;
 		const { message: msg } = result;
 
 		try {
 			const allMessages = await conversationsStore.getConversationMessages(activeConv.id);
 			const rootMessage = allMessages.find((m) => m.type === 'root' && m.parent === null);
-			const isFirstUserMessage = rootMessage && msg.parent === rootMessage.id;
+			const isFirstUserMessage =
+				msg.role === 'user' && rootMessage && msg.parent === rootMessage.id;
 
 			const parentId = msg.parent || rootMessage?.id;
 			if (!parentId) return;
@@ -1037,7 +1059,10 @@ class ChatStore {
 				);
 			}
 			await conversationsStore.refreshActiveMessages();
-			await this.generateResponseForMessage(newMessage.id);
+
+			if (msg.role === 'user') {
+				await this.generateResponseForMessage(newMessage.id);
+			}
 		} catch (error) {
 			console.error('Failed to edit message with branching:', error);
 		}
diff --git a/tools/server/webui/src/lib/types/chat.d.ts b/tools/server/webui/src/lib/types/chat.d.ts
index 0eafb80cbf..0e706b72b6 100644
--- a/tools/server/webui/src/lib/types/chat.d.ts
+++ b/tools/server/webui/src/lib/types/chat.d.ts
@@ -1,4 +1,4 @@
-export type ChatMessageType = 'root' | 'text' | 'think';
+export type ChatMessageType = 'root' | 'text' | 'think' | 'system';
 export type ChatRole = 'user' | 'assistant' | 'system';
 
 export interface ChatUploadedFile {

From e31b5c55c3dbbf5c8e47c8dcb17ba92250f82303 Mon Sep 17 00:00:00 2001
From: Aleksander Grygier <aleksander.grygier@gmail.com>
Date: Sat, 6 Dec 2025 13:23:29 +0100
Subject: [PATCH 003/135] webui: Fix context available value in Multi-model
 Router mode (#17804)

* fix: Use context size from `/props?model=...` in ROUTER mode

* chore: update webui build output
---
 tools/server/public/index.html.gz             | Bin 1442523 -> 1441700 bytes
 .../webui/src/lib/stores/chat.svelte.ts       |  21 +++++++++++++++++-
 .../webui/src/lib/stores/models.svelte.ts     |  17 ++++++++++++++
 3 files changed, 37 insertions(+), 1 deletion(-)

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index 38927736683ab580fd9dceb450e6022427d773a7..a9dba62abeb2190ce21f7a99cae86cd4d400eb21 100644
GIT binary patch
delta 1151425
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diff --git a/tools/server/webui/src/lib/stores/chat.svelte.ts b/tools/server/webui/src/lib/stores/chat.svelte.ts
index dd6b77e71a..167f24288f 100644
--- a/tools/server/webui/src/lib/stores/chat.svelte.ts
+++ b/tools/server/webui/src/lib/stores/chat.svelte.ts
@@ -2,7 +2,11 @@ import { DatabaseService, ChatService } from '$lib/services';
 import { conversationsStore } from '$lib/stores/conversations.svelte';
 import { config } from '$lib/stores/settings.svelte';
 import { contextSize, isRouterMode } from '$lib/stores/server.svelte';
-import { selectedModelName, modelsStore } from '$lib/stores/models.svelte';
+import {
+	selectedModelName,
+	modelsStore,
+	selectedModelContextSize
+} from '$lib/stores/models.svelte';
 import {
 	normalizeModelName,
 	filterByLeafNodeId,
@@ -261,6 +265,13 @@ class ChatStore {
 			return activeState.contextTotal;
 		}
 
+		if (isRouterMode()) {
+			const modelContextSize = selectedModelContextSize();
+			if (modelContextSize && modelContextSize > 0) {
+				return modelContextSize;
+			}
+		}
+
 		const propsContextSize = contextSize();
 		if (propsContextSize && propsContextSize > 0) {
 			return propsContextSize;
@@ -458,6 +469,14 @@ class ChatStore {
 		onError?: (error: Error) => void,
 		modelOverride?: string | null
 	): Promise<void> {
+		// Ensure model props are cached before streaming (for correct n_ctx in processing info)
+		if (isRouterMode()) {
+			const modelName = modelOverride || selectedModelName();
+			if (modelName && !modelsStore.getModelProps(modelName)) {
+				await modelsStore.fetchModelProps(modelName);
+			}
+		}
+
 		let streamedContent = '';
 		let streamedReasoningContent = '';
 		let streamedToolCallContent = '';
diff --git a/tools/server/webui/src/lib/stores/models.svelte.ts b/tools/server/webui/src/lib/stores/models.svelte.ts
index 2e834af5a0..29416c2fe5 100644
--- a/tools/server/webui/src/lib/stores/models.svelte.ts
+++ b/tools/server/webui/src/lib/stores/models.svelte.ts
@@ -158,6 +158,22 @@ class ModelsStore {
 		return this.modelPropsCache.get(modelId) ?? null;
 	}
 
+	/**
+	 * Get context size (n_ctx) for a specific model from cached props
+	 */
+	getModelContextSize(modelId: string): number | null {
+		const props = this.modelPropsCache.get(modelId);
+		return props?.default_generation_settings?.n_ctx ?? null;
+	}
+
+	/**
+	 * Get context size for the currently selected model or null if no model is selected
+	 */
+	get selectedModelContextSize(): number | null {
+		if (!this.selectedModelName) return null;
+		return this.getModelContextSize(this.selectedModelName);
+	}
+
 	/**
 	 * Check if props are being fetched for a model
 	 */
@@ -579,3 +595,4 @@ export const loadedModelIds = () => modelsStore.loadedModelIds;
 export const loadingModelIds = () => modelsStore.loadingModelIds;
 export const propsCacheVersion = () => modelsStore.propsCacheVersion;
 export const singleModelName = () => modelsStore.singleModelName;
+export const selectedModelContextSize = () => modelsStore.selectedModelContextSize;

From a28e3c7567ae2f064d663db7019ca2d5da73df55 Mon Sep 17 00:00:00 2001
From: Aleksander Grygier <aleksander.grygier@gmail.com>
Date: Sat, 6 Dec 2025 13:29:15 +0100
Subject: [PATCH 004/135] webui: Stop generation from chat sidebar (#17806)

* feat: Add stop generation button for Conversation Item

* chore: update webui build output
---
 tools/server/public/index.html.gz             | Bin 1441700 -> 1442044 bytes
 .../app/chat/ChatSidebar/ChatSidebar.svelte   |   6 ++
 .../ChatSidebarConversationItem.svelte        |  53 +++++++++++++++++-
 .../webui/src/lib/stores/chat.svelte.ts       |  14 +++--
 4 files changed, 66 insertions(+), 7 deletions(-)

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index a9dba62abeb2190ce21f7a99cae86cd4d400eb21..2db04e9522742e0d7bb36034054bcbb58a958520 100644
GIT binary patch
delta 1393401
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diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte
index a113acbcb7..1d313e284e 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte
@@ -8,6 +8,7 @@
 	import * as AlertDialog from '$lib/components/ui/alert-dialog';
 	import Input from '$lib/components/ui/input/input.svelte';
 	import { conversationsStore, conversations } from '$lib/stores/conversations.svelte';
+	import { chatStore } from '$lib/stores/chat.svelte';
 	import ChatSidebarActions from './ChatSidebarActions.svelte';
 
 	const sidebar = Sidebar.useSidebar();
@@ -98,6 +99,10 @@
 
 		await goto(`#/chat/${id}`);
 	}
+
+	function handleStopGeneration(id: string) {
+		chatStore.stopGenerationForChat(id);
+	}
 </script>
 
 <ScrollArea class="h-[100vh]">
@@ -132,6 +137,7 @@
 							onSelect={selectConversation}
 							onEdit={handleEditConversation}
 							onDelete={handleDeleteConversation}
+							onStop={handleStopGeneration}
 						/>
 					</Sidebar.MenuItem>
 				{/each}
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarConversationItem.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarConversationItem.svelte
index 82685f83a5..bf2fa4f9e9 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarConversationItem.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarConversationItem.svelte
@@ -1,6 +1,7 @@
 <script lang="ts">
-	import { Trash2, Pencil, MoreHorizontal, Download, Loader2 } from '@lucide/svelte';
+	import { Trash2, Pencil, MoreHorizontal, Download, Loader2, Square } from '@lucide/svelte';
 	import { ActionDropdown } from '$lib/components/app';
+	import * as Tooltip from '$lib/components/ui/tooltip';
 	import { getAllLoadingChats } from '$lib/stores/chat.svelte';
 	import { conversationsStore } from '$lib/stores/conversations.svelte';
 	import { onMount } from 'svelte';
@@ -12,6 +13,7 @@
 		onDelete?: (id: string) => void;
 		onEdit?: (id: string) => void;
 		onSelect?: (id: string) => void;
+		onStop?: (id: string) => void;
 	}
 
 	let {
@@ -20,6 +22,7 @@
 		onDelete,
 		onEdit,
 		onSelect,
+		onStop,
 		isActive = false
 	}: Props = $props();
 
@@ -38,8 +41,14 @@
 		onDelete?.(conversation.id);
 	}
 
+	function handleStop(event: Event) {
+		event.stopPropagation();
+		onStop?.(conversation.id);
+	}
+
 	function handleGlobalEditEvent(event: Event) {
 		const customEvent = event as CustomEvent<{ conversationId: string }>;
+
 		if (customEvent.detail.conversationId === conversation.id && isActive) {
 			handleEdit(event);
 		}
@@ -88,8 +97,28 @@
 >
 	<div class="flex min-w-0 flex-1 items-center gap-2">
 		{#if isLoading}
-			<Loader2 class="h-3.5 w-3.5 shrink-0 animate-spin text-muted-foreground" />
+			<Tooltip.Root>
+				<Tooltip.Trigger>
+					<div
+						class="stop-button flex h-4 w-4 shrink-0 cursor-pointer items-center justify-center rounded text-muted-foreground transition-colors hover:text-foreground"
+						onclick={handleStop}
+						onkeydown={(e) => e.key === 'Enter' && handleStop(e)}
+						role="button"
+						tabindex="0"
+						aria-label="Stop generation"
+					>
+						<Loader2 class="loading-icon h-3.5 w-3.5 animate-spin" />
+
+						<Square class="stop-icon hidden h-3 w-3 fill-current text-destructive" />
+					</div>
+				</Tooltip.Trigger>
+
+				<Tooltip.Content>
+					<p>Stop generation</p>
+				</Tooltip.Content>
+			</Tooltip.Root>
 		{/if}
+
 		<!-- svelte-ignore a11y_click_events_have_key_events -->
 		<!-- svelte-ignore a11y_no_static_element_interactions -->
 		<span class="truncate text-sm font-medium" onclick={handleMobileSidebarItemClick}>
@@ -147,5 +176,25 @@
 				opacity: 1 !important;
 			}
 		}
+
+		.stop-button {
+			:global(.stop-icon) {
+				display: none;
+			}
+
+			:global(.loading-icon) {
+				display: block;
+			}
+		}
+
+		&:is(:hover) .stop-button {
+			:global(.stop-icon) {
+				display: block;
+			}
+
+			:global(.loading-icon) {
+				display: none;
+			}
+		}
 	}
 </style>
diff --git a/tools/server/webui/src/lib/stores/chat.svelte.ts b/tools/server/webui/src/lib/stores/chat.svelte.ts
index 167f24288f..4f78840a57 100644
--- a/tools/server/webui/src/lib/stores/chat.svelte.ts
+++ b/tools/server/webui/src/lib/stores/chat.svelte.ts
@@ -701,13 +701,17 @@ class ChatStore {
 
 		if (!activeConv) return;
 
-		await this.savePartialResponseIfNeeded(activeConv.id);
+		await this.stopGenerationForChat(activeConv.id);
+	}
+
+	async stopGenerationForChat(convId: string): Promise<void> {
+		await this.savePartialResponseIfNeeded(convId);
 
 		this.stopStreaming();
-		this.abortRequest(activeConv.id);
-		this.setChatLoading(activeConv.id, false);
-		this.clearChatStreaming(activeConv.id);
-		this.clearProcessingState(activeConv.id);
+		this.abortRequest(convId);
+		this.setChatLoading(convId, false);
+		this.clearChatStreaming(convId);
+		this.clearProcessingState(convId);
 	}
 
 	/**

From f334b79494008e8eac6b41edbb62639226394d82 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Sat, 6 Dec 2025 13:45:36 +0100
Subject: [PATCH 005/135] HIP: fix RDNA3 FP16/BF16 matrix multiplication
 (#17817)

---
 ggml/src/ggml-cuda/mmf.cu | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-cuda/mmf.cu b/ggml/src/ggml-cuda/mmf.cu
index be2ad1c6b6..7cf33f0ddf 100644
--- a/ggml/src/ggml-cuda/mmf.cu
+++ b/ggml/src/ggml-cuda/mmf.cu
@@ -160,9 +160,9 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const
         case GGML_TYPE_F32:
             return ampere_mma_available(cc);
         case GGML_TYPE_F16:
-            return volta_mma_available(cc) || turing_mma_available(cc) || amd_wmma_available(cc);
+            return volta_mma_available(cc) || turing_mma_available(cc) || (amd_wmma_available(cc) && GGML_CUDA_CC_IS_RDNA4(cc));
         case GGML_TYPE_BF16:
-            return ampere_mma_available(cc) || amd_wmma_available(cc);
+            return ampere_mma_available(cc) || (amd_wmma_available(cc) && GGML_CUDA_CC_IS_RDNA4(cc));
         default:
             return false;
     }

From 09c7c50e64c98adac452d090406b6e5f6c320a41 Mon Sep 17 00:00:00 2001
From: Phylliida Dev <phylliida.dev@gmail.com>
Date: Sat, 6 Dec 2025 06:07:02 -0800
Subject: [PATCH 006/135] ggml : add circular tiling support to pad, for
 Vulkan, CUDA, and CPU (used for making seamless textures) (#16985)

* Feat: Added vulkan circular tiling support

* Feat: Added cpu circular

* Feat: Added cuda kernels

* Added tests

* Added tests

* Removed non-pad operations

* Removed unneded changes

* removed backend non pad tests

* Update test-backend-ops.cpp

* Fixed comment on pad test

* removed trailing whitespace

* Removed unneded test in test-backend-ops

* Removed removed test from calls

* Update ggml/src/ggml-vulkan/vulkan-shaders/pad.comp

Co-authored-by: Ruben Ortlam <picard12@live.de>

* Fixed alignment

* Formatting

Co-authored-by: Aman Gupta <amangupta052@gmail.com>

* Format pad

* Format

* Clang format

* format

* format

* don't change so much stuff

* clang format and update to bool

* fix duplicates

* don't need to fix the padding

* make circular bool

* duplicate again

* rename vulkan to wrap around

* Don't need indent

* moved to const expr

* removed unneded extra line break

* More readable method calls

* Minor wording changes

* Added final newline

* Update ggml/include/ggml.h

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Update ggml/include/ggml.h

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Added circular pad ext tests

* Gate non circular pad devices

* Cleaned gating of non-circular pad devices

---------

Co-authored-by: Phylliida <phylliidadev@gmail.com>
Co-authored-by: Ruben Ortlam <picard12@live.de>
Co-authored-by: Aman Gupta <amangupta052@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 ggml/include/ggml.h                          | 22 +++++
 ggml/src/ggml-cann/ggml-cann.cpp             |  2 +
 ggml/src/ggml-cpu/ops.cpp                    | 49 +++++++---
 ggml/src/ggml-cuda/pad.cu                    | 99 +++++++++++++-------
 ggml/src/ggml-metal/ggml-metal-device.m      |  5 +
 ggml/src/ggml-opencl/ggml-opencl.cpp         |  4 +
 ggml/src/ggml-sycl/ggml-sycl.cpp             |  4 +
 ggml/src/ggml-vulkan/ggml-vulkan.cpp         |  2 +
 ggml/src/ggml-vulkan/vulkan-shaders/pad.comp | 25 ++++-
 ggml/src/ggml.c                              | 32 +++++++
 tests/test-backend-ops.cpp                   | 30 ++++--
 11 files changed, 214 insertions(+), 60 deletions(-)

diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index b0e10f5768..6bc762c069 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -2196,6 +2196,15 @@ extern "C" {
             int                  p2,
             int                  p3);
 
+    // pad each dimension with values on the other side of the torus (looping around)
+    GGML_API struct ggml_tensor * ggml_pad_circular(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   p0,
+            int                   p1,
+            int                   p2,
+            int                   p3);
+
     GGML_API struct ggml_tensor * ggml_pad_ext(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -2209,6 +2218,19 @@ extern "C" {
             int                  rp3
             );
 
+    // pad each dimension with values on the other side of the torus (looping around)
+    GGML_API struct ggml_tensor * ggml_pad_ext_circular(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   lp0,
+            int                   rp0,
+            int                   lp1,
+            int                   rp1,
+            int                   lp2,
+            int                   rp2,
+            int                   lp3,
+            int                   rp3);
+
     // pad each dimension with reflection: [a, b, c, d] -> [b, a, b, c, d, c]
     GGML_API struct ggml_tensor * ggml_pad_reflect_1d(
             struct ggml_context * ctx,
diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index 544c1e2a50..23dc0433c1 100644
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -2551,6 +2551,8 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
         case GGML_OP_ACC:
         case GGML_OP_GROUP_NORM:
         case GGML_OP_PAD:
+            // TODO: add circular padding support for cann, see https://github.com/ggml-org/llama.cpp/pull/16985
+            return ggml_get_op_params_i32(op, 8) == 0;
         case GGML_OP_ARANGE:
         case GGML_OP_TIMESTEP_EMBEDDING:
         case GGML_OP_LEAKY_RELU:
diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp
index ac16b3681b..3032783971 100644
--- a/ggml/src/ggml-cpu/ops.cpp
+++ b/ggml/src/ggml-cpu/ops.cpp
@@ -6554,8 +6554,13 @@ static void ggml_call_mul_mat(ggml_type type, const ggml_compute_params * params
     ggml_compute_forward_mul_mat(params, &dst);
 }
 
+static inline int64_t ggml_wrap_around(int64_t coord, int64_t size) {
+    return (coord  + size) % size; // adding size avoids negative number weirdness
+}
+
 // ggml_compute_forward_conv_2d
 
+
 static void ggml_compute_forward_conv_2d_impl(const ggml_compute_params * params,
                                               const ggml_tensor *         kernel,  // [KW, KH, IC, OC]
                                               const ggml_tensor *         src,     // [W, H, C, N]
@@ -7591,6 +7596,7 @@ void ggml_compute_forward_upscale(
 
 // ggml_compute_forward_pad
 
+template<bool circular_t>
 static void ggml_compute_forward_pad_f32(
     const ggml_compute_params * params,
           ggml_tensor * dst) {
@@ -7615,23 +7621,40 @@ static void ggml_compute_forward_pad_f32(
     const int32_t lp3 = ggml_get_op_params_i32(dst, 6);
     const int32_t rp3 = ggml_get_op_params_i32(dst, 7);
 
-
     // TODO: optimize
 
     for (int64_t i2 = 0; i2 < ne2; ++i2) {
         for (int64_t i1 = ith; i1 < ne1; i1 += nth) {
             for (int64_t i0 = 0; i0 < ne0; ++i0) {
                 for (int64_t i3 = 0; i3 < ne3; ++i3) {
-                    const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0;
-                    if ((i0 >= lp0 && i0 < ne0 - rp0) \
-                         && (i1 >= lp1 && i1 < ne1 - rp1) \
-                         && (i2 >= lp2 && i2 < ne2 - rp2) \
-                         && (i3 >= lp3 && i3 < ne3 - rp3)) {
-                        const int64_t src_idx = (i3 - lp3)*nb03 + (i2 - lp2)*nb02 + (i1 - lp1)*nb01 + (i0 - lp0)*nb00;
+                    // circular means wrap around on a torus, so x and y loop around
+                    if constexpr (circular_t) {
+                        const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0;
+                        const int64_t src_i0 = ggml_wrap_around(i0 - lp0, ne00);
+                        const int64_t src_i1 = ggml_wrap_around(i1 - lp1, ne01);
+                        const int64_t src_i2 = ggml_wrap_around(i2 - lp2, ne02);
+                        const int64_t src_i3 = ggml_wrap_around(i3 - lp3, ne03);
+
+                        const int64_t src_idx =
+                            src_i3*nb03 +
+                            src_i2*nb02 +
+                            src_i1*nb01 +
+                            src_i0*nb00;
+
                         const float * src_ptr = (const float *)((char *) src0->data + src_idx);
                         dst_ptr[dst_idx] = *src_ptr;
                     } else {
-                        dst_ptr[dst_idx] = 0;
+                        const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0;
+                        if ((i0 >= lp0 && i0 < ne0 - rp0) \
+                            && (i1 >= lp1 && i1 < ne1 - rp1) \
+                            && (i2 >= lp2 && i2 < ne2 - rp2) \
+                            && (i3 >= lp3 && i3 < ne3 - rp3)) {
+                            const int64_t src_idx = (i3 - lp3)*nb03 + (i2 - lp2)*nb02 + (i1 - lp1)*nb01 + (i0 - lp0)*nb00;
+                            const float * src_ptr = (const float *)((char *) src0->data + src_idx);
+                            dst_ptr[dst_idx] = *src_ptr;
+                        } else {
+                            dst_ptr[dst_idx] = 0;
+                        }
                     }
                 }
             }
@@ -7639,16 +7662,20 @@ static void ggml_compute_forward_pad_f32(
     }
 }
 
+
 void ggml_compute_forward_pad(
     const ggml_compute_params * params,
     ggml_tensor * dst) {
-
     const ggml_tensor * src0 = dst->src[0];
-
+    const bool circular = (bool) ggml_get_op_params_i32(dst, 8);
     switch (src0->type) {
         case GGML_TYPE_F32:
             {
-                ggml_compute_forward_pad_f32(params, dst);
+                if (circular) {
+                    ggml_compute_forward_pad_f32<true>(params, dst);
+                } else {
+                    ggml_compute_forward_pad_f32<false>(params, dst);
+                }
             } break;
         default:
             {
diff --git a/ggml/src/ggml-cuda/pad.cu b/ggml/src/ggml-cuda/pad.cu
index 29aef33c1a..660c192e48 100644
--- a/ggml/src/ggml-cuda/pad.cu
+++ b/ggml/src/ggml-cuda/pad.cu
@@ -1,9 +1,17 @@
 #include "pad.cuh"
 
+#include <stdint.h>
+
+__device__ __forceinline__ int64_t wrap_around(int64_t coord, int64_t size) {
+    // + size ensures negatives are handled properly
+    return (coord + size) % size;
+}
+
 static __global__ void pad_f32(const float * src, float * dst,
                                const int lp0, const int rp0, const int lp1, const int rp1,
                                const int lp2, const int rp2, const int lp3, const int rp3,
-                               const int ne0, const int ne1, const int ne2, const int ne3) {
+                               const int ne0, const int ne1, const int ne2, const int ne3,
+                               const bool circular) {
     // blockIdx.z: i3*ne2+i2
     // blockIdx.y: i1
     // blockIDx.x: i0 / CUDA_PAD_BLOCK_SIZE
@@ -12,61 +20,84 @@ static __global__ void pad_f32(const float * src, float * dst,
     int i1 = blockIdx.y;
     int i2 = blockIdx.z % ne2;
     int i3 = blockIdx.z / ne2;
+
     if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) {
         return;
     }
 
-    // operation
-    const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0;
-    if ((i0 >= lp0 && i0 < ne0 - rp0) &&
-        (i1 >= lp1 && i1 < ne1 - rp1) &&
-        (i2 >= lp2 && i2 < ne2 - rp2) &&
-        (i3 >= lp3 && i3 < ne3 - rp3)) {
-        const int64_t i00 = i0 - lp0;
-        const int64_t i01 = i1 - lp1;
-        const int64_t i02 = i2 - lp2;
-        const int64_t i03 = i3 - lp3;
-        const int64_t ne02 = ne2 - lp2 - rp2;
-        const int64_t ne01 = ne1 - lp1 - rp1;
-        const int64_t ne00 = ne0 - lp0 - rp0;
+    const int64_t dst_idx = i3 * (ne0 * ne1 * ne2) + i2 * (ne0 * ne1) + i1 * ne0 + i0;
 
-        const int64_t src_idx = i03*(ne00*ne01*ne02) + i02*(ne00*ne01) + i01*ne00 + i00;
+    if (!circular) {
+        if ((i0 >= lp0 && i0 < ne0 - rp0) && (i1 >= lp1 && i1 < ne1 - rp1) && (i2 >= lp2 && i2 < ne2 - rp2) &&
+            (i3 >= lp3 && i3 < ne3 - rp3)) {
+            const int64_t i00  = i0 - lp0;
+            const int64_t i01  = i1 - lp1;
+            const int64_t i02  = i2 - lp2;
+            const int64_t i03  = i3 - lp3;
+            const int64_t ne02 = ne2 - lp2 - rp2;
+            const int64_t ne01 = ne1 - lp1 - rp1;
+            const int64_t ne00 = ne0 - lp0 - rp0;
+
+            const int64_t src_idx = i03 * (ne00 * ne01 * ne02) + i02 * (ne00 * ne01) + i01 * ne00 + i00;
+
+            dst[dst_idx] = src[src_idx];
+        } else {
+            dst[dst_idx] = 0.0f;
+        }
+    }
+    // circular means on a torus, so x and y wrap around
+    else {
+        const int64_t ne00 = ne0 - lp0 - rp0;
+        const int64_t ne01 = ne1 - lp1 - rp1;
+        const int64_t ne02 = ne2 - lp2 - rp2;
+        const int64_t ne03 = ne3 - lp3 - rp3;
+
+        const int64_t i00 = wrap_around(i0 - lp0, ne00);
+        const int64_t i01 = wrap_around(i1 - lp1, ne01);
+        const int64_t i02 = wrap_around(i2 - lp2, ne02);
+        const int64_t i03 = wrap_around(i3 - lp3, ne03);
+
+        const int64_t src_idx = i03 * (ne00 * ne01 * ne02) + i02 * (ne00 * ne01) + i01 * ne00 + i00;
 
         dst[dst_idx] = src[src_idx];
-    } else {
-        dst[dst_idx] = 0.0f;
     }
 }
 
+
 static void pad_f32_cuda(const float * src, float * dst,
     const int lp0, const int rp0, const int lp1, const int rp1,
     const int lp2, const int rp2, const int lp3, const int rp3,
-    const int ne0, const int ne1, const int ne2, const int ne3, cudaStream_t stream) {
-    int num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE;
-    dim3 gridDim(num_blocks, ne1, ne2*ne3);
-    pad_f32<<<gridDim, CUDA_PAD_BLOCK_SIZE, 0, stream>>>(src, dst, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, ne0, ne1, ne2, ne3);
+    const int ne0, const int ne1, const int ne2, const int ne3,
+    const bool circular, cudaStream_t stream) {
+    int  num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE;
+    dim3 gridDim(num_blocks, ne1, ne2 * ne3);
+    pad_f32<<<gridDim, CUDA_PAD_BLOCK_SIZE, 0, stream>>>(src, dst,
+                                                         lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3,
+                                                         ne0, ne1, ne2, ne3, circular);
 }
 
 void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * src0 = dst->src[0];
-    const float * src0_d = (const float *)src0->data;
-    float * dst_d = (float *)dst->data;
-    cudaStream_t stream = ctx.stream();
+    const ggml_tensor * src0   = dst->src[0];
+    const float *       src0_d = (const float *) src0->data;
+    float *             dst_d  = (float *) dst->data;
+    cudaStream_t        stream = ctx.stream();
 
     GGML_ASSERT(src0->type == GGML_TYPE_F32);
     GGML_ASSERT(dst->type == GGML_TYPE_F32);
     GGML_ASSERT(ggml_is_contiguous(src0));
 
-    const int32_t lp0 = ((const int32_t*)(dst->op_params))[0];
-    const int32_t rp0 = ((const int32_t*)(dst->op_params))[1];
-    const int32_t lp1 = ((const int32_t*)(dst->op_params))[2];
-    const int32_t rp1 = ((const int32_t*)(dst->op_params))[3];
-    const int32_t lp2 = ((const int32_t*)(dst->op_params))[4];
-    const int32_t rp2 = ((const int32_t*)(dst->op_params))[5];
-    const int32_t lp3 = ((const int32_t*)(dst->op_params))[6];
-    const int32_t rp3 = ((const int32_t*)(dst->op_params))[7];
+    const int32_t lp0      = ((const int32_t *) (dst->op_params))[0];
+    const int32_t rp0      = ((const int32_t *) (dst->op_params))[1];
+    const int32_t lp1      = ((const int32_t *) (dst->op_params))[2];
+    const int32_t rp1      = ((const int32_t *) (dst->op_params))[3];
+    const int32_t lp2      = ((const int32_t *) (dst->op_params))[4];
+    const int32_t rp2      = ((const int32_t *) (dst->op_params))[5];
+    const int32_t lp3      = ((const int32_t *) (dst->op_params))[6];
+    const int32_t rp3      = ((const int32_t *) (dst->op_params))[7];
+    const int32_t circular = ((const int32_t *) (dst->op_params))[8];
 
     pad_f32_cuda(src0_d, dst_d,
                  lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3,
-                 dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], stream);
+                 dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3],
+                 (bool) circular, stream);
 }
diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m
index 64fd9bdbf6..7b7d1c1233 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.m
+++ b/ggml/src/ggml-metal/ggml-metal-device.m
@@ -1037,6 +1037,11 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
         case GGML_OP_POOL_2D:
             return op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_PAD:
+            // TODO: add circular padding support for metal, see https://github.com/ggml-org/llama.cpp/pull/16985
+            if (ggml_get_op_params_i32(op, 8) != 0) {
+                return false;
+            }
+
             return (ggml_get_op_params_i32(op, 0) == 0) && (ggml_get_op_params_i32(op, 2) == 0) &&
                    (ggml_get_op_params_i32(op, 4) == 0) && (ggml_get_op_params_i32(op, 6) == 0);
         case GGML_OP_PAD_REFLECT_1D:
diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp
index 277a30d30e..0d37587f60 100644
--- a/ggml/src/ggml-opencl/ggml-opencl.cpp
+++ b/ggml/src/ggml-opencl/ggml-opencl.cpp
@@ -3083,6 +3083,10 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
         case GGML_OP_REPEAT:
             return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32; // Assuming F32 for now, can be expanded
         case GGML_OP_PAD:
+            // TODO: add circular padding support for opencl, see https://github.com/ggml-org/llama.cpp/pull/16985
+            if (ggml_get_op_params_i32(op, 8) != 0) {
+                return false;
+            }
             return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
         case GGML_OP_UPSCALE: {
             ggml_scale_mode mode = (ggml_scale_mode)(ggml_get_op_params_i32(op, 0) & 0xFF);
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index a264ade0b7..7449a91609 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -4613,6 +4613,10 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_ACC:
             return true;
         case GGML_OP_PAD:
+            // TODO: add circular padding support for syscl, see https://github.com/ggml-org/llama.cpp/pull/16985
+            if (ggml_get_op_params_i32(op, 8) != 0) {
+                return false;
+            }
             return ggml_is_contiguous(op->src[0]);
         case GGML_OP_LEAKY_RELU:
         case GGML_OP_TIMESTEP_EMBEDDING:
diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index d7c35ed101..2787f63d0e 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -1050,6 +1050,7 @@ struct vk_op_pad_push_constants {
     uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03;
     uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13;
     uint32_t misalign_offsets;
+    uint32_t circular;
 
     uint32_t lp0; uint32_t rp0;
     uint32_t lp1; uint32_t rp1;
@@ -1092,6 +1093,7 @@ static vk_op_pad_push_constants vk_op_pad_push_constants_init(const ggml_tensor
     p.rp2 = dst->op_params[5];
     p.lp3 = dst->op_params[6];
     p.rp3 = dst->op_params[7];
+    p.circular = dst->op_params[8];
 
     return p; // fastdiv values and offsets are initialized later in ggml_vk_op
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp b/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp
index f3c8176872..5abd2f6fc6 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp
@@ -8,6 +8,7 @@ layout (push_constant) uniform parameter
     uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03;
     uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13;
     uint misalign_offsets;
+    uint circular;
 
     uint lp0; uint rp0;
     uint lp1; uint rp1;
@@ -18,6 +19,10 @@ layout (push_constant) uniform parameter
 uint get_aoffset() { return p.misalign_offsets >> 16; }
 uint get_doffset() { return p.misalign_offsets & 0xFFFF; }
 
+uint wrap_around(int coord, uint size) {
+    return (uint(coord + int(size))) % size; // add size to avoid issues with negative
+}
+
 layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
 layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
 
@@ -40,10 +45,20 @@ void main() {
     const uint src0_idx = (i3 - p.lp3)*p.nb03 + (i2 - p.lp2)*p.nb02 + (i1 - p.lp1)*p.nb01 + (i0 - p.lp0)*p.nb00;
     const uint dst_idx = i3*p.nb13 + i2*p.nb12 + i1*p.nb11 + i0*p.nb10;
 
-    const bool is_src0 = i0 >= p.lp0 && i0 < p.ne10 - p.rp0 &&
-                         i1 >= p.lp1 && i1 < p.ne11 - p.rp1 &&
-                         i2 >= p.lp2 && i2 < p.ne12 - p.rp2 &&
-                         i3 >= p.lp3 && i3 < p.ne13 - p.rp3;
+    if (p.circular != 0u) {
+        const uint ci0 = wrap_around(int(i0) - int(p.lp0), p.ne00);
+        const uint ci1 = wrap_around(int(i1) - int(p.lp1), p.ne01);
+        const uint ci2 = wrap_around(int(i2) - int(p.lp2), p.ne02);
+        const uint ci3 = wrap_around(int(i3) - int(p.lp3), p.ne03);
+        const uint circular_src_idx = ci3*p.nb03 + ci2*p.nb02 + ci1*p.nb01 + ci0*p.nb00;
+        data_d[get_doffset() + dst_idx] = D_TYPE(data_a[get_aoffset() + circular_src_idx]);
+    } else {
+        const bool is_src0 = i0 >= p.lp0 && i0 < p.ne10 - p.rp0 &&
+                             i1 >= p.lp1 && i1 < p.ne11 - p.rp1 &&
+                             i2 >= p.lp2 && i2 < p.ne12 - p.rp2 &&
+                             i3 >= p.lp3 && i3 < p.ne13 - p.rp3;
+        data_d[get_doffset() + dst_idx] = D_TYPE(is_src0 ? data_a[get_aoffset() + src0_idx] : 0.0f);
+    }
+
 
-    data_d[get_doffset() + dst_idx] = D_TYPE(is_src0 ? data_a[get_aoffset() + src0_idx] : 0.0f);
 }
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 17cf4d84bb..9f5cdc1398 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -4947,6 +4947,18 @@ struct ggml_tensor * ggml_pad(
     return ggml_pad_ext(ctx, a, 0, p0, 0, p1, 0, p2, 0, p3);
 }
 
+// ggml_pad_circular
+
+struct ggml_tensor * ggml_pad_circular(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                   p0,
+        int                   p1,
+        int                   p2,
+        int                   p3) {
+    return ggml_pad_ext_circular(ctx, a, 0, p0, 0, p1, 0, p2, 0, p3);
+}
+
 struct ggml_tensor * ggml_pad_ext(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -4973,6 +4985,7 @@ struct ggml_tensor * ggml_pad_ext(
     ggml_set_op_params_i32(result, 5, rp2);
     ggml_set_op_params_i32(result, 6, lp3);
     ggml_set_op_params_i32(result, 7, rp3);
+    ggml_set_op_params_i32(result, 8, 0); // not circular by default
 
 
     result->op     = GGML_OP_PAD;
@@ -4981,6 +4994,25 @@ struct ggml_tensor * ggml_pad_ext(
     return result;
 }
 
+// ggml_pad_ext_circular
+
+struct ggml_tensor * ggml_pad_ext_circular(
+        struct ggml_context * ctx,
+        struct ggml_tensor  * a,
+        int                  lp0,
+        int                  rp0,
+        int                  lp1,
+        int                  rp1,
+        int                  lp2,
+        int                  rp2,
+        int                  lp3,
+        int                  rp3
+        ) {
+    struct ggml_tensor * result = ggml_pad_ext(ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3);
+    ggml_set_op_params_i32(result, 8, 1); // circular
+    return result;
+}
+
 // ggml_pad_reflect_1d
 
 struct ggml_tensor * ggml_pad_reflect_1d(
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 316b589769..2e94a53da2 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -5604,21 +5604,24 @@ struct test_pad : public test_case {
     const std::array<int64_t, 4> ne_a;
     const int pad_0;
     const int pad_1;
+    const bool circular;
 
     std::string vars() override {
-        return VARS_TO_STR4(type, ne_a, pad_0, pad_1);
+        return VARS_TO_STR5(type, ne_a, pad_0, pad_1, circular);
     }
 
     test_pad(ggml_type type = GGML_TYPE_F32,
             std::array<int64_t, 4> ne_a = {512, 512, 1, 1},
-            int pad_0 = 1, int pad_1 = 1)
-        : type(type), ne_a(ne_a), pad_0(pad_0), pad_1(pad_1)  {}
+            int pad_0 = 1, int pad_1 = 1, bool circular = false)
+        : type(type), ne_a(ne_a), pad_0(pad_0), pad_1(pad_1), circular(circular) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
         ggml_set_name(a, "a");
 
-        ggml_tensor * out = ggml_pad(ctx, a, pad_0, pad_1, 0, 0);
+        ggml_tensor * out = circular
+            ? ggml_pad_circular(ctx, a, pad_0, pad_1, 0, 0)
+            : ggml_pad(ctx, a, pad_0, pad_1, 0, 0);
         ggml_set_name(out, "out");
 
         return out;
@@ -5638,17 +5641,19 @@ struct test_pad_ext : public test_case {
     const int lp3;
     const int rp3;
     const bool v;
+    const bool circular;
 
     std::string vars() override {
-        return VARS_TO_STR11(type, ne_a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, v);
+        return VARS_TO_STR12(type, ne_a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, v, circular);
     }
 
     test_pad_ext(ggml_type type = GGML_TYPE_F32,
             std::array<int64_t, 4> ne_a = {512, 512, 3, 1},
             int lp0 = 1, int rp0 = 1, int lp1 = 1, int rp1 = 1,
             int lp2 = 1, int rp2 = 1, int lp3 = 1, int rp3 = 1,
-            bool v = false)
-        : type(type), ne_a(ne_a), lp0(lp0), rp0(rp0), lp1(lp1), rp1(rp1), lp2(lp2), rp2(rp2), lp3(lp3), rp3(rp3), v(v) {}
+            bool v = false, bool circular = false)
+        : type(type), ne_a(ne_a), lp0(lp0), rp0(rp0), lp1(lp1), rp1(rp1), lp2(lp2), rp2(rp2), lp3(lp3), rp3(rp3),
+          v(v), circular(circular) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
         ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data());
@@ -5659,7 +5664,9 @@ struct test_pad_ext : public test_case {
             ggml_set_name(a, "view of a");
         }
 
-        ggml_tensor * out = ggml_pad_ext(ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3);
+        ggml_tensor * out = circular
+            ? ggml_pad_ext_circular(ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3)
+            : ggml_pad_ext(ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3);
         ggml_set_name(out, "out");
 
         return out;
@@ -7782,6 +7789,7 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_group_norm_mul_add(GGML_TYPE_F32, {9, 9, 1280, 1}));
     test_cases.emplace_back(new test_acc());
     test_cases.emplace_back(new test_pad());
+    test_cases.emplace_back(new test_pad(GGML_TYPE_F32, {33, 17, 2, 1}, 4, 3, true)); // circular
     test_cases.emplace_back(new test_pad_ext());
     test_cases.emplace_back(new test_pad_reflect_1d());
     test_cases.emplace_back(new test_pad_reflect_1d(GGML_TYPE_F32, {3000, 384, 4, 1}));
@@ -7829,8 +7837,10 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 300, 64, 4, 4 }));
 
     for (bool v : {false, true}) {
-        test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {512, 512, 1, 1}, 0, 1, 0, 1, 0, 0, 0, 0, v));
-        test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {11, 22, 33, 44}, 1, 2, 3, 4, 5, 6, 7, 8, v));
+        for (bool circular : {false, true}) {
+            test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {512, 512, 1, 1}, 0, 1, 0, 1, 0, 0, 0, 0, v, circular));
+            test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {11, 22, 33, 44}, 1, 2, 3, 4, 5, 6, 7, 8, v, circular));
+        }
     }
 
     for (int hsk : { 40, 64, 72, 80, 96, 128, 192, 256, 576 }) {

From c42712b056fb2cf03902ef57fd314c531e356965 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Sat, 6 Dec 2025 15:54:38 +0100
Subject: [PATCH 007/135] server: support multiple generations from one prompt
 (OAI "n" option) (#17775)

* backend support

* server: support multiple generations from one prompt (OAI "n" option)

* fix invalid batch

* format oai

* clean up

* disable ctx shift

* add test

* update comments

* fix style

* add n_cmpl to docs [no ci]

* allowing using both n_cmpl and n
---
 tools/server/README.md                        |  2 +
 tools/server/server-common.cpp                | 24 +++---
 tools/server/server-common.h                  |  2 +
 tools/server/server-context.cpp               | 85 +++++++++++++++++--
 tools/server/server-task.cpp                  |  9 +-
 tools/server/server-task.h                    | 24 ++++++
 .../server/tests/unit/test_chat_completion.py | 19 +++++
 7 files changed, 146 insertions(+), 19 deletions(-)

diff --git a/tools/server/README.md b/tools/server/README.md
index cb2fbcf8eb..bf274db79d 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -493,6 +493,8 @@ Note for `multimodal_data` in JSON object prompts. This should be an array of st
 `n_keep`: Specify the number of tokens from the prompt to retain when the context size is exceeded and tokens need to be discarded. The number excludes the BOS token.
 By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to retain all tokens from the prompt.
 
+`n_cmpl`: Number of completions to generate from the current prompt. If input has multiple prompts, the output will have N prompts times `n_cmpl` entries.
+
 `stream`: Allows receiving each predicted token in real-time instead of waiting for the completion to finish (uses a different response format). To enable this, set to `true`.
 
 `stop`: Specify a JSON array of stopping strings.
diff --git a/tools/server/server-common.cpp b/tools/server/server-common.cpp
index cfdd0c656f..b403864e0e 100644
--- a/tools/server/server-common.cpp
+++ b/tools/server/server-common.cpp
@@ -494,6 +494,18 @@ int32_t server_tokens::process_chunk(
     return 0;
 }
 
+server_tokens server_tokens::clone() const {
+    server_tokens res;
+    res.has_mtmd = has_mtmd;
+    res.tokens   = tokens;
+    for (auto it = map_idx_to_media.begin(); it != map_idx_to_media.end(); ++it) {
+        size_t idx = it->first;
+        const mtmd::input_chunk_ptr & chunk = it->second;
+        res.map_idx_to_media[idx] = mtmd::input_chunk_ptr(mtmd_input_chunk_copy(chunk.get()));
+    }
+    return res;
+}
+
 //
 // tokenizer and input processing utils
 //
@@ -745,12 +757,6 @@ json oaicompat_completion_params_parse(const json & body) {
         llama_params["stop"] = json_value(body, "stop", json::array());
     }
 
-    // Handle "n" field
-    int n_choices = json_value(body, "n", 1);
-    if (n_choices != 1) {
-        throw std::runtime_error("Only one completion choice is allowed");
-    }
-
     // Handle "echo" field
     if (json_value(body, "echo", false)) {
         throw std::runtime_error("Only no echo is supported");
@@ -1049,12 +1055,6 @@ json oaicompat_chat_params_parse(
         llama_params["chat_parser"] = chat_params.parser;
     }
 
-    // Handle "n" field
-    int n_choices = json_value(body, "n", 1);
-    if (n_choices != 1) {
-        throw std::invalid_argument("Only one completion choice is allowed");
-    }
-
     // Handle "logprobs" field
     // TODO: The response format of this option is not yet OAI-compatible, but seems like no one really using it; We may need to fix it in the future
     if (json_value(body, "logprobs", false)) {
diff --git a/tools/server/server-common.h b/tools/server/server-common.h
index bb04e82b4f..0c4d84ffa0 100644
--- a/tools/server/server-common.h
+++ b/tools/server/server-common.h
@@ -215,6 +215,8 @@ public:
                 llama_pos pos,
                 int32_t seq_id,
                 size_t & n_tokens_out) const;
+
+    server_tokens clone() const;
 };
 
 
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index f3f2edc0cc..12a4e94e5d 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -35,7 +35,8 @@ constexpr int HTTP_POLLING_SECONDS = 1;
 // state diagram: https://github.com/ggml-org/llama.cpp/pull/9283
 enum slot_state {
     SLOT_STATE_IDLE,
-    SLOT_STATE_STARTED, // TODO: this state is only used for setting up the initial prompt processing; maybe merge it with launch_slot_with_task in the future
+    SLOT_STATE_WAIT_OTHER, // after assigning a task, but waiting for parent slot to process prompt
+    SLOT_STATE_STARTED,    // after assigning a task and about to process prompt
     SLOT_STATE_PROCESSING_PROMPT,
     SLOT_STATE_DONE_PROMPT,
     SLOT_STATE_GENERATING,
@@ -254,6 +255,15 @@ struct server_slot {
         generated_token_probs.push_back(token);
     }
 
+    // note: a slot can also be either a parent or a child
+    bool is_parent() const {
+        return is_processing() && task->n_children > 0;
+    }
+
+    bool is_child() const {
+        return is_processing() && task->id_parent >= 0;
+    }
+
     void release() {
         if (is_processing()) {
             GGML_ASSERT(task);
@@ -383,6 +393,17 @@ struct server_slot {
 
         return res;
     }
+
+    void copy_state_to(server_slot & other) const {
+        llama_memory_seq_rm(llama_get_memory(ctx), other.id, 0, -1);
+        llama_memory_seq_cp(llama_get_memory(ctx), id, other.id, 0, -1);
+        other.n_decoded   = n_decoded;
+        other.n_remaining = n_remaining;
+        other.i_batch     = i_batch;
+        other.n_prompt_tokens_cache     = n_prompt_tokens_cache;
+        other.n_prompt_tokens_processed = n_prompt_tokens_processed;
+        other.prompt = prompt.clone();
+    }
 };
 
 
@@ -1022,7 +1043,9 @@ struct server_context_impl {
 
         slot.task = std::make_unique<const server_task>(std::move(task));
 
-        slot.state = SLOT_STATE_STARTED;
+        slot.state = slot.is_child()
+            ? SLOT_STATE_WAIT_OTHER // wait for the parent to process prompt
+            : SLOT_STATE_STARTED;
 
         SLT_INF(slot, "%s", "processing task\n");
 
@@ -1684,6 +1707,12 @@ struct server_context_impl {
                     GGML_ABORT("not supported by multimodal");
                 }
 
+                if (slot.is_parent() || slot.is_child()) {
+                    send_error(slot, "context shift cannot be used for shared prompt", ERROR_TYPE_SERVER);
+                    slot.release();
+                    continue;
+                }
+
                 // Shift context
                 int n_keep = slot.task->params.n_keep < 0 ? slot.task->n_tokens() : slot.task->params.n_keep;
 
@@ -2308,6 +2337,26 @@ struct server_context_impl {
             n_batch = llama_n_batch(ctx);
 
             for (auto & slot : slots) {
+                // may need to copy state to other slots
+                if (slot.state == SLOT_STATE_DONE_PROMPT && slot.is_parent()) {
+                    std::vector<server_slot *> child_slots;
+                    for (auto & other : slots) {
+                        if (other.state == SLOT_STATE_WAIT_OTHER && slot.task->id == other.task->id_parent) {
+                            child_slots.push_back(&other);
+                        }
+                    }
+
+                    // we can only proceed if all child slots are having the correct tasks
+                    if (child_slots.size() == slot.task->n_children) {
+                        // copy state to the child slots
+                        for (auto & child : child_slots) {
+                            SLT_INF(slot, "copying state to child %d\n", child->id);
+                            slot.copy_state_to(*child);
+                            child->state = SLOT_STATE_DONE_PROMPT;
+                        }
+                    }
+                }
+
                 // optionally send prompt processing progress
                 if (slot.state == SLOT_STATE_PROCESSING_PROMPT || slot.state == SLOT_STATE_DONE_PROMPT) {
                     if (slot.task->params.stream && slot.task->params.return_progress) {
@@ -2593,11 +2642,12 @@ static std::unique_ptr<server_res_generator> handle_completions_impl(
         }
         tasks.reserve(inputs.size());
         states.reserve(inputs.size());
+        int idx = 0;
         for (size_t i = 0; i < inputs.size(); i++) {
             server_task task = server_task(type);
 
             task.id    = ctx_server.queue_tasks.get_new_id();
-            task.index = i;
+            task.index = idx++;
 
             task.tokens = std::move(inputs[i]);
             task.params = server_task::params_from_json_cmpl(
@@ -2612,6 +2662,18 @@ static std::unique_ptr<server_res_generator> handle_completions_impl(
             task.params.oaicompat_model   = ctx_server.model_name;
             states.push_back(task.params.oaicompat_chat_syntax);
 
+            if (task.params.n_cmpl > 1) {
+                task.n_children = task.params.n_cmpl - 1;
+                for (size_t j = 0; j < task.n_children; j++) {
+                    server_task child = task.create_child(
+                        task.id,
+                        ctx_server.queue_tasks.get_new_id(),
+                        idx++);
+                    states.push_back(child.params.oaicompat_chat_syntax);
+                    tasks.push_back(std::move(child));
+                }
+            }
+
             tasks.push_back(std::move(task));
         }
 
@@ -2638,8 +2700,21 @@ static std::unique_ptr<server_res_generator> handle_completions_impl(
                 GGML_ASSERT(dynamic_cast<server_task_result_cmpl_final*>(res.get()) != nullptr);
                 arr.push_back(res->to_json());
             }
-            // if single request, return single object instead of array
-            res->ok(arr.size() == 1 ? arr[0] : arr);
+            GGML_ASSERT(!arr.empty() && "empty results");
+            if (arr.size() == 1) {
+                // if single request, return single object instead of array
+                res->ok(arr[0]);
+            } else if (res_type == TASK_RESPONSE_TYPE_OAI_CHAT || res_type == TASK_RESPONSE_TYPE_OAI_CMPL) {
+                // if multiple results in OAI format, we need to re-format them
+                json & choices = arr[0]["choices"];
+                for (size_t i = 1; i < arr.size(); i++) {
+                    choices.push_back(std::move(arr[i]["choices"][0]));
+                }
+                res->ok(arr[0]);
+            } else {
+                // multi-results, non-OAI compat
+                res->ok(arr);
+            }
         }
     } else {
         // in streaming mode, the first error must be treated as non-stream response
diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp
index df06626477..c401f47a78 100644
--- a/tools/server/server-task.cpp
+++ b/tools/server/server-task.cpp
@@ -175,6 +175,7 @@ task_params server_task::params_from_json_cmpl(
     params.n_indent         = json_value(data,       "n_indent",           defaults.n_indent);
     params.n_keep           = json_value(data,       "n_keep",             defaults.n_keep);
     params.n_discard        = json_value(data,       "n_discard",          defaults.n_discard);
+    params.n_cmpl           = json_value(data,       "n_cmpl",             json_value(data, "n", 1));
     //params.t_max_prompt_ms  = json_value(data,       "t_max_prompt_ms",    defaults.t_max_prompt_ms); // TODO: implement
     params.t_max_predict_ms = json_value(data,       "t_max_predict_ms",   defaults.t_max_predict_ms);
     params.response_fields  = json_value(data,       "response_fields",    std::vector<std::string>());
@@ -453,6 +454,10 @@ task_params server_task::params_from_json_cmpl(
         }
     }
 
+    if (params.n_cmpl > params_base.n_parallel) {
+        throw std::runtime_error("n_cmpl cannot be greater than the number of slots, please increase -np");
+    }
+
     return params;
 }
 
@@ -664,7 +669,7 @@ json server_task_result_cmpl_final::to_json_oaicompat_chat() {
 
     json choice {
         {"finish_reason", finish_reason},
-        {"index", 0},
+        {"index", index},
         {"message", msg.to_json_oaicompat<json>()},
     };
 
@@ -1064,7 +1069,7 @@ json server_task_result_cmpl_partial::to_json_oaicompat_chat() {
             {"choices", json::array({
                 json {
                     {"finish_reason", nullptr},
-                    {"index", 0},
+                    {"index", index},
                     {"delta", delta},
                 },
             })},
diff --git a/tools/server/server-task.h b/tools/server/server-task.h
index 8e7b9e3e31..4e4840fc83 100644
--- a/tools/server/server-task.h
+++ b/tools/server/server-task.h
@@ -53,6 +53,7 @@ struct task_params {
     int32_t n_discard =  0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half
     int32_t n_predict = -1; // new tokens to predict
     int32_t n_indent  =  0; // minimum line indentation for the generated text in number of whitespace characters
+    int32_t n_cmpl    =  1; // number of completions to generate from this prompt
 
     int64_t t_max_prompt_ms  = -1; // TODO: implement
     int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit
@@ -89,6 +90,10 @@ struct server_task {
     int id_target = -1;
     int id_slot   = -1;
 
+    // used by parallel sampling (multiple completions from same prompt)
+    size_t n_children =  0; // number of tasks reusing this prompt
+    int    id_parent  = -1;
+
     // used by SERVER_TASK_TYPE_INFERENCE
     task_params   params;
     server_tokens tokens;
@@ -130,6 +135,17 @@ struct server_task {
         }
         return ids;
     }
+
+    server_task create_child(int id_parent, int id_child, int idx) const {
+        server_task copy;
+        copy.id        = id_child;
+        copy.index     = idx;
+        copy.id_parent = id_parent;
+        copy.params    = params;
+        copy.type      = type;
+        copy.tokens    = tokens.clone();
+        return copy;
+    }
 };
 
 struct result_timings {
@@ -466,6 +482,14 @@ struct server_prompt {
     int n_tokens() const {
         return tokens.size();
     }
+
+    server_prompt clone() const {
+        return server_prompt {
+            tokens.clone(),
+            data,
+            checkpoints
+        };
+    }
 };
 
 struct server_prompt_cache {
diff --git a/tools/server/tests/unit/test_chat_completion.py b/tools/server/tests/unit/test_chat_completion.py
index aa6229c93a..64f3158b98 100644
--- a/tools/server/tests/unit/test_chat_completion.py
+++ b/tools/server/tests/unit/test_chat_completion.py
@@ -477,3 +477,22 @@ def test_return_progress(n_batch, batch_count, reuse_cache):
     assert last_progress["total"] > 0
     assert last_progress["processed"] == last_progress["total"]
     assert total_batch_count == batch_count
+
+
+def test_chat_completions_multiple_choices():
+    global server
+    server.start()
+    res = server.make_request("POST", "/chat/completions", data={
+        "max_tokens": 8,
+        "n": 2,
+        "messages": [
+            {"role": "system", "content": "Book"},
+            {"role": "user", "content": "What is the best book"},
+        ],
+    })
+    assert res.status_code == 200
+    assert len(res.body["choices"]) == 2
+    for choice in res.body["choices"]:
+        assert "assistant" == choice["message"]["role"]
+        assert match_regex("Suddenly", choice["message"]["content"])
+        assert choice["finish_reason"] == "length"

From 017761daf5f543e3f064d956784a9c9421dc0b66 Mon Sep 17 00:00:00 2001
From: Vishal Singh <vishal@zettabolt.com>
Date: Sat, 6 Dec 2025 21:43:33 +0530
Subject: [PATCH 008/135] ggml-zendnn : add ZenDNN backend for AMD CPUs
 (#17690)

* ggml-zennn: add ZenDNN backend support

* ggml-zendnn : address ZenDNN backend review fixes and suggestions

* docs : apply blockquote syntax to ZenDNN docs

---------

Co-authored-by: Manoj Kumar <mkumar@zettabolt.com>
---
 README.md                            |     1 +
 docs/backend/ZenDNN.md               |   258 +
 docs/backend/zDNN.md                 |     5 +
 docs/build.md                        |    32 +
 docs/ops.md                          |   216 +-
 docs/ops/ZenDNN.csv                  | 18741 +++++++++++++++++++++++++
 ggml/CMakeLists.txt                  |     4 +
 ggml/include/ggml-zendnn.h           |    22 +
 ggml/src/CMakeLists.txt              |     1 +
 ggml/src/ggml-backend-reg.cpp        |     8 +
 ggml/src/ggml-zendnn/CMakeLists.txt  |    92 +
 ggml/src/ggml-zendnn/ggml-zendnn.cpp |   466 +
 tools/llama-bench/llama-bench.cpp    |     3 +-
 13 files changed, 19740 insertions(+), 109 deletions(-)
 create mode 100644 docs/backend/ZenDNN.md
 create mode 100644 docs/ops/ZenDNN.csv
 create mode 100644 ggml/include/ggml-zendnn.h
 create mode 100644 ggml/src/ggml-zendnn/CMakeLists.txt
 create mode 100644 ggml/src/ggml-zendnn/ggml-zendnn.cpp

diff --git a/README.md b/README.md
index 2e44ae7d0c..eac8d66cc2 100644
--- a/README.md
+++ b/README.md
@@ -276,6 +276,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
 | [MUSA](docs/build.md#musa) | Moore Threads GPU |
 | [CUDA](docs/build.md#cuda) | Nvidia GPU |
 | [HIP](docs/build.md#hip) | AMD GPU |
+| [ZenDNN](docs/build.md#zendnn) | AMD CPU |
 | [Vulkan](docs/build.md#vulkan) | GPU |
 | [CANN](docs/build.md#cann) | Ascend NPU |
 | [OpenCL](docs/backend/OPENCL.md) | Adreno GPU |
diff --git a/docs/backend/ZenDNN.md b/docs/backend/ZenDNN.md
new file mode 100644
index 0000000000..b57fd97b69
--- /dev/null
+++ b/docs/backend/ZenDNN.md
@@ -0,0 +1,258 @@
+# llama.cpp for AMD ZenDNN
+
+> [!WARNING]
+> **Note:** ZenDNN is **not** the same as zDNN.
+> - **ZenDNN** (this page): AMD's deep learning library for AMD EPYC CPUs
+> - **zDNN**: IBM's Deep Neural Network acceleration library for IBM Z & LinuxONE Mainframes ([see zDNN documentation](zDNN.md))
+
+- [Background](#background)
+- [OS](#os)
+- [Hardware](#hardware)
+- [Supported Operations](#supported-operations)
+- [DataType Supports](#datatype-supports)
+- [Linux](#linux)
+- [Environment Variable](#environment-variable)
+- [Performance Optimization](#performance-optimization)
+- [Known Issues](#known-issues)
+- [TODO](#todo)
+
+## Background
+
+**ZenDNN** (Zen Deep Neural Network Library) is AMD's high-performance deep learning inference library optimized for AMD EPYC™ CPUs. It provides optimized implementations of key deep learning primitives and operations, delivering significant performance improvements for neural network workloads on AMD Zen-based processor architectures.
+
+**Llama.cpp + ZenDNN**
+
+The llama.cpp ZenDNN backend leverages AMD's optimized matrix multiplication primitives to accelerate inference on AMD CPUs. It utilizes ZenDNN's **LowOHA (Low Overhead Hardware Accelerated)** MatMul operator for efficient GEMM operations with minimal execution overhead, built-in weight caching, and direct access to backend libraries (AOCL BLIS, LibXSMM, OneDNN).
+
+For more information about ZenDNN, visit: https://www.amd.com/en/developer/zendnn.html
+
+## OS
+
+| OS      | Status  | Verified                                       |
+|:-------:|:-------:|:----------------------------------------------:|
+| Linux   | Support | Ubuntu 20.04, 22.04, 24.04                     |
+
+For the latest list of supported operating systems, see the [ZenDNN Supported OS](https://github.com/amd/ZenDNN/blob/zendnnl/README.md#15-supported-os).
+
+## Hardware
+
+### AMD CPUs
+
+**Recommended Processors**
+
+ZenDNN is optimized for AMD EPYC™ processors and AMD Ryzen™ processors based on "Zen" microarchitecture and newer.
+
+| CPU Family                    | Status  | Notes                              |
+|:-----------------------------:|:-------:|:----------------------------------:|
+| AMD EPYC™ 9005 Series (Turin)| Support | 5th Gen - Zen 5 architecture       |
+| AMD EPYC™ 9004 Series (Genoa)| Support | 4th Gen - Zen 4 architecture       |
+| AMD EPYC™ 7003 Series (Milan)| Support | 3rd Gen - Zen 3 architecture       |
+| AMD Ryzen™ AI MAX (Strix Halo)| Support | High-performance mobile processors |
+
+*Notes:*
+
+- Best performance is achieved on AMD EPYC™ processors with high core counts (e.g., EPYC 9005 series).
+- ZenDNN leverages AMD's advanced CPU features including AVX2 and AVX-512 instruction sets.
+- For optimal performance, ensure your system has sufficient memory bandwidth.
+
+## Supported Operations
+
+The ZenDNN backend currently accelerates **matrix multiplication (MUL_MAT)** operations only. Other operations are handled by the standard CPU backend.
+
+| Operation    | Status  | Notes                                          |
+|:-------------|:-------:|:----------------------------------------------:|
+| MUL_MAT      |    ✓    | Accelerated via ZenDNN LowOHA MatMul           |
+
+*Note:* Since only MUL_MAT is accelerated, models will benefit most from ZenDNN when matrix multiplications dominate the computational workload (which is typical for transformer-based LLMs).
+
+## DataType Supports
+
+| DataType               | Status  | Notes                                         |
+|:----------------------:|:-------:|:---------------------------------------------:|
+| FP32                   | Support | Full precision floating point                 |
+| BF16                   | Support | BFloat16 (best performance on Zen 4/Zen 5)    |
+
+*Notes:*
+
+- **BF16** provides best performance on Zen 4 and Zen 5 EPYC™ processors (Genoa, Turin).
+
+## Linux
+
+### I. Setup Environment
+
+You have two options to set up ZenDNN:
+
+#### Option 1: Automatic Download and Build (Recommended)
+
+CMake will automatically download and build ZenDNN for you:
+
+```sh
+# Build llama.cpp - ZenDNN will be automatically downloaded and built
+cmake -B build -DGGML_ZENDNN=ON -DCMAKE_BUILD_TYPE=Release
+cmake --build build --config Release -j $(nproc)
+```
+
+No manual ZenDNN installation required. CMake will handle everything automatically.
+
+#### Option 2: Use Custom ZenDNN Installation
+
+If you want to build ZenDNN yourself or use a specific version:
+
+**Step 1: Build ZenDNN from source**
+
+```sh
+# Clone ZenDNN repository
+git clone https://github.com/amd/ZenDNN.git
+cd ZenDNN
+git checkout zendnnl
+
+# Build and install (requires CMake >= 3.25)
+mkdir build && cd build
+cmake ..
+cmake --build . --target all
+```
+
+Default installation path: `ZenDNN/build/install`
+
+**For detailed build instructions**, refer to the [ZenDNN README](https://github.com/amd/ZenDNN/blob/zendnnl/README.md).
+
+**Step 2: Build llama.cpp with custom ZenDNN path**
+
+```sh
+# Using environment variable
+export ZENDNN_ROOT=/path/to/ZenDNN/build/install
+cmake -B build -DGGML_ZENDNN=ON -DCMAKE_BUILD_TYPE=Release
+cmake --build build --config Release -j $(nproc)
+
+# OR specify path directly in CMake
+cmake -B build -DGGML_ZENDNN=ON -DZENDNN_ROOT=/path/to/ZenDNN/build/install -DCMAKE_BUILD_TYPE=Release
+cmake --build build --config Release -j $(nproc)
+```
+
+### II. Run the Server
+
+#### 1. Download Model
+
+Download LLaMA 3.1 8B Instruct BF16 model:
+
+```sh
+# Download from Hugging Face
+huggingface-cli download meta-llama/Llama-3.1-8B-Instruct-GGUF --local-dir models/
+```
+
+#### 2. Start Server
+
+Run llama.cpp server with ZenDNN acceleration:
+
+```sh
+# Set optimal configuration
+export OMP_NUM_THREADS=64  # Adjust to your CPU core count
+export ZENDNNL_MATMUL_ALGO=2  # Blocked AOCL BLIS for best performance
+
+# Start server
+./build/bin/llama-server \
+    -m models/Llama-3.1-8B-Instruct.BF16.gguf \
+    --host 0.0.0.0 \
+    --port 8080 \
+    -t 64
+```
+
+Access the server at `http://localhost:8080`.
+
+**Performance tips**:
+- Set `OMP_NUM_THREADS` to match your physical core count
+- Use `ZENDNNL_MATMUL_ALGO=2` for optimal performance
+- For NUMA systems: `numactl --cpunodebind=0 --membind=0 ./build/bin/llama-server ...`
+
+## Environment Variable
+
+### Build Time
+
+| Name               | Value                                 | Function                                    |
+|--------------------|---------------------------------------|---------------------------------------------|
+| GGML_ZENDNN        | ON/OFF                                | Enable ZenDNN backend support               |
+| ZENDNN_ROOT        | Path to ZenDNN installation           | Set ZenDNN installation directory           |
+| GGML_OPENMP        | ON/OFF (recommended: ON)              | Enable OpenMP for multi-threading           |
+
+### Runtime
+
+| Name                    | Value                    | Function                                                          |
+|-------------------------|--------------------------|-------------------------------------------------------------------|
+| OMP_NUM_THREADS         | Number (e.g., 64)        | Set number of OpenMP threads (recommended: physical core count)   |
+| ZENDNNL_MATMUL_ALGO     | 0-5                      | Select MatMul backend algorithm (see Performance Optimization)    |
+| ZENDNNL_PROFILE_LOG_LEVEL | 0-4                    | Profiling log level (0=disabled, 4=verbose)                       |
+| ZENDNNL_ENABLE_PROFILER | 0 or 1                   | Enable detailed profiling (1=enabled)                             |
+| ZENDNNL_API_LOG_LEVEL   | 0-4                      | API log level (0=disabled, 4=verbose)                             |
+
+**Example**:
+
+```sh
+export OMP_NUM_THREADS=64
+export ZENDNNL_MATMUL_ALGO=2  # Use Blocked AOCL BLIS for best performance
+./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Test" -n 100
+```
+
+## Performance Optimization
+
+### MatMul Algorithm Selection
+
+ZenDNN's LowOHA MatMul supports multiple backend algorithms. For **best performance**, use the **Blocked AOCL BLIS** algorithm:
+
+```sh
+export ZENDNNL_MATMUL_ALGO=2  # Blocked AOCL BLIS (recommended)
+```
+
+**Available algorithms**:
+
+| Value | Algorithm              | Description                                    |
+|:-----:|:-----------------------|:----------------------------------------------|
+| 0     | Dynamic Dispatch       | Automatic backend selection (default)         |
+| 1     | AOCL BLIS              | AOCL BLIS backend                             |
+| 2     | AOCL BLIS Blocked      | **Blocked AOCL BLIS (recommended)**           |
+| 3     | OneDNN                 | OneDNN backend                                |
+| 4     | OneDNN Blocked         | Blocked OneDNN                                |
+| 5     | LibXSMM                | LibXSMM backend                               |
+
+### Profiling and Debugging
+
+For detailed profiling and logging options, refer to the [ZenDNN Logging Documentation](https://github.com/amd/ZenDNN/blob/zendnnl/docs/logging.md).
+
+## Known Issues
+
+- **Limited operation support**: Currently only matrix multiplication (MUL_MAT) is accelerated via ZenDNN. Other operations fall back to the standard CPU backend.
+- **BF16 support**: BF16 operations require AMD Zen 4 or Zen 5 architecture (EPYC 9004/9005 series). On older CPUs, operations will use FP32.
+- **NUMA awareness**: For multi-socket systems, manual NUMA binding may be required for optimal performance.
+
+## Q&A
+
+**Q: How do I verify that ZenDNN backend is being used?**
+
+A: Check the log output when running llama.cpp. You should see messages indicating the ZenDNN backend is initialized. You can also check the backend name in the output.
+
+**Q: What performance improvement can I expect?**
+
+A: Performance gains vary depending on the model size, batch size, and CPU architecture. On AMD EPYC processors, you can typically expect 1.1x-2x speedup compared to standard CPU inference for matrix multiplication operations.
+
+**Q: Can I use ZenDNN on non-AMD processors?**
+
+A: ZenDNN is optimized specifically for AMD processors. While it may work on other x86-64 CPUs, performance benefits are only guaranteed on AMD Zen-based architectures.
+
+**Q: Does ZenDNN support quantized models?**
+
+A: Currently, ZenDNN primarily supports FP32 and BF16 data types. Quantized model support is not available at this time.
+
+**Q: Why is my inference not faster with ZenDNN?**
+
+A: Ensure:
+1. You're using an AMD EPYC or Ryzen processor (Zen 2 or newer)
+2. `OMP_NUM_THREADS` is set appropriately (physical core count)
+3. `ZENDNNL_MATMUL_ALGO=2` is set for best performance (Blocked AOCL BLIS)
+4. You're using a sufficiently large model (small models may not benefit as much)
+5. Enable profiling to verify ZenDNN MatMul is being called
+
+### **GitHub Contribution**:
+Please add the **[ZenDNN]** prefix/tag in issues/PRs titles to help the ZenDNN-team check/address them without delay.
+
+## TODO
+
+- Expand operation support beyond MUL_MAT (attention operations, activations, etc.)
diff --git a/docs/backend/zDNN.md b/docs/backend/zDNN.md
index 8d2e111772..94108f5ef4 100644
--- a/docs/backend/zDNN.md
+++ b/docs/backend/zDNN.md
@@ -1,5 +1,10 @@
 # llama.cpp for IBM zDNN Accelerator
 
+> [!WARNING]
+> **Note:** zDNN is **not** the same as ZenDNN.
+> - **zDNN** (this page): IBM's Deep Neural Network acceleration library for IBM Z & LinuxONE Mainframes
+> - **ZenDNN**: AMD's deep learning library for AMD EPYC CPUs ([see ZenDNN documentation](ZenDNN.md))
+
 ## Background
 
 IBM zDNN (Z Deep Neural Network) is a hardware acceleration library designed specifically to leverage the IBM NNPA (Neural Network Processor Assist) accelerator located within IBM Telum I and II processors. It provides significant performance improvements for neural network inference operations.
diff --git a/docs/build.md b/docs/build.md
index 316269288d..4a6911778c 100644
--- a/docs/build.md
+++ b/docs/build.md
@@ -495,6 +495,38 @@ llama_new_context_with_model:       CANN compute buffer size =  1260.81 MiB
 
 For detailed info, such as model/device supports, CANN install, please refer to [llama.cpp for CANN](./backend/CANN.md).
 
+## ZenDNN
+
+ZenDNN provides optimized deep learning primitives for AMD EPYC™ CPUs. It accelerates matrix multiplication operations for inference workloads.
+
+### Compilation
+
+- Using `CMake` on Linux (automatic build):
+
+    ```bash
+    cmake -B build -DGGML_ZENDNN=ON
+    cmake --build build --config Release
+    ```
+
+    The first build will automatically download and build ZenDNN, which may take 5-10 minutes. Subsequent builds will be much faster.
+
+- Using `CMake` with custom ZenDNN installation:
+
+    ```bash
+    cmake -B build -DGGML_ZENDNN=ON -DZENDNN_ROOT=/path/to/zendnn/install
+    cmake --build build --config Release
+    ```
+
+### Testing
+
+You can test with:
+
+```bash
+./build/bin/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -n 50
+```
+
+For detailed information about hardware support, setup instructions, and performance optimization, refer to [llama.cpp for ZenDNN](./backend/ZenDNN.md).
+
 ## Arm® KleidiAI™
 KleidiAI is a library of optimized microkernels for AI workloads, specifically designed for Arm CPUs. These microkernels enhance performance and can be enabled for use by the CPU backend.
 
diff --git a/docs/ops.md b/docs/ops.md
index 65e33b5120..ef1febccae 100644
--- a/docs/ops.md
+++ b/docs/ops.md
@@ -12,111 +12,111 @@ Legend:
 - 🟡 Partially supported by this backend
 - ❌ Not supported by this backend
 
-| Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan | WebGPU | zDNN |
-|-----------|------|------|------|------|------|------|------|------|------|------|
-|                              ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ |
-|                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ |
-|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
-|                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ |
-|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
-|                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ |
-|                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ |
-|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
-|                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
-|                              COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ |
-|                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
-|               CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                           CUMSUM | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
-|                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ |
-|                              DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ |
-|                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ |
-|                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ |
-|                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                             FILL | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
-|                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ |
-|                            FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ |
-|                GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ |
-|                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ |
-|                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ |
-|                             GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ |
-|                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ |
-|                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ |
-|                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
-|                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|               GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ |
-|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ |
-|                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
-|                          L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ |
-|                              LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ❌ | ❌ |
-|                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ |
-|                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
-|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ |
-|                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ |
-|                             NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ |
-|                     NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
-|                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
-|                         OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
-|                              PAD | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ |
-|                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ |
-|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ❌ | ❌ |
-|                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
-|                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                 RMS_NORM_MUL_ADD | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
-|                             ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                             ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
-|                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
-|                            ROUND | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ |
-|                        RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
-|                              SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
-|                         SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ |
-|                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ |
-|                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ |
-|                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ |
-|                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
-|                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ |
-|                          SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ |
-|                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
-|                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ |
-|                        SOLVE_TRI | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ |
-|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ |
-|                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ |
-|                         SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ |
-|                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ |
-|                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ |
-|                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ |
-|                              SUM | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ |
-|                         SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ |
-|                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ |
-|                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ |
-|               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
-|                            TOP_K | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ |
-|                              TRI | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ |
-|                            TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ |
-|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ |
-|                            XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ |
+| Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan | WebGPU | ZenDNN | zDNN |
+|-----------|------|------|------|------|------|------|------|------|------|------|------|
+|                              ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
+|                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                       CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                              COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
+|               CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                           CUMSUM | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
+|                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                             FILL | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
+|                            FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                        GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                      GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                             GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                         GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                       GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                         GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
+|                    GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                       GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|               GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                      HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                        HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                           IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                          L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
+|                              LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
+|                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
+|                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
+|                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                             NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ |
+|                     NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                         OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ |
+|                              PAD | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | ❌ |
+|                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
+|                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                             RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                           REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ❌ | ❌ | ❌ |
+|                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                 RMS_NORM_MUL_ADD | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                             ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                             ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                            ROUND | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                        RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                        RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                            SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ |
+|                         SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
+|                              SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
+|                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                          SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
+|                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
+|                        SOLVE_TRI | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
+|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                         SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
+|                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              SUM | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                         SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | ❌ |
+|                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ |
+|                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
+|               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                            TOP_K | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
+|                              TRI | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                            TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                            XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
diff --git a/docs/ops/ZenDNN.csv b/docs/ops/ZenDNN.csv
new file mode 100644
index 0000000000..1b955624a3
--- /dev/null
+++ b/docs/ops/ZenDNN.csv
@@ -0,0 +1,18741 @@
+"backend_name","op_name","op_params","test_mode","supported","error_message","backend_reg_name"
+"ZenDNN","ABS","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
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+"ZenDNN","SGN","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","SGN","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","NEG","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","NEG","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","STEP","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","STEP","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","TANH","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","TANH","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","ELU","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","ELU","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","RELU","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","RELU","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","SIGMOID","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","SIGMOID","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","GELU","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","GELU","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","GELU_QUICK","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
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+"ZenDNN","SILU","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","SILU","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","HARDSWISH","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","HARDSWISH","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
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+"ZenDNN","HARDSIGMOID","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","EXP","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","EXP","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","EXPM1","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","EXPM1","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","SOFTPLUS","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","CEIL","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","CEIL","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","ROUND","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","ZenDNN"
+"ZenDNN","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","ZenDNN"
+"ZenDNN","ABS","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","ZenDNN"
+"ZenDNN","ABS","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","ZenDNN"
+"ZenDNN","SGN","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","ZenDNN"
+"ZenDNN","SGN","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","ZenDNN"
+"ZenDNN","NEG","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","ZenDNN"
+"ZenDNN","NEG","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","ZenDNN"
+"ZenDNN","STEP","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","ZenDNN"
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+"ZenDNN","RELU","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","ZenDNN"
+"ZenDNN","RELU","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","ZenDNN"
+"ZenDNN","SIGMOID","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","ZenDNN"
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+"ZenDNN","GELU","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","ZenDNN"
+"ZenDNN","GELU","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","ZenDNN"
+"ZenDNN","GELU_QUICK","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","ZenDNN"
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+"ZenDNN","SILU","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","ZenDNN"
+"ZenDNN","SILU","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","ZenDNN"
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+"ZenDNN","SILU","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","ZenDNN"
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+"ZenDNN","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[141,1,25,2],ne_kernel=[11,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,3,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[1,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[2,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[1,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[3,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f32,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D","ne_input=[141,133,25,2],ne_kernel=[11,11,25,12],type_kernel=f16,stride0=3,stride1=5,padding0=5,padding1=5,dilation0=2,dilation1=4,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],stride=1,padding=0,dilation=1,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D_DW","ne_input=[17,34,9,1],ne_kernel=[3,3,1,9],stride=1,padding=0,dilation=1,cwhn=1","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=0","support","0","no","ZenDNN"
+"ZenDNN","CONV_2D_DW","ne_input=[32,8,64,1],ne_kernel=[3,3,1,64],stride=2,padding=1,dilation=1,cwhn=1","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=4,ID=8,IH=8,IW=8,OC=8,KD=1,KH=1,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","ZenDNN"
+"ZenDNN","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","ZenDNN"
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+"ZenDNN","CPY","type_src=q8_0,type_dst=q8_0,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q8_0,type_dst=q8_0,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q8_0,type_dst=q8_0,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q8_0,type_dst=q8_0,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=mxfp4,type_dst=mxfp4,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q2_K,type_dst=q2_K,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q3_K,type_dst=q3_K,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q4_K,type_dst=q4_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q4_K,type_dst=q4_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q4_K,type_dst=q4_K,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q4_K,type_dst=q4_K,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
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+"ZenDNN","CPY","type_src=q4_K,type_dst=q4_K,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
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+"ZenDNN","CPY","type_src=q4_K,type_dst=q4_K,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q4_K,type_dst=q4_K,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=q5_K,type_dst=q5_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
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+"ZenDNN","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
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+"ZenDNN","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
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+"ZenDNN","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
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+"ZenDNN","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
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+"ZenDNN","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3],_src_transpose=0","support","0","no","ZenDNN"
+"ZenDNN","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=1,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f16,type_b=f32,m=64,n=2,k=128,bs=[8,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f16,type_b=f32,m=83,n=2,k=128,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f16,type_b=f32,m=64,n=2,k=64,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f16,type_b=f32,m=83,n=2,k=64,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f16,type_b=f32,m=64,n=45,k=128,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=45,k=64,bs=[8,1],nr=[4,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=193,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=67,bs=[1,1],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=64,n=77,k=77,bs=[12,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=q4_0,type_b=f32,m=576,n=512,k=576,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","ZenDNN"
+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","ZenDNN"
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+"ZenDNN","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","ZenDNN"
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+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","ZenDNN"
+"ZenDNN","CROSS_ENTROPY_LOSS","type=f32,ne=[10,5,4,3]","support","0","no","ZenDNN"
+"ZenDNN","CROSS_ENTROPY_LOSS","type=f32,ne=[30000,1,1,1]","support","0","no","ZenDNN"
+"ZenDNN","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[10,5,4,3]","support","0","no","ZenDNN"
+"ZenDNN","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[30000,1,1,1]","support","0","no","ZenDNN"
+"ZenDNN","OPT_STEP_ADAMW","type=f32,ne=[10,5,4,3]","support","0","no","ZenDNN"
+"ZenDNN","OPT_STEP_SGD","type=f32,ne=[10,5,4,3]","support","0","no","ZenDNN"
diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 0ccd901921..6b69ad8281 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -253,6 +253,9 @@ option(GGML_HEXAGON                         "ggml: enable Hexagon backend"
 # toolchain for vulkan-shaders-gen
 set   (GGML_VULKAN_SHADERS_GEN_TOOLCHAIN "" CACHE FILEPATH "ggml: toolchain file for vulkan-shaders-gen")
 
+option(GGML_ZENDNN                          "ggml: use ZenDNN"                                OFF)
+option(ZENDNN_ROOT                          "ggml: path to ZenDNN installation"               "")
+
 # extra artifacts
 option(GGML_BUILD_TESTS    "ggml: build tests"    ${GGML_STANDALONE})
 option(GGML_BUILD_EXAMPLES "ggml: build examples" ${GGML_STANDALONE})
@@ -314,6 +317,7 @@ set(GGML_PUBLIC_HEADERS
     include/ggml-sycl.h
     include/ggml-vulkan.h
     include/ggml-webgpu.h
+    include/ggml-zendnn.h
     include/gguf.h)
 
 set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}")
diff --git a/ggml/include/ggml-zendnn.h b/ggml/include/ggml-zendnn.h
new file mode 100644
index 0000000000..a30a3a9808
--- /dev/null
+++ b/ggml/include/ggml-zendnn.h
@@ -0,0 +1,22 @@
+#pragma once
+
+#include "ggml-backend.h"
+#include "ggml.h"
+
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+// backend API
+GGML_BACKEND_API ggml_backend_t ggml_backend_zendnn_init(void);
+
+GGML_BACKEND_API bool ggml_backend_is_zendnn(ggml_backend_t backend);
+
+// number of threads used for zendnn operations
+GGML_BACKEND_API void ggml_backend_zendnn_set_n_threads(ggml_backend_t backend_zendnn, int n_threads);
+
+GGML_BACKEND_API ggml_backend_reg_t ggml_backend_zendnn_reg(void);
+
+#ifdef __cplusplus
+}
+#endif
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index 98606e9cf1..4c04c33003 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -440,6 +440,7 @@ ggml_add_backend(WebGPU)
 ggml_add_backend(zDNN)
 ggml_add_backend(OpenCL)
 ggml_add_backend(Hexagon)
+ggml_add_backend(ZenDNN)
 
 foreach (target ggml-base ggml)
     target_include_directories(${target} PUBLIC    $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/../include> $<INSTALL_INTERFACE:include>)
diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp
index 88e6dc45c7..4181a714ad 100644
--- a/ggml/src/ggml-backend-reg.cpp
+++ b/ggml/src/ggml-backend-reg.cpp
@@ -73,6 +73,10 @@
 #include "ggml-cann.h"
 #endif
 
+#ifdef GGML_USE_ZENDNN
+#include "ggml-zendnn.h"
+#endif
+
 // disable C++17 deprecation warning for std::codecvt_utf8
 #if defined(__clang__)
 #    pragma clang diagnostic push
@@ -203,6 +207,9 @@ struct ggml_backend_registry {
 #ifdef GGML_USE_OPENCL
         register_backend(ggml_backend_opencl_reg());
 #endif
+#ifdef GGML_USE_ZENDNN
+        register_backend(ggml_backend_zendnn_reg());
+#endif
 #ifdef GGML_USE_HEXAGON
         register_backend(ggml_backend_hexagon_reg());
 #endif
@@ -605,6 +612,7 @@ void ggml_backend_load_all_from_path(const char * dir_path) {
 #endif
 
     ggml_backend_load_best("blas", silent, dir_path);
+    ggml_backend_load_best("zendnn", silent, dir_path);
     ggml_backend_load_best("cann", silent, dir_path);
     ggml_backend_load_best("cuda", silent, dir_path);
     ggml_backend_load_best("hip", silent, dir_path);
diff --git a/ggml/src/ggml-zendnn/CMakeLists.txt b/ggml/src/ggml-zendnn/CMakeLists.txt
new file mode 100644
index 0000000000..bdbfc74369
--- /dev/null
+++ b/ggml/src/ggml-zendnn/CMakeLists.txt
@@ -0,0 +1,92 @@
+ggml_add_backend_library(ggml-zendnn
+                         ggml-zendnn.cpp)
+
+# Get ZenDNN path
+if (NOT DEFINED ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "")
+    set(ZENDNN_ROOT "$ENV{ZENDNN_ROOT}")
+endif()
+
+# Check if path is still empty or OFF
+if (NOT ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "" OR ZENDNN_ROOT STREQUAL "OFF")
+    message(STATUS "ZENDNN_ROOT not set. Automatically downloading and building ZenDNN...")
+    message(STATUS "This will take several minutes on first build...")
+
+    include(ExternalProject)
+
+    set(ZENDNN_PREFIX      ${CMAKE_BINARY_DIR}/_deps/zendnn-prefix)
+    set(ZENDNN_SOURCE_DIR  ${ZENDNN_PREFIX}/src/zendnn)
+    set(ZENDNN_BUILD_DIR   ${ZENDNN_PREFIX}/build)
+    set(ZENDNN_INSTALL_DIR ${ZENDNN_BUILD_DIR}/install)
+
+    ExternalProject_Add(
+        zendnn
+        GIT_REPOSITORY https://github.com/amd/ZenDNN.git
+        GIT_TAG zendnnl
+        PREFIX      ${ZENDNN_PREFIX}
+        SOURCE_DIR  ${ZENDNN_SOURCE_DIR}
+        BINARY_DIR  ${ZENDNN_BUILD_DIR}
+        CMAKE_ARGS
+            -DCMAKE_BUILD_TYPE=Release
+            -DCMAKE_INSTALL_PREFIX=${ZENDNN_INSTALL_DIR}
+            -DZENDNNL_BUILD_EXAMPLES=OFF
+            -DZENDNNL_BUILD_DOXYGEN=OFF
+            -DZENDNNL_BUILD_GTEST=OFF
+            -DZENDNNL_BUILD_BENCHDNN=OFF
+            # Enable ALL matmul algorithm backends
+            -DZENDNNL_DEPENDS_AOCLDLP=ON
+            -DZENDNNL_DEPENDS_ONEDNN=ON
+            -DZENDNNL_DEPENDS_LIBXSMM=ON
+        BUILD_COMMAND   ${CMAKE_COMMAND} --build ${ZENDNN_BUILD_DIR} --target zendnnl
+        INSTALL_COMMAND ${CMAKE_COMMAND} --build ${ZENDNN_BUILD_DIR} --target install
+        BUILD_ALWAYS OFF
+        LOG_DOWNLOAD ON
+        LOG_CONFIGURE ON
+        LOG_BUILD ON
+        LOG_INSTALL ON
+    )
+
+    # Add dependency so ZenDNN builds before our library
+    add_dependencies(ggml-zendnn zendnn)
+
+    # Set ZENDNN_ROOT to the installation directory
+    set(ZENDNN_ROOT ${ZENDNN_INSTALL_DIR})
+
+    message(STATUS "ZenDNN will be built to: ${ZENDNN_ROOT}")
+else()
+    message(STATUS "Using custom ZenDNN installation at: ${ZENDNN_ROOT}")
+endif()
+
+# ZenDNN headers + libs
+target_include_directories(ggml-zendnn PRIVATE
+    ${ZENDNN_ROOT}/zendnnl/include
+    ${ZENDNN_ROOT}/deps/aocldlp/include
+    ${ZENDNN_ROOT}/deps/aoclutils/include
+    ${ZENDNN_ROOT}/deps/json/include
+    ${ZENDNN_ROOT}/deps/libxsmm/include
+    ${ZENDNN_ROOT}/deps/onednn/include
+)
+
+target_link_directories(ggml-zendnn PRIVATE
+    ${ZENDNN_ROOT}/zendnnl/lib
+    ${ZENDNN_ROOT}/deps/aocldlp/lib
+    ${ZENDNN_ROOT}/deps/aoclutils/lib
+    ${ZENDNN_ROOT}/deps/libxsmm/lib
+    ${ZENDNN_ROOT}/deps/onednn/lib
+)
+
+target_link_libraries(ggml-zendnn PRIVATE
+    zendnnl_archive    # ZenDNN main
+    aocl-dlp           # AOCL libraries
+    aoclutils
+    au_cpuid
+    dnnl               # OneDNN
+    xsmm               # libxsmm small matrix math
+    xsmmext
+    xsmmnoblas
+    m
+    pthread
+)
+
+if (GGML_OPENMP)
+    target_link_libraries(ggml-zendnn PRIVATE OpenMP::OpenMP_CXX)
+endif()
diff --git a/ggml/src/ggml-zendnn/ggml-zendnn.cpp b/ggml/src/ggml-zendnn/ggml-zendnn.cpp
new file mode 100644
index 0000000000..fd07f983da
--- /dev/null
+++ b/ggml/src/ggml-zendnn/ggml-zendnn.cpp
@@ -0,0 +1,466 @@
+#include "ggml-zendnn.h"
+
+#include "ggml-backend-impl.h"
+#include "ggml-impl.h"
+#include "ggml-cpu.h"
+#include "zendnnl.hpp"
+
+#include <cstring>
+
+
+struct ggml_backend_zendnn_context {
+    int n_threads = GGML_DEFAULT_N_THREADS;
+    std::unique_ptr<char[]> work_data;
+    size_t work_size = 0;
+};
+
+template<typename T>
+zendnnl::common::data_type_t ggml_to_zendnn_type() {
+    if constexpr (std::is_same_v<T, float>) {
+        return zendnnl::common::data_type_t::f32;
+    } else if constexpr (std::is_same_v<T, ggml_bf16_t>) {
+        return zendnnl::common::data_type_t::bf16;
+    } else {
+        return zendnnl::common::data_type_t::none;
+    }
+}
+
+/**
+ * ZenDNN matmul: computes C = B * A.
+ *
+ * - A: weights, shape (k, m), column-major (each column is a weight vector for one output).
+ * - B: input, shape (n, k), row-major (each row is an input sample).
+ * - C: output, shape (n, m), row-major.
+ *
+ * Dimensions:
+ *   m = output features (columns of C, columns of A)
+ *   n = batch size      (rows of C, rows of B)
+ *   k = inner dimension (columns of B, rows of A)
+ */
+template <typename TA, typename TB, typename TC>
+static bool ggml_zendnn_matmul(ggml_backend_zendnn_context * ctx, int64_t m, int64_t n, int64_t k,
+                               const TA * A, int64_t lda, const TB * B, int64_t ldb, TC * C,
+                               int64_t ldc) {
+
+    zendnnl::lowoha::lowoha_params params;
+    params.dtypes.src = ggml_to_zendnn_type<TB>();
+    params.dtypes.wei = ggml_to_zendnn_type<TA>();
+    params.dtypes.dst = ggml_to_zendnn_type<TC>();
+    params.num_threads = ctx->n_threads;
+
+    zendnnl::lowoha::status_t status = zendnnl::lowoha::matmul_direct(
+        'r', false, true,   // row-major, don't transpose B, transpose A (because it's column-major)
+        n,                  // M: rows of B and C
+        m,                  // N: cols of A^T and C
+        k,                  // K: cols of B, rows of A
+        1.0f,               // alpha
+        B, ldb,             // src: B[n,k]
+        A, lda,             // weight: A[k,m] column-major (transposed)
+        nullptr,            // bias
+        0.0f,               // beta
+        C, ldc,             // output C[n,m]
+        true,               // is_weights_const
+        {},                 // batch_params
+        params              // params
+    );
+
+    if (status != zendnnl::lowoha::status_t::success) {
+        GGML_LOG_ERROR("%s, ZenDNN matmul failed: status=%d\n", __func__, static_cast<int>(status));
+        return false;
+    }
+    return true;
+}
+
+static bool ggml_zendnn_sgemm(ggml_backend_zendnn_context * ctx, int64_t m, int64_t n, int64_t k,
+                              const void * A, int64_t lda, const void * B, int64_t ldb, void * C,
+                              int64_t ldc, int Atype, int Btype, int Ctype) {
+
+    assert(m >= 0);
+    assert(n >= 0);
+    assert(k >= 0);
+    assert(lda >= k);
+    assert(ldb >= k);
+    assert(ldc >= m);
+
+    // categorize types
+    switch (Atype) {
+        case GGML_TYPE_F32:
+            if (Btype != GGML_TYPE_F32 || Ctype != GGML_TYPE_F32)
+                return false;
+            return ggml_zendnn_matmul<float, float, float>(
+                ctx, m, n, k,
+                (const float *)A, lda,
+                (const float *)B, ldb,
+                (float *)C, ldc);
+        case GGML_TYPE_BF16:
+            if (Btype != GGML_TYPE_BF16)
+                return false;
+            if (Ctype == GGML_TYPE_BF16)
+                return ggml_zendnn_matmul<ggml_bf16_t, ggml_bf16_t, ggml_bf16_t>(
+                    ctx, m, n, k,
+                    (const ggml_bf16_t *)A, lda,
+                    (const ggml_bf16_t *)B, ldb,
+                    (ggml_bf16_t *)C, ldc);
+            if (Ctype == GGML_TYPE_F32)
+                return ggml_zendnn_matmul<ggml_bf16_t, ggml_bf16_t, float>(
+                    ctx, m, n, k,
+                    (const ggml_bf16_t *)A, lda,
+                    (const ggml_bf16_t *)B, ldb,
+                    (float *)C, ldc);
+            return false;
+        default:
+            return false; // unsupported type
+    }
+}
+
+static void ggml_zendnn_compute_forward_mul_mat(
+    ggml_backend_zendnn_context * ctx,
+    ggml_tensor * dst) {
+
+    const ggml_tensor * src0 = dst->src[0];  // weights
+    const ggml_tensor * src1 = dst->src[1];  // inputs
+
+    GGML_TENSOR_BINARY_OP_LOCALS
+
+    ggml_type         const vec_dot_type = ggml_get_type_traits_cpu(src0->type)->vec_dot_type;
+    ggml_from_float_t const from_float = ggml_get_type_traits_cpu(vec_dot_type)->from_float;
+
+    GGML_ASSERT(ne0 == ne01);
+    GGML_ASSERT(ne1 == ne11);
+    GGML_ASSERT(ne2 == ne12);
+    GGML_ASSERT(ne3 == ne13);
+
+    // we don't support permuted src0 or src1
+    GGML_ASSERT(nb00 == ggml_type_size(src0->type));
+    GGML_ASSERT(nb10 == ggml_type_size(src1->type));
+
+    // dst cannot be transposed or permuted
+    GGML_ASSERT(nb0 == sizeof(float));
+    GGML_ASSERT(nb0 <= nb1);
+    GGML_ASSERT(nb1 <= nb2);
+    GGML_ASSERT(nb2 <= nb3);
+
+    // broadcast factors
+    const int64_t r2 = ne12/ne02;
+    const int64_t r3 = ne13/ne03;
+
+    void * work_data = ctx->work_data.get();
+    if (src1->type != vec_dot_type) {
+        const size_t nbw1 = ggml_row_size(vec_dot_type, ne10);
+        const size_t nbw2 = nbw1 * ne11;
+        const size_t nbw3 = nbw2 * ne12;
+        const size_t desired_wsize = ne13 * nbw3;
+        if (ctx->work_size < desired_wsize) {
+            ctx->work_data.reset(new char[desired_wsize]);
+            ctx->work_size = desired_wsize;
+        }
+        work_data = ctx->work_data.get();
+
+        // #pragma omp parallel for num_threads(ctx->n_threads)
+        #pragma omp parallel for collapse(3) num_threads(ctx->n_threads) schedule(static)
+        for (int64_t i13 = 0; i13 < ne13; ++i13) {
+            for (int64_t i12 = 0; i12 < ne12; ++i12) {
+                for (int64_t i11 = 0; i11 < ne11; ++i11) {
+                    const float * src1_f32 = (float *)((char *)src1->data + i11*nb11 + i12*nb12 + i13*nb13);
+                    void * src1_conv = (char *)work_data + i11*nbw1 + i12*nbw2 + i13*nbw3;
+                    from_float(src1_f32, src1_conv, ne10);
+                }
+            }
+        }
+    }
+
+    for (int64_t i13 = 0; i13 < ne13; i13++) {
+        for (int64_t i12 = 0; i12 < ne12; i12++) {
+            const void* wdata = src1->type == vec_dot_type ? src1->data : work_data;
+            const size_t row_size = ggml_row_size(vec_dot_type, ne10);
+            if (!ggml_zendnn_sgemm(ctx,
+                                  ne01,     // m
+                                  ne11,     // n
+                                  ne10,     // k
+                                  static_cast<const char *>(src0->data) + (i12/r2)*nb02 + (i13/r3)*nb03,
+                                  ne00,     // lda
+                                  static_cast<const char *>(wdata) + (i12*ne11 + i13*ne12*ne11)*row_size,
+                                  ne10,     // ldb
+                                  static_cast<char *>(dst->data) + i12*nb2 + i13*nb3,
+                                  ne01,     // ldc
+                                  src0->type,
+                                  vec_dot_type,
+                                  dst->type))
+                GGML_ABORT("%s: ZenDNN sgemm failed\n", __func__);
+        }
+    }
+}
+
+// backend interface
+
+static const char * ggml_backend_zendnn_get_name(ggml_backend_t backend) {
+    return "ZenDNN";
+
+    GGML_UNUSED(backend);
+}
+
+static void ggml_backend_zendnn_free(ggml_backend_t backend) {
+    ggml_backend_zendnn_context * ctx = (ggml_backend_zendnn_context *)backend->context;
+    delete ctx;
+    delete backend;
+}
+
+static ggml_status ggml_backend_zendnn_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) {
+    ggml_backend_zendnn_context * ctx = (ggml_backend_zendnn_context *)backend->context;
+
+    for (int i = 0; i < cgraph->n_nodes; i++) {
+        struct ggml_tensor * node = cgraph->nodes[i];
+
+        switch (node->op) {
+            case GGML_OP_MUL_MAT:
+                ggml_zendnn_compute_forward_mul_mat(ctx, node);
+                break;
+            case GGML_OP_NONE:
+            case GGML_OP_RESHAPE:
+            case GGML_OP_VIEW:
+            case GGML_OP_PERMUTE:
+            case GGML_OP_TRANSPOSE:
+                break;
+
+            default:
+                GGML_ABORT("%s: unsupported op %s\n", __func__, ggml_op_desc(node));
+        }
+    }
+
+    return GGML_STATUS_SUCCESS;
+
+    GGML_UNUSED(backend);
+}
+
+static struct ggml_backend_i ggml_backend_zendnn_i = {
+    /* .get_name                = */ ggml_backend_zendnn_get_name,
+    /* .free                    = */ ggml_backend_zendnn_free,
+    /* .set_tensor_async        = */ NULL,
+    /* .get_tensor_async        = */ NULL,
+    /* .cpy_tensor_async        = */ NULL,
+    /* .synchronize             = */ NULL,
+    /* .graph_plan_create       = */ NULL,
+    /* .graph_plan_free         = */ NULL,
+    /* .graph_plan_update       = */ NULL,
+    /* .graph_plan_compute      = */ NULL,
+    /* .graph_compute           = */ ggml_backend_zendnn_graph_compute,
+    /* .event_record            = */ NULL,
+    /* .event_wait              = */ NULL,
+    /* .graph_optimize          = */ NULL,
+};
+
+static ggml_guid_t ggml_backend_zendnn_guid(void) {
+    static const char * guid_str = "AMD-ZENDNN-ACCEL";
+    return reinterpret_cast<ggml_guid_t>(const_cast<char*>(guid_str));
+}
+
+ggml_backend_t ggml_backend_zendnn_init(void) {
+    ggml_backend_zendnn_context * ctx = new ggml_backend_zendnn_context;
+
+    ggml_backend_t backend = new ggml_backend {
+        /* .guid    = */ ggml_backend_zendnn_guid(),
+        /* .iface   = */ ggml_backend_zendnn_i,
+        /* .device  = */ ggml_backend_reg_dev_get(ggml_backend_zendnn_reg(), 0),
+        /* .context = */ ctx,
+    };
+
+    return backend;
+}
+
+bool ggml_backend_is_zendnn(ggml_backend_t backend) {
+    return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_zendnn_guid());
+}
+
+void ggml_backend_zendnn_set_n_threads(ggml_backend_t backend_zendnn, int n_threads) {
+    GGML_ASSERT(ggml_backend_is_zendnn(backend_zendnn));
+
+    ggml_backend_zendnn_context * ctx = (ggml_backend_zendnn_context *)backend_zendnn->context;
+    ctx->n_threads = n_threads;
+}
+
+// device interface
+static const char * ggml_backend_zendnn_device_get_name(ggml_backend_dev_t dev) {
+    return "ZenDNN";
+
+    GGML_UNUSED(dev);
+}
+/**
+ * ZenDNN is AMD's performance library providing optimized primitives and implementations
+ * for deep learning workloads on AMD CPUs. It targets improved performance for common
+ * neural network operations on AMD architectures. For more information, see:
+ * https://www.amd.com/en/developer/zendnn.html
+ */
+static const char * ggml_backend_zendnn_device_get_description(ggml_backend_dev_t dev) {
+    return "ZenDNN: AMD optimized primitives backend for GGML (optimized for AMD CPUs)";
+
+    GGML_UNUSED(dev);
+}
+
+static void ggml_backend_zendnn_device_get_memory(ggml_backend_dev_t dev, size_t * free, size_t * total) {
+    *free  = 0;
+    *total = 0;
+
+    GGML_UNUSED(dev);
+}
+
+static enum ggml_backend_dev_type ggml_backend_zendnn_device_get_type(ggml_backend_dev_t dev) {
+    return GGML_BACKEND_DEVICE_TYPE_ACCEL;
+
+    GGML_UNUSED(dev);
+}
+
+static void ggml_backend_zendnn_device_get_props(ggml_backend_dev_t dev, struct ggml_backend_dev_props * props) {
+    props->name        = ggml_backend_zendnn_device_get_name(dev);
+    props->description = ggml_backend_zendnn_device_get_description(dev);
+    props->type        = ggml_backend_zendnn_device_get_type(dev);
+    ggml_backend_zendnn_device_get_memory(dev, &props->memory_free, &props->memory_total);
+    props->caps = {
+        /* .async                = */ false,
+        /* .host_buffer          = */ false,
+        /* .buffer_from_host_ptr = */ true,
+        /* .events               = */ false
+    };
+}
+
+static ggml_backend_t ggml_backend_zendnn_device_init_backend(ggml_backend_dev_t dev, const char * params) {
+    ggml_backend_t backend = ggml_backend_zendnn_init();
+    if (backend == NULL) {
+        GGML_LOG_ERROR("%s: error: failed to initialize ZenDNN backend\n", __func__);
+        return NULL;
+    }
+
+    return backend;
+
+    GGML_UNUSED(dev);
+    GGML_UNUSED(params);
+}
+
+static ggml_backend_buffer_type_t ggml_backend_zendnn_device_get_buffer_type(ggml_backend_dev_t dev) {
+    return ggml_backend_cpu_buffer_type();
+
+    GGML_UNUSED(dev);
+}
+
+static ggml_backend_buffer_t ggml_backend_zendnn_device_buffer_from_host_ptr(ggml_backend_dev_t dev, void * ptr, size_t size, size_t max_tensor_size) {
+    return ggml_backend_cpu_buffer_from_ptr(ptr, size);
+
+    GGML_UNUSED(dev);
+    GGML_UNUSED(max_tensor_size);
+}
+
+static bool ggml_backend_zendnn_device_supports_op(ggml_backend_dev_t dev, const struct ggml_tensor * op) {
+    switch (op->op) {
+        case GGML_OP_NONE:
+        case GGML_OP_RESHAPE:
+        case GGML_OP_VIEW:
+        case GGML_OP_PERMUTE:
+        case GGML_OP_TRANSPOSE:
+            return true;
+
+        case GGML_OP_MUL_MAT:
+        {
+            const ggml_tensor * weights = op->src[0];
+            const ggml_tensor * inputs = op->src[1];
+
+            const int64_t ne10 = inputs->ne[0];
+            const int64_t ne0 = op->ne[0];
+            const int64_t ne1 = op->ne[1];
+
+            const int64_t min_batch = 1;
+            if (!ggml_is_contiguous(weights) || !ggml_is_contiguous(inputs) ||
+                ne0 < min_batch || ne1 < min_batch || ne10 < min_batch) {
+                    return false;
+            }
+            switch (weights->type) {
+                case GGML_TYPE_F32:
+                case GGML_TYPE_BF16:
+                    return true;
+                default:
+                    return false;
+            }
+        } break;
+
+        default:
+            return false;
+    }
+
+    GGML_UNUSED(dev);
+}
+
+static bool ggml_backend_zendnn_device_supports_buft(ggml_backend_dev_t dev, ggml_backend_buffer_type_t buft) {
+    return ggml_backend_buft_is_host(buft);
+
+    GGML_UNUSED(dev);
+}
+
+static const struct ggml_backend_device_i ggml_backend_zendnn_device_i = {
+    /* .get_name               = */ ggml_backend_zendnn_device_get_name,
+    /* .get_description        = */ ggml_backend_zendnn_device_get_description,
+    /* .get_memory             = */ ggml_backend_zendnn_device_get_memory,
+    /* .get_type               = */ ggml_backend_zendnn_device_get_type,
+    /* .get_props              = */ ggml_backend_zendnn_device_get_props,
+    /* .init_backend           = */ ggml_backend_zendnn_device_init_backend,
+    /* .get_buffer_type        = */ ggml_backend_zendnn_device_get_buffer_type,
+    /* .get_host_buffer_type   = */ NULL,
+    /* .buffer_from_host_ptr   = */ ggml_backend_zendnn_device_buffer_from_host_ptr,
+    /* .supports_op            = */ ggml_backend_zendnn_device_supports_op,
+    /* .supports_buft          = */ ggml_backend_zendnn_device_supports_buft,
+    /* .offload_op             = */ NULL,
+    /* .event_new              = */ NULL,
+    /* .event_free             = */ NULL,
+    /* .event_synchronize      = */ NULL,
+};
+
+// backend reg interface
+static const char * ggml_backend_zendnn_reg_get_name(ggml_backend_reg_t reg) {
+    return "ZenDNN";
+
+    GGML_UNUSED(reg);
+}
+
+static size_t ggml_backend_zendnn_reg_get_device_count(ggml_backend_reg_t reg) {
+    return 1;
+
+    GGML_UNUSED(reg);
+}
+
+static ggml_backend_dev_t ggml_backend_zendnn_reg_get_device(ggml_backend_reg_t reg, size_t index) {
+    GGML_ASSERT(index == 0);
+
+    static ggml_backend_device ggml_backend_zendnn_device = {
+        /* .iface   = */ ggml_backend_zendnn_device_i,
+        /* .reg     = */ reg,
+        /* .context = */ nullptr,
+    };
+
+    return &ggml_backend_zendnn_device;
+}
+
+static void * ggml_backend_zendnn_get_proc_address(ggml_backend_reg_t reg, const char * name) {
+    if (std::strcmp(name, "ggml_backend_set_n_threads") == 0) {
+        return (void *) ggml_backend_zendnn_set_n_threads;
+    }
+    return NULL;
+
+    GGML_UNUSED(reg);
+    GGML_UNUSED(name);
+}
+
+static const struct ggml_backend_reg_i ggml_backend_zendnn_reg_i = {
+    /* .get_name         = */ ggml_backend_zendnn_reg_get_name,
+    /* .get_device_count = */ ggml_backend_zendnn_reg_get_device_count,
+    /* .get_device       = */ ggml_backend_zendnn_reg_get_device,
+    /* .get_proc_address = */ ggml_backend_zendnn_get_proc_address,
+};
+
+ggml_backend_reg_t ggml_backend_zendnn_reg(void) {
+    static struct ggml_backend_reg ggml_backend_zendnn_reg = {
+        /* .api_version = */ GGML_BACKEND_API_VERSION,
+        /* .iface       = */ ggml_backend_zendnn_reg_i,
+        /* .context     = */ NULL,
+    };
+
+    return &ggml_backend_zendnn_reg;
+}
+
+GGML_BACKEND_DL_IMPL(ggml_backend_zendnn_reg)
diff --git a/tools/llama-bench/llama-bench.cpp b/tools/llama-bench/llama-bench.cpp
index 852a512451..0be6ed6948 100644
--- a/tools/llama-bench/llama-bench.cpp
+++ b/tools/llama-bench/llama-bench.cpp
@@ -1737,7 +1737,8 @@ struct markdown_printer : public printer {
         fields.emplace_back("params");
         fields.emplace_back("backend");
         bool is_cpu_backend = test::get_backend().find("CPU") != std::string::npos ||
-                              test::get_backend().find("BLAS") != std::string::npos;
+                              test::get_backend().find("BLAS") != std::string::npos ||
+                              test::get_backend().find("ZenDNN") != std::string::npos;
         if (!is_cpu_backend) {
             fields.emplace_back("n_gpu_layers");
         }

From db97837385edfbc772230debbd49e5efae843a71 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 6 Dec 2025 11:46:46 -0600
Subject: [PATCH 009/135] vulkan: perf_logger improvements (#17672)

* vulkan: perf_logger improvements

- Move perf_logger from device to ctx.
- Add an env var to control the frequency we dump the stats. If you set a very
large value, it just dumps when the ctx is destroyed.
- Add a fusion info string to the tracking, only log one item per fused op.
- Fix MUL_MAT_ID flops calculation.

* fix vector sizes
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 120 +++++++++++++++++++--------
 1 file changed, 85 insertions(+), 35 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 2787f63d0e..c6f5809ccd 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -777,11 +777,6 @@ struct vk_device_struct {
     std::unique_ptr<vk_memory_logger> memory_logger;
 #endif
 
-    // for GGML_VK_PERF_LOGGER
-    std::unique_ptr<vk_perf_logger> perf_logger;
-    vk::QueryPool query_pool;
-    int32_t num_queries;
-
     ~vk_device_struct() {
         VK_LOG_DEBUG("destroy device " << name);
 
@@ -1523,12 +1518,21 @@ private:
 #define VK_LOG_MEMORY(msg) ((void) 0)
 #endif // GGML_VULKAN_MEMORY_DEBUG
 
+static bool vk_perf_logger_enabled = false;
+// number of calls between perf logger prints
+static uint32_t vk_perf_logger_frequency = 1;
+
 class vk_perf_logger {
   public:
-    void print_timings() {
+    void print_timings(bool force = false) {
         if (timings.empty()) {
             return;
         }
+        print_count++;
+        if ((print_count % vk_perf_logger_frequency) != 0 && !force) {
+            return;
+        }
+        print_count = 0;
         uint64_t total_all_op_times = 0;
         std::cerr << "----------------\nVulkan Timings:" << std::endl;
         for (const auto & t : timings) {
@@ -1565,16 +1569,20 @@ class vk_perf_logger {
         flops.clear();
     }
 
-    void log_timing(const ggml_tensor * node, uint64_t time) {
+    void log_timing(const ggml_tensor * node, const char *fusion_name, uint64_t time) {
+        std::string fusion_str;
+        if (fusion_name) {
+            fusion_str = fusion_name + std::string(" ");
+        }
         if (node->op == GGML_OP_UNARY) {
-            timings[ggml_unary_op_name(ggml_get_unary_op(node))].push_back(time);
+            timings[fusion_str + ggml_unary_op_name(ggml_get_unary_op(node))].push_back(time);
             return;
         }
         if (node->op == GGML_OP_MUL_MAT || node->op == GGML_OP_MUL_MAT_ID) {
-            const uint64_t m     = node->src[0]->ne[1];
-            const uint64_t n     = (node->op == GGML_OP_MUL_MAT) ? node->ne[1] : node->ne[2];
+            const uint64_t m     = node->ne[0];
+            const uint64_t n     = node->ne[1];
             const uint64_t k     = node->src[1]->ne[0];
-            const uint64_t batch = node->src[1]->ne[2] * node->src[1]->ne[3];
+            const uint64_t batch = node->ne[2] * node->ne[3];
             std::string    name  = ggml_op_name(node->op);
             if ((node->op == GGML_OP_MUL_MAT && n <= mul_mat_vec_max_cols) ||
                 (node->op == GGML_OP_MUL_MAT_ID && node->src[2]->ne[1] == 1)) {
@@ -1583,9 +1591,13 @@ class vk_perf_logger {
             name += " ";
             name += ggml_type_name(node->src[0]->type);
             name += " m=" + std::to_string(m) + " n=" + std::to_string(n) + " k=" + std::to_string(k);
+            if (node->op == GGML_OP_MUL_MAT_ID) {
+                name += " n_expert=" + std::to_string(node->src[0]->ne[2]);
+            }
             if (batch > 1) {
                 name += " batch=" + std::to_string(batch);
             }
+            name = fusion_str + name;
             timings[name].push_back(time);
             flops[name].push_back(m * n * (k + (k - 1)) * batch);
             return;
@@ -1607,6 +1619,7 @@ class vk_perf_logger {
             uint64_t      n_flops = size_M * size_N * (size_K + (size_K - 1));
             name += " M=Cout=" + std::to_string(size_M) + ", K=Cin*KW*KH=" + std::to_string(size_K) +
                     ", N=N*OW*OH=" + std::to_string(size_N);
+            name = fusion_str + name;
             flops[name].push_back(n_flops);
             timings[name].push_back(time);
             return;
@@ -1614,6 +1627,7 @@ class vk_perf_logger {
         if (node->op == GGML_OP_RMS_NORM) {
             std::string   name    = ggml_op_name(node->op);
             name += "(" + std::to_string(node->ne[0]) + "," + std::to_string(node->ne[1]) + "," + std::to_string(node->ne[2]) + "," + std::to_string(node->ne[3]) + ")";
+            name = fusion_str + name;
             timings[name].push_back(time);
             return;
         }
@@ -1624,6 +1638,7 @@ class vk_perf_logger {
             const ggml_tensor * v = node->src[2];
             const ggml_tensor * m = node->src[3];
             std::stringstream name;
+            name << fusion_str;
             name << ggml_op_name(node->op) <<
                 " dst(" << dst->ne[0] << "," << dst->ne[1] << "," << dst->ne[2] << "," << dst->ne[3] << "), " <<
                 " q(" << q->ne[0] << "," << q->ne[1] << "," << q->ne[2] << "," << q->ne[3] << "), " <<
@@ -1635,17 +1650,19 @@ class vk_perf_logger {
         }
         if (node->op == GGML_OP_TOP_K) {
             std::stringstream name;
+            name << fusion_str;
             name << ggml_op_name(node->op) <<
                 " K=" << node->ne[0] <<
                 " (" << node->src[0]->ne[0] << "," << node->src[0]->ne[1] << "," << node->src[0]->ne[2] << "," << node->src[0]->ne[3] << ")";
             timings[name.str()].push_back(time);
             return;
         }
-        timings[ggml_op_name(node->op)].push_back(time);
+        timings[fusion_str + ggml_op_name(node->op)].push_back(time);
     }
   private:
     std::map<std::string, std::vector<uint64_t>> timings;
     std::map<std::string, std::vector<uint64_t>> flops;
+    uint32_t print_count {};
 };
 
 struct ggml_backend_vk_context {
@@ -1699,6 +1716,14 @@ struct ggml_backend_vk_context {
     // Bit 'i' means nodes[start_of_fusion + i] writes to memory.
     // If there's no fusion, bit 0 is still set.
     int fused_ops_write_mask {};
+
+    // for GGML_VK_PERF_LOGGER
+    std::unique_ptr<vk_perf_logger> perf_logger;
+    vk::QueryPool query_pool;
+    std::vector<const char *> query_fusion_names;
+    std::vector<ggml_tensor *> query_nodes;
+    int32_t num_queries {};
+    int32_t query_idx {};
 };
 
 static void * const vk_ptr_base = (void *)(uintptr_t) 0x1000;  // NOLINT
@@ -1824,8 +1849,6 @@ struct vk_instance_t {
 static bool vk_instance_initialized = false;
 static vk_instance_t vk_instance;
 
-static bool vk_perf_logger_enabled = false;
-
 #ifdef GGML_VULKAN_CHECK_RESULTS
 static size_t vk_skip_checks;
 static size_t vk_output_tensor;
@@ -4205,9 +4228,6 @@ static vk_device ggml_vk_get_device(size_t idx) {
 #ifdef GGML_VULKAN_MEMORY_DEBUG
         device->memory_logger = std::unique_ptr<vk_memory_logger>(new vk_memory_logger());
 #endif
-        if (vk_perf_logger_enabled) {
-            device->perf_logger = std::unique_ptr<vk_perf_logger>(new vk_perf_logger());
-        }
 
         size_t dev_num = vk_instance.device_indices[idx];
 
@@ -5153,6 +5173,11 @@ static void ggml_vk_instance_init() {
     }
 
     vk_perf_logger_enabled = getenv("GGML_VK_PERF_LOGGER") != nullptr;
+    const char* GGML_VK_PERF_LOGGER_FREQUENCY = getenv("GGML_VK_PERF_LOGGER_FREQUENCY");
+
+    if (GGML_VK_PERF_LOGGER_FREQUENCY != nullptr) {
+        vk_perf_logger_frequency = std::stoul(GGML_VK_PERF_LOGGER_FREQUENCY);
+    }
 
     // See https://github.com/KhronosGroup/Vulkan-Hpp?tab=readme-ov-file#extensions--per-device-function-pointers-
     VULKAN_HPP_DEFAULT_DISPATCHER.init(vk_instance.instance);
@@ -5330,6 +5355,10 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) {
     ctx->compute_cmd_pool.init(ctx->device, &ctx->device->compute_queue);
     ctx->transfer_cmd_pool.init(ctx->device, &ctx->device->transfer_queue);
 
+    if (vk_perf_logger_enabled) {
+        ctx->perf_logger = std::unique_ptr<vk_perf_logger>(new vk_perf_logger());
+    }
+
 #ifdef GGML_VULKAN_CHECK_RESULTS
     const char* skip_checks = getenv("GGML_VULKAN_SKIP_CHECKS");
     vk_skip_checks = (skip_checks == NULL ? 0 : atoi(skip_checks));
@@ -12205,6 +12234,9 @@ static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) {
 
     ctx->compute_cmd_pool.destroy(ctx->device->device);
     ctx->transfer_cmd_pool.destroy(ctx->device->device);
+    if (vk_perf_logger_enabled) {
+        ctx->perf_logger->print_timings(true);
+    }
 }
 
 static int ggml_vk_get_device_count() {
@@ -13003,24 +13035,29 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
     vk_context compute_ctx;
     if (vk_perf_logger_enabled) {
         // allocate/resize the query pool
-        if (ctx->device->num_queries < cgraph->n_nodes + 1) {
-            if (ctx->device->query_pool) {
-                ctx->device->device.destroyQueryPool(ctx->device->query_pool);
+        if (ctx->num_queries < cgraph->n_nodes + 1) {
+            if (ctx->query_pool) {
+                ctx->device->device.destroyQueryPool(ctx->query_pool);
             }
             vk::QueryPoolCreateInfo query_create_info;
             query_create_info.queryType = vk::QueryType::eTimestamp;
             query_create_info.queryCount = cgraph->n_nodes + 100;
-            ctx->device->query_pool = ctx->device->device.createQueryPool(query_create_info);
-            ctx->device->num_queries = query_create_info.queryCount;
+            ctx->query_pool = ctx->device->device.createQueryPool(query_create_info);
+            ctx->num_queries = query_create_info.queryCount;
+            ctx->query_fusion_names.resize(ctx->num_queries);
+            ctx->query_nodes.resize(ctx->num_queries);
         }
 
-        ctx->device->device.resetQueryPool(ctx->device->query_pool, 0, cgraph->n_nodes+1);
+        ctx->device->device.resetQueryPool(ctx->query_pool, 0, cgraph->n_nodes+1);
+        std::fill(ctx->query_fusion_names.begin(), ctx->query_fusion_names.end(), nullptr);
+        std::fill(ctx->query_nodes.begin(), ctx->query_nodes.end(), nullptr);
 
         GGML_ASSERT(ctx->compute_ctx.expired());
         compute_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
         ctx->compute_ctx = compute_ctx;
         ggml_vk_ctx_begin(ctx->device, compute_ctx);
-        compute_ctx->s->buffer.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->device->query_pool, 0);
+        ctx->query_idx = 0;
+        compute_ctx->s->buffer.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->query_pool, ctx->query_idx++);
     }
 
     ctx->prealloc_y_last_pipeline_used = nullptr;
@@ -13061,52 +13098,66 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
             total_mul_mat_bytes += bytes;
         }
 
+        const char *fusion_string {};
         if (!ctx->device->disable_fusion) {
             uint32_t num_adds = ggml_vk_fuse_multi_add(ctx, cgraph, i);
             if (num_adds) {
                 ctx->num_additional_fused_ops = num_adds - 1;
+                fusion_string = "MULTI_ADD";
             } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT, GGML_OP_ADD, GGML_OP_ADD })) {
                 ctx->num_additional_fused_ops = 2;
+                fusion_string = "MUL_MAT_ADD_ADD";
             } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT, GGML_OP_ADD })) {
                 ctx->num_additional_fused_ops = 1;
+                fusion_string = "MUL_MAT_ADD";
             } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT_ID, GGML_OP_ADD_ID, GGML_OP_MUL })) {
                 ctx->num_additional_fused_ops = 2;
+                fusion_string = "MUL_MAT_ID_ADD_ID_MUL";
             } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT_ID, GGML_OP_ADD_ID })) {
                 ctx->num_additional_fused_ops = 1;
+                fusion_string = "MUL_MAT_ID_ADD_ID";
             } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT_ID, GGML_OP_MUL })) {
                 ctx->num_additional_fused_ops = 1;
+                fusion_string = "MUL_MAT_ID_MUL";
             } else if (ggml_can_fuse_subgraph(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS }, { i + 4 }) &&
                        ggml_check_edges(cgraph, i, rms_norm_mul_rope_view_set_rows_edges) &&
                        ggml_vk_can_fuse_rms_norm_mul_rope(ctx, cgraph, i) &&
                        ggml_vk_can_fuse_rope_set_rows(ctx, cgraph, i + 2)) {
                 ctx->num_additional_fused_ops = 4;
+                fusion_string = "RMS_NORM_MUL_ROPE_VIEW_SET_ROWS";
             } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ROPE })&&
                        ggml_vk_can_fuse_rms_norm_mul_rope(ctx, cgraph, i)) {
                 ctx->num_additional_fused_ops = 2;
+                fusion_string = "RMS_NORM_MUL_ROPE";
             } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
                 ctx->num_additional_fused_ops = 1;
+                fusion_string = "RMS_NORM_MUL";
             } else if (ggml_can_fuse_subgraph(cgraph, i, { GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS }, { i + 2 }) &&
                        ggml_check_edges(cgraph, i, rope_view_set_rows_edges) &&
                        ggml_vk_can_fuse_rope_set_rows(ctx, cgraph, i)) {
                 ctx->num_additional_fused_ops = 2;
+                fusion_string = "ROPE_VIEW_SET_ROWS";
             } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_early_softmax_norm, { i + 3, i + 9 }) &&
                        ggml_check_edges(cgraph, i, topk_moe_early_softmax_norm_edges) &&
                        ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_EARLY_SOFTMAX_NORM)) {
                 ctx->num_additional_fused_ops = topk_moe_early_softmax_norm.size() - 1;
                 // view of argsort writes to memory
                 ctx->fused_ops_write_mask |= 1 << 3;
+                fusion_string = "TOPK_MOE_EARLY_SOFTMAX_NORM";
             } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_early_softmax, { i + 3, i + 4 }) &&
                        ggml_check_edges(cgraph, i, topk_moe_early_softmax_edges) &&
                        ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_EARLY_SOFTMAX)) {
                 ctx->num_additional_fused_ops = topk_moe_early_softmax.size() - 1;
                 // view of argsort writes to memory
                 ctx->fused_ops_write_mask |= 1 << 3;
+                fusion_string = "TOPK_MOE_EARLY_SOFTMAX";
             } else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_late_softmax, { i + 1, i + 5 }) &&
                        ggml_check_edges(cgraph, i, topk_moe_late_softmax_edges) &&
                        ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_LATE_SOFTMAX)) {
                 ctx->num_additional_fused_ops = topk_moe_late_softmax.size() - 1;
                 // view of argsort writes to memory
                 ctx->fused_ops_write_mask |= 1 << 1;
+                fusion_string = "TOPK_MOE_LATE_SOFTMAX";
             }
         }
         ctx->fused_ops_write_mask |= 1 << ctx->num_additional_fused_ops;
@@ -13120,7 +13171,7 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
 
         bool enqueued = ggml_vk_build_graph(ctx, cgraph, i, cgraph->nodes[submit_node_idx], submit_node_idx, i + ctx->num_additional_fused_ops >= last_node, almost_ready, submit);
 
-        if (vk_perf_logger_enabled) {
+        if (vk_perf_logger_enabled && enqueued) {
             if (ctx->compute_ctx.expired()) {
                 compute_ctx = ggml_vk_create_context(ctx, ctx->compute_cmd_pool);
                 ctx->compute_ctx = compute_ctx;
@@ -13128,10 +13179,9 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
             } else {
                 compute_ctx = ctx->compute_ctx.lock();
             }
-            // If there are fused ops, just write out timestamps for all nodes to keep the accounting simple
-            for (int j = 0; j < ctx->num_additional_fused_ops + 1; ++j) {
-                compute_ctx->s->buffer.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->device->query_pool, i+j+1);
-            }
+            ctx->query_nodes[ctx->query_idx] = cgraph->nodes[i];
+            ctx->query_fusion_names[ctx->query_idx] = fusion_string;
+            compute_ctx->s->buffer.writeTimestamp(vk::PipelineStageFlagBits::eAllCommands, ctx->query_pool, ctx->query_idx++);
         }
 
         if (enqueued) {
@@ -13172,14 +13222,14 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
 
         // Get the results and pass them to the logger
         std::vector<uint64_t> timestamps(cgraph->n_nodes + 1);
-        VK_CHECK(ctx->device->device.getQueryPoolResults(ctx->device->query_pool, 0, cgraph->n_nodes + 1, (cgraph->n_nodes + 1)*sizeof(uint64_t), timestamps.data(), sizeof(uint64_t), vk::QueryResultFlagBits::e64 | vk::QueryResultFlagBits::eWait), "get timestamp results");
-        for (int i = 0; i < cgraph->n_nodes; i++) {
-            if (!ggml_vk_is_empty(cgraph->nodes[i])) {
-                ctx->device->perf_logger->log_timing(cgraph->nodes[i], uint64_t((timestamps[i+1] - timestamps[i]) * ctx->device->properties.limits.timestampPeriod));
-            }
+        VK_CHECK(ctx->device->device.getQueryPoolResults(ctx->query_pool, 0, ctx->query_idx, (cgraph->n_nodes + 1)*sizeof(uint64_t), timestamps.data(), sizeof(uint64_t), vk::QueryResultFlagBits::e64 | vk::QueryResultFlagBits::eWait), "get timestamp results");
+        for (int i = 1; i < ctx->query_idx; i++) {
+            auto node = ctx->query_nodes[i];
+            auto name = ctx->query_fusion_names[i];
+            ctx->perf_logger->log_timing(node, name, uint64_t((timestamps[i] - timestamps[i-1]) * ctx->device->properties.limits.timestampPeriod));
         }
 
-        ctx->device->perf_logger->print_timings();
+        ctx->perf_logger->print_timings();
     }
 
     if (!ctx->device->support_async) {

From d9e03db1e701e34ed0b764615025110041729864 Mon Sep 17 00:00:00 2001
From: Law Po Ying <30721578+yingying0906@users.noreply.github.com>
Date: Sun, 7 Dec 2025 09:18:18 +0800
Subject: [PATCH 010/135] sycl: add missing BF16 conversion support for Intel
 oneAPI (#17780)

* sycl: add missing BF16 conversion support for Intel oneAPI

* Fix Line 645: Trailing whitespace
---
 ggml/src/ggml-sycl/convert.cpp | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp
index 96d2583b13..7c6ea8a57a 100644
--- a/ggml/src/ggml-sycl/convert.cpp
+++ b/ggml/src/ggml-sycl/convert.cpp
@@ -2,6 +2,13 @@
 #include "dequantize.hpp"
 #include "presets.hpp"
 
+#if defined(__INTEL_LLVM_COMPILER)
+    #if __has_include(<sycl/ext/oneapi/bfloat16.hpp>)
+        #include <sycl/ext/oneapi/bfloat16.hpp>
+        #define GGML_SYCL_HAS_BF16
+    #endif
+#endif
+
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel, typename dst_t>
 static void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k,
                              const sycl::nd_item<3> &item_ct1) {
@@ -566,6 +573,10 @@ to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) {
             return dequantize_row_iq4_nl_sycl;
         case GGML_TYPE_F32:
             return convert_unary_sycl<float>;
+#ifdef GGML_SYCL_HAS_BF16
+        case GGML_TYPE_BF16:
+            return convert_unary_sycl<sycl::ext::oneapi::bfloat16>;
+#endif
         default:
             return nullptr;
     }
@@ -627,6 +638,10 @@ to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst) {
             return dequantize_row_iq4_nl_sycl;
         case GGML_TYPE_F16:
             return convert_unary_sycl<sycl::half>;
+#ifdef GGML_SYCL_HAS_BF16
+        case GGML_TYPE_BF16:
+            return convert_unary_sycl<sycl::ext::oneapi::bfloat16>;
+#endif
         default:
             return nullptr;
     }
@@ -636,6 +651,10 @@ to_fp16_nc_sycl_t get_to_fp16_nc_sycl(ggml_type type) {
     switch (type) {
         case GGML_TYPE_F32:
             return convert_unary_nc_sycl<float>;
+#ifdef GGML_SYCL_HAS_BF16
+        case GGML_TYPE_BF16:
+            return convert_unary_nc_sycl<sycl::ext::oneapi::bfloat16>;
+#endif
         default:
             return nullptr;
     }

From 22577583a38ec0d236e6b4d45357c5e79021da07 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Sun, 7 Dec 2025 03:43:50 +0100
Subject: [PATCH 011/135] common : change --color to accept on/off/auto,
 default to auto (#17827)

---
 common/arg.cpp    | 24 ++++++++++++++++++------
 common/common.cpp | 26 ++++++++++++++++++++++++++
 common/common.h   |  7 +++++++
 common/log.cpp    | 29 +++--------------------------
 4 files changed, 54 insertions(+), 32 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 9e062ee7a1..4203da4a0a 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -708,6 +708,8 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         params.use_jinja = true;
     }
 
+    params.use_color = tty_can_use_colors();
+
     // load dynamic backends
     ggml_backend_load_all();
 
@@ -790,10 +792,20 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_MAIN}));
     add_opt(common_arg(
-        {"-co", "--color"},
-        string_format("colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false"),
-        [](common_params & params) {
-            params.use_color = true;
+        {"-co", "--color"}, "[on|off|auto]",
+        "Colorize output to distinguish prompt and user input from generations ('on', 'off', or 'auto', default: 'auto')\n"
+        "'auto' enables colors when output is to a terminal",
+        [](common_params & params, const std::string & value) {
+            if (is_truthy(value)) {
+                params.use_color = true;
+            } else if (is_falsey(value)) {
+                params.use_color = false;
+            } else if (is_autoy(value)) {
+                params.use_color = tty_can_use_colors();
+            } else {
+                throw std::invalid_argument(
+                    string_format("error: unknown value for --color: '%s'\n", value.c_str()));
+            }
         }
     ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP}));
     add_opt(common_arg(
@@ -1022,7 +1034,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                                params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_AUTO;
                            } else {
                                throw std::runtime_error(
-                                   string_format("error: unkown value for --flash-attn: '%s'\n", value.c_str()));
+                                   string_format("error: unknown value for --flash-attn: '%s'\n", value.c_str()));
                            }
                        }).set_env("LLAMA_ARG_FLASH_ATTN"));
     add_opt(common_arg(
@@ -2696,7 +2708,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 common_log_set_colors(common_log_main(), LOG_COLORS_AUTO);
             } else {
                 throw std::invalid_argument(
-                    string_format("error: unkown value for --log-colors: '%s'\n", value.c_str()));
+                    string_format("error: unknown value for --log-colors: '%s'\n", value.c_str()));
             }
         }
     ).set_env("LLAMA_LOG_COLORS"));
diff --git a/common/common.cpp b/common/common.cpp
index a42f488435..0497f90a28 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -982,6 +982,32 @@ std::vector<common_file_info> fs_list(const std::string & path, bool include_dir
     return files;
 }
 
+//
+// TTY utils
+//
+
+bool tty_can_use_colors() {
+    // Check NO_COLOR environment variable (https://no-color.org/)
+    if (const char * no_color = std::getenv("NO_COLOR")) {
+        if (no_color[0] != '\0') {
+            return false;
+        }
+    }
+
+    // Check TERM environment variable
+    if (const char * term = std::getenv("TERM")) {
+        if (std::strcmp(term, "dumb") == 0) {
+            return false;
+        }
+    }
+
+    // Check if stdout and stderr are connected to a terminal
+    // We check both because log messages can go to either
+    bool stdout_is_tty = isatty(fileno(stdout));
+    bool stderr_is_tty = isatty(fileno(stderr));
+
+    return stdout_is_tty || stderr_is_tty;
+}
 
 //
 // Model utils
diff --git a/common/common.h b/common/common.h
index 179113a4db..d28e48991c 100644
--- a/common/common.h
+++ b/common/common.h
@@ -655,6 +655,13 @@ struct common_file_info {
 };
 std::vector<common_file_info> fs_list(const std::string & path, bool include_directories);
 
+//
+// TTY utils
+//
+
+// Auto-detect if colors can be enabled based on terminal and environment
+bool tty_can_use_colors();
+
 //
 // Model utils
 //
diff --git a/common/log.cpp b/common/log.cpp
index b6c9ff79a4..00a03f158d 100644
--- a/common/log.cpp
+++ b/common/log.cpp
@@ -1,3 +1,4 @@
+#include "common.h"
 #include "log.h"
 
 #include <chrono>
@@ -26,30 +27,6 @@ void common_log_set_verbosity_thold(int verbosity) {
     common_log_verbosity_thold = verbosity;
 }
 
-// Auto-detect if colors should be enabled based on terminal and environment
-static bool common_log_should_use_colors_auto() {
-    // Check NO_COLOR environment variable (https://no-color.org/)
-    if (const char * no_color = std::getenv("NO_COLOR")) {
-        if (no_color[0] != '\0') {
-            return false;
-        }
-    }
-
-    // Check TERM environment variable
-    if (const char * term = std::getenv("TERM")) {
-        if (std::strcmp(term, "dumb") == 0) {
-            return false;
-        }
-    }
-
-    // Check if stdout and stderr are connected to a terminal
-    // We check both because log messages can go to either
-    bool stdout_is_tty = isatty(fileno(stdout));
-    bool stderr_is_tty = isatty(fileno(stderr));
-
-    return stdout_is_tty || stderr_is_tty;
-}
-
 static int64_t t_us() {
     return std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::system_clock::now().time_since_epoch()).count();
 }
@@ -391,7 +368,7 @@ struct common_log * common_log_main() {
     static std::once_flag    init_flag;
     std::call_once(init_flag, [&]() {
         // Set default to auto-detect colors
-        log.set_colors(common_log_should_use_colors_auto());
+        log.set_colors(tty_can_use_colors());
     });
 
     return &log;
@@ -422,7 +399,7 @@ void common_log_set_file(struct common_log * log, const char * file) {
 
 void common_log_set_colors(struct common_log * log, log_colors colors) {
     if (colors == LOG_COLORS_AUTO) {
-        log->set_colors(common_log_should_use_colors_auto());
+        log->set_colors(tty_can_use_colors());
         return;
     }
 

From 0a540f9abd98915edb99fed47d80078ed8d2f343 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Sun, 7 Dec 2025 14:02:04 +0100
Subject: [PATCH 012/135] ci : add windows-cuda 13.1 release (#17839)

---
 .github/actions/windows-setup-cuda/action.yml | 31 +++++++++++++++++++
 .github/workflows/release.yml                 |  6 ++--
 2 files changed, 35 insertions(+), 2 deletions(-)

diff --git a/.github/actions/windows-setup-cuda/action.yml b/.github/actions/windows-setup-cuda/action.yml
index 5575caeca3..6ad61582a5 100644
--- a/.github/actions/windows-setup-cuda/action.yml
+++ b/.github/actions/windows-setup-cuda/action.yml
@@ -65,3 +65,34 @@ runs:
           echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4\libnvvp" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
           echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
           echo "CUDA_PATH_V12_4=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
+
+    - name: Install Cuda Toolkit 13.1
+      if: ${{ inputs.cuda_version == '13.1' }}
+      shell: pwsh
+      run: |
+          mkdir -p "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1"
+          choco install unzip -y
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_crt/windows-x86_64/cuda_crt-windows-x86_64-13.1.80-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cudart/windows-x86_64/cuda_cudart-windows-x86_64-13.1.80-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvcc/windows-x86_64/cuda_nvcc-windows-x86_64-13.1.80-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvrtc/windows-x86_64/cuda_nvrtc-windows-x86_64-13.1.80-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libcublas/windows-x86_64/libcublas-windows-x86_64-13.2.0.9-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/libnvvm/windows-x86_64/libnvvm-windows-x86_64-13.1.80-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_nvtx/windows-x86_64/cuda_nvtx-windows-x86_64-13.1.68-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_profiler_api/windows-x86_64/cuda_profiler_api-windows-x86_64-13.1.80-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/visual_studio_integration/windows-x86_64/visual_studio_integration-windows-x86_64-13.1.68-archive.zip"
+          curl -O "https://developer.download.nvidia.com/compute/cuda/redist/cuda_cccl/windows-x86_64/cuda_cccl-windows-x86_64-13.1.78-archive.zip"
+          unzip '*.zip' -d "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1"
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\cuda_crt-windows-x86_64-13.1.80-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\cuda_cudart-windows-x86_64-13.1.80-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\cuda_nvcc-windows-x86_64-13.1.80-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\cuda_nvrtc-windows-x86_64-13.1.80-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\libcublas-windows-x86_64-13.2.0.9-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\libnvvm-windows-x86_64-13.1.80-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\cuda_nvtx-windows-x86_64-13.1.68-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\cuda_profiler_api-windows-x86_64-13.1.80-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\visual_studio_integration-windows-x86_64-13.1.68-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
+          xcopy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\cuda_cccl-windows-x86_64-13.1.78-archive\*" "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" /E /I /H /Y
+          echo "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1\bin" | Out-File -FilePath $env:GITHUB_PATH -Encoding utf8 -Append
+          echo "CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
+          echo "CUDA_PATH_V13_1=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.1" | Out-File -FilePath $env:GITHUB_ENV -Append -Encoding utf8
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 9e064ae1db..3668e4e2c9 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -421,7 +421,7 @@ jobs:
 
     strategy:
       matrix:
-        cuda: ['12.4']
+        cuda: ['12.4', '13.1']
 
     steps:
       - name: Clone
@@ -476,6 +476,7 @@ jobs:
           $dst='.\build\bin\cudart\'
           robocopy "${{env.CUDA_PATH}}\bin" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll
           robocopy "${{env.CUDA_PATH}}\lib" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll
+          robocopy "${{env.CUDA_PATH}}\bin\x64" $dst cudart64_*.dll cublas64_*.dll cublasLt64_*.dll
           7z a cudart-llama-bin-win-cuda-${{ matrix.cuda }}-x64.zip $dst\*
 
       - name: Upload Cuda runtime
@@ -835,7 +836,8 @@ jobs:
             **Windows:**
             - [Windows x64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-x64.zip)
             - [Windows arm64 (CPU)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cpu-arm64.zip)
-            - [Windows x64 (CUDA)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-12.4-x64.zip)
+            - [Windows x64 (CUDA 12)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-12.4-x64.zip)
+            - [Windows x64 (CUDA 13)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-cuda-13.1-x64.zip)
             - [Windows x64 (Vulkan)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-vulkan-x64.zip)
             - [Windows x64 (SYCL)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip)
             - [Windows x64 (HIP)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-hip-radeon-x64.zip)

From 08f9d3cc1d40169082ccf2416ebadb8a5afca9d9 Mon Sep 17 00:00:00 2001
From: lovedheart <6277001+lovedheart@users.noreply.github.com>
Date: Sun, 7 Dec 2025 18:40:42 +0100
Subject: [PATCH 013/135] Vulkan: improve mul_mat_vec_iq1_m (#16907)

* Optimize Vulkan shader for matrix-vector multiplication

* Revert changes on compute_outputs and main

Refactor compute_outputs to handle remaining rows correctly.

* Fix trailing whitespace
---
 .../vulkan-shaders/mul_mat_vec_iq1_m.comp     | 86 +++++++++++++++----
 1 file changed, 68 insertions(+), 18 deletions(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_m.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_m.comp
index 4cb292380c..e5cc7ff862 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_m.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_m.comp
@@ -7,35 +7,85 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
 FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
 
-void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) {
+void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i,
+                               const uint num_blocks_per_row, const uint first_row, const uint num_rows) {
+    // Compute starting index in matrix B for this superblock
     const uint y_idx = i * QUANT_K + 32 * ib32;
-
     uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i;
+
+    // Precompute indices for quantization lookup tables
+    const uint qh_base = 2 * ib32;
+    const uint qs_base = 4 * ib32;
+    const uint sc_index = ib32 / 2;
+    const uint sc_shift = 6 * (ib32 & 1);
+
+    // Loop over rows in the superblock
     [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+        // Load per-block scales and shift for quantization
         const uint16_t[4] scales = data_a[ibi].scales;
         const u16vec4 s = u16vec4(scales[0], scales[1], scales[2], scales[3]) >> 12;
         const float d = float(unpackHalf2x16(s.x | (s.y << 4) | (s.z << 8) | (s.w << 12)).x);
+        const uint sc = data_a[ibi].scales[sc_index] >> sc_shift;
 
-        const uint sc = data_a[ibi].scales[ib32 / 2] >> (6 * (ib32 & 1));
+        // Temporary caches for decoding
+        FLOAT_TYPE dl_cache[4];
+        uint16_t gvf_cache[4];
+        float delta_cache[4];
+
+        // Precompute the multiplier and lookup values for 4 sub-blocks
         [[unroll]] for (uint l = 0; l < 4; ++l) {
-            const uint qh = data_a[ibi].qh[2 * ib32 + l / 2] >> (4 * (l&1));
-            const uint qs = data_a[ibi].qs[4 * ib32 + l];
-            const float delta = ((qh & 8) != 0) ? -IQ1M_DELTA : IQ1M_DELTA;
-            const float dl = d * (2 * bitfieldExtract(sc, 3 * int(l / 2), 3) + 1);
+            dl_cache[l] = FLOAT_TYPE(d * (2 * bitfieldExtract(sc, 3 * int(l / 2), 3) + 1));
+            const uint qh = data_a[ibi].qh[qh_base + l / 2] >> (4 * (l & 1));
+            const uint qs = data_a[ibi].qs[qs_base + l];
+            gvf_cache[l] = iq1s_grid[qs | ((qh & 7) << 8)];
+            delta_cache[l] = ((qh & 8) != 0) ? -IQ1M_DELTA : IQ1M_DELTA;
+        }
 
-            const int16_t grid = int16_t(iq1s_grid[qs | ((qh & 7) << 8)]);
+        // Loop over columns of the output
+        [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+            // Compute base index for matrix B
+            const uint base_b_idx = (j * p.batch_stride_b + b_offset + y_idx) / 4;
+            vec4 b_vals[8];
 
-            [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
-                vec4 b0 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 0]);
-                vec4 b4 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 1]);
-
-                FLOAT_TYPE sum = FLOAT_TYPE(0.0);
-                [[unroll]] for (int k = 0; k < 4; ++k) {
-                    sum = fma(FLOAT_TYPE(b0[k]), bitfieldExtract(grid, 2 * k, 2) + delta,
-                          fma(FLOAT_TYPE(b4[k]), bitfieldExtract(grid, 8 + 2 * k, 2) + delta, sum));
-                }
-                temp[j][n] = fma(dl, sum, temp[j][n]);
+            // Load 8 vec4 values from matrix B
+            [[unroll]] for (int idx = 0; idx < 8; ++idx) {
+                b_vals[idx] = vec4(data_b_v4[base_b_idx + idx]);
             }
+
+            FLOAT_TYPE col_sum = FLOAT_TYPE(0.0);
+
+            // Loop over sub-blocks
+            [[unroll]] for (uint l = 0; l < 4; ++l) {
+                const uint16_t grid = gvf_cache[l];
+                const float dl = dl_cache[l];
+
+                // Decode 8 2-bit fbits from gvf_cache
+                float f0 = float(bitfieldExtract(grid, 0, 2));
+                float f1 = float(bitfieldExtract(grid, 2, 2));
+                float f2 = float(bitfieldExtract(grid, 4, 2));
+                float f3 = float(bitfieldExtract(grid, 6, 2));
+                float f4 = float(bitfieldExtract(grid, 8, 2));
+                float f5 = float(bitfieldExtract(grid, 10, 2));
+                float f6 = float(bitfieldExtract(grid, 12, 2));
+                float f7 = float(bitfieldExtract(grid, 14, 2));
+
+                // Pack into vec4 for vectorized FMA
+                const vec4 fbits_v0 = vec4(f0, f1, f2, f3);
+                const vec4 fbits_v1 = vec4(f4, f5, f6, f7);
+                const vec4 delta_v = vec4(delta_cache[l]);
+
+                // Vectorized fused multiply-add
+                vec4 sum_v = fma(b_vals[2*l + 0], fbits_v0 + delta_v, vec4(0.0));
+                sum_v      = fma(b_vals[2*l + 1], fbits_v1 + delta_v, sum_v);
+
+                // Horizontal add to get scalar sum
+                FLOAT_TYPE sum = sum_v.x + sum_v.y + sum_v.z + sum_v.w;
+
+                // Accumulate to column sum
+                col_sum = fma(dl, sum, col_sum);
+            }
+            // Write result to temporary buffer
+            temp[j][n] += col_sum;
         }
         ibi += num_blocks_per_row;
     }

From 4d3726278bfd3f42ad9a5b5db2ae056a8eef2ee2 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Sun, 7 Dec 2025 22:29:54 +0100
Subject: [PATCH 014/135] model: add llama 4 scaling for mistral-large
 (deepseek arch) (#17744)

---
 src/llama-model.cpp      |  4 ++++
 src/models/deepseek2.cpp | 18 ++++++++++++++++++
 2 files changed, 22 insertions(+)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index c3675dbdc4..7d09d7abd5 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1628,6 +1628,10 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 }
                 ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, false);
 
+                // (optional) temperature tuning - used by mistral-large
+                ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE,  hparams.f_attn_temp_scale,       false);
+                ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.n_attn_temp_floor_scale, false);
+
                 switch (hparams.n_layer) {
                     case 27: type = LLM_TYPE_16B; break;
                     case 60: type = LLM_TYPE_236B; break;
diff --git a/src/models/deepseek2.cpp b/src/models/deepseek2.cpp
index 0b41f7ba8e..dbaa8297be 100644
--- a/src/models/deepseek2.cpp
+++ b/src/models/deepseek2.cpp
@@ -30,6 +30,12 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
     // {n_embd, n_tokens}
     inpL = build_inp_embd(model.tok_embd);
 
+    // (optional) temperature tuning - used by mistral-large
+    ggml_tensor * inp_attn_scale = nullptr;
+    if (hparams.f_attn_temp_scale != 0.0f) {
+        inp_attn_scale = build_inp_attn_scale();
+    }
+
     // inp_pos - contains the positions
     ggml_tensor * inp_pos = build_inp_pos();
 
@@ -128,6 +134,12 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
                 ggml_tensor * Vcur = kv_cmpr;
                 cb(Vcur, "Vcur", il);
 
+                if (inp_attn_scale) {
+                    // apply llama 4 temperature scaling
+                    Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);
+                    cb(Qcur, "Qcur_attn_temp_scaled", il);
+                }
+
                 // note: MLA with the absorption optimzation converts into MQA (ie: GQA with 1 group)
                 cur = build_attn(inp_attn,
                         model.layers[il].wo, NULL,
@@ -160,6 +172,12 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
                 ggml_tensor * Kcur = ggml_concat(ctx0, ggml_repeat(ctx0, k_pe, q_pe), k_nope, 0);
                 cb(Kcur, "Kcur", il);
 
+                if (inp_attn_scale) {
+                    // apply llama 4 temperature scaling
+                    Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);
+                    cb(Qcur, "Qcur_attn_temp_scaled", il);
+                }
+
                 // note: MLA without the absorption optimization converts into MHA (ie: GQA with full n_head groups)
                 cur = build_attn(inp_attn,
                             model.layers[il].wo, NULL,

From 79d61896d35f37b79f432ae935698c5459ba8a41 Mon Sep 17 00:00:00 2001
From: ixgbe <1113177880@qq.com>
Date: Mon, 8 Dec 2025 16:41:34 +0800
Subject: [PATCH 015/135] ggml-cpu: add ggml_thread_cpu_relax with Zihintpause
 support (#17784)

* ggml-cpu: add ggml_thread_cpu_relax with Zihintpause support

Signed-off-by: Wang Yang <yangwang@iscas.ac.cn>

* cmake: enable RISC-V zihintpause extension for Spacemit builds

* readme : add ZIHINTPAUSE support for RISC-V

---------

Signed-off-by: Wang Yang <yangwang@iscas.ac.cn>
---
 .github/workflows/build-linux-cross.yml | 1 +
 README.md                               | 2 +-
 docs/build-riscv64-spacemit.md          | 1 +
 ggml/CMakeLists.txt                     | 1 +
 ggml/src/ggml-cpu/CMakeLists.txt        | 3 +++
 ggml/src/ggml-cpu/ggml-cpu.c            | 9 +++++++++
 6 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/build-linux-cross.yml b/.github/workflows/build-linux-cross.yml
index 36201281f0..c2c6ea12ae 100644
--- a/.github/workflows/build-linux-cross.yml
+++ b/.github/workflows/build-linux-cross.yml
@@ -291,6 +291,7 @@ jobs:
                          -DGGML_RVV=ON \
                          -DGGML_RV_ZFH=ON \
                          -DGGML_RV_ZICBOP=ON \
+                         -DGGML_RV_ZIHINTPAUSE=ON \
                          -DRISCV64_SPACEMIT_IME_SPEC=RISCV64_SPACEMIT_IME1 \
                          -DCMAKE_TOOLCHAIN_FILE=${PWD}/cmake/riscv64-spacemit-linux-gnu-gcc.cmake
 
diff --git a/README.md b/README.md
index eac8d66cc2..7dd2bfd8a1 100644
--- a/README.md
+++ b/README.md
@@ -61,7 +61,7 @@ range of hardware - locally and in the cloud.
 - Plain C/C++ implementation without any dependencies
 - Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
 - AVX, AVX2, AVX512 and AMX support for x86 architectures
-- RVV, ZVFH, ZFH and ZICBOP support for RISC-V architectures
+- RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE support for RISC-V architectures
 - 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
 - Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA)
 - Vulkan and SYCL backend support
diff --git a/docs/build-riscv64-spacemit.md b/docs/build-riscv64-spacemit.md
index eaa6532546..79bd4de63a 100644
--- a/docs/build-riscv64-spacemit.md
+++ b/docs/build-riscv64-spacemit.md
@@ -19,6 +19,7 @@ cmake -B build \
     -DGGML_RVV=ON \
     -DGGML_RV_ZFH=ON \
     -DGGML_RV_ZICBOP=ON \
+    -DGGML_RV_ZIHINTPAUSE=ON \
     -DRISCV64_SPACEMIT_IME_SPEC=RISCV64_SPACEMIT_IME1 \
     -DCMAKE_TOOLCHAIN_FILE=${PWD}/cmake/riscv64-spacemit-linux-gnu-gcc.cmake \
     -DCMAKE_INSTALL_PREFIX=build/installed
diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index 6b69ad8281..ab5b4760e2 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -168,6 +168,7 @@ option(GGML_RVV              "ggml: enable rvv"              ON)
 option(GGML_RV_ZFH           "ggml: enable riscv zfh"        ON)
 option(GGML_RV_ZVFH          "ggml: enable riscv zvfh"       ON)
 option(GGML_RV_ZICBOP        "ggml: enable riscv zicbop"     ON)
+option(GGML_RV_ZIHINTPAUSE   "ggml: enable riscv zihintpause "  ON)
 option(GGML_XTHEADVECTOR     "ggml: enable xtheadvector"     OFF)
 option(GGML_VXE              "ggml: enable vxe"              ${GGML_NATIVE})
 
diff --git a/ggml/src/ggml-cpu/CMakeLists.txt b/ggml/src/ggml-cpu/CMakeLists.txt
index 7e53a57b7b..fc31089f3e 100644
--- a/ggml/src/ggml-cpu/CMakeLists.txt
+++ b/ggml/src/ggml-cpu/CMakeLists.txt
@@ -469,6 +469,9 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
             if (GGML_RV_ZICBOP)
                 string(APPEND MARCH_STR "_zicbop")
             endif()
+            if (GGML_RV_ZIHINTPAUSE)
+                string(APPEND MARCH_STR "_zihintpause")
+            endif()
             list(APPEND ARCH_FLAGS "-march=${MARCH_STR}" -mabi=lp64d)
         else()
             # Begin with the lowest baseline
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 8507557267..b468b115a1 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -490,6 +490,15 @@ static inline void ggml_thread_cpu_relax(void) {
 static inline void ggml_thread_cpu_relax(void) {
     _mm_pause();
 }
+#elif defined(__riscv)
+static inline void ggml_thread_cpu_relax(void) {
+    #ifdef __riscv_zihintpause
+        __asm__ __volatile__ ("pause");
+    #else
+        /* Encoding of the pause instruction */
+        __asm__ __volatile__ (".4byte 0x100000F");
+    #endif
+}
 #else
 static inline void ggml_thread_cpu_relax(void) {;}
 #endif

From 5814b4dce18f9c5cbebef175e381a7b0ff147d72 Mon Sep 17 00:00:00 2001
From: wsbagnsv1 <sclumpfpapa36@gmail.com>
Date: Mon, 8 Dec 2025 10:41:08 +0100
Subject: [PATCH 016/135] cuda: optimize SOLVE_TRI using registers and FMAF
 (#17703)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* ggml-cuda: optimize solve_tri_f32_fast and fix stride handling

- Switch from using shared memory for the RHS/solution matrix to a register-based approach (x_low, x_high), reducing shared memory pressure and bank conflicts.
- Implement explicit `fmaf` instructions for the reduction loop.
- Update kernel arguments to pass strides in bytes rather than elements to align with standard ggml tensor arithmetic (casting to `char *` before addition).
- Remove unused `MAX_K_FAST` definition.

* Small cleanup

* Remove comments in solve_tri.cu

* Update ggml/src/ggml-cuda/solve_tri.cu

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* Update ggml/src/ggml-cuda/solve_tri.cu

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* Update ggml/src/ggml-cuda/solve_tri.cu

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

* Use const for variables in solve_tri.cu

* Replace fmaf with more readable code

* remove last fmaf

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---
 ggml/src/ggml-cuda/solve_tri.cu | 68 +++++++++++++++------------------
 1 file changed, 30 insertions(+), 38 deletions(-)

diff --git a/ggml/src/ggml-cuda/solve_tri.cu b/ggml/src/ggml-cuda/solve_tri.cu
index 2e2b39720f..e161d4dc43 100644
--- a/ggml/src/ggml-cuda/solve_tri.cu
+++ b/ggml/src/ggml-cuda/solve_tri.cu
@@ -3,7 +3,6 @@
 #include "solve_tri.cuh"
 
 #define MAX_N_FAST 64
-#define MAX_K_FAST 32
 
 // ======================
 // Fast Kernel (n <= 64, k <= 32) - Warp-based parallel reduction
@@ -48,65 +47,58 @@ static __global__ void solve_tri_f32_fast(const float * __restrict__ A,
     float *             X_batch = (float *) (X + i02 * nb2 + i03 * nb3);
 
     __shared__ float sA[MAX_N_FAST * MAX_N_FAST];
-    __shared__ float sXt[MAX_N_FAST * (MAX_K_FAST + 1)];
 
     const int offset = threadIdx.x + threadIdx.y * blockDim.x;
 
 #pragma unroll
     for (int i = 0; i < n * n; i += k * WARP_SIZE) {
-        int i0 = i + offset;
+        const int i0 = i + offset;
         if (i0 < n * n) {
             sA[i0] = A_batch[i0];
         }
     }
 
-    const int rows_per_warp = (n + WARP_SIZE - 1) / WARP_SIZE;
-
-#pragma unroll
-    for (int i = 0; i < rows_per_warp; i++) {
-        const int i0 = lane + i * WARP_SIZE;
-        if (i0 < n) {
-            sXt[col_idx * n + i0] = B_batch[i0 * k + col_idx];
-        }
-    }
-
     __syncthreads();
 
+    float x_low  = (lane < n) ? B_batch[lane * k + col_idx] : 0.0f;
+    float x_high = (WARP_SIZE + lane < n) ? B_batch[(WARP_SIZE + lane) * k + col_idx] : 0.0f;
+
+    const int half = WARP_SIZE;
+    const int nrows_low = (n < half) ? n : half;
+
 #pragma unroll
-    for (int row = 0; row < n; ++row) {
+    for (int row = 0; row < nrows_low; ++row) {
         float sum = 0.0f;
-
-        {
-            int j = lane;
-            if (j < row) {
-                sum += sA[row * n + j] * sXt[col_idx * n + j];
-            }
+        if (lane < row) {
+            sum += sA[row * n + lane] * x_low;
         }
-        if (row >= WARP_SIZE) {
-            int j = WARP_SIZE + lane;
-            if (j < row) {
-                sum += sA[row * n + j] * sXt[col_idx * n + j];
-            }
-        }
-
         sum = warp_reduce_sum(sum);
 
-        if (lane == 0) {
-            const float b_val      = sXt[col_idx * n + row];
-            const float a_diag     = sA[row * n + row];
-            // no safeguards for division by zero because that indicates corrupt
-            // data anyway
-            sXt[col_idx * n + row] = (b_val - sum) / a_diag;
+        if (lane == row) {
+            x_low = (x_low - sum) / sA[row * n + row];
         }
     }
 
-    __syncthreads();
+#pragma unroll
+    for (int row = half; row < n; ++row) {
+        float sum = sA[row * n + lane] * x_low;
+        const int j = half + lane;
+        if (j < row) {
+            sum += sA[row * n + j] * x_high;
+        }
+        sum = warp_reduce_sum(sum);
+
+        if (lane == row - half) {
+            x_high = (x_high - sum) / sA[row * n + row];
+        }
+    }
 
 #pragma unroll
-    for (int i = 0; i < rows_per_warp; i++) {
-        const int i0 = lane + i * WARP_SIZE;
-        if (i0 < n) {
-            X_batch[i0 * k + col_idx] = sXt[col_idx * n + i0];
+    for (int rr = 0; rr < 2; ++rr) {
+        const int row = rr * WARP_SIZE + lane;
+        if (row < n) {
+            const float val = (row < half) ? x_low : x_high;
+            X_batch[row * k + col_idx] = val;
         }
     }
 }

From 2bc96931d2583cad9e85b9a967b78f42ef76b8a7 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Mon, 8 Dec 2025 12:43:12 +0200
Subject: [PATCH 017/135] server : make cache_reuse configurable per request
 (#17858)

---
 tools/server/README.md          |  2 ++
 tools/server/server-context.cpp | 17 +++++++++++++----
 tools/server/server-task.cpp    | 12 +++++++-----
 tools/server/server-task.h      | 15 +++++++++------
 4 files changed, 31 insertions(+), 15 deletions(-)

diff --git a/tools/server/README.md b/tools/server/README.md
index bf274db79d..9deb241b07 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -495,6 +495,8 @@ By default, this value is set to `0`, meaning no tokens are kept. Use `-1` to re
 
 `n_cmpl`: Number of completions to generate from the current prompt. If input has multiple prompts, the output will have N prompts times `n_cmpl` entries.
 
+`n_cache_reuse`: Min chunk size to attempt reusing from the cache via KV shifting. For more info, see `--cache-reuse` arg. Default: `0`, which is disabled.
+
 `stream`: Allows receiving each predicted token in real-time instead of waiting for the completion to finish (uses a different response format). To enable this, set to `true`.
 
 `stop`: Specify a JSON array of stopping strings.
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 12a4e94e5d..d0039631d4 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -1880,8 +1880,18 @@ struct server_context_impl {
                                     n_past = std::min(n_past, slot.alora_invocation_start - 1);
                                 }
 
+                                const auto n_cache_reuse = slot.task->params.n_cache_reuse;
+
+                                const bool can_cache_reuse =
+                                    llama_memory_can_shift(llama_get_memory(ctx)) &&
+                                    !slot.prompt.tokens.has_mtmd;
+
+                                if (!can_cache_reuse && n_cache_reuse > 0) {
+                                    SLT_WRN(slot, "cache reuse is not supported - ignoring n_cache_reuse = %d\n", n_cache_reuse);
+                                }
+
                                 // reuse chunks from the cached prompt by shifting their KV cache in the new position
-                                if (params_base.n_cache_reuse > 0) {
+                                if (can_cache_reuse && n_cache_reuse > 0) {
                                     GGML_ASSERT(!slot.prompt.tokens.has_mtmd);
 
                                     size_t head_c = n_past; // cache
@@ -1892,7 +1902,7 @@ struct server_context_impl {
                                         GGML_ABORT("not supported by multimodal");
                                     }
 
-                                    SLT_DBG(slot, "trying to reuse chunks with size > %d, n_past = %d\n", params_base.n_cache_reuse, n_past);
+                                    SLT_DBG(slot, "trying to reuse chunks with size > %d, n_past = %d\n", n_cache_reuse, n_past);
 
                                     while (head_c < slot.prompt.tokens.size() &&
                                            head_p < input_tokens.size()) {
@@ -1901,11 +1911,10 @@ struct server_context_impl {
                                         while (head_c + n_match < slot.prompt.tokens.size() &&
                                                head_p + n_match < input_tokens.size()       &&
                                                slot.prompt.tokens[head_c + n_match] == input_tokens[head_p + n_match]) {
-
                                             n_match++;
                                         }
 
-                                        if (n_match >= (size_t) params_base.n_cache_reuse) {
+                                        if (n_match >= (size_t) n_cache_reuse) {
                                             SLT_INF(slot, "reusing chunk with size %zu, shifting KV cache [%zu, %zu) -> [%zu, %zu)\n", n_match, head_c, head_c + n_match, head_p, head_p + n_match);
                                             //for (size_t i = head_p; i < head_p + n_match; i++) {
                                             //    SLT_DBG(slot, "cache token %3zu: %6d '%s'\n", i, prompt_tokens[i], common_token_to_piece(ctx, prompt_tokens[i]).c_str());
diff --git a/tools/server/server-task.cpp b/tools/server/server-task.cpp
index c401f47a78..360826062b 100644
--- a/tools/server/server-task.cpp
+++ b/tools/server/server-task.cpp
@@ -155,11 +155,12 @@ task_params server_task::params_from_json_cmpl(
 
     // Sampling parameter defaults are loaded from the global server context (but individual requests can still them)
     task_params defaults;
-    defaults.sampling    = params_base.sampling;
-    defaults.speculative = params_base.speculative;
-    defaults.n_keep      = params_base.n_keep;
-    defaults.n_predict   = params_base.n_predict;
-    defaults.antiprompt  = params_base.antiprompt;
+    defaults.sampling      = params_base.sampling;
+    defaults.speculative   = params_base.speculative;
+    defaults.n_keep        = params_base.n_keep;
+    defaults.n_predict     = params_base.n_predict;
+    defaults.n_cache_reuse = params_base.n_cache_reuse;
+    defaults.antiprompt    = params_base.antiprompt;
 
     // enabling this will output extra debug information in the HTTP responses from the server
     params.verbose           = params_base.verbosity > 9;
@@ -176,6 +177,7 @@ task_params server_task::params_from_json_cmpl(
     params.n_keep           = json_value(data,       "n_keep",             defaults.n_keep);
     params.n_discard        = json_value(data,       "n_discard",          defaults.n_discard);
     params.n_cmpl           = json_value(data,       "n_cmpl",             json_value(data, "n", 1));
+    params.n_cache_reuse    = json_value(data,       "n_cache_reuse",      defaults.n_cache_reuse);
     //params.t_max_prompt_ms  = json_value(data,       "t_max_prompt_ms",    defaults.t_max_prompt_ms); // TODO: implement
     params.t_max_predict_ms = json_value(data,       "t_max_predict_ms",   defaults.t_max_predict_ms);
     params.response_fields  = json_value(data,       "response_fields",    std::vector<std::string>());
diff --git a/tools/server/server-task.h b/tools/server/server-task.h
index 4e4840fc83..da4e22a7cd 100644
--- a/tools/server/server-task.h
+++ b/tools/server/server-task.h
@@ -55,6 +55,8 @@ struct task_params {
     int32_t n_indent  =  0; // minimum line indentation for the generated text in number of whitespace characters
     int32_t n_cmpl    =  1; // number of completions to generate from this prompt
 
+    int32_t n_cache_reuse = 0; // min chunk size to attempt reusing from the cache via KV shifting (0 = disabled)
+
     int64_t t_max_prompt_ms  = -1; // TODO: implement
     int64_t t_max_predict_ms = -1; // if positive, limit the generation phase to this time limit
 
@@ -62,18 +64,19 @@ struct task_params {
 
     std::vector<std::string> antiprompt;
     std::vector<std::string> response_fields;
-    bool timings_per_token = false;
+
+    bool timings_per_token   = false;
     bool post_sampling_probs = false;
 
     struct common_params_sampling sampling;
     struct common_params_speculative speculative;
 
     // response formatting
-    bool                         verbose                   = false;
-    task_response_type           res_type                  = TASK_RESPONSE_TYPE_NONE;
-    std::string                  oaicompat_model;
-    std::string                  oaicompat_cmpl_id;
-    common_chat_syntax           oaicompat_chat_syntax;
+    bool               verbose  = false;
+    task_response_type res_type = TASK_RESPONSE_TYPE_NONE;
+    std::string        oaicompat_model;
+    std::string        oaicompat_cmpl_id;
+    common_chat_syntax oaicompat_chat_syntax;
 
     // Embeddings
     int32_t embd_normalize = 2; // (-1=none, 0=max absolute int16, 1=taxicab, 2=Euclidean/L2, >2=p-norm)

From 37a4f632442cb0e6d046b714d36cecb619833f4e Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Mon, 8 Dec 2025 13:54:58 +0100
Subject: [PATCH 018/135] server : add development documentation (#17760)

* first draft

* rewrite

* update & remove duplicated sections
---
 tools/server/README-dev.md | 151 +++++++++++++++++++++++++++++++++++++
 tools/server/README.md     | 145 +++++------------------------------
 2 files changed, 170 insertions(+), 126 deletions(-)
 create mode 100644 tools/server/README-dev.md

diff --git a/tools/server/README-dev.md b/tools/server/README-dev.md
new file mode 100644
index 0000000000..67ebe1aafe
--- /dev/null
+++ b/tools/server/README-dev.md
@@ -0,0 +1,151 @@
+# llama-server Development Documentation
+
+This document provides an in-depth technical overview of `llama-server`, intended for maintainers and contributors.
+
+If you are an end user consuming `llama-server` as a product, please refer to the main [README](./README.md) instead.
+
+## Backend
+
+### Overview
+
+The server supports two primary operating modes:
+
+- **Inference mode**: The default mode for performing inference with a single loaded GGUF model.
+- **Router mode**: Enables management of multiple inference server instances behind a single API endpoint. Requests are automatically routed to the appropriate backend instance based on the requested model.
+
+The core architecture consists of the following components:
+
+- `server_context`: Holds the primary inference state, including the main `llama_context` and all active slots.
+- `server_slot`: An abstraction over a single “sequence” in llama.cpp, responsible for managing individual parallel inference requests.
+- `server_routes`: Middleware layer between `server_context` and the HTTP interface; handles JSON parsing/formatting and request routing logic.
+- `server_http_context`: Implements the HTTP server using `cpp-httplib`.
+- `server_queue`: Thread-safe queue used by HTTP workers to submit new tasks to `server_context`.
+- `server_response`: Thread-safe queue used by `server_context` to return results to HTTP workers.
+- `server_response_reader`: Higher-level wrapper around the two queues above for cleaner code.
+- `server_task`: Unit of work pushed into `server_queue`.
+- `server_task_result`: Unit of result pushed into `server_response`.
+- `server_tokens`: Unified representation of token sequences (supports both text and multimodal tokens); used by `server_task` and `server_slot`.
+- `server_prompt_checkpoint`: For recurrent (e.g., RWKV) and SWA models, stores snapshots of KV cache state. Enables reuse when subsequent requests share the same prompt prefix, saving redundant computation.
+- `server_models`: Standalone component for managing multiple backend instances (used in router mode). It is completely independent of `server_context`.
+
+```mermaid
+graph TD
+    API_User <--> server_http_context
+    server_http_context <-- router mode --> server_models
+    server_http_context <-- inference mode --> server_routes
+    server_routes -- server_task --> server_queue
+    subgraph server_context
+        server_queue --> server_slot
+        server_slot -- server_task_result --> server_response
+        server_slot[multiple server_slot]
+    end
+    server_response --> server_routes
+```
+
+TODO: mention about how batching is handled by `server_slot`
+
+### Thread Management
+
+`server_context` runs on a dedicated single thread. Because it is single-threaded, heavy post-processing (especially after token generation) should be avoided, as it directly impacts multi-sequence throughput.
+
+Each incoming HTTP request is handled by its own thread managed by the HTTP library. The following operations are performed in HTTP worker threads:
+
+- JSON request parsing
+- Chat template application
+- Tokenization
+- Conversion of `server_task_result` into final JSON response
+- Error formatting into JSON
+- Tracking of partial/incremental responses (e.g., streaming tool calls or reasoning steps)
+
+**Best practices to follow:**
+
+- All JSON formatting and chat template logic must stay in the HTTP layer.
+- Avoid passing raw JSON between the HTTP layer and `server_slot`. Instead, parse everything into native C++ types as early as possible.
+
+### Testing
+
+`llama-server` includes an automated test suite based on `pytest`.
+
+The framework automatically starts a `llama-server` instance, sends requests, and validates responses.
+
+For detailed instructions, see the [test documentation](./tests/README.md).
+
+### Notable Related PRs
+
+- Initial server implementation: https://github.com/ggml-org/llama.cpp/pull/1443
+- Parallel decoding support: https://github.com/ggml-org/llama.cpp/pull/3228
+- Refactor introducing `server_queue` and `server_response`: https://github.com/ggml-org/llama.cpp/pull/5065
+- Reranking endpoint: https://github.com/ggml-org/llama.cpp/pull/9510
+- Multimodal model support (`libmtmd`): https://github.com/ggml-org/llama.cpp/pull/12898
+- Unified KV cache handling: https://github.com/ggml-org/llama.cpp/pull/16736
+- Separation of HTTP logic into dedicated files: https://github.com/ggml-org/llama.cpp/pull/17216
+- Large-scale code base split into smaller files: https://github.com/ggml-org/llama.cpp/pull/17362
+- Introduction of router mode: https://github.com/ggml-org/llama.cpp/pull/17470
+
+
+
+
+## Web UI
+
+The project includes a web-based user interface for interacting with `llama-server`. It supports both single-model (`MODEL` mode) and multi-model (`ROUTER` mode) operation.
+
+The SvelteKit-based Web UI is introduced in this PR: https://github.com/ggml-org/llama.cpp/pull/14839
+
+### Features
+
+-   **Chat interface** with streaming responses
+-   **Multi-model support** (ROUTER mode) - switch between models, auto-load on selection
+-   **Modality validation** - ensures selected model supports conversation's attachments (images, audio)
+-   **Conversation management** - branching, regeneration, editing with history preservation
+-   **Attachment support** - images, audio, PDFs (with vision/text fallback)
+-   **Configurable parameters** - temperature, top_p, etc. synced with server defaults
+-   **Dark/light theme**
+
+### Tech Stack
+
+-   **SvelteKit** - frontend framework with Svelte 5 runes for reactive state
+-   **TailwindCSS** + **shadcn-svelte** - styling and UI components
+-   **Vite** - build tooling
+-   **IndexedDB** (Dexie) - local storage for conversations
+-   **LocalStorage** - user settings persistence
+
+### Architecture
+
+The WebUI follows a layered architecture:
+
+```
+Routes → Components → Hooks → Stores → Services → Storage/API
+```
+
+-   **Stores** - reactive state management (`chatStore`, `conversationsStore`, `modelsStore`, `serverStore`, `settingsStore`)
+-   **Services** - stateless API/database communication (`ChatService`, `ModelsService`, `PropsService`, `DatabaseService`)
+-   **Hooks** - reusable logic (`useModelChangeValidation`, `useProcessingState`)
+
+For detailed architecture diagrams, see [`tools/server/webui/docs/`](webui/docs/):
+
+-   `high-level-architecture.mmd` - full architecture with all modules
+-   `high-level-architecture-simplified.mmd` - simplified overview
+-   `data-flow-simplified-model-mode.mmd` - data flow for single-model mode
+-   `data-flow-simplified-router-mode.mmd` - data flow for multi-model mode
+-   `flows/*.mmd` - detailed per-domain flows (chat, conversations, models, etc.)
+
+### Development
+
+```sh
+# make sure you have Node.js installed
+cd tools/server/webui
+npm i
+
+# run dev server (with hot reload)
+npm run dev
+
+# run tests
+npm run test
+
+# build production bundle
+npm run build
+```
+
+After `public/index.html.gz` has been generated, rebuild `llama-server` as described in the [build](#build) section to include the updated UI.
+
+**Note:** The Vite dev server automatically proxies API requests to `http://localhost:8080`. Make sure `llama-server` is running on that port during development.
diff --git a/tools/server/README.md b/tools/server/README.md
index 9deb241b07..f98fb44c7b 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -2,7 +2,7 @@
 
 Fast, lightweight, pure C/C++ HTTP server based on [httplib](https://github.com/yhirose/cpp-httplib), [nlohmann::json](https://github.com/nlohmann/json) and **llama.cpp**.
 
-Set of LLM REST APIs and a simple web front end to interact with llama.cpp.
+Set of LLM REST APIs and a web UI to interact with llama.cpp.
 
 **Features:**
  * LLM inference of F16 and quantized models on GPU and CPU
@@ -19,7 +19,7 @@ Set of LLM REST APIs and a simple web front end to interact with llama.cpp.
  * Speculative decoding
  * Easy-to-use web UI
 
-The project is under active development, and we are [looking for feedback and contributors](https://github.com/ggml-org/llama.cpp/issues/4216).
+For the ful list of features, please refer to [server's changelog](https://github.com/ggml-org/llama.cpp/issues/9291)
 
 ## Usage
 
@@ -289,69 +289,6 @@ For more details, please refer to [multimodal documentation](../../docs/multimod
   cmake --build build --config Release -t llama-server
   ```
 
-## Web UI
-
-The project includes a web-based user interface for interacting with `llama-server`. It supports both single-model (`MODEL` mode) and multi-model (`ROUTER` mode) operation.
-
-### Features
-
--   **Chat interface** with streaming responses
--   **Multi-model support** (ROUTER mode) - switch between models, auto-load on selection
--   **Modality validation** - ensures selected model supports conversation's attachments (images, audio)
--   **Conversation management** - branching, regeneration, editing with history preservation
--   **Attachment support** - images, audio, PDFs (with vision/text fallback)
--   **Configurable parameters** - temperature, top_p, etc. synced with server defaults
--   **Dark/light theme**
-
-### Tech Stack
-
--   **SvelteKit** - frontend framework with Svelte 5 runes for reactive state
--   **TailwindCSS** + **shadcn-svelte** - styling and UI components
--   **Vite** - build tooling
--   **IndexedDB** (Dexie) - local storage for conversations
--   **LocalStorage** - user settings persistence
-
-### Architecture
-
-The WebUI follows a layered architecture:
-
-```
-Routes → Components → Hooks → Stores → Services → Storage/API
-```
-
--   **Stores** - reactive state management (`chatStore`, `conversationsStore`, `modelsStore`, `serverStore`, `settingsStore`)
--   **Services** - stateless API/database communication (`ChatService`, `ModelsService`, `PropsService`, `DatabaseService`)
--   **Hooks** - reusable logic (`useModelChangeValidation`, `useProcessingState`)
-
-For detailed architecture diagrams, see [`tools/server/webui/docs/`](webui/docs/):
-
--   `high-level-architecture.mmd` - full architecture with all modules
--   `high-level-architecture-simplified.mmd` - simplified overview
--   `data-flow-simplified-model-mode.mmd` - data flow for single-model mode
--   `data-flow-simplified-router-mode.mmd` - data flow for multi-model mode
--   `flows/*.mmd` - detailed per-domain flows (chat, conversations, models, etc.)
-
-### Development
-
-```sh
-# make sure you have Node.js installed
-cd tools/server/webui
-npm i
-
-# run dev server (with hot reload)
-npm run dev
-
-# run tests
-npm run test
-
-# build production bundle
-npm run build
-```
-
-After `public/index.html.gz` has been generated, rebuild `llama-server` as described in the [build](#build) section to include the updated UI.
-
-**Note:** The Vite dev server automatically proxies API requests to `http://localhost:8080`. Make sure `llama-server` is running on that port during development.
-
 ## Quick Start
 
 To get started right away, run the following command, making sure to use the correct path for the model you have:
@@ -380,7 +317,7 @@ docker run -p 8080:8080 -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:se
 docker run -p 8080:8080 -v /path/to/models:/models --gpus all ghcr.io/ggml-org/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99
 ```
 
-## Testing with CURL
+## Using with CURL
 
 Using [curl](https://curl.se/). On Windows, `curl.exe` should be available in the base OS.
 
@@ -391,46 +328,6 @@ curl --request POST \
     --data '{"prompt": "Building a website can be done in 10 simple steps:","n_predict": 128}'
 ```
 
-## Advanced testing
-
-We implemented a [server test framework](./tests/README.md) using human-readable scenario.
-
-*Before submitting an issue, please try to reproduce it with this format.*
-
-## Node JS Test
-
-You need to have [Node.js](https://nodejs.org/en) installed.
-
-```bash
-mkdir llama-client
-cd llama-client
-```
-
-Create an index.js file and put this inside:
-
-```javascript
-const prompt = "Building a website can be done in 10 simple steps:"
-
-async function test() {
-    let response = await fetch("http://127.0.0.1:8080/completion", {
-        method: "POST",
-        body: JSON.stringify({
-            prompt,
-            n_predict: 64,
-        })
-    })
-    console.log((await response.json()).content)
-}
-
-test()
-```
-
-And run it:
-
-```bash
-node index.js
-```
-
 ## API Endpoints
 
 ### GET `/health`: Returns health check result
@@ -1638,6 +1535,22 @@ Response:
 }
 ```
 
+## API errors
+
+`llama-server` returns errors in the same format as OAI: https://github.com/openai/openai-openapi
+
+Example of an error:
+
+```json
+{
+    "error": {
+        "code": 401,
+        "message": "Invalid API Key",
+        "type": "authentication_error"
+    }
+}
+```
+
 ## More examples
 
 ### Interactive mode
@@ -1657,26 +1570,6 @@ Run with bash:
 bash chat.sh
 ```
 
-### OAI-like API
-
-The HTTP `llama-server` supports an OAI-like API: https://github.com/openai/openai-openapi
-
-### API errors
-
-`llama-server` returns errors in the same format as OAI: https://github.com/openai/openai-openapi
-
-Example of an error:
-
-```json
-{
-    "error": {
-        "code": 401,
-        "message": "Invalid API Key",
-        "type": "authentication_error"
-    }
-}
-```
-
 Apart from error types supported by OAI, we also have custom types that are specific to functionalities of llama.cpp:
 
 **When /metrics or /slots endpoint is disabled**

From 51e0c2d917c21826585e84be1c27f75147325de0 Mon Sep 17 00:00:00 2001
From: Jay Zenith <162098309+JayZenith@users.noreply.github.com>
Date: Mon, 8 Dec 2025 05:10:12 -0800
Subject: [PATCH 019/135] cuda : add FILL op support (#17851)

* cuda : add FILL op support

* cuda : add missing FILL op files
---
 ggml/src/ggml-cuda/fill.cu      | 37 +++++++++++++++++++++++++++++++++
 ggml/src/ggml-cuda/fill.cuh     |  3 +++
 ggml/src/ggml-cuda/ggml-cuda.cu |  5 +++++
 3 files changed, 45 insertions(+)
 create mode 100644 ggml/src/ggml-cuda/fill.cu
 create mode 100644 ggml/src/ggml-cuda/fill.cuh

diff --git a/ggml/src/ggml-cuda/fill.cu b/ggml/src/ggml-cuda/fill.cu
new file mode 100644
index 0000000000..eb8ccb7802
--- /dev/null
+++ b/ggml/src/ggml-cuda/fill.cu
@@ -0,0 +1,37 @@
+#include "fill.cuh"
+#include "convert.cuh"
+
+#define CUDA_FILL_BLOCK_SIZE 256
+
+template <typename T>
+static __global__ void fill_kernel(T * __restrict__ dst, const int64_t k, const T value) {
+    const int64_t i = (int64_t)blockDim.x * blockIdx.x + threadIdx.x;
+    if (i >= k) {
+        return;
+    }
+    dst[i] = value;
+}
+
+void ggml_cuda_op_fill(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    void * dst_d = dst->data;
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(ggml_is_contiguous(dst));
+
+    float value;
+    memcpy(&value, dst->op_params, sizeof(float));
+
+    const int64_t k = ggml_nelements(dst);
+    const int64_t num_blocks = (k + CUDA_FILL_BLOCK_SIZE - 1) / CUDA_FILL_BLOCK_SIZE;
+
+    switch (dst->type) {
+        case GGML_TYPE_F32:
+            fill_kernel<<<num_blocks, CUDA_FILL_BLOCK_SIZE, 0, stream>>>((float *)dst_d, k, value);
+            break;
+        case GGML_TYPE_F16:
+            fill_kernel<<<num_blocks, CUDA_FILL_BLOCK_SIZE, 0, stream>>>((half *)dst_d, k, ggml_cuda_cast<half>(value));
+            break;
+        default:
+            GGML_ABORT("unsupported type");
+    }
+}
diff --git a/ggml/src/ggml-cuda/fill.cuh b/ggml/src/ggml-cuda/fill.cuh
new file mode 100644
index 0000000000..8443c83620
--- /dev/null
+++ b/ggml/src/ggml-cuda/fill.cuh
@@ -0,0 +1,3 @@
+#include "common.cuh"
+
+void ggml_cuda_op_fill(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 235d94d500..d0463388c5 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -56,6 +56,7 @@
 #include "ggml-cuda/solve_tri.cuh"
 #include "ggml-cuda/tri.cuh"
 #include "ggml-cuda/cumsum.cuh"
+#include "ggml-cuda/fill.cuh"
 #include "ggml.h"
 
 #include <algorithm>
@@ -2730,6 +2731,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_SOLVE_TRI:
             ggml_cuda_op_solve_tri(ctx, dst);
             break;
+        case GGML_OP_FILL:
+            ggml_cuda_op_fill(ctx, dst);
+            break;
         default:
             return false;
     }
@@ -4617,6 +4621,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
         case GGML_OP_OPT_STEP_ADAMW:
         case GGML_OP_OPT_STEP_SGD:
+        case GGML_OP_FILL:
         case GGML_OP_CUMSUM:
         case GGML_OP_TRI:
             return true;

From 636fc17a376dacc01da20d508e6986a299b1f819 Mon Sep 17 00:00:00 2001
From: hksdpc255 <43977088+hksdpc255@users.noreply.github.com>
Date: Tue, 9 Dec 2025 00:32:04 +1100
Subject: [PATCH 020/135] Fix Kimi-K2 tool-call parsing issues (#17376)

* Fix kimi-k2 parsing

* fix template & add more tests for kimi-k2

* Another fix for Kimi-K2 chat template.

* enable allow_toolcall_in_think for Kimi-K2

* Refine key-value separator and value end format

* Enable tool call in think for kimi-k2

* allow_toolcall_in_think is now tested with Kimi-K2

* Remove outdated TODO comment in XML tool call parser

Removed TODO comment about untested tool call feature.

* Rename function from "utf8_truncate_safe" to "utf8_truncate_safe_len"
---
 common/chat-parser-xml-toolcall.cpp     |  54 +++++---
 common/chat-parser-xml-toolcall.h       |   2 +-
 common/chat-parser.cpp                  |   5 +-
 models/templates/Kimi-K2-Instruct.jinja |  10 +-
 models/templates/Kimi-K2-Thinking.jinja |  10 +-
 tests/test-chat.cpp                     | 163 +++++++++++++++++++++---
 6 files changed, 194 insertions(+), 50 deletions(-)

diff --git a/common/chat-parser-xml-toolcall.cpp b/common/chat-parser-xml-toolcall.cpp
index 7349895550..a80900ff8d 100644
--- a/common/chat-parser-xml-toolcall.cpp
+++ b/common/chat-parser-xml-toolcall.cpp
@@ -724,16 +724,10 @@ inline void parse_msg_with_xml_tool_calls(common_chat_msg_parser & builder, cons
         if (reasoning_unclosed) {
             if (auto pos = content.find(end_think); pos == std::string::npos && builder.pos() != builder.input().size()) {
                 unclosed_reasoning_content += content;
-                if (form.allow_toolcall_in_think) {
-                    builder.move_to(tc->groups[0].begin);
-                    if (!builder.try_consume_xml_tool_calls(form)) {
-                        unclosed_reasoning_content += tool_call_start;
-                        builder.move_to(tc->groups[0].end);
-                    }
-                } else {
+                if (!(form.allow_toolcall_in_think && tc)) {
                     unclosed_reasoning_content += tool_call_start;
+                    continue;
                 }
-                continue;
             } else {
                 reasoning_unclosed = false;
                 std::string reasoning_content;
@@ -781,8 +775,12 @@ inline void parse_msg_with_xml_tool_calls(common_chat_msg_parser & builder, cons
                 }
             } else {
                 // This <tool_call> start is in thinking block, skip this tool call
-                auto pos = think_start + start_think.size();
-                unclosed_reasoning_content = content.substr(pos) + tool_call_start;
+                // This <tool_call> start is in thinking block
+                if (form.allow_toolcall_in_think) {
+                    unclosed_reasoning_content = content.substr(think_start + start_think.size());
+                } else {
+                    unclosed_reasoning_content = content.substr(think_start + start_think.size()) + tool_call_start;
+                }
                 reasoning_unclosed = true;
                 content.resize(think_start);
                 toolcall_in_think = true;
@@ -805,14 +803,35 @@ inline void parse_msg_with_xml_tool_calls(common_chat_msg_parser & builder, cons
         }
 
         // remove potential partial suffix
-        if (content.size() > 0 && builder.pos() == builder.input().size() && unclosed_reasoning_content.empty()) {
-            rstrip(content);
-            trim_potential_partial_word(content);
-            rstrip(content);
+        if (builder.pos() == builder.input().size()) {
+            if (unclosed_reasoning_content.empty()) {
+                rstrip(content);
+                trim_potential_partial_word(content);
+                rstrip(content);
+            } else {
+                rstrip(unclosed_reasoning_content);
+                trim_potential_partial_word(unclosed_reasoning_content);
+                rstrip(unclosed_reasoning_content);
+            }
+        }
+
+        // consume unclosed_reasoning_content if allow_toolcall_in_think is set
+        if (form.allow_toolcall_in_think && !unclosed_reasoning_content.empty()) {
+            if (builder.syntax().reasoning_format != COMMON_REASONING_FORMAT_NONE && !builder.syntax().reasoning_in_content) {
+                builder.add_reasoning_content(unclosed_reasoning_content);
+            } else {
+                if (content.empty()) {
+                    content = start_think + unclosed_reasoning_content;
+                } else {
+                    content += "\n\n" + start_think;
+                    content += unclosed_reasoning_content;
+                }
+            }
+            unclosed_reasoning_content.clear();
         }
 
         // Add content
-        if (content.size() != 0) {
+        if (!content.empty()) {
             // If there are multiple content blocks
             if (builder.syntax().reasoning_format != COMMON_REASONING_FORMAT_NONE && !builder.syntax().reasoning_in_content && builder.result().content.size() != 0) {
                 builder.add_content("\n\n");
@@ -820,7 +839,7 @@ inline void parse_msg_with_xml_tool_calls(common_chat_msg_parser & builder, cons
             builder.add_content(content);
         }
 
-        // This <tool_call> start is in thinking block, skip this tool call
+        // This <tool_call> start is in thinking block and toolcall_in_think not set, skip this tool call
         if (toolcall_in_think && !form.allow_toolcall_in_think) {
             continue;
         }
@@ -829,7 +848,7 @@ inline void parse_msg_with_xml_tool_calls(common_chat_msg_parser & builder, cons
         if (!tc) {
             GGML_ASSERT(builder.pos() == builder.input().size());
             GGML_ASSERT(unclosed_reasoning_content.empty());
-            GGML_ASSERT(!reasoning_unclosed);
+            if (!form.allow_toolcall_in_think) GGML_ASSERT(!reasoning_unclosed);
             break;
         }
 
@@ -854,7 +873,6 @@ inline void parse_msg_with_xml_tool_calls(common_chat_msg_parser & builder, cons
 
 /**
  * Parse content uses reasoning and XML-Style tool call
- * TODO: Note that form.allow_toolcall_in_think is not tested yet. If anyone confirms it works, this comment can be removed.
  */
 void common_chat_msg_parser::consume_reasoning_with_xml_tool_calls(const struct xml_tool_call_format & form, const std::string & start_think, const std::string & end_think) {
     parse_msg_with_xml_tool_calls(*this, form, start_think, end_think);
diff --git a/common/chat-parser-xml-toolcall.h b/common/chat-parser-xml-toolcall.h
index 67face2b94..b309fb6670 100644
--- a/common/chat-parser-xml-toolcall.h
+++ b/common/chat-parser-xml-toolcall.h
@@ -31,7 +31,7 @@ struct xml_tool_call_format {
     std::optional<std::string> last_val_end = std::nullopt;
     std::optional<std::string> last_tool_end = std::nullopt;
     bool trim_raw_argval = false;
-    bool allow_toolcall_in_think = false; // TODO: UNTESTED!!!
+    bool allow_toolcall_in_think = false;
 };
 
 // make a GBNF that accept any strings except those containing any of the forbidden strings.
diff --git a/common/chat-parser.cpp b/common/chat-parser.cpp
index fe3e80037f..d740dac065 100644
--- a/common/chat-parser.cpp
+++ b/common/chat-parser.cpp
@@ -917,12 +917,13 @@ static void common_chat_parse_kimi_k2(common_chat_msg_parser & builder) {
         form.tool_start  = "<|tool_call_begin|>";
         form.tool_sep    = "<|tool_call_argument_begin|>{";
         form.key_start   = "\"";
-        form.key_val_sep = "\": ";
-        form.val_end     = ", ";
+        form.key_val_sep = "\":";
+        form.val_end     = ",";
         form.tool_end    = "}<|tool_call_end|>";
         form.scope_end   = "<|tool_calls_section_end|>";
         form.raw_argval  = false;
         form.last_val_end = "";
+        form.allow_toolcall_in_think = true;
         return form;
     })();
     builder.consume_reasoning_with_xml_tool_calls(form, "<think>", "</think>");
diff --git a/models/templates/Kimi-K2-Instruct.jinja b/models/templates/Kimi-K2-Instruct.jinja
index a9439135ba..6204fb3960 100644
--- a/models/templates/Kimi-K2-Instruct.jinja
+++ b/models/templates/Kimi-K2-Instruct.jinja
@@ -14,7 +14,7 @@
 {%- endmacro %}
 
 {%- set tool_response_queue = namespace(ids=[]) -%}
-{%- set tool_call_counter = namespace(value=1) -%}
+{%- set tool_call_counter = namespace(value=0) -%}
 
 {%- if tools -%}
   <|im_system|>tool_declare<|im_middle|>{{ tools | tojson }}<|im_end|>
@@ -36,12 +36,8 @@
   {%- if message['role'] == 'assistant' and message.get('tool_calls') -%}
     {{render_content(message)}}<|tool_calls_section_begin|>
     {%- for tool_call in message['tool_calls'] -%}
-      {%- if tool_call['id'] is defined -%}
-        {%- set formatted_id = tool_call['id'] -%}
-      {%- else -%}
-        {%- set formatted_id = 'functions.' + tool_call['function']['name'] + ':' + (tool_call_counter.value | string) -%}
-        {%- set tool_call_counter.value = tool_call_counter.value + 1 -%}
-      {%- endif -%}
+      {%- set formatted_id = 'functions.' + tool_call['function']['name'] + ':' + (tool_call_counter.value | string) -%}
+      {%- set tool_call_counter.value = tool_call_counter.value + 1 -%}
       {%- set _ = tool_response_queue.ids.append(formatted_id) -%}
       <|tool_call_begin|>{{ formatted_id }}<|tool_call_argument_begin|>{% if tool_call['function']['arguments'] is string %}{{ tool_call['function']['arguments'] }}{% else %}{{ tool_call['function']['arguments'] | tojson }}{% endif %}<|tool_call_end|>
     {%- endfor -%}
diff --git a/models/templates/Kimi-K2-Thinking.jinja b/models/templates/Kimi-K2-Thinking.jinja
index 4c2af6a783..5641429f53 100644
--- a/models/templates/Kimi-K2-Thinking.jinja
+++ b/models/templates/Kimi-K2-Thinking.jinja
@@ -25,17 +25,13 @@
 {%- endmacro -%}
 
 {%- set tool_response_queue = namespace(ids=[]) -%}
-{%- set tool_call_counter = namespace(value=1) -%}
+{%- set tool_call_counter = namespace(value=0) -%}
 
 {%- macro render_toolcalls(message) -%}
   <|tool_calls_section_begin|>
   {%- for tool_call in message['tool_calls'] -%}
-    {%- if tool_call['id'] is defined -%}
-      {%- set formatted_id = tool_call['id'] -%}
-    {%- else -%}
-      {%- set formatted_id = 'functions.' + tool_call['function']['name'] + ':' + (tool_call_counter.value | string) -%}
-      {%- set tool_call_counter.value = tool_call_counter.value + 1 -%}
-    {%- endif -%}
+    {%- set formatted_id = 'functions.' + tool_call['function']['name'] + ':' + (tool_call_counter.value | string) -%}
+    {%- set tool_call_counter.value = tool_call_counter.value + 1 -%}
     {%- set _ = tool_response_queue.ids.append(formatted_id) -%}
     <|tool_call_begin|>{{ formatted_id }}<|tool_call_argument_begin|>{% if tool_call['function']['arguments'] is string %}{{ tool_call['function']['arguments'] }}{% else %}{{ tool_call['function']['arguments'] | tojson }}{% endif %}<|tool_call_end|>
   {%- endfor -%}
diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp
index 62dd1583fa..f765bda62c 100644
--- a/tests/test-chat.cpp
+++ b/tests/test-chat.cpp
@@ -428,10 +428,38 @@ static void test_templates(const struct common_chat_templates * tmpls, const std
  */
 template <typename T>
 static void test_parser_with_streaming(const common_chat_msg & expected, const std::string & raw_message, T parse_msg) {
+    constexpr auto utf8_truncate_safe_len = [](const std::string_view s) -> size_t {
+        auto len = s.size();
+        if (len == 0) return 0;
+        auto i = len;
+        for (size_t back = 0; back < 4 && i > 0; ++back) {
+            --i;
+            unsigned char c = s[i];
+            if ((c & 0x80) == 0) {
+                return len;
+            } else if ((c & 0xC0) == 0xC0) {
+                size_t expected_len = 0;
+                if ((c & 0xE0) == 0xC0) expected_len = 2;
+                else if ((c & 0xF0) == 0xE0) expected_len = 3;
+                else if ((c & 0xF8) == 0xF0) expected_len = 4;
+                else return i;
+                if (len - i >= expected_len) {
+                    return len;
+                } else {
+                    return i;
+                }
+            }
+        }
+        return len - std::min(len, size_t(3));
+    };
+    constexpr auto utf8_truncate_safe_view = [utf8_truncate_safe_len](const std::string_view s) {
+        return s.substr(0, utf8_truncate_safe_len(s));
+    };
+
     auto merged = simple_assist_msg("");
     auto last_msg = parse_msg("");
     for (size_t i = 1; i <= raw_message.size(); ++i) {
-        auto curr_msg = parse_msg(raw_message.substr(0, i));
+        auto curr_msg = parse_msg(std::string(utf8_truncate_safe_view(std::string_view(raw_message).substr(0, i))));
         if (curr_msg == simple_assist_msg("")) continue;
         LOG_INF("Streaming msg: %s\n", common_chat_msgs_to_json_oaicompat<json>({curr_msg}).dump().c_str());
         for (auto diff: common_chat_msg_diff::compute_diffs(last_msg, curr_msg)) {
@@ -2659,14 +2687,14 @@ Hey there!<|im_end|>
         // Test parsing tool calls
         assert_msg_equals(message_assist_call,
             common_chat_parse(
-                "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:1<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>",
+                "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>",
                 /* is_partial= */ false,
                 {COMMON_CHAT_FORMAT_KIMI_K2}));
 
         // Test parsing tool calls with thinking
         assert_msg_equals(message_assist_call_thoughts,
             common_chat_parse(
-                "<think>I'm\nthinking</think><|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:1<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>",
+                "<think>I'm\nthinking</think><|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>",
                 /* is_partial= */ false,
                 {
                     /*  .format = */ COMMON_CHAT_FORMAT_KIMI_K2,
@@ -2676,7 +2704,7 @@ Hey there!<|im_end|>
         // Test tool calls with extra content
         assert_msg_equals(message_assist_call_content,
             common_chat_parse(
-                "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:1<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>Hello, world!\nWhat's up?",
+                "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>Hello, world!\nWhat's up?",
                 /* is_partial= */ false,
                 {COMMON_CHAT_FORMAT_KIMI_K2}
             ));
@@ -2684,7 +2712,7 @@ Hey there!<|im_end|>
         // Test tool calls with extra content AND thinking
         assert_msg_equals(message_assist_call_thoughts_content,
             common_chat_parse(
-                "<think>I'm\nthinking</think><|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:1<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>Hello, world!\nWhat's up?",
+                "<think>I'm\nthinking</think><|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>Hello, world!\nWhat's up?",
                 /* is_partial= */ false,
                 {
                     /*  .format = */ COMMON_CHAT_FORMAT_KIMI_K2,
@@ -2693,47 +2721,152 @@ Hey there!<|im_end|>
 
         // Test streaming
         test_parser_with_streaming(message_assist_call_thoughts_content,
-            "<think>I'm\nthinking\n</think>Hello, world!\nWhat's up?\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:1<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>",
+            "<think>I'm\nthinking\n</think>Hello, world!\nWhat's up?\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>",
             [&](const std::string &msg) { return common_chat_parse(msg, /* is_partial= */ true, {
                     /*  .format = */ COMMON_CHAT_FORMAT_KIMI_K2,
                     /*  .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK
             }); });
         test_parser_with_streaming(message_assist_call_thoughts_unparsed,
-            "<think>I'm\nthinking</think>\n\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:1<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>",
+            "<think>I'm\nthinking</think>\n\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>",
             [&](const std::string &msg) { return common_chat_parse(msg, /* is_partial= */ true, {
                     /*  .format = */ COMMON_CHAT_FORMAT_KIMI_K2,
                     /*  .reasoning_format = */ COMMON_REASONING_FORMAT_NONE
             }); });
         test_parser_with_streaming(message_assist_call_thoughts_content,
-            "<think>I'm\nthinking\n</think>\n\nHello, world!\nWhat's up?\n\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:1<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>\n",
+            "<think>I'm\nthinking\n</think>\n\nHello, world!\nWhat's up?\n\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>\n",
             [&](const std::string &msg) { return common_chat_parse(msg, /* is_partial= */ true, {
                     /*  .format = */ COMMON_CHAT_FORMAT_KIMI_K2,
                     /*  .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK
             }); });
         test_parser_with_streaming(message_assist_call_withopt,
-            "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function_with_opt:1<|tool_call_argument_begin|>{\"arg1\": 1, \"arg2\": 2}<|tool_call_end|><|tool_calls_section_end|>",
+            "<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function_with_opt:0<|tool_call_argument_begin|>{\"arg1\": 1, \"arg2\": 2}<|tool_call_end|><|tool_calls_section_end|>",
             [&](const std::string &msg) { return common_chat_parse(msg, /* is_partial= */ true, {
                     /*  .format = */ COMMON_CHAT_FORMAT_KIMI_K2,
                     /*  .reasoning_format = */ COMMON_REASONING_FORMAT_NONE
             }); });
         test_parser_with_streaming(simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking", "special_function", "{\"arg1\": \"123456\"}"),
-            "<think>I'm\nthinking</think>Hello, world!\nWhat's up?\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:1<|tool_call_argument_begin|>{\"arg1\": \"123456\"}<|tool_call_end|><|tool_calls_section_end|>",
+            "<think>I'm\nthinking</think>Hello, world!\nWhat's up?\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": \"123456\"}<|tool_call_end|><|tool_calls_section_end|>",
             [&](const std::string &msg) { return common_chat_parse(msg, /* is_partial= */ true, {
                     /*  .format = */ COMMON_CHAT_FORMAT_KIMI_K2,
                     /*  .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK
             }); });
         test_parser_with_streaming(simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking", "special_function", "{\"arg1\": [1, 2, \"345\", 6]}"),
-            "<think>I'm\nthinking</think>Hello, world!\nWhat's up?\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:1<|tool_call_argument_begin|>{\"arg1\": [1, 2, \"345\", 6]}<|tool_call_end|><|tool_calls_section_end|>",
+            "<think>I'm\nthinking</think>Hello, world!\nWhat's up?\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": [1, 2, \"345\", 6]}<|tool_call_end|><|tool_calls_section_end|>",
             [&](const std::string &msg) { return common_chat_parse(msg, /* is_partial= */ true, {
                     /*  .format = */ COMMON_CHAT_FORMAT_KIMI_K2,
                     /*  .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK
             }); });
         test_parser_with_streaming(simple_assist_msg("Hello, world!\nWhat's up?", "I'm\nthinking", "special_function", "{\"arg1\": {\"12\": 34, \"5\": [67, 8], \"9\": \"10\"}}"),
-            "<think>I'm\nthinking</think>Hello, world!\nWhat's up?\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:1<|tool_call_argument_begin|>{\"arg1\": {\"12\": 34, \"5\": [67, 8], \"9\": \"10\"}}<|tool_call_end|><|tool_calls_section_end|>",
+            "<think>I'm\nthinking</think>Hello, world!\nWhat's up?\n<|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": {\"12\": 34, \"5\": [67, 8], \"9\": \"10\"}}<|tool_call_end|><|tool_calls_section_end|>",
             [&](const std::string &msg) { return common_chat_parse(msg, /* is_partial= */ true, {
                     /*  .format = */ COMMON_CHAT_FORMAT_KIMI_K2,
                     /*  .reasoning_format = */ COMMON_REASONING_FORMAT_DEEPSEEK
             }); });
+        test_parser_with_streaming(
+                simple_assist_msg("", "", "complex_function", "{\"name\":\"John Doe\",\"age\":30,\"active\":true,\"score\":95.5}"),
+                "<|tool_calls_section_begin|><|tool_call_begin|>functions.complex_function:0<|tool_call_argument_begin|>"
+                "{\"name\": \"John Doe\", \"age\": 30, \"active\": true, \"score\": 95.5}"
+                "<|tool_call_end|><|tool_calls_section_end|>",
+            [&](const std::string &msg) { return common_chat_parse(msg, /* is_partial= */ true, {COMMON_CHAT_FORMAT_KIMI_K2}); });
+        test_parser_with_streaming(
+                simple_assist_msg("", "", "web_search", "{\"query\":\"\\\"From Zero\\\" Linkin Park album tracklist complete songs\",\"limit\":3,\"type\":\"text\"}"),
+                "<|tool_calls_section_begin|><|tool_call_begin|>functions.web_search:0<|tool_call_argument_begin|>"
+                "{\"query\":\"\\\"From Zero\\\" Linkin Park album tracklist complete songs\",\"limit\":3,\"type\":\"text\"}"
+                "<|tool_call_end|><|tool_calls_section_end|>",
+            [&](const std::string &msg) { return common_chat_parse(msg, /* is_partial= */ true, {COMMON_CHAT_FORMAT_KIMI_K2}); });
+        test_parser_with_streaming(
+                simple_assist_msg("", "", "read_file", "{\"args\": [{\"path\": \"src/providers/ThemeProvider.tsx\"}, {\"path\": \"src/components/Header.tsx\"}, {\"path\": \"src/components/ThemeToggle.tsx\"}, {\"path\": \"src/app/globals.css\"}, {\"path\": \"src/app/layout.tsx\"}]}"),
+                "<|tool_calls_section_begin|><|tool_call_begin|>functions.read_file:0<|tool_call_argument_begin|>"
+                "{\"args\": [{\"path\": \"src/providers/ThemeProvider.tsx\"}, {\"path\": \"src/components/Header.tsx\"}, {\"path\": \"src/components/ThemeToggle.tsx\"}, {\"path\": \"src/app/globals.css\"}, {\"path\": \"src/app/layout.tsx\"}]}"
+                "<|tool_call_end|><|tool_calls_section_end|>",
+            [&](const std::string &msg) { return common_chat_parse(msg, /* is_partial= */ true, {COMMON_CHAT_FORMAT_KIMI_K2}); });
+        test_parser_with_streaming(
+                simple_assist_msg(
+                        "Let me start by examining the relevant files to understand the current implementation.", "",
+                        "read_file",
+                        "{\"files\": [{\"path\": \"src/app/Partners.tsx\", \"line_ranges\": [\"1-100\"]}]}"),
+                "Let me start by examining the relevant files to understand the current implementation."
+                "<|tool_calls_section_begin|><|tool_call_begin|>functions.read_file:0<|tool_call_argument_begin|>"
+                "{\"files\":[{\"path\":\"src/app/Partners.tsx\",\"line_ranges\":[\"1-100\"]}]}"
+                "<|tool_call_end|><|tool_calls_section_end|>",
+            [&](const std::string &msg) { return common_chat_parse(msg, /* is_partial= */ true, {COMMON_CHAT_FORMAT_KIMI_K2}); });
+        auto multi_tool_msg = simple_assist_msg("Let me call multiple tools.", "I'm thinking.");
+        multi_tool_msg.tool_calls.push_back({ "read_file", "{\"files\": [{\"path\": \"src/app/Partners.tsx\", \"line_ranges\": [\"1-100\"]}]}", "" });
+        multi_tool_msg.tool_calls.push_back({ "web_search", "{\"query\":\"\\\"From Zero\\\" Linkin Park album tracklist complete songs\",\"limit\":3,\"type\":\"text\"}", "" });
+        multi_tool_msg.tool_calls.push_back({ "complex_function", "{\"name\": \"John Doe\", \"age\": 30, \"active\": true, \"score\": 95.5}", "" });
+        multi_tool_msg.tool_calls.push_back({ "emoji_function", "{\"message\":\"Hello! 👋 🌟 🚀 Testing emojis: 😀😃😄😁 and symbols: ∑∏∆∇\"}", "" });
+        test_parser_with_streaming(multi_tool_msg,
+                "<think>I'm thinking.</think>Let me call multiple tools."
+                "<|tool_calls_section_begin|>"
+                "<|tool_call_begin|>functions.read_file:0<|tool_call_argument_begin|>"
+                "{\"files\":[{\"path\":\"src/app/Partners.tsx\",\"line_ranges\":[\"1-100\"]}]}"
+                "<|tool_call_end|>"
+                "<|tool_call_begin|>functions.web_search:1<|tool_call_argument_begin|>"
+                "{\"query\":\"\\\"From Zero\\\" Linkin Park album tracklist complete songs\",\"limit\":3,\"type\":\"text\"}"
+                "<|tool_call_end|>"
+                "<|tool_call_begin|>functions.complex_function:2<|tool_call_argument_begin|>"
+                "{\"name\": \"John Doe\", \"age\": 30, \"active\": true, \"score\": 95.5}"
+                "<|tool_call_end|>"
+                "<|tool_call_begin|>functions.emoji_function:3<|tool_call_argument_begin|>"
+                "{\"message\":\"Hello! 👋 🌟 🚀 Testing emojis: 😀😃😄😁 and symbols: ∑∏∆∇\"}"
+                "<|tool_call_end|>"
+                "<|tool_calls_section_end|>",
+            [&](const std::string &msg) { return common_chat_parse(msg, /* is_partial= */ true, {
+                    COMMON_CHAT_FORMAT_KIMI_K2,
+                    COMMON_REASONING_FORMAT_DEEPSEEK
+            }); });
+        test_parser_with_streaming(
+                simple_assist_msg("", "I'm thinking", "complex_function_in_think", "{\"name\":\"John Doe\",\"age\":30,\"active\":true,\"score\":95.5}"),
+                "<think>I'm thinking<|tool_calls_section_begin|><|tool_call_begin|>functions.complex_function_in_think:0<|tool_call_argument_begin|>"
+                "{\"name\": \"John Doe\", \"age\": 30, \"active\": true, \"score\": 95.5}"
+                "<|tool_call_end|><|tool_calls_section_end|>",
+            [&](const std::string &msg) { return common_chat_parse(msg, /* is_partial= */ true, {
+                    COMMON_CHAT_FORMAT_KIMI_K2,
+                    COMMON_REASONING_FORMAT_DEEPSEEK
+            }); });
+        test_parser_with_streaming(
+                simple_assist_msg("Hello", "I'm thinkingI'm still thinking", "complex_function_in_think", "{\"name\":\"John Doe\",\"age\":30,\"active\":true,\"score\":95.5}"),
+                "<think>I'm thinking<|tool_calls_section_begin|><|tool_call_begin|>functions.complex_function_in_think:0<|tool_call_argument_begin|>"
+                "{\"name\": \"John Doe\", \"age\": 30, \"active\": true, \"score\": 95.5}"
+                "<|tool_call_end|><|tool_calls_section_end|>I'm still thinking</think>Hello",
+            [&](const std::string &msg) { return common_chat_parse(msg, /* is_partial= */ true, {
+                    COMMON_CHAT_FORMAT_KIMI_K2,
+                    COMMON_REASONING_FORMAT_DEEPSEEK
+            }); });
+
+        // Test template rendering
+        common_chat_templates_inputs conversation_with_tools = inputs_tools;
+        conversation_with_tools.messages.push_back(simple_assist_msg("Let's do it", "Think first", "complex_function", "{\"name\":\"John Doe\",\"age\":30,\"active\":true,\"score\":95.5}"));
+        conversation_with_tools.messages.push_back({
+            "tool",
+            "Tool response 1",
+            /* .content_parts = */ {},
+            /* .tool_calls = */ {},
+            /* .reasoning_content = */ "",
+            /* .tool_name = */ "complex_function",
+            /* .tool_call_id = */ "",
+        });
+        conversation_with_tools.messages.push_back(simple_assist_msg("Continue", "Think next", "web_search", "{\"query\":\"\\\"From Zero\\\" Linkin Park album tracklist complete songs\",\"limit\":3,\"type\":\"text\"}"));
+        conversation_with_tools.messages.push_back({
+            "tool",
+            "Tool response 2",
+            /* .content_parts = */ {},
+            /* .tool_calls = */ {},
+            /* .reasoning_content = */ "",
+            /* .tool_name = */ "web_search",
+            /* .tool_call_id = */ "",
+        });
+        conversation_with_tools.messages.push_back(simple_assist_msg("CC", "Think last", "read_file", "{\"args\": [{\"path\": \"src/providers/ThemeProvider.tsx\"}, {\"path\": \"src/components/Header.tsx\"}, {\"path\": \"src/components/ThemeToggle.tsx\"}, {\"path\": \"src/app/globals.css\"}, {\"path\": \"src/app/layout.tsx\"}]}"));
+        conversation_with_tools.messages.push_back({
+            "tool",
+            "Tool response 3",
+            /* .content_parts = */ {},
+            /* .tool_calls = */ {},
+            /* .reasoning_content = */ "",
+            /* .tool_name = */ "read_file",
+            /* .tool_call_id = */ "",
+        });
+        assert_equals(common_chat_templates_apply(tmpls.get(), conversation_with_tools).prompt, std::string("<|im_system|>tool_declare<|im_middle|>[{\"type\": \"function\", \"function\": {\"name\": \"special_function\", \"description\": \"I'm special\", \"parameters\": {\"type\": \"object\", \"properties\": {\"arg1\": {\"type\": \"integer\", \"description\": \"The arg.\"}}, \"required\": [\"arg1\"]}}}]<|im_end|><|im_system|>system<|im_middle|>You are Kimi, an AI assistant created by Moonshot AI.<|im_end|><|im_user|>user<|im_middle|>Hey there!<|im_end|><|im_assistant|>assistant<|im_middle|><think>Think first</think>Let's do it<|tool_calls_section_begin|><|tool_call_begin|>functions.complex_function:0<|tool_call_argument_begin|>{\"name\":\"John Doe\",\"age\":30,\"active\":true,\"score\":95.5}<|tool_call_end|><|tool_calls_section_end|><|im_end|><|im_system|>complex_function<|im_middle|>## Return of functions.complex_function:0\nTool response 1<|im_end|><|im_assistant|>assistant<|im_middle|><think>Think next</think>Continue<|tool_calls_section_begin|><|tool_call_begin|>functions.web_search:1<|tool_call_argument_begin|>{\"query\":\"\\\"From Zero\\\" Linkin Park album tracklist complete songs\",\"limit\":3,\"type\":\"text\"}<|tool_call_end|><|tool_calls_section_end|><|im_end|><|im_system|>web_search<|im_middle|>## Return of functions.web_search:1\nTool response 2<|im_end|><|im_assistant|>assistant<|im_middle|><think>Think last</think>CC<|tool_calls_section_begin|><|tool_call_begin|>functions.read_file:2<|tool_call_argument_begin|>{\"args\": [{\"path\": \"src/providers/ThemeProvider.tsx\"}, {\"path\": \"src/components/Header.tsx\"}, {\"path\": \"src/components/ThemeToggle.tsx\"}, {\"path\": \"src/app/globals.css\"}, {\"path\": \"src/app/layout.tsx\"}]}<|tool_call_end|><|tool_calls_section_end|><|im_end|><|im_system|>read_file<|im_middle|>## Return of functions.read_file:2\nTool response 3<|im_end|><|im_assistant|>assistant<|im_middle|>"));
 
         // Test template generation for regular content
         test_templates(tmpls.get(), end_tokens, message_assist, tools,
@@ -2742,7 +2875,7 @@ Hey there!<|im_end|>
 
         // Test template generation for tool calls
         test_templates(tmpls.get(), end_tokens, message_assist_call, tools,
-                      "<think></think><|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:1<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>",
+                      "<think></think><|tool_calls_section_begin|><|tool_call_begin|>functions.special_function:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>",
                       /* expect_grammar_triggered= */ true,
                       /* test_grammar_if_triggered= */ true,
                       /* common_reasoning_format= */ COMMON_REASONING_FORMAT_DEEPSEEK,
@@ -2751,14 +2884,14 @@ Hey there!<|im_end|>
 
         // Test template generation for tools with optional parameters
         test_templates(tmpls.get(), end_tokens, message_assist_call_noopt, tools,
-                      "<think></think><|tool_calls_section_begin|><|tool_call_begin|>functions.special_function_with_opt:1<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>",
+                      "<think></think><|tool_calls_section_begin|><|tool_call_begin|>functions.special_function_with_opt:0<|tool_call_argument_begin|>{\"arg1\": 1}<|tool_call_end|><|tool_calls_section_end|>",
                       /* expect_grammar_triggered= */ true,
                       /* test_grammar_if_triggered= */ true,
                       /* common_reasoning_format= */ COMMON_REASONING_FORMAT_DEEPSEEK,
                       /* ignore_whitespace_differences= */ true
         );
         test_templates(tmpls.get(), end_tokens, message_assist_call_withopt, tools,
-                      "<think></think><|tool_calls_section_begin|><|tool_call_begin|>functions.special_function_with_opt:1<|tool_call_argument_begin|>{\"arg1\": 1, \"arg2\": 2}<|tool_call_end|><|tool_calls_section_end|>",
+                      "<think></think><|tool_calls_section_begin|><|tool_call_begin|>functions.special_function_with_opt:0<|tool_call_argument_begin|>{\"arg1\": 1, \"arg2\": 2}<|tool_call_end|><|tool_calls_section_end|>",
                       /* expect_grammar_triggered= */ true,
                       /* test_grammar_if_triggered= */ true,
                       /* common_reasoning_format= */ COMMON_REASONING_FORMAT_DEEPSEEK,

From e4e9c4329c088d3aa97b8c242e18ff79bfe66248 Mon Sep 17 00:00:00 2001
From: "Piotr Wilkin (ilintar)" <piotr.wilkin@syndatis.com>
Date: Mon, 8 Dec 2025 14:32:41 +0100
Subject: [PATCH 021/135] Make graph_max_nodes vary by ubatch size (#17794)

* Make graph_max_nodes vary by ubatch size for models where chunking might explode the graph

* Update src/llama-context.h

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* Add missing const

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 src/llama-context.cpp | 12 ++++++------
 src/llama-context.h   |  2 +-
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index e04f0fc4f9..4171400713 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -248,7 +248,10 @@ llama_context::llama_context(
 
         LLAMA_LOG_DEBUG("%s: backend_ptrs.size() = %zu\n", __func__, backend_ptrs.size());
 
-        const size_t max_nodes = this->graph_max_nodes();
+        const uint32_t n_seqs = cparams.n_seq_max;
+        const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
+
+        const size_t max_nodes = this->graph_max_nodes(n_tokens);
 
         LLAMA_LOG_DEBUG("%s: max_nodes = %zu\n", __func__, max_nodes);
 
@@ -300,9 +303,6 @@ llama_context::llama_context(
 
         cross.v_embd.clear();
 
-        const uint32_t n_seqs = cparams.n_seq_max;
-        const uint32_t n_tokens = std::min(cparams.n_ctx, cparams.n_ubatch);
-
         // avoid reserving graphs with zero outputs - assume one output per sequence
         n_outputs = n_seqs;
 
@@ -1386,9 +1386,9 @@ void llama_context::output_reorder() {
 // graph
 //
 
-uint32_t llama_context::graph_max_nodes() const {
+uint32_t llama_context::graph_max_nodes(uint32_t n_tokens) const {
     if (model.arch == LLM_ARCH_QWEN3NEXT) {
-        return std::max<uint32_t>(8192u, 32u*model.n_tensors());
+        return std::max<uint32_t>(n_tokens * 40, 32u * model.n_tensors());
     }
     return std::max<uint32_t>(1024u, 8u*model.n_tensors());
 }
diff --git a/src/llama-context.h b/src/llama-context.h
index 20cbd78955..cd26eafe18 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -197,7 +197,7 @@ private:
     //
 
 public:
-    uint32_t graph_max_nodes() const;
+    uint32_t graph_max_nodes(uint32_t n_tokens) const;
 
     // can reuse the llm_graph_result instance of the context (for example to update a memory module)
     llm_graph_result * get_gf_res_reserve() const;

From f896d2c34f7bb502c13986830b3ed7d85aac67d9 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Mon, 8 Dec 2025 14:35:28 +0100
Subject: [PATCH 022/135] server: improve speed of speculative decoding
 (#17808)

* server: improve speed of speculative decoding

* fix small draft case

* add link to the PR

* server : fix generation time measurement

* server : fix draft acceptance logs (add SRV_CNT, SLT_CNT macros)

* server : add comment

* add PR to docs

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 tools/server/README-dev.md      |   1 +
 tools/server/server-common.h    |   2 +
 tools/server/server-context.cpp | 181 ++++++++++++++++++--------------
 3 files changed, 108 insertions(+), 76 deletions(-)

diff --git a/tools/server/README-dev.md b/tools/server/README-dev.md
index 67ebe1aafe..df165c34a3 100644
--- a/tools/server/README-dev.md
+++ b/tools/server/README-dev.md
@@ -81,6 +81,7 @@ For detailed instructions, see the [test documentation](./tests/README.md).
 - Separation of HTTP logic into dedicated files: https://github.com/ggml-org/llama.cpp/pull/17216
 - Large-scale code base split into smaller files: https://github.com/ggml-org/llama.cpp/pull/17362
 - Introduction of router mode: https://github.com/ggml-org/llama.cpp/pull/17470
+- Speculative decoding: https://github.com/ggml-org/llama.cpp/pull/17808 and rework in https://github.com/ggml-org/llama.cpp/pull/17808
 
 
 
diff --git a/tools/server/server-common.h b/tools/server/server-common.h
index 0c4d84ffa0..0629bb5edd 100644
--- a/tools/server/server-common.h
+++ b/tools/server/server-common.h
@@ -18,11 +18,13 @@ const static std::string build_info("b" + std::to_string(LLAMA_BUILD_NUMBER) + "
 using json = nlohmann::ordered_json;
 
 #define SLT_INF(slot, fmt, ...) LOG_INF("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__)
+#define SLT_CNT(slot, fmt, ...) LOG_CNT(""                                 fmt,                                                                __VA_ARGS__)
 #define SLT_WRN(slot, fmt, ...) LOG_WRN("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__)
 #define SLT_ERR(slot, fmt, ...) LOG_ERR("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__)
 #define SLT_DBG(slot, fmt, ...) LOG_DBG("slot %12.*s: id %2d | task %d | " fmt, 12, __func__, (slot).id, ((slot).task ? (slot).task->id : -1), __VA_ARGS__)
 
 #define SRV_INF(fmt, ...) LOG_INF("srv  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
+#define SRV_CNT(fmt, ...) LOG_CNT(""              fmt,               __VA_ARGS__)
 #define SRV_WRN(fmt, ...) LOG_WRN("srv  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
 #define SRV_ERR(fmt, ...) LOG_ERR("srv  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
 #define SRV_DBG(fmt, ...) LOG_DBG("srv  %12.*s: " fmt, 12, __func__, __VA_ARGS__)
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index d0039631d4..3bf9051026 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -102,6 +102,11 @@ struct server_slot {
     std::string  generated_text;
     llama_tokens generated_tokens;
 
+    // idx of draft tokens in the main batch
+    // non-empty if we went to evaluate draft tokens
+    // ref: https://github.com/ggml-org/llama.cpp/pull/17808
+    std::vector<int32_t> i_batch_dft;
+
     std::vector<completion_token_output> generated_token_probs;
 
     bool has_next_token = true;
@@ -150,7 +155,8 @@ struct server_slot {
 
     struct common_sampler * smpl = nullptr;
 
-    llama_token sampled;
+    llama_token sampled; // in speculative mode, this is the last accepted token
+    llama_tokens drafted;
 
     // stats
     size_t n_sent_text = 0; // number of sent text character
@@ -180,6 +186,8 @@ struct server_slot {
         stopping_word  = "";
         n_sent_text    = 0;
 
+        drafted.clear();
+        i_batch_dft.clear();
         generated_tokens.clear();
         generated_token_probs.clear();
         json_schema = json();
@@ -255,6 +263,31 @@ struct server_slot {
         generated_token_probs.push_back(token);
     }
 
+    int get_n_draft_max() const {
+        if (!can_speculate()) {
+            return 0;
+        }
+
+        // determine the max draft that fits the current slot state
+        int n_draft_max = task->params.speculative.n_max;
+
+        // note: slot.prompt is not yet expanded with the `id` token sampled above
+        //       also, need to leave space for 1 extra token to allow context shifts
+        n_draft_max = std::min(n_draft_max, n_ctx - prompt.n_tokens() - 2);
+
+        if (n_remaining > 0) {
+            n_draft_max = std::min(n_draft_max, n_remaining - 1);
+        }
+
+        SLT_DBG(*this, "max possible draft: %d\n", n_draft_max);
+
+        if (n_draft_max < task->params.speculative.n_min) {
+            SLT_DBG(*this, "the max possible draft is too small: %d < %d - skipping speculative decoding\n", n_draft_max, task->params.speculative.n_min);
+            n_draft_max = 0;
+        }
+        return n_draft_max;
+    }
+
     // note: a slot can also be either a parent or a child
     bool is_parent() const {
         return is_processing() && task->n_children > 0;
@@ -353,8 +386,7 @@ struct server_slot {
 
         if (n_draft_total > 0) {
             const float draft_ratio = (float) n_draft_accepted / n_draft_total;
-            SLT_INF(*this,
-                    "\n"
+            SLT_CNT(*this,
                     "draft acceptance rate = %0.5f (%5d accepted / %5d generated)\n",
                     draft_ratio, n_draft_accepted, n_draft_total
             );
@@ -1774,14 +1806,57 @@ struct server_context_impl {
                 continue;
             }
 
-            slot.i_batch = batch.n_tokens;
+            // generate draft tokens in speculative decoding mode
+            // TODO: rework to have a single draft llama_context shared across all slots [TAG_SERVER_SPEC_REWORK]
+            //       perform the speculative drafting for all sequences at the same time in a single batch
+            int n_draft_max = slot.get_n_draft_max();
+            if (n_draft_max > 0) {
+                if (mctx) {
+                    // we should never reach this, as speculative is automatically disabled if mmproj is loaded
+                    GGML_ABORT("not supported by multimodal");
+                }
 
-            common_batch_add(batch, slot.sampled, slot.prompt.tokens.pos_next(), { slot.id }, true);
+                struct common_speculative_params params_spec;
+                params_spec.n_draft = n_draft_max;
+                params_spec.n_reuse = llama_n_ctx(slot.ctx_dft) - slot.task->params.speculative.n_max;
+                params_spec.p_min   = slot.task->params.speculative.p_min;
+                const llama_tokens & cached_text_tokens = slot.prompt.tokens.get_text_tokens();
+                llama_tokens draft = common_speculative_gen_draft(slot.spec, params_spec, cached_text_tokens, slot.sampled);
 
-            slot.prompt.tokens.push_back(slot.sampled);
+                // add the sampled token to the batch
+                slot.i_batch_dft.push_back(batch.n_tokens);
+                common_batch_add(batch, slot.sampled, slot.prompt.tokens.pos_next(), { slot.id }, true);
+                slot.prompt.tokens.push_back(slot.sampled);
 
-            SLT_DBG(slot, "slot decode token, n_ctx = %d, n_tokens = %d, truncated = %d\n",
-                    slot.n_ctx, slot.prompt.n_tokens(), slot.truncated);
+                if (slot.task->params.speculative.n_min > (int) draft.size()) {
+                    SLT_DBG(slot, "ignoring small draft: %d < %d\n", (int) draft.size(), slot.task->params.speculative.n_min);
+                    // fallback to normal decoding
+                    slot.i_batch = slot.i_batch_dft[0];
+                    slot.drafted.clear();
+                    slot.i_batch_dft.clear();
+                } else {
+                    // keep track of total number of drafted tokens tested
+                    slot.n_draft_total += draft.size();
+
+                    // add all drafted tokens to the batch
+                    for (size_t i = 0; i < draft.size(); i++) {
+                        slot.i_batch_dft.push_back(batch.n_tokens);
+                        common_batch_add(batch, draft[i], slot.prompt.tokens.pos_next(), { slot.id }, true);
+                        slot.prompt.tokens.push_back(draft[i]);
+                    }
+                    slot.drafted = std::move(draft);
+                }
+            } else {
+                // no speculative decoding
+                slot.i_batch = batch.n_tokens;
+
+                common_batch_add(batch, slot.sampled, slot.prompt.tokens.pos_next(), { slot.id }, true);
+
+                slot.prompt.tokens.push_back(slot.sampled);
+
+                SLT_DBG(slot, "slot decode token, n_ctx = %d, n_tokens = %d, truncated = %d\n",
+                        slot.n_ctx, slot.prompt.n_tokens(), slot.truncated);
+            }
         }
 
         // process in chunks of params.n_batch
@@ -2345,6 +2420,10 @@ struct server_context_impl {
             // on successful decode, restore the original batch size
             n_batch = llama_n_batch(ctx);
 
+            // technically, measuring the time here excludes the sampling time for the last batch
+            // but on the other hand, we don't want to do too many system calls to measure the time, so it's ok
+            const int64_t t_current = ggml_time_us();
+
             for (auto & slot : slots) {
                 // may need to copy state to other slots
                 if (slot.state == SLOT_STATE_DONE_PROMPT && slot.is_parent()) {
@@ -2399,6 +2478,10 @@ struct server_context_impl {
                     continue; // continue loop of slots
                 }
 
+                if (slot.i_batch_dft.size() > 0) {
+                    continue; // sample using speculative decoding
+                }
+
                 const int tok_idx = slot.i_batch - i;
 
                 llama_token id = common_sampler_sample(slot.smpl, ctx, tok_idx);
@@ -2409,8 +2492,6 @@ struct server_context_impl {
 
                 slot.n_decoded += 1;
 
-                const int64_t t_current = ggml_time_us();
-
                 if (slot.n_decoded == 1) {
                     slot.t_start_generation = t_current;
                     slot.t_prompt_processing = (slot.t_start_generation - slot.t_start_process_prompt) / 1e3;
@@ -2439,84 +2520,32 @@ struct server_context_impl {
                 }
             }
 
-            // do speculative decoding
-            // TODO: rework to have a single draft llama_context shared across all slots [TAG_SERVER_SPEC_REWORK]
-            //       perform the speculative drafting for all sequences at the same time in a single batch
+            // speculative decoding - main model sample and accept
             for (auto & slot : slots) {
-                if (!slot.is_processing() || !slot.can_speculate()) {
+                if (slot.state != SLOT_STATE_GENERATING || slot.i_batch_dft.empty()) {
                     continue;
                 }
 
-                if (slot.state != SLOT_STATE_GENERATING) {
-                    continue;
-                }
-
-                if (mctx) {
-                    // we should never reach this, as speculative is automatically disabled if mmproj is loaded
-                    GGML_ABORT("not supported by multimodal");
-                }
-
-                // determine the max draft that fits the current slot state
-                int n_draft_max = slot.task->params.speculative.n_max;
-
-                // note: slot.prompt is not yet expanded with the `id` token sampled above
-                //       also, need to leave space for 1 extra token to allow context shifts
-                n_draft_max = std::min(n_draft_max, slot.n_ctx - slot.prompt.n_tokens() - 2);
-
-                if (slot.n_remaining > 0) {
-                    n_draft_max = std::min(n_draft_max, slot.n_remaining - 1);
-                }
-
-                SLT_DBG(slot, "max possible draft: %d\n", n_draft_max);
-
-                if (n_draft_max < slot.task->params.speculative.n_min) {
-                    SLT_DBG(slot, "the max possible draft is too small: %d < %d - skipping speculative decoding\n", n_draft_max, slot.task->params.speculative.n_min);
-
-                    continue;
-                }
-
-                llama_token id = slot.sampled;
-
-                struct common_speculative_params params_spec;
-                params_spec.n_draft = n_draft_max;
-                params_spec.n_reuse = llama_n_ctx(slot.ctx_dft) - slot.task->params.speculative.n_max;
-                params_spec.p_min   = slot.task->params.speculative.p_min;
-
-                const llama_tokens & cached_text_tokens = slot.prompt.tokens.get_text_tokens();
-                llama_tokens draft = common_speculative_gen_draft(slot.spec, params_spec, cached_text_tokens, id);
-
-                // ignore small drafts
-                if (slot.task->params.speculative.n_min > (int) draft.size()) {
-                    SLT_DBG(slot, "ignoring small draft: %d < %d\n", (int) draft.size(), slot.task->params.speculative.n_min);
-
-                    continue;
-                }
-
-                // keep track of total number of drafted tokens tested
-                slot.n_draft_total += draft.size();
-
-                // construct the speculation batch
-                common_batch_clear(slot.batch_spec);
-                common_batch_add  (slot.batch_spec, id, slot.prompt.tokens.pos_next(), { slot.id }, true);
-
-                for (size_t i = 0; i < draft.size(); ++i) {
-                    common_batch_add(slot.batch_spec, draft[i], slot.prompt.tokens.pos_next() + 1 + i, { slot.id }, true);
-                }
-
-                SLT_DBG(slot, "decoding speculative batch, size = %d\n", slot.batch_spec.n_tokens);
-
-                llama_decode(ctx, slot.batch_spec);
+                size_t n_draft = slot.drafted.size();
 
                 // the accepted tokens from the speculation
-                const auto ids = common_sampler_sample_and_accept_n(slot.smpl, ctx, draft);
+                const auto ids = common_sampler_sample_and_accept_n(slot.smpl, ctx, slot.i_batch_dft, slot.drafted);
+                slot.i_batch_dft.clear();
+                slot.drafted.clear();
 
                 slot.n_decoded += ids.size();
 
+                slot.t_token_generation = std::max<int64_t>(1, t_current - slot.t_start_generation) / 1e3;
+
                 // update how many tokens out of those tested were accepted
                 slot.n_draft_accepted += ids.size() - 1;
 
-                slot.prompt.tokens.push_back(id);
+                // rollback to the state before sampling the draft tokens
+                slot.prompt.tokens.keep_first(slot.prompt.n_tokens() - n_draft);
+
+                // add accepted tokens to the prompt
                 slot.prompt.tokens.insert({ids.begin(), ids.end() - 1});
+                slot.sampled = ids.back(); // last accepted token
 
                 llama_memory_seq_rm(llama_get_memory(ctx), slot.id, slot.prompt.n_tokens(), -1);
 
@@ -2539,7 +2568,7 @@ struct server_context_impl {
                     }
                 }
 
-                SLT_DBG(slot, "accepted %d/%d draft tokens, new n_tokens = %d\n", (int) ids.size() - 1, (int) draft.size(), slot.prompt.n_tokens());
+                SLT_DBG(slot, "accepted %d/%d draft tokens, new n_tokens = %d\n", (int) ids.size() - 1, (int) slot.drafted.size(), slot.prompt.n_tokens());
             }
         }
 

From 68522c678daa7b65718f8a3de89bb2fbb139e26f Mon Sep 17 00:00:00 2001
From: Neo Zhang <zhang.jianyu@outlook.com>
Date: Mon, 8 Dec 2025 22:09:39 +0800
Subject: [PATCH 023/135] ci : support bfloat16 SYCL release package (#17855)

* support bfloat16 release package

* add fallback file
---
 .github/workflows/release.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 3668e4e2c9..77aec20c11 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -546,6 +546,8 @@ jobs:
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libmmd.dll" ./build/bin
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libiomp5md.dll" ./build/bin
           cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/sycl-ls.exe" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libsycl-fallback-bfloat16.spv" ./build/bin
+          cp "${{ env.ONEAPI_ROOT }}/compiler/latest/bin/libsycl-native-bfloat16.spv" ./build/bin
 
           cp "${{ env.ONEAPI_ROOT }}/dnnl/latest/bin/dnnl.dll" ./build/bin
           cp "${{ env.ONEAPI_ROOT }}/tbb/latest/bin/tbb12.dll" ./build/bin

From 951520ddb05402bb8844509a7683d1a9a517dfc6 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Mon, 8 Dec 2025 17:04:38 +0100
Subject: [PATCH 024/135] server: delegate result_state creation to server_task
 (#17835)

* server: delegate result_state creation to server_task

* remove unued states

* add more docs
---
 tools/server/README-dev.md      | 27 +++++++++++++++++++-
 tools/server/server-context.cpp | 21 +++++++---------
 tools/server/server-context.h   |  5 ++--
 tools/server/server-queue.cpp   | 13 ++++++++--
 tools/server/server-queue.h     |  6 ++---
 tools/server/server-task.h      | 44 +++++++++++++++++++--------------
 6 files changed, 76 insertions(+), 40 deletions(-)

diff --git a/tools/server/README-dev.md b/tools/server/README-dev.md
index df165c34a3..fbcd6bc1f9 100644
--- a/tools/server/README-dev.md
+++ b/tools/server/README-dev.md
@@ -42,7 +42,15 @@ graph TD
     server_response --> server_routes
 ```
 
-TODO: mention about how batching is handled by `server_slot`
+### Batching
+
+The server context maintains a single batch shared across all slots. When `update_slots()` is invoked, the system iterates through all active slots to populate this batch. For each slot, either a generated token from the previous decoding step or available prompt tokens are added to the batch.
+
+Batching constraints apply: slots can only be batched together if they share compatible configurations. For instance, slots using a specific LoRA adapter can be batched with each other, but not with slots using a different LoRA adapter or no adapter at all.
+
+Once the batch reaches capacity or all slots have been processed, `llama_decode` is called to execute the inference. This operation represents the primary computational bottleneck in `update_slots()`.
+
+Following decoding, the system either retrieves embeddings or samples the next token using `common_sampler_sample`. If a slot has remaining prompt tokens to process, it yields until the next `update_slots()` iteration.
 
 ### Thread Management
 
@@ -62,6 +70,23 @@ Each incoming HTTP request is handled by its own thread managed by the HTTP libr
 - All JSON formatting and chat template logic must stay in the HTTP layer.
 - Avoid passing raw JSON between the HTTP layer and `server_slot`. Instead, parse everything into native C++ types as early as possible.
 
+### Example trace of a request
+
+Here is an example trace of an API request for text completion:
+
+- A request arrives at the HTTP layer.
+- The request is routed to the corresponding handler inside `server_routes`. In this case, `handle_completions_impl` is invoked.
+- The handler parses the input request, constructs a new `server_task`, and passes it to `server_res_generator`.
+- `server_res_generator` creates a new `task_result_state` for each task:
+    - `task_result_state` stays in the HTTP layer, responsible for keeping track of the current state of the response (e.g., parsing tool calls or thinking messages).
+    - `server_task` is moved into `server_queue` inside `server_context`.
+- `server_context` launches the task by moving it into an available slot (see `launch_slot_with_task()`).
+- `update_slot()` processes the task as described in the "Batching" section above.
+- Results may be sent using `send_partial_response` or `send_final_response`, which creates a new `server_task_result` and pushes it to the response queue.
+- At the same time, `server_res_generator` listens to the response queue and retrieves this response.
+- As the response is stateless, `server_res_generator` calls `response->update()` to update the response with the current state.
+- `server_res_generator` then calls `response->to_json()` and passes the response to the HTTP layer.
+
 ### Testing
 
 `llama-server` includes an automated test suite based on `pytest`.
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 3bf9051026..4578f8d7a9 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -2589,6 +2589,10 @@ struct server_context_impl {
     int get_slot_n_ctx() {
         return slots.back().n_ctx;
     }
+
+    server_response_reader get_response_reader() {
+        return server_response_reader(queue_tasks, queue_results, HTTP_POLLING_SECONDS);
+    }
 };
 
 //
@@ -2618,8 +2622,8 @@ llama_context * server_context::get_llama_context() const {
     return impl->ctx;
 }
 
-std::pair<server_queue &, server_response &> server_context::get_queues() {
-    return { impl->queue_tasks, impl->queue_results };
+server_response_reader server_context::get_response_reader() {
+    return impl->get_response_reader();
 }
 
 
@@ -2628,7 +2632,7 @@ std::pair<server_queue &, server_response &> server_context::get_queues() {
 struct server_res_generator : server_http_res {
     server_response_reader rd;
     server_res_generator(server_context_impl & ctx_server)
-        : rd({ctx_server.queue_tasks, ctx_server.queue_results}, HTTP_POLLING_SECONDS) {}
+        : rd(ctx_server.queue_tasks, ctx_server.queue_results, HTTP_POLLING_SECONDS) {}
     void ok(const json & response_data) {
         status = 200;
         data = safe_json_to_str(response_data);
@@ -2661,9 +2665,6 @@ static std::unique_ptr<server_res_generator> handle_completions_impl(
     try {
         std::vector<server_task> tasks;
 
-        // tracking generation state and partial tool calls
-        std::vector<task_result_state> states;
-
         const auto & prompt = data.at("prompt");
         // TODO: this log can become very long, put it behind a flag or think about a more compact format
         //SRV_DBG("Prompt: %s\n", prompt.is_string() ? prompt.get<std::string>().c_str() : prompt.dump(2).c_str());
@@ -2679,7 +2680,6 @@ static std::unique_ptr<server_res_generator> handle_completions_impl(
             inputs = tokenize_input_prompts(ctx_server.vocab, ctx_server.mctx, prompt, true, true);
         }
         tasks.reserve(inputs.size());
-        states.reserve(inputs.size());
         int idx = 0;
         for (size_t i = 0; i < inputs.size(); i++) {
             server_task task = server_task(type);
@@ -2698,7 +2698,6 @@ static std::unique_ptr<server_res_generator> handle_completions_impl(
             task.params.res_type          = res_type;
             task.params.oaicompat_cmpl_id = completion_id;
             task.params.oaicompat_model   = ctx_server.model_name;
-            states.push_back(task.params.oaicompat_chat_syntax);
 
             if (task.params.n_cmpl > 1) {
                 task.n_children = task.params.n_cmpl - 1;
@@ -2707,7 +2706,6 @@ static std::unique_ptr<server_res_generator> handle_completions_impl(
                         task.id,
                         ctx_server.queue_tasks.get_new_id(),
                         idx++);
-                    states.push_back(child.params.oaicompat_chat_syntax);
                     tasks.push_back(std::move(child));
                 }
             }
@@ -2715,7 +2713,6 @@ static std::unique_ptr<server_res_generator> handle_completions_impl(
             tasks.push_back(std::move(task));
         }
 
-        rd.set_states(std::move(states));
         rd.post_tasks(std::move(tasks));
     } catch (const std::exception & e) {
         res->error(format_error_response(e.what(), ERROR_TYPE_INVALID_REQUEST));
@@ -3445,7 +3442,7 @@ void server_routes::init_routes() {
 
         // create and queue the task
         json responses = json::array();
-        server_response_reader rd({ctx_server.queue_tasks, ctx_server.queue_results}, HTTP_POLLING_SECONDS);
+        server_response_reader rd = ctx_server.get_response_reader();
         {
             std::vector<server_task> tasks;
             tasks.reserve(documents.size());
@@ -3705,7 +3702,7 @@ std::unique_ptr<server_res_generator> server_routes::handle_embeddings_impl(cons
 
     // create and queue the task
     json responses = json::array();
-    server_response_reader rd({ctx_server.queue_tasks, ctx_server.queue_results}, HTTP_POLLING_SECONDS);
+    server_response_reader rd = ctx_server.get_response_reader();
     {
         std::vector<server_task> tasks;
         for (size_t i = 0; i < tokenized_prompts.size(); i++) {
diff --git a/tools/server/server-context.h b/tools/server/server-context.h
index 05b4afaeeb..eaa1380877 100644
--- a/tools/server/server-context.h
+++ b/tools/server/server-context.h
@@ -31,9 +31,8 @@ struct server_context {
     // get the underlaying llama_context
     llama_context * get_llama_context() const;
 
-    // get the underlaying queue_tasks and queue_results
-    // used by CLI application
-    std::pair<server_queue &, server_response &> get_queues();
+    // get a new response reader, used by CLI application
+    server_response_reader get_response_reader();
 };
 
 
diff --git a/tools/server/server-queue.cpp b/tools/server/server-queue.cpp
index 10196128db..3cceb2bbe2 100644
--- a/tools/server/server-queue.cpp
+++ b/tools/server/server-queue.cpp
@@ -271,12 +271,21 @@ void server_response::terminate() {
 // server_response_reader
 //
 
-void server_response_reader::set_states(std::vector<task_result_state> && states) {
-    this->states = std::move(states);
+void server_response_reader::post_task(server_task && task) {
+    GGML_ASSERT(id_tasks.empty() && "post_task() can only be called once per reader");
+    id_tasks.insert(task.id);
+    states.push_back(task.create_state());
+    queue_results.add_waiting_task_id(task.id);
+    queue_tasks.post(std::move(task));
 }
 
 void server_response_reader::post_tasks(std::vector<server_task> && tasks) {
+    GGML_ASSERT(id_tasks.empty() && "post_tasks() can only be called once per reader");
     id_tasks = server_task::get_list_id(tasks);
+    states.reserve(tasks.size());
+    for (size_t i = 0; i < tasks.size(); i++) {
+        states.push_back(tasks[i].create_state());
+    }
     queue_results.add_waiting_tasks(tasks);
     queue_tasks.post(std::move(tasks));
 }
diff --git a/tools/server/server-queue.h b/tools/server/server-queue.h
index a5c3179d8c..726eadf4ef 100644
--- a/tools/server/server-queue.h
+++ b/tools/server/server-queue.h
@@ -129,13 +129,13 @@ struct server_response_reader {
     std::vector<task_result_state> states;
 
     // should_stop function will be called each polling_interval_seconds
-    server_response_reader(std::pair<server_queue &, server_response &> server_queues, int polling_interval_seconds)
-        : queue_tasks(server_queues.first), queue_results(server_queues.second), polling_interval_seconds(polling_interval_seconds) {}
+    server_response_reader(server_queue & queue_tasks, server_response & queue_results, int polling_interval_seconds)
+        : queue_tasks(queue_tasks), queue_results(queue_results), polling_interval_seconds(polling_interval_seconds) {}
     ~server_response_reader() {
         stop();
     }
 
-    void set_states(std::vector<task_result_state> && states);
+    void post_task(server_task && tasks);
     void post_tasks(std::vector<server_task> && tasks);
     bool has_next() const;
 
diff --git a/tools/server/server-task.h b/tools/server/server-task.h
index da4e22a7cd..9011ff944b 100644
--- a/tools/server/server-task.h
+++ b/tools/server/server-task.h
@@ -85,6 +85,25 @@ struct task_params {
     json to_json(bool only_metrics = false) const;
 };
 
+// struct for tracking the state of a task (e.g., for streaming)
+struct task_result_state {
+    // tracking diffs for partial tool calls
+    std::vector<common_chat_msg_diff> diffs;
+    common_chat_syntax oaicompat_chat_syntax;
+    common_chat_msg chat_msg;
+    std::string generated_text; // append new chunks of generated text here
+    std::vector<std::string> generated_tool_call_ids;
+
+    task_result_state(const common_chat_syntax & oaicompat_chat_syntax)
+        : oaicompat_chat_syntax(oaicompat_chat_syntax) {}
+
+    // parse partial tool calls and update the internal state
+    common_chat_msg update_chat_msg(
+        const std::string & text_added,
+        bool is_partial,
+        std::vector<common_chat_msg_diff> & diffs);
+};
+
 struct server_task {
     int id    = -1; // to be filled by server_queue
     int index = -1; // used when there are multiple prompts (batch request)
@@ -149,6 +168,12 @@ struct server_task {
         copy.tokens    = tokens.clone();
         return copy;
     }
+
+    // the task will be moved into queue, then onto slots
+    // however, the state must be kept by caller (e.g., HTTP thread)
+    task_result_state create_state() const {
+        return task_result_state(params.oaicompat_chat_syntax);
+    }
 };
 
 struct result_timings {
@@ -180,25 +205,6 @@ struct result_prompt_progress {
     json to_json() const;
 };
 
-// struct for tracking the state of a task (e.g., for streaming)
-struct task_result_state {
-    // tracking diffs for partial tool calls
-    std::vector<common_chat_msg_diff> diffs;
-    common_chat_syntax oaicompat_chat_syntax;
-    common_chat_msg chat_msg;
-    std::string generated_text; // append new chunks of generated text here
-    std::vector<std::string> generated_tool_call_ids;
-
-    task_result_state(const common_chat_syntax & oaicompat_chat_syntax)
-        : oaicompat_chat_syntax(oaicompat_chat_syntax) {}
-
-    // parse partial tool calls and update the internal state
-    common_chat_msg update_chat_msg(
-        const std::string & text_added,
-        bool is_partial,
-        std::vector<common_chat_msg_diff> & diffs);
-};
-
 struct server_task_result {
     int id           = -1;
     int id_slot      = -1;

From 2fa51c19b028180b35d316e9ed06f5f0f7ada2c1 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Mon, 8 Dec 2025 17:13:08 +0100
Subject: [PATCH 025/135] model-conversion : add token ids to prompt token
 output [no ci] (#17863)

This commit adds the token ids to the printed prompt outputs.

The motivation for this is that is can be useful to see the actual token
ids alongside the token strings for debugging.
---
 examples/model-conversion/logits.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/model-conversion/logits.cpp b/examples/model-conversion/logits.cpp
index bbd095e603..5bcf063267 100644
--- a/examples/model-conversion/logits.cpp
+++ b/examples/model-conversion/logits.cpp
@@ -144,7 +144,7 @@ int main(int argc, char ** argv) {
             return 1;
         }
         std::string s(buf, n);
-        printf("%s", s.c_str());
+        printf("%s (%d)", s.c_str(), id);
     }
     printf("\n");
 

From c8554b66e0ed397f7457ed5f3ce3b466dd508d5c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Mon, 8 Dec 2025 21:29:59 +0100
Subject: [PATCH 026/135] graph : use fill instead of scale_bias in grouped
 expert selection (#17867)

* use fill instead of scale_bias in grouped expert selection

* do not explicitly use _inplace
---
 src/llama-graph.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 42ccb5b76a..43620df780 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -973,7 +973,7 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
 
         // mask out the other groups
         selection_probs = ggml_get_rows(ctx0, selection_groups, expert_groups); // [n_exp_per_group, n_group_used, n_tokens]
-        selection_probs = ggml_set_rows(ctx0, ggml_scale_bias(ctx0, selection_groups, 0.0f, -INFINITY), selection_probs, expert_groups); // [n_exp_per_group, n_expert_groups, n_tokens]
+        selection_probs = ggml_set_rows(ctx0, ggml_fill(ctx0, selection_groups, -INFINITY), selection_probs, expert_groups); // [n_exp_per_group, n_expert_groups, n_tokens]
         selection_probs = ggml_reshape_2d(ctx0, selection_probs, n_expert, n_tokens); // [n_expert, n_tokens]
         cb(selection_probs, "ffn_moe_probs_masked", il);
     }

From 1d2a1ab73d927644d58f44235209affab7e4043e Mon Sep 17 00:00:00 2001
From: philip-essential <169196560+philip-essential@users.noreply.github.com>
Date: Mon, 8 Dec 2025 19:49:03 -0800
Subject: [PATCH 027/135] model : support Rnj-1 (#17811)

* add support for rnj1

* refactor gemma3 to support rnj-1

* address review comments
---
 convert_hf_to_gguf.py                      | 35 +++++++++++++++-------
 src/CMakeLists.txt                         |  2 +-
 src/llama-model.cpp                        | 23 ++++++++++----
 src/models/{gemma3-iswa.cpp => gemma3.cpp} | 35 ++++++++++++++++++----
 src/models/models.h                        |  5 ++--
 5 files changed, 76 insertions(+), 24 deletions(-)
 rename src/models/{gemma3-iswa.cpp => gemma3.cpp} (78%)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index c641989c19..2cc2a38823 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -5825,9 +5825,11 @@ class Gemma3Model(TextModel):
     norm_shift = 1.0  # Gemma3RMSNorm adds 1.0 to the norm value
 
     def set_vocab(self):
-        self._set_vocab_sentencepiece()
-
-        self.gguf_writer.add_add_space_prefix(False)
+        if (self.dir_model / "tokenizer.model").is_file():
+            self._set_vocab_sentencepiece()
+            self.gguf_writer.add_add_space_prefix(False)
+        else:
+            self._set_vocab_gpt2()
 
     def set_gguf_parameters(self):
         hparams = self.hparams
@@ -5845,13 +5847,24 @@ class Gemma3Model(TextModel):
         self.gguf_writer.add_rope_freq_base(hparams.get("rope_theta", 1_000_000.0)) # for global layers
         # attn_logit_softcapping is removed in Gemma3
         assert hparams.get("attn_logit_softcapping") is None
-        self.gguf_writer.add_sliding_window(hparams["sliding_window"])
+        if (final_logit_softcap := hparams.get("final_logit_softcapping")):
+            self.gguf_writer.add_final_logit_softcapping(final_logit_softcap)
+        if hparams.get("sliding_window_pattern") != 1:
+            self.gguf_writer.add_sliding_window(hparams["sliding_window"])
         self.gguf_writer.add_head_count_kv(hparams.get("num_key_value_heads", 4))
         if hparams.get("rope_scaling") is not None:
-            assert hparams["rope_scaling"]["rope_type"] == "linear"
-            # important: this rope_scaling is only applied for global layers, and not used by 1B model
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
-            self.gguf_writer.add_rope_scaling_factor(hparams["rope_scaling"]["factor"])
+            rope_scaling = hparams["rope_scaling"]
+            if rope_scaling["rope_type"] == "linear":
+                # important: this rope_scaling is only applied for global layers, and not used by 1B model
+                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
+                self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
+            elif rope_scaling["rope_type"] == "yarn":
+                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
+                self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
+                self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
+                self.gguf_writer.add_rope_scaling_yarn_ext_factor(rope_scaling["extrapolation_factor"])
+                self.gguf_writer.add_rope_scaling_yarn_beta_fast(rope_scaling["beta_fast"])
+                self.gguf_writer.add_rope_scaling_yarn_beta_slow(rope_scaling["beta_slow"])
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
@@ -5865,8 +5878,10 @@ class Gemma3Model(TextModel):
 
         # remove OOV (out-of-vocabulary) rows in token_embd
         if "embed_tokens.weight" in name:
-            vocab = self._create_vocab_sentencepiece()
-            tokens = vocab[0]
+            if (self.dir_model / "tokenizer.model").is_file():
+                tokens = self._create_vocab_sentencepiece()[0]
+            else:
+                tokens = self.get_vocab_base()[0]
             data_torch = data_torch[:len(tokens)]
 
         # ref code in Gemma3RMSNorm
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index fbd538109b..84a0c2934e 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -67,7 +67,7 @@ add_library(llama
             models/gemma-embedding.cpp
             models/gemma.cpp
             models/gemma2-iswa.cpp
-            models/gemma3-iswa.cpp
+            models/gemma3.cpp
             models/gemma3n-iswa.cpp
             models/glm4-moe.cpp
             models/glm4.cpp
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 7d09d7abd5..e09d59e2c1 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1264,18 +1264,25 @@ void llama_model::load_hparams(llama_model_loader & ml) {
             } break;
         case LLM_ARCH_GEMMA3:
             {
-                hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
-                hparams.set_swa_pattern(6);
+                const bool found_swa = ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
+                if (found_swa && hparams.n_swa > 0) {
+                    hparams.swa_type = LLAMA_SWA_TYPE_STANDARD;
+                    hparams.set_swa_pattern(6);
 
-                hparams.rope_freq_base_train_swa  = 10000.0f;
-                hparams.rope_freq_scale_train_swa = 1.0f;
+                    hparams.rope_freq_base_train_swa  = 10000.0f;
+                    hparams.rope_freq_scale_train_swa = 1.0f;
+                } else {
+                    hparams.swa_type = LLAMA_SWA_TYPE_NONE;
+                }
 
-                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW,    hparams.n_swa);
+                hparams.f_final_logit_softcapping = 0.0f;
+                ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
                 switch (hparams.n_layer) {
                     case 18: type = LLM_TYPE_270M; break;
                     case 26: type = LLM_TYPE_1B; break;
+                    case 32: type = LLM_TYPE_8B; break; // Rnj-1
                     case 34: type = LLM_TYPE_4B; break;
                     case 48: type = LLM_TYPE_12B; break;
                     case 62: type = LLM_TYPE_27B; break;
@@ -7304,7 +7311,11 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             } break;
         case LLM_ARCH_GEMMA3:
             {
-                llm = std::make_unique<llm_build_gemma3_iswa>(*this, params);
+                if (hparams.swa_type == LLAMA_SWA_TYPE_STANDARD) {
+                    llm = std::make_unique<llm_build_gemma3<true>>(*this, params);
+                } else {
+                    llm = std::make_unique<llm_build_gemma3<false>>(*this, params);
+                }
             } break;
         case LLM_ARCH_GEMMA3N:
             {
diff --git a/src/models/gemma3-iswa.cpp b/src/models/gemma3.cpp
similarity index 78%
rename from src/models/gemma3-iswa.cpp
rename to src/models/gemma3.cpp
index 839ff6d3d9..ae60ef4790 100644
--- a/src/models/gemma3-iswa.cpp
+++ b/src/models/gemma3.cpp
@@ -1,6 +1,7 @@
 #include "models.h"
 
-llm_build_gemma3_iswa::llm_build_gemma3_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+template <bool iswa>
+llm_build_gemma3<iswa>::llm_build_gemma3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     const int64_t n_embd_head = hparams.n_embd_head_k;
 
     ggml_tensor * cur;
@@ -17,13 +18,28 @@ llm_build_gemma3_iswa::llm_build_gemma3_iswa(const llama_model & model, const ll
     ggml_tensor * inp_pos = build_inp_pos();
 
     // TODO: is causal == true correct? might need some changes
-    auto * inp_attn = build_attn_inp_kv_iswa();
+    using inp_attn_type = std::conditional_t<iswa, llm_graph_input_attn_kv_iswa, llm_graph_input_attn_kv>;
+    inp_attn_type * inp_attn = nullptr;
+
+    if constexpr (iswa) {
+        inp_attn = build_attn_inp_kv_iswa();
+    } else {
+        inp_attn = build_attn_inp_kv();
+    }
 
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
     for (int il = 0; il < n_layer; ++il) {
-        const float freq_base_l  = model.get_rope_freq_base (cparams, il);
-        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
+        float freq_base_l  = 0.0f;
+        float freq_scale_l = 0.0f;
+
+        if constexpr (iswa) {
+            freq_base_l  = model.get_rope_freq_base (cparams, il);
+            freq_scale_l = model.get_rope_freq_scale(cparams, il);
+        } else {
+            freq_base_l  = freq_base;
+            freq_scale_l = freq_scale;
+        }
 
         // norm
         cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
@@ -102,7 +118,7 @@ llm_build_gemma3_iswa::llm_build_gemma3_iswa(const llama_model & model, const ll
         cur = build_norm(cur,
                 model.layers[il].ffn_post_norm, NULL,
                 LLM_NORM_RMS, -1);
-        cb(cur, "ffn_post_norm", -1);
+        cb(cur, "ffn_post_norm", il);
 
         cur = ggml_add(ctx0, cur, sa_out);
 
@@ -124,8 +140,17 @@ llm_build_gemma3_iswa::llm_build_gemma3_iswa(const llama_model & model, const ll
     // lm_head
     cur = build_lora_mm(model.output, cur);
 
+    if (hparams.f_final_logit_softcapping) {
+        cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_final_logit_softcapping);
+        cur = ggml_tanh(ctx0, cur);
+        cur = ggml_scale(ctx0, cur, hparams.f_final_logit_softcapping);
+    }
+
     cb(cur, "result_output", -1);
     res->t_logits = cur;
 
     ggml_build_forward_expand(gf, cur);
 }
+
+template struct llm_build_gemma3<false>;
+template struct llm_build_gemma3<true>;
diff --git a/src/models/models.h b/src/models/models.h
index d93601ad06..6494f54501 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -179,8 +179,9 @@ struct llm_build_gemma2_iswa : public llm_graph_context {
     llm_build_gemma2_iswa(const llama_model & model, const llm_graph_params & params);
 };
 
-struct llm_build_gemma3_iswa : public llm_graph_context {
-    llm_build_gemma3_iswa(const llama_model & model, const llm_graph_params & params);
+template <bool iswa>
+struct llm_build_gemma3 : public llm_graph_context {
+    llm_build_gemma3(const llama_model & model, const llm_graph_params & params);
 };
 
 struct llm_build_gemma3n_iswa : public llm_graph_context {

From e39502e74b46c54b359ddbbefc9bba4f98704906 Mon Sep 17 00:00:00 2001
From: Aldehir Rojas <hello@alde.dev>
Date: Tue, 9 Dec 2025 00:32:57 -0600
Subject: [PATCH 028/135] llama : add token matching support to llama-grammar
 (#17816)

* llama : add token support to llama-grammar

* fix inverse token comment

* refactor trigger_patterns to replay tokens instead of the entire string

* add token documentation

* fix test-llama-grammar

* improve test cases for tokens
---
 grammars/README.md                 |  24 +++
 src/llama-grammar.cpp              | 266 +++++++++++++++++++++++++----
 src/llama-grammar.h                |  21 ++-
 tests/test-grammar-integration.cpp | 111 +++++++++++-
 tests/test-grammar-parser.cpp      |  14 ++
 tests/test-llama-grammar.cpp       |   2 +-
 6 files changed, 400 insertions(+), 38 deletions(-)

diff --git a/grammars/README.md b/grammars/README.md
index a63198b5ae..11e3b6dd90 100644
--- a/grammars/README.md
+++ b/grammars/README.md
@@ -67,6 +67,30 @@ Parentheses `()` can be used to group sequences, which allows for embedding alte
 - `{m,n}` repeats the precedent symbol or sequence at between `m` and `n` times (included)
 - `{0,n}` repeats the precedent symbol or sequence at most `n` times (included)
 
+## Tokens
+
+Tokens allow grammars to match specific tokenizer tokens rather than character sequences. This is useful for constraining outputs based on special tokens (like `<think>` or `</think>`).
+
+Tokens can be specified in two ways:
+
+1. **Token ID**: Use angle brackets with the token ID in square brackets: `<[token-id]>`. For example, `<[1000]>` matches the token with ID 1000.
+
+2. **Token string**: Use angle brackets with the token text directly: `<token>`. For example, `<think>` will match the token whose text is exactly `<think>`. This only works if the string tokenizes to exactly one token in the vocabulary, otherwise the grammar will fail to parse.
+
+You can negate token matches using the `!` prefix: `!<[1000]>` or `!<think>` matches any token *except* the specified one.
+
+```
+# Match a thinking block: <think>...</think>
+# Using token strings (requires these to be single tokens in the vocab)
+root ::= <think> thinking </think> .*
+thinking ::= !</think>*
+
+# Equivalent grammar using explicit token IDs
+# Assumes token 1000 = <think>, token 1001 = </think>
+root ::= <[1000]> thinking <[1001]> .*
+thinking ::= !<[1001]>*
+```
+
 ## Comments and newlines
 
 Comments can be specified with `#`:
diff --git a/src/llama-grammar.cpp b/src/llama-grammar.cpp
index b3c5eb5717..75d5d750c3 100644
--- a/src/llama-grammar.cpp
+++ b/src/llama-grammar.cpp
@@ -181,6 +181,52 @@ static std::pair<uint32_t, const char *> parse_char(const char * src) {
     throw std::runtime_error("unexpected end of input");
 }
 
+static std::pair<uint32_t, const char *> parse_token(const llama_vocab * vocab, const char * src) {
+    const char * pos = src;
+    if (*pos != '<') {
+        throw std::runtime_error(std::string("expecting '<' at ") + pos);
+    }
+    pos++;
+
+    // Parse <[id]>
+    if (*pos == '[') {
+        pos++;
+        const char * int_end = parse_int(pos);
+        uint32_t token_id = std::stoul(std::string(pos, int_end - pos));
+        pos = int_end;
+        if (*pos != ']') {
+            throw std::runtime_error(std::string("expecting ']' at ") + pos);
+        }
+        pos++;
+        if (*pos != '>') {
+            throw std::runtime_error(std::string("expecting '>' at ") + pos);
+        }
+        pos++;
+        return std::make_pair(token_id, pos);
+    }
+
+    if (vocab == nullptr) {
+        throw std::runtime_error(std::string("no vocab to parse token at ") + src);
+    }
+
+    // Parse <token> and tokenize to obtain the token id
+    while (*pos != 0 && *pos != '>') {
+        pos++;
+    }
+    if (*pos != '>') {
+        throw std::runtime_error(std::string("expecting '>' at ") + pos);
+    }
+    pos++;
+
+    llama_token tokens[2];
+    int32_t n_tokens = vocab->tokenize(src, static_cast<int32_t>(pos - src), tokens, 2, false, true);
+    if (n_tokens != 1) {
+        // must tokenize to exactly 1 token
+        throw std::runtime_error("invalid token '" + std::string(src, pos - src) + "'");
+    }
+    return std::make_pair(tokens[0], pos);
+}
+
 static void print_grammar_char(FILE * file, uint32_t c) {
     if (0x20 <= c && c <= 0x7f) {
         fprintf(file, "%c", static_cast<char>(c));
@@ -212,6 +258,8 @@ static void print_rule_binary(FILE * file, const llama_grammar_rule & rule) {
             case LLAMA_GRETYPE_CHAR_RNG_UPPER: fprintf(file, "CHAR_RNG_UPPER"); break;
             case LLAMA_GRETYPE_CHAR_ALT:       fprintf(file, "CHAR_ALT");       break;
             case LLAMA_GRETYPE_CHAR_ANY:       fprintf(file, "CHAR_ANY");       break;
+            case LLAMA_GRETYPE_TOKEN:          fprintf(file, "TOKEN");          break;
+            case LLAMA_GRETYPE_TOKEN_NOT:      fprintf(file, "TOKEN_NOT");      break;
         }
         switch (elem.type) {
             case LLAMA_GRETYPE_END:
@@ -228,6 +276,17 @@ static void print_rule_binary(FILE * file, const llama_grammar_rule & rule) {
                 print_grammar_char(file, elem.value);
                 fprintf(file, "\") ");
                 break;
+            case LLAMA_GRETYPE_TOKEN:
+                fprintf(file, "<[");
+                fprintf(file, "%u", elem.value);
+                fprintf(file, "]> ");
+                break;
+            case LLAMA_GRETYPE_TOKEN_NOT:
+                fprintf(file, "!");
+                fprintf(file, "<[");
+                fprintf(file, "%u", elem.value);
+                fprintf(file, "]> ");
+                break;
         }
     }
     fprintf(file, "\n");
@@ -284,6 +343,17 @@ static void print_rule(
             case LLAMA_GRETYPE_CHAR_ANY:
                 fprintf(file, ".");
                 break;
+            case LLAMA_GRETYPE_TOKEN:
+                fprintf(file, "<[");
+                fprintf(file, "%u", elem.value);
+                fprintf(file, "]> ");
+                break;
+            case LLAMA_GRETYPE_TOKEN_NOT:
+                fprintf(file, "!");
+                fprintf(file, "<[");
+                fprintf(file, "%u", elem.value);
+                fprintf(file, "]> ");
+                break;
         }
         if (is_char_element(elem)) {
             switch (rule[i + 1].type) {
@@ -444,6 +514,17 @@ const char * llama_grammar_parser::parse_sequence(
                 }
             }
             pos = parse_space(pos + 1, is_nested);
+        } else if (*pos == '<' || *pos == '!') { // token
+            auto type = LLAMA_GRETYPE_TOKEN;
+            if (*pos == '!') { // token inverse
+                type = LLAMA_GRETYPE_TOKEN_NOT;
+                pos++;
+            }
+            auto token_pair = parse_token(vocab, pos);
+            const char * token_end  = token_pair.second;
+            last_sym_start = rule.size();
+            rule.push_back({type, token_pair.first});
+            pos = parse_space(token_end, is_nested);
         } else if (is_word_char(*pos)) { // rule reference
             const char * name_end    = parse_name(pos);
             uint32_t ref_rule_id = get_symbol_id(pos, name_end - pos);
@@ -691,6 +772,21 @@ static bool llama_grammar_match_partial_char(
     return !is_positive_char;
 }
 
+// returns true iff token matches the rule at pos (regular or inverse)
+// asserts that pos is pointing to a token element
+static bool llama_grammar_match_token(
+    const llama_grammar_element * pos,
+    const llama_token             token) {
+    GGML_ASSERT(pos->type == LLAMA_GRETYPE_TOKEN || pos->type == LLAMA_GRETYPE_TOKEN_NOT);
+    if (pos->type == LLAMA_GRETYPE_TOKEN) {
+        return pos->value == static_cast<uint32_t>(token);
+    }
+    if (pos->type == LLAMA_GRETYPE_TOKEN_NOT) {
+        return pos->value != static_cast<uint32_t>(token);
+    }
+    return false;
+}
+
 // transforms a grammar pushdown stack into N possible stacks, all ending
 // at a character range (terminal element)
 static void llama_grammar_advance_stack(
@@ -738,6 +834,8 @@ static void llama_grammar_advance_stack(
         case LLAMA_GRETYPE_CHAR:
         case LLAMA_GRETYPE_CHAR_NOT:
         case LLAMA_GRETYPE_CHAR_ANY:
+        case LLAMA_GRETYPE_TOKEN:
+        case LLAMA_GRETYPE_TOKEN_NOT:
             if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
                 // only add the stack if it's not a duplicate of one we already have
                 new_stacks.emplace_back(stack);
@@ -831,26 +929,38 @@ llama_grammar_stacks & llama_grammar_get_stacks(struct llama_grammar * grammar)
     return grammar->stacks;
 }
 
+static void llama_grammar_accept_chr(
+        struct llama_grammar       & grammar,
+        const llama_grammar_stack  & stack,
+              uint32_t               chr,
+              llama_grammar_stacks & new_stacks) {
+    if (stack.empty()) {
+        return;
+    }
+
+    const llama_grammar_element * pos = stack.back();
+
+    // ignore if this turns into a token
+    if (pos->type == LLAMA_GRETYPE_TOKEN || pos->type == LLAMA_GRETYPE_TOKEN_NOT) {
+        return;
+    }
+
+    auto match = llama_grammar_match_char(pos, chr);
+    if (match.first) {
+        llama_grammar_stack new_stack(stack.begin(), stack.end() - 1);
+        if (!llama_grammar_is_end_of_sequence(match.second)) {
+            new_stack.push_back(match.second);
+        }
+        llama_grammar_advance_stack(grammar.rules, new_stack, new_stacks);
+    }
+}
+
 void llama_grammar_accept(struct llama_grammar * grammar, uint32_t chr) {
     llama_grammar_stacks stacks_new;
     stacks_new.reserve(grammar->stacks.size());
 
     for (const auto & stack : grammar->stacks) {
-        if (stack.empty()) {
-            continue;
-        }
-
-        auto match = llama_grammar_match_char(stack.back(), chr);
-        if (match.first) {
-            const llama_grammar_element * pos = match.second;
-
-            // update top of stack to next element, if any
-            llama_grammar_stack new_stack(stack.begin(), stack.end() - 1);
-            if (!llama_grammar_is_end_of_sequence(pos)) {
-                new_stack.push_back(pos);
-            }
-            llama_grammar_advance_stack(grammar->rules, new_stack, stacks_new);
-        }
+        llama_grammar_accept_chr(*grammar, stack, chr, stacks_new);
     }
 
     grammar->stacks = std::move(stacks_new);
@@ -875,6 +985,22 @@ llama_grammar_candidates llama_grammar_reject_candidates_for_stack(
 
     const llama_grammar_element * stack_pos = stack.back();
 
+    // if the top of the stack is a token rule, then we only need to check the token id
+    if (stack_pos->type == LLAMA_GRETYPE_TOKEN || stack_pos->type == LLAMA_GRETYPE_TOKEN_NOT) {
+        for (const auto & tok : candidates) {
+            if (*tok.code_points == 0) {
+                // reached the end of a token consumed by char rules, reject iff it ended
+                // in a partial response
+                if (tok.partial_utf8.n_remain != 0) {
+                    rejects.push_back(tok);
+                }
+            } else if (!llama_grammar_match_token(stack_pos, tok.id)) {
+                rejects.push_back(tok);
+            }
+        }
+        return rejects;
+    }
+
     llama_grammar_candidates next_candidates;
     next_candidates.reserve(candidates.size());
 
@@ -887,7 +1013,7 @@ llama_grammar_candidates llama_grammar_reject_candidates_for_stack(
                 rejects.push_back(tok);
             }
         } else if (llama_grammar_match_char(stack_pos, *tok.code_points).first) {
-            next_candidates.push_back({ tok.index, tok.code_points + 1, tok.partial_utf8 });
+            next_candidates.push_back({ tok.index, tok.code_points + 1, tok.partial_utf8, tok.id });
         } else {
             rejects.push_back(tok);
         }
@@ -905,7 +1031,7 @@ llama_grammar_candidates llama_grammar_reject_candidates_for_stack(
 
     auto next_rejects = llama_grammar_reject_candidates(rules, next_stacks, next_candidates);
     for (const auto & tok : next_rejects) {
-        rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8 });
+        rejects.push_back({ tok.index, tok.code_points - 1, tok.partial_utf8, tok.id });
     }
 
     return rejects;
@@ -972,12 +1098,13 @@ struct llama_grammar * llama_grammar_init_impl(
         vocab,
         std::move(vec_rules),
         std::move(stacks),
-        /* .partial_utf8 = */     {},
-        /* .lazy =*/              false,
-        /* .awaiting_trigger = */ false,
-        /* .trigger_buffer = */   "",
-        /* .trigger_tokens   = */ {},
-        /* .trigger_patterns    = */ {},
+        /* .partial_utf8 = */             {},
+        /* .lazy = */                     false,
+        /* .awaiting_trigger = */         false,
+        /* .trigger_buffer = */           "",
+        /* .trigger_buffer_positions = */ {},
+        /* .trigger_tokens = */           {},
+        /* .trigger_patterns = */         {},
     };
 }
 
@@ -990,7 +1117,7 @@ struct llama_grammar * llama_grammar_init_impl(
                             size_t num_trigger_patterns,
                const llama_token * trigger_tokens,
                             size_t num_trigger_tokens) {
-    llama_grammar_parser parser;
+    llama_grammar_parser parser(vocab);
 
     // if there is a grammar, parse it
     // rules will be empty (default) if there are parse errors
@@ -1077,10 +1204,11 @@ struct llama_grammar * llama_grammar_init_impl(
         vocab,
         std::move(vec_rules),
         std::move(stacks),
-        /* .partial_utf8 = */     {},
-        /* .lazy = */             lazy,
-        /* .awaiting_trigger = */ lazy,
-        /* .trigger_buffer = */   "",
+        /* .partial_utf8 = */             {},
+        /* .lazy = */                     lazy,
+        /* .awaiting_trigger = */         lazy,
+        /* .trigger_buffer = */           "",
+        /* .trigger_buffer_positions = */ {},
         std::move(vec_trigger_tokens),
         std::move(vec_trigger_patterns),
     };
@@ -1103,6 +1231,7 @@ struct llama_grammar * llama_grammar_clone_impl(const struct llama_grammar & gra
         grammar.lazy,
         grammar.awaiting_trigger,
         grammar.trigger_buffer,
+        grammar.trigger_buffer_positions,
         grammar.trigger_tokens,
         grammar.trigger_patterns,
     };
@@ -1156,7 +1285,7 @@ void llama_grammar_apply_impl(const struct llama_grammar & grammar, llama_token_
             cur_p->data[i].logit = -INFINITY;
         } else {
             candidates_decoded.push_back(decode_utf8(piece, grammar.partial_utf8));
-            candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second });
+            candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second, id });
         }
     }
 
@@ -1175,10 +1304,12 @@ void llama_grammar_accept_impl(struct llama_grammar & grammar, llama_token token
         if (std::find(grammar.trigger_tokens.begin(), grammar.trigger_tokens.end(), token) != grammar.trigger_tokens.end()) {
             grammar.awaiting_trigger = false;
             grammar.trigger_buffer.clear();
-            llama_grammar_accept_str(grammar, piece);
+            llama_grammar_accept_token(grammar, token, piece);
             LLAMA_LOG_DEBUG("Grammar triggered on token %u (`%s`)", token, piece.c_str());
             return;
         } else {
+            auto position = std::make_pair(grammar.trigger_buffer.size(), grammar.trigger_buffer.size() + piece.size());
+            grammar.trigger_buffer_positions.push_back(std::make_pair(token, position));
             grammar.trigger_buffer += piece;
 
             std::smatch match;
@@ -1196,10 +1327,23 @@ void llama_grammar_accept_impl(struct llama_grammar & grammar, llama_token token
                     if (start == std::string::npos) {
                         start = match.position(0);
                     }
+
+                    // replay tokens that overlap with [start, end)
+                    for (const auto & [tok, tok_pos] : grammar.trigger_buffer_positions) {
+                        auto [tok_start, tok_end] = tok_pos;
+                        if (tok_end <= start) {
+                            continue;
+                        }
+
+                        size_t piece_start = (tok_start < start) ? start : tok_start; // allow for partial token pieces
+                        size_t piece_len = tok_end - piece_start;
+                        auto tok_piece = grammar.trigger_buffer.substr(piece_start, piece_len);
+                        llama_grammar_accept_token(grammar, tok, tok_piece);
+                    }
+
                     auto constrained_str = grammar.trigger_buffer.substr(start);
-                    // std::string constrained_str(match[1].first, grammar.trigger_buffer.end());
                     grammar.trigger_buffer.clear();
-                    llama_grammar_accept_str(grammar, constrained_str);
+                    grammar.trigger_buffer_positions.clear();
                     LLAMA_LOG_DEBUG("Grammar triggered on regex: '%s'\n", constrained_str.c_str());
                     return;
                 }
@@ -1218,7 +1362,7 @@ void llama_grammar_accept_impl(struct llama_grammar & grammar, llama_token token
         GGML_ABORT("fatal error");
     }
 
-    llama_grammar_accept_str(grammar, piece);
+    llama_grammar_accept_token(grammar, token, piece);
 }
 
 void llama_grammar_accept_str(struct llama_grammar & grammar, const std::string & piece) {
@@ -1235,3 +1379,59 @@ void llama_grammar_accept_str(struct llama_grammar & grammar, const std::string
         throw std::runtime_error("Unexpected empty grammar stack after accepting piece: " + piece);
     }
 }
+
+void llama_grammar_accept_token(struct llama_grammar & grammar, llama_token token, const std::string & piece) {
+    // Note terminating 0 in decoded string
+    const auto   decoded     = decode_utf8(piece, grammar.partial_utf8);
+    const auto & code_points = decoded.first;
+
+    llama_grammar_stacks stacks_new;
+    stacks_new.reserve(grammar.stacks.size());
+
+    for (const auto & stack : grammar.stacks) {
+        if (stack.empty()) {
+            continue;
+        }
+
+        const llama_grammar_element * pos = stack.back();
+
+        if (pos->type == LLAMA_GRETYPE_TOKEN || pos->type == LLAMA_GRETYPE_TOKEN_NOT) {
+            if (llama_grammar_match_token(pos, token)) {
+                llama_grammar_stack new_stack(stack.begin(), stack.end() - 1);
+                if (!llama_grammar_is_end_of_sequence(pos + 1)) {
+                    new_stack.push_back(pos + 1);
+                }
+                llama_grammar_advance_stack(grammar.rules, new_stack, stacks_new);
+            }
+        } else {
+            llama_grammar_stacks current_stacks = {stack};
+
+            for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
+                llama_grammar_stacks next_stacks;
+
+                for (const auto & cur_stack : current_stacks) {
+                    llama_grammar_accept_chr(grammar, cur_stack, *it, next_stacks);
+                }
+
+                current_stacks = std::move(next_stacks);
+                if (current_stacks.empty()) {
+                    break;
+                }
+            }
+
+            for (auto & surviving_stack : current_stacks) {
+                if (std::find(stacks_new.begin(), stacks_new.end(), surviving_stack) == stacks_new.end()) {
+                    stacks_new.emplace_back(surviving_stack);
+                }
+            }
+        }
+    }
+
+    grammar.stacks = std::move(stacks_new);
+    grammar.partial_utf8 = decoded.second;
+
+    if (grammar.stacks.empty()) {
+        throw std::runtime_error("Unexpected empty grammar stack after accepting piece: " + piece + " (" + std::to_string(token) + ")");
+    }
+}
+
diff --git a/src/llama-grammar.h b/src/llama-grammar.h
index f8c291de99..a4c978ac11 100644
--- a/src/llama-grammar.h
+++ b/src/llama-grammar.h
@@ -36,11 +36,17 @@ enum llama_gretype {
 
     // any character (.)
     LLAMA_GRETYPE_CHAR_ANY       = 7,
+
+    // terminal element: token (<[token-id]>)
+    LLAMA_GRETYPE_TOKEN          = 8,
+
+    // inverse token (!<[token-id]>)
+    LLAMA_GRETYPE_TOKEN_NOT      = 9,
 };
 
 typedef struct llama_grammar_element {
     enum llama_gretype type;
-    uint32_t           value; // Unicode code point or rule ID
+    uint32_t           value; // Unicode code point, rule ID, or token ID
 } llama_grammar_element;
 
 struct llama_partial_utf8 {
@@ -52,6 +58,7 @@ struct llama_grammar_candidate {
     size_t               index;
     const uint32_t     * code_points;
     llama_partial_utf8   partial_utf8;
+    llama_token          id;
 };
 
 using llama_grammar_rule  = std::vector<      llama_grammar_element>;
@@ -77,10 +84,13 @@ std::vector<llama_grammar_candidate> llama_grammar_reject_candidates_for_stack(
         const llama_grammar_candidates & candidates);
 
 struct llama_grammar_parser {
+    const llama_vocab * vocab;
     std::map<std::string, uint32_t> symbol_ids;
 
     llama_grammar_rules rules;
 
+    llama_grammar_parser(const struct llama_vocab * vocab = nullptr) : vocab(vocab) {}
+
     llama_grammar_stack c_rules() const;
 
     uint32_t get_symbol_id(const char * src, size_t len);
@@ -112,6 +122,9 @@ struct llama_grammar_trigger_pattern {
 };
 
 struct llama_grammar {
+    // maintain a list of llama_tokens and their positions in the trigger_buffer
+    using token_pos = std::pair<llama_token, std::pair<size_t, size_t>>;
+
     // note: allow null vocab for testing (not great)
     const llama_vocab * vocab;
 
@@ -127,6 +140,7 @@ struct llama_grammar {
     bool                     lazy             = false;
     bool                     awaiting_trigger = false; // Initialized to true for lazy grammars only
     std::string              trigger_buffer;           // Output buffered by lazy grammar. Will be cleared once trigger is found.
+    std::vector<token_pos>   trigger_buffer_positions; // Tokens buffered by lazy grammar. Used to replay when a trigger is found.
     std::vector<llama_token> trigger_tokens;           // Tokens that trigger a lazy grammar, or tokens to force printing of (even if special).
     std::vector<llama_grammar_trigger_pattern>
                              trigger_patterns;         // Regular expressions that trigger a lazy grammar. Must be a full match of the entire generated
@@ -171,3 +185,8 @@ void llama_grammar_accept_impl(
 void llama_grammar_accept_str(
               struct llama_grammar & grammar,
                  const std::string & piece);
+
+void llama_grammar_accept_token(
+              struct llama_grammar & grammar,
+                       llama_token   token,
+                 const std::string & piece);
diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp
index 82fae671ed..7aa7e58a5c 100644
--- a/tests/test-grammar-integration.cpp
+++ b/tests/test-grammar-integration.cpp
@@ -32,13 +32,66 @@ static bool test_build_grammar_fails(const std::string & grammar_str) {
     return grammar_fails;
 }
 
+struct token_and_piece {
+    llama_token token;
+    std::string piece;
+};
+
+// token() encodes a 32-bit ID as 5 bytes: a 0xff marker followed by the ID in big-endian order.
+static std::string token(llama_token id) {
+    return std::string{
+        static_cast<char>(0xff),
+        static_cast<char>((id >> 24) & 0xff),
+        static_cast<char>((id >> 16) & 0xff),
+        static_cast<char>((id >> 8) & 0xff),
+        static_cast<char>(id & 0xff)
+    };
+}
+
+// parse_tokens() parses the token encodes above and UTF-8 text.
+static std::vector<token_and_piece> parse_tokens(const std::string & input) {
+    std::vector<token_and_piece> result;
+    result.reserve(input.size());
+    size_t offset = 0;
+    while (offset < input.size()) {
+        try {
+            if (static_cast<unsigned char>(input[offset]) == 0xff) {
+                if (offset + 5 > input.size()) {
+                    throw std::runtime_error("not enough bytes for token id");
+                }
+                uint32_t val =
+                    (static_cast<unsigned char>(input[offset + 1]) << 24) |
+                    (static_cast<unsigned char>(input[offset + 2]) << 16) |
+                    (static_cast<unsigned char>(input[offset + 3]) << 8)  |
+                    (static_cast<unsigned char>(input[offset + 4]));
+                auto piece = "<[" + std::to_string(val) + "]>";
+                result.push_back({static_cast<llama_token>(val), piece});
+                offset += 5;
+            } else {
+                uint32_t cpt = unicode_cpt_from_utf8(input, offset);
+                result.push_back({0, unicode_cpt_to_utf8(cpt)});
+            }
+        } catch (const std::invalid_argument & /*ex*/) {
+            // Silently ignore invalid UTF-8 input to avoid leaking the exception beyond llama_tokenize
+            ++offset;
+            result.push_back({0, unicode_cpt_to_utf8(0xFFFD)}); // replacement character
+        }
+    }
+    return result;
+}
+
 static bool match_string(const std::string & input, llama_grammar * grammar) {
-    const auto cpts = unicode_cpts_from_utf8(input);
+    const auto parsed = parse_tokens(input);
 
     auto & stacks_cur = llama_grammar_get_stacks(grammar);
 
-    for (const auto & cpt : cpts) {
-        llama_grammar_accept(grammar, cpt);
+    for (const auto & in : parsed) {
+        try {
+            llama_grammar_accept_token(*grammar, in.token, in.piece);
+        } catch (const std::runtime_error & /*e*/) {
+            // normally this shouldn't get hit because of llama_grammar_apply
+            return false;
+        }
 
         if (stacks_cur.empty()) {
             // no stacks means that the grammar failed to match at this point
@@ -426,6 +479,30 @@ static void test_simple_grammar() {
             "12a45",
         }
     );
+
+    // Test case for a simple grammar with tokens
+    test_grammar(
+        "simple grammar with tokens",
+        R"""(
+            root ::= <[10]> content <[11]>
+            content ::= (!<[11]>)*)""",
+        // Passing strings
+        {
+            token(10) + "hello world" + token(11),
+            token(10) + "text with " + token(12) + " other tokens " + token(13) + " mixed in" + token(11),
+            token(10) + token(11),
+            token(10) + token(12) + token(13) + token(14) + token(15) + token(11),
+            token(10) + "a" + token(11),
+        },
+        // Failing strings
+        {
+            token(10) + "missing end token",
+            token(10),
+            "missing start token" + token(11),
+            token(10) + token(11) + token(11),  // double end token
+            token(11) + "wrong order" + token(10),
+        }
+    );
 }
 
 static void test_complex_grammar() {
@@ -487,6 +564,34 @@ static void test_complex_grammar() {
             "123+456*789-123/456+789*123-456/789+123*456-789/123+456*789-123/456+789*123-456/",
         }
     );
+
+    // Test case for a more complex grammar with tokens
+    test_grammar(
+        "complex grammar with tokens",
+        R"""(
+            root ::= reasoning+ content tool-call*
+            reasoning ::= <[10]> (!<[11]>)* <[11]>
+            content ::= <[20]> (!<[21]>)* <[21]>
+            tool-call ::= <[12]> name <[13]> args <[14]>
+            name ::= (!<[13]>)+
+            args ::= (!<[14]>)*)""",
+        // Passing strings
+        {
+            token(10) + "I am thinking" + token(11) + token(20) + "hello world!" + token(21) + token(12) + "search" + token(13) + "query=test" + token(14),
+            token(10) + "reasoning 1" + token(11) + token(10) + "reasoning 2" + token(11) + token(20) + token(21) + token(12) + "tool" + token(13) + token(14),
+            token(10) + token(11) + token(20) + "content" + token(21),
+            token(10) + "think" + token(12) + " nested" + token(11) + token(20) + token(10) + "more content" + token(21) + token(12) + "fn" + token(13) + "x=1,y=2" + token(14) + token(12) + "fn2" + token(13) + token(14),
+            token(10) + "reasoning" + token(11) + token(10) + "more" + token(11) + token(10) + "even more" + token(11) + token(20) + "text" + token(21) + token(12) + "a" + token(13) + "b" + token(14) + token(12) + "c" + token(13) + "d" + token(14),
+        },
+        // Failing strings
+        {
+            token(20) + "content only" + token(21),
+            token(10) + "no closing reasoning",
+            token(10) + token(11) + token(20) + "no closing content",
+            token(10) + token(11) + token(20) + token(21) + token(12) + "incomplete tool",
+            token(10) + token(11) + token(11) + token(20) + token(21),
+        }
+    );
 }
 
 static void test_special_chars() {
diff --git a/tests/test-grammar-parser.cpp b/tests/test-grammar-parser.cpp
index 67821a2d5c..03ae78ff73 100644
--- a/tests/test-grammar-parser.cpp
+++ b/tests/test-grammar-parser.cpp
@@ -515,5 +515,19 @@ int main()
         {LLAMA_GRETYPE_END, 0},
     });
 
+    // <[1000]> = "<think>"
+    // <[1001]> = "</think>"
+    verify_parsing(R"""(
+        root  ::= <[1000]> !<[1001]> <[1001]>
+    )""", {
+        {"root", 0}
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_TOKEN, 1000},
+        {LLAMA_GRETYPE_TOKEN_NOT, 1001},
+        {LLAMA_GRETYPE_TOKEN, 1001},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
     return 0;
 }
diff --git a/tests/test-llama-grammar.cpp b/tests/test-llama-grammar.cpp
index cc198f3e3c..fd45d5ada8 100644
--- a/tests/test-llama-grammar.cpp
+++ b/tests/test-llama-grammar.cpp
@@ -202,7 +202,7 @@ int main()
         uint32_t *cp = new uint32_t[2]; // dynamically allocate memory for code_point
         cp[0] = 37 + i;
         cp[1] = 0;
-        next_candidates[i] = {i, cp, {}};
+        next_candidates[i] = {i, cp, {}, 0};
     }
 
     std::vector<std::vector<std::pair<uint32_t, uint16_t>>> expected_reject = {

From 0cdce38a97bd05462416272fbb912f4d7ecd2940 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Tue, 9 Dec 2025 09:34:02 +0100
Subject: [PATCH 029/135] CUDA: fix FP16 overflow in tile FA kernel (#17875)

---
 ggml/src/ggml-cuda/fattn-tile.cuh | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/ggml/src/ggml-cuda/fattn-tile.cuh b/ggml/src/ggml-cuda/fattn-tile.cuh
index 8afc1daaeb..7c4d6fe67f 100644
--- a/ggml/src/ggml-cuda/fattn-tile.cuh
+++ b/ggml/src/ggml-cuda/fattn-tile.cuh
@@ -564,6 +564,12 @@ static __device__ __forceinline__ void flash_attn_tile_iter(
         for (int i_KQ_0 = 0; i_KQ_0 < nbatch_fa; i_KQ_0 += np*warp_size) {
             const int i_KQ = i_KQ_0 + (threadIdx.y % np)*warp_size + threadIdx.x;
 
+#if defined(FAST_FP16_AVAILABLE) && !defined(V_DOT2_F32_F16_AVAILABLE)
+            // Without the v_dot2_f32_f16 instruction there is a higher risk of numerical overflow in the KQ calculation.
+            // Therefore, scale down Q values and apply the inverse scale the FP32 KQ values afterwards again.
+            KQ_acc[i_KQ_0/(np*warp_size)*cpw + jc0] *= 4.0f;
+#endif // defined(FAST_FP16_AVAILABLE) && !defined(V_DOT2_F32_F16_AVAILABLE)
+
             if (use_logit_softcap) {
                 KQ_acc[(i_KQ_0/(np*warp_size))*cpw + jc0] = logit_softcap * tanhf(KQ_acc[(i_KQ_0/(np*warp_size))*cpw + jc0]);
             }
@@ -858,6 +864,11 @@ static __global__ void flash_attn_tile(
 #pragma unroll
                 for (int i1 = 0; i1 < cpy_ne_D; i1 += 2) {
                     tmp_h2[i1/2] = make_half2(tmp_f[i1 + 0], tmp_f[i1 + 1]);
+#if defined(FAST_FP16_AVAILABLE) && !defined(V_DOT2_F32_F16_AVAILABLE)
+                    // Without the v_dot2_f32_f16 instruction there is a higher risk of numerical overflow in the KQ calculation.
+                    // Therefore, scale down Q values and apply the inverse scale the FP32 KQ values afterwards again.
+                    tmp_h2[i1/2] *= make_half2(0.25f, 0.25f);
+#endif // defined(FAST_FP16_AVAILABLE) && !defined(V_DOT2_F32_F16_AVAILABLE)
                 }
                 ggml_cuda_memcpy_1<sizeof(tmp_h2)>(
                     &Q_tmp[jc*(DKQ/2) + i0/2 + (threadIdx.y % np)*(warp_size*cpy_ne_D/2) + threadIdx.x*(cpy_ne_D/2)],

From ca709e427b1c5cf0134aaabc9bbc0183f32d1df0 Mon Sep 17 00:00:00 2001
From: Chenguang Li <757486878@qq.com>
Date: Tue, 9 Dec 2025 17:53:23 +0800
Subject: [PATCH 030/135] CANN: add support for partial RoPE and Vision mode
 (#17543)

* cann: add support for partial RoPE and Vision mode

Add support for two important RoPE variants: partial rotation (rope_dims < ne0)
and Vision mode rotation.

1. Support for partial RoPE (rope_dims < ne0):
   - Split tensor into head (first rope_dims dimensions) and tail portions
   - Apply rotation only to head portion using RotaryPositionEmbedding operator
   - Copy unrotated tail portion directly from source to destination
   - Handle both contiguous and non-contiguous tensor layouts

2. Support for Vision mode (GGML_ROPE_TYPE_VISION):
   - Set rope_dims = ne0 for Vision mode to rotate entire tensor
   - Vision mode pairs dimension i with dimension i+n_dims (where n_dims = ne0/2)
   - No tail handling needed since entire tensor is rotated

Implementation details:
   - Use has_tail flag to determine execution path: head/tail splitting when
     rope_dims < ne0, or full tensor rotation when rope_dims == ne0
   - Support both F32 and F16 data types with intermediate F32 conversion
   - Copy non-contiguous tensors to contiguous buffers before calling
     RotaryPositionEmbedding operator for compatibility
   - Improve cache invalidation logic to include rope_dims and indep_sects
     parameters

These enhancements enable CANN backend to handle various RoPE configurations
used in modern vision-language models and models with partial rotation.

* cann: fix review comment
---
 ggml/src/ggml-cann/aclnn_ops.cpp | 216 ++++++++++++++++++++++---------
 ggml/src/ggml-cann/common.h      |   4 +-
 ggml/src/ggml-cann/ggml-cann.cpp |  14 +-
 3 files changed, 162 insertions(+), 72 deletions(-)

diff --git a/ggml/src/ggml-cann/aclnn_ops.cpp b/ggml/src/ggml-cann/aclnn_ops.cpp
index 48f4b7db69..835b53f659 100644
--- a/ggml/src/ggml-cann/aclnn_ops.cpp
+++ b/ggml/src/ggml-cann/aclnn_ops.cpp
@@ -2251,12 +2251,12 @@ static void aclnn_rope_cache_init(ggml_backend_cann_context & ctx,
                                   int                         sections[4],
                                   bool                        mrope_used,
                                   bool                        is_imrope,
-                                  bool                        indep_sects) {
-    ggml_tensor * src0 = dst->src[0];  // input
+                                  bool                        indep_sects,
+                                  int64_t                     rope_dims) {
     ggml_tensor * src1 = dst->src[1];  // position
     ggml_tensor * src2 = dst->src[2];  // freq_factors
 
-    int64_t theta_scale_length = src0->ne[0] / 2;
+    int64_t theta_scale_length = rope_dims / 2;
     int64_t position_length    = dst->ne[2];
 
     // TODO: check theta_scale_length and position_length.
@@ -2331,18 +2331,17 @@ static void aclnn_rope_cache_init(ggml_backend_cann_context & ctx,
         ACL_CHECK(aclrtMemcpyAsync(ctx.rope_cache.theta_scale_cache, theta_scale_length * sizeof(float),
                                    ctx.rope_cache.theta_scale_exp_host, theta_scale_length * sizeof(float),
                                    ACL_MEMCPY_HOST_TO_DEVICE, ctx.stream()));
-
-        acl_theta_scale_tensor = ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float),
-                                                         theta_scale_ne, theta_scale_nb, 1);
     }
+    acl_theta_scale_tensor = ggml_cann_create_tensor(ctx.rope_cache.theta_scale_cache, ACL_FLOAT, sizeof(float),
+                                                     theta_scale_ne, theta_scale_nb, 1);
 
     // Step1.2: prepare rope_yarn_ramp, if this part updated, should update theta_scale_tensor.
+    // TODO: acl_yarn_ramp_tensor use rope cache.
     bool                 yarn_ramp_tensor_updated = false;
     ggml_cann_pool_alloc yarn_ramp_allocator(ctx.pool());
     acl_tensor_ptr       acl_yarn_ramp_tensor;
-    if (ext_factor != 0 &&
-        // TODO: check more parameter.
-        (ctx.rope_cache.theta_scale_length != theta_scale_length || ctx.rope_cache.freq_scale != freq_scale)) {
+    if (ext_factor != 0 && (theta_scale_updated || ctx.rope_cache.theta_scale_length != theta_scale_length ||
+                            ctx.rope_cache.freq_scale != freq_scale)) {
         yarn_ramp_tensor_updated = true;
 
         // -rope_yarn_ramp
@@ -2590,7 +2589,7 @@ static void aclnn_rope_cache_init(ggml_backend_cann_context & ctx,
         aclnn_muls(ctx, acl_cos_tensor.get(), attn_factor, nullptr, true);
     }
 
-    int64_t sin_reshape_ne[4] = { src0->ne[0], 1, dst->ne[2], 1 };
+    int64_t sin_reshape_ne[4] = { rope_dims, 1, dst->ne[2], 1 };
     size_t  sin_reshape_nb[GGML_MAX_DIMS];
     sin_reshape_nb[0] = sizeof(float);
     for (int i = 1; i < GGML_MAX_DIMS; i++) {
@@ -2645,7 +2644,7 @@ void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
 
     // param
     float     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
-    int sections[4];
+    int       sections[4];
     // const int n_past     = ((int32_t *) dst->op_params)[0];
     const int n_dims     = ((int32_t *) dst->op_params)[1];
     const int mode       = ((int32_t *) dst->op_params)[2];
@@ -2654,44 +2653,60 @@ void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
 
     GGML_TENSOR_UNARY_OP_LOCALS
 
-    memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
-    memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
-    memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
-    memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
-    memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
-    memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
-    memcpy(&sections,    (int32_t *) dst->op_params + 11, sizeof(int)*4);
+    memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float));
+    memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float));
+    memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float));
+    memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float));
+    memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float));
+    memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float));
+    memcpy(&sections, (int32_t *) dst->op_params + 11, sizeof(int) * 4);
 
-    // TODO: n_dims <= ne0
-    GGML_ASSERT(n_dims == ne0);
     GGML_ASSERT(n_dims % 2 == 0);
+    GGML_ASSERT(n_dims <= ne00);
 
     const float theta_scale = powf(freq_base, -2.0f / n_dims);
 
     float corr_dims[2];
     ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
 
-    bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
-    const bool is_imrope = mode == GGML_ROPE_TYPE_IMROPE; // qwen3vl apply interleaved mrope
-    const bool mrope_used = mode & GGML_ROPE_TYPE_MROPE;  // ggml_rope_multi, note: also true for vision (24 & 8 == true) and for imrope
-    const bool is_vision = mode == GGML_ROPE_TYPE_VISION;
+    bool       is_neox    = mode & GGML_ROPE_TYPE_NEOX;
+    const bool is_imrope  = mode == GGML_ROPE_TYPE_IMROPE;  // qwen3vl apply interleaved mrope
+    // mrope_used means the GGML_ROPE_TYPE_MROPE bit is set.
+    // Note: this bit is also set for imrope and some vision modes,
+    // so mrope_used does NOT exclusively indicate pure mrope.
+    const bool mrope_used = mode & GGML_ROPE_TYPE_MROPE;
+    const bool is_vision  = mode == GGML_ROPE_TYPE_VISION;
 
     if (mrope_used) {
         GGML_ASSERT(sections[0] > 0 || sections[1] > 0 || sections[2] > 0);
     }
 
     if (is_vision) {
-        GGML_ASSERT(n_dims == ne0/2);
+        GGML_ASSERT(n_dims == ne0 / 2);
     }
 
     if (is_imrope || mrope_used) {
         is_neox = true;
     }
 
-    // init ctx.rope_cos/rope_sin cache
-    aclnn_rope_cache_init(ctx, dst, corr_dims, ext_factor, theta_scale, freq_scale, attn_factor, is_neox, sections, mrope_used, is_imrope, is_vision);
+    int64_t rope_dims = n_dims;
 
-    int64_t sin_reshape_ne[4] = { ne00, 1, ne02, 1 };
+    //Our current RotaryPositionEmbedding does not support the VISION mode,
+    //but essentially it only modifies theta_base in mrope,
+    //then repeats it at the end in the same way as is_neox.
+    //In fact, RoPE is still applied across all dimensions.
+    if (is_vision) {
+        rope_dims = src0->ne[0];
+    }
+    int64_t tail_dims = ne00 - rope_dims;
+    bool    has_tail  = tail_dims > 0;
+
+    // init ctx.rope_cos/rope_sin cache
+    aclnn_rope_cache_init(ctx, dst, corr_dims, ext_factor, theta_scale, freq_scale, attn_factor, is_neox, sections,
+                          mrope_used, is_imrope, is_vision, rope_dims);
+
+    // Cache is generated with ne00 dimensions, so we use ne00 for reshape
+    int64_t sin_reshape_ne[4] = { rope_dims, 1, ne02, 1 };
     size_t  sin_reshape_nb[GGML_MAX_DIMS];
     sin_reshape_nb[0] = sizeof(float);
     for (int i = 1; i < GGML_MAX_DIMS; i++) {
@@ -2704,7 +2719,6 @@ void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
 
     acl_tensor_ptr acl_src = ggml_cann_create_tensor(src0);
     acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
-
 #ifdef ASCEND_310P
     // Special ROPE operation for 310P
 
@@ -2844,46 +2858,124 @@ void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
     }
     return;
 #endif
-
     int64_t acl_mode = is_neox ? 0 : 1;
 
-    switch (src0->type) {
-        case GGML_TYPE_F32:
-            {
-                GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src.get(), acl_cos_reshape_tensor.get(),
-                                        acl_sin_reshape_tensor.get(), acl_mode, acl_dst.get());
-                break;
-            }
-        case GGML_TYPE_F16:
-            {
-                ggml_cann_pool_alloc src_trans_allocator(ctx.pool(), ggml_nelements(src0) * sizeof(float));
-                void *               src_trans_buffer = src_trans_allocator.get();
-                ggml_cann_pool_alloc dst_trans_allocator(ctx.pool(), ggml_nelements(dst) * sizeof(float));
-                void *               dst_trans_buffer = dst_trans_allocator.get();
+    // Pre-define head and tail dimensions for reuse
+    int64_t head_ne[GGML_MAX_DIMS] = { rope_dims, ne01, ne02, ne03 };
+    int64_t tail_ne[GGML_MAX_DIMS] = { tail_dims, ne01, ne02, ne03 };
 
-                size_t src_trans_nb[GGML_MAX_DIMS];
-                src_trans_nb[0] = sizeof(float);
-                for (int i = 1; i < GGML_MAX_DIMS; i++) {
-                    src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
-                }
+    // Step 1: Prepare trans tensors for F16 type conversion to F32 if needed
+    bool                 src_dst_need_trans = false;
+    ggml_cann_pool_alloc src_trans_allocator(ctx.pool());
+    ggml_cann_pool_alloc dst_trans_allocator(ctx.pool());
+    acl_tensor_ptr       acl_src_trans_tensor;
+    acl_tensor_ptr       acl_dst_trans_tensor;
+    void *               src_trans_buffer = nullptr;
+    void *               dst_trans_buffer = nullptr;
+    size_t               src_dst_trans_nb[GGML_MAX_DIMS];
+    if (src0->type == GGML_TYPE_F16) {
+        src_dst_need_trans = true;
+        src_trans_buffer   = src_trans_allocator.alloc(ggml_nelements(src0) * sizeof(float));
+        dst_trans_buffer   = dst_trans_allocator.alloc(ggml_nelements(dst) * sizeof(float));
 
-                acl_tensor_ptr acl_src_trans_tensor = ggml_cann_create_tensor(
-                    src_trans_buffer, ACL_FLOAT, sizeof(float), src0->ne, src_trans_nb, GGML_MAX_DIMS);
-                acl_tensor_ptr acl_dst_trans_tensor = ggml_cann_create_tensor(
-                    dst_trans_buffer, ACL_FLOAT, sizeof(float), dst->ne, src_trans_nb, GGML_MAX_DIMS);
+        src_dst_trans_nb[0] = sizeof(float);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            src_dst_trans_nb[i] = src_dst_trans_nb[i - 1] * src0->ne[i - 1];
+        }
+        acl_src_trans_tensor = ggml_cann_create_tensor(src_trans_buffer, ACL_FLOAT, sizeof(float), src0->ne,
+                                                       src_dst_trans_nb, GGML_MAX_DIMS);
+        acl_dst_trans_tensor = ggml_cann_create_tensor(dst_trans_buffer, ACL_FLOAT, sizeof(float), dst->ne,
+                                                       src_dst_trans_nb, GGML_MAX_DIMS);
+        aclnn_cast(ctx, acl_src.get(), acl_src_trans_tensor.get(), ACL_FLOAT);
+    }
 
-                aclnn_cast(ctx, acl_src.get(), acl_src_trans_tensor.get(), ACL_FLOAT);
+    // Step 2: Prepare head tensors for tail splitting if needed
+    acl_tensor_ptr acl_src_head;
+    acl_tensor_ptr acl_dst_head;
+    if (has_tail) {
+        // Create head views for RotaryPositionEmbedding (only first rope_dims dimensions)
+        // RotaryPositionEmbedding requires contiguous dst tensor, so we use a temporary buffer
+        if (src_dst_need_trans) {
+            // Use F32 trans tensor strides
+            acl_src_head = ggml_cann_create_tensor((char *) src_trans_buffer, ACL_FLOAT, sizeof(float), head_ne,
+                                                   src_dst_trans_nb, GGML_MAX_DIMS);
+        } else {
+            // Use original F32 tensor strides
+            acl_src_head = ggml_cann_create_tensor((char *) src0->data, ACL_FLOAT, sizeof(float), head_ne, src0->nb,
+                                                   GGML_MAX_DIMS);
+        }
 
-                GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src_trans_tensor.get(),
-                                        acl_cos_reshape_tensor.get(), acl_sin_reshape_tensor.get(), acl_mode,
-                                        acl_dst_trans_tensor.get());
+        int64_t              head_elements = rope_dims * ne01 * ne02 * ne03;
+        ggml_cann_pool_alloc dst_head_contiguous_allocator(ctx.pool(), head_elements * sizeof(float));
+        void *               dst_head_contiguous_buffer = dst_head_contiguous_allocator.get();
 
-                aclnn_cast(ctx, acl_dst_trans_tensor.get(), acl_dst.get(), ACL_FLOAT16);
-                break;
-            }
-        default:
-            GGML_ABORT("Unsupported tensor type for GGML_OP_ROPE");
-            break;
+        size_t head_contiguous_nb[GGML_MAX_DIMS];
+        head_contiguous_nb[0] = sizeof(float);
+        for (int i = 1; i < GGML_MAX_DIMS; i++) {
+            head_contiguous_nb[i] = head_contiguous_nb[i - 1] * head_ne[i - 1];
+        }
+        acl_dst_head = ggml_cann_create_tensor(dst_head_contiguous_buffer, ACL_FLOAT, sizeof(float), head_ne,
+                                               head_contiguous_nb, GGML_MAX_DIMS);
+    }
+
+    // Step 3: Execute RotaryPositionEmbedding
+    if (has_tail) {
+        // Rotate only the head portion (first rope_dims dimensions)
+        GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src_head.get(), acl_cos_reshape_tensor.get(),
+                                acl_sin_reshape_tensor.get(), acl_mode, acl_dst_head.get());
+
+        // Copy head result from contiguous buffer back to destination tensor
+        if (src_dst_need_trans) {
+            acl_tensor_ptr acl_dst_head_target = ggml_cann_create_tensor(
+                (char *) dst_trans_buffer, ACL_FLOAT, sizeof(float), head_ne, src_dst_trans_nb, GGML_MAX_DIMS);
+            cann_copy(ctx, acl_dst_head.get(), acl_dst_head_target.get());
+        } else {
+            acl_tensor_ptr acl_dst_head_target =
+                ggml_cann_create_tensor((char *) dst->data, ACL_FLOAT, sizeof(float), head_ne, dst->nb, GGML_MAX_DIMS);
+            cann_copy(ctx, acl_dst_head.get(), acl_dst_head_target.get());
+        }
+    } else if (src_dst_need_trans) {
+        // Rotate full tensor (no tail), using trans tensors
+        GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src_trans_tensor.get(), acl_cos_reshape_tensor.get(),
+                                acl_sin_reshape_tensor.get(), acl_mode, acl_dst_trans_tensor.get());
+    } else {
+        // Rotate full tensor (no tail), using original tensors
+        GGML_CANN_CALL_ACLNN_OP(ctx, RotaryPositionEmbedding, acl_src.get(), acl_cos_reshape_tensor.get(),
+                                acl_sin_reshape_tensor.get(), acl_mode, acl_dst.get());
+    }
+
+    // Step 4: Copy unrotated tail portion from source to destination
+    if (has_tail) {
+        size_t src_tail_offset;
+        size_t dst_tail_offset;
+
+        auto copy_tail_device = [&](void * src_ptr, void * dst_ptr, aclDataType dtype, size_t elem_size,
+                                    size_t * nb_src_arr, size_t * nb_dst_arr) {
+            acl_tensor_ptr acl_src_tail =
+                ggml_cann_create_tensor(src_ptr, dtype, elem_size, tail_ne, nb_src_arr, GGML_MAX_DIMS);
+            acl_tensor_ptr acl_dst_tail =
+                ggml_cann_create_tensor(dst_ptr, dtype, elem_size, tail_ne, nb_dst_arr, GGML_MAX_DIMS);
+            cann_copy(ctx, acl_src_tail.get(), acl_dst_tail.get());
+        };
+
+        if (src_dst_need_trans) {
+            // Use F32 trans tensor strides and offsets
+            src_tail_offset = rope_dims * src_dst_trans_nb[0];
+            dst_tail_offset = rope_dims * src_dst_trans_nb[0];
+            copy_tail_device((char *) src_trans_buffer + src_tail_offset, (char *) dst_trans_buffer + dst_tail_offset,
+                             ACL_FLOAT, sizeof(float), src_dst_trans_nb, src_dst_trans_nb);
+        } else {
+            // Use original tensor strides and offsets
+            src_tail_offset = rope_dims * nb00;
+            dst_tail_offset = rope_dims * nb0;
+            copy_tail_device((char *) src0->data + src_tail_offset, (char *) dst->data + dst_tail_offset,
+                             ggml_cann_type_mapping(dst->type), ggml_element_size(dst), src0->nb, dst->nb);
+        }
+    }
+
+    // Step 5: Cast back to F16 if needed
+    if (src_dst_need_trans) {
+        aclnn_cast(ctx, acl_dst_trans_tensor.get(), acl_dst.get(), ACL_FLOAT16);
     }
 }
 
diff --git a/ggml/src/ggml-cann/common.h b/ggml/src/ggml-cann/common.h
index b17445bb9a..45c7294e68 100644
--- a/ggml/src/ggml-cann/common.h
+++ b/ggml/src/ggml-cann/common.h
@@ -315,7 +315,7 @@ struct ggml_cann_rope_cache {
         if (theta_scale_exp_host) {
             free(theta_scale_exp_host);
         }
-        if(position_select_index_host) {
+        if (position_select_index_host) {
             free(position_select_index_host);
         }
     }
@@ -340,7 +340,7 @@ struct ggml_cann_rope_cache {
 
     void set(int64_t theta_scale_length,
              int64_t position_length,
-             float    ext_factor,
+             float   ext_factor,
              float   theta_scale,
              float   freq_scale,
              float   attn_factor,
diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index 23dc0433c1..81288464c7 100644
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -2308,7 +2308,7 @@ static enum ggml_status ggml_backend_cann_graph_compute(ggml_backend_t backend,
 
     bool cann_graph_update_required = false;
 #ifdef USE_ACL_GRAPH
-    bool use_cann_graph             = true;
+    bool use_cann_graph = true;
 
     static bool prefill_use_graph = parse_bool(get_env("GGML_CANN_PREFILL_USE_GRAPH").value_or(""));
     if (!prefill_use_graph) {
@@ -2338,7 +2338,7 @@ static enum ggml_status ggml_backend_cann_graph_compute(ggml_backend_t backend,
         }
     }
 #else
-    bool use_cann_graph             = false;
+    bool use_cann_graph = false;
 #endif  // USE_ACL_GRAPH
     evaluate_and_capture_cann_graph(cann_ctx, cgraph, use_cann_graph, cann_graph_update_required);
 
@@ -2474,16 +2474,14 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
             }
         case GGML_OP_ROPE:
             {
-                // TODO: with ops-test v == 1
-                // TODO: n_dims <= ne0
-                if (op->src[0]->ne[0] != op->op_params[1]) {
-                    return false;
-                }
-
                 if (op->src[0]->ne[0] > 896) {
                     return false;
                 }
 #ifdef ASCEND_310P
+                // TODO: Support rope_dim < ne00(dim)
+                if (op->src[0]->ne[0] != op->op_params[1]) {
+                    return false;
+                }
                 if (!ggml_is_contiguous(op->src[0])) {
                     return false;
                 }

From 4e842d5120f0349fe4c014a1cc8b9a55c5715c66 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Tue, 9 Dec 2025 11:53:59 +0100
Subject: [PATCH 031/135] console: allow using arrow left/right, home/end keys
 and history mode (#17836)

* console: allow using arrow left/right to edit the line (with UTF-8 support)

* console: fix arrow keys on Windows using private-use Unicode

* console: add Home/End key support for Windows and Linux

* console: add basic Up/Down history navigation

* fix build

* console: allow using arrow left/right to edit the line (with UTF-8 support)

* console: fix arrow keys on Windows using private-use Unicode

* console: add Home/End key support for Windows and Linux

* console: add basic Up/Down history navigation

* console: remove unreachable wc == 0 check after VK switch

* console: add Ctrl+Left/Right word navigation

- Add KEY_CTRL_ARROW_LEFT and KEY_CTRL_ARROW_RIGHT codes
- Windows: detect CTRL modifier via dwControlKeyState
- Linux: parse ANSI sequences with modifier (1;5D/C)
- Implement move_word_left/right with space-skipping logic
- Refactor escape sequence parsing to accumulate params

* console: add Delete key support

- Windows: VK_DELETE detection
- Linux: ESC[3~ sequence parsing
- Forward character deletion with UTF-8 support

* console: implement bash-style history editing

- Edit any history line during UP/DOWN navigation, edits persist
- Pressing Enter appends edited version as new history entry
- Original line stay untouched in their positions

* clean up

* better history impl

* fix decode_utf8

---------

Co-authored-by: Pascal <admin@serveurperso.com>
---
 common/console.cpp | 613 ++++++++++++++++++++++++++++++++++++++++++---
 1 file changed, 583 insertions(+), 30 deletions(-)

diff --git a/common/console.cpp b/common/console.cpp
index 078a8d678d..5e9901e4a2 100644
--- a/common/console.cpp
+++ b/common/console.cpp
@@ -1,6 +1,11 @@
 #include "console.h"
 #include <vector>
 #include <iostream>
+#include <cassert>
+#include <cstddef>
+#include <cctype>
+#include <cwctype>
+#include <cstdint>
 
 #if defined(_WIN32)
 #define WIN32_LEAN_AND_MEAN
@@ -35,9 +40,26 @@
 
 namespace console {
 
+#if defined (_WIN32)
+    namespace {
+        // Use private-use unicode values to represent special keys that are not reported
+        // as characters (e.g. arrows on Windows). These values should never clash with
+        // real input and let the rest of the code handle navigation uniformly.
+        static constexpr char32_t KEY_ARROW_LEFT       = 0xE000;
+        static constexpr char32_t KEY_ARROW_RIGHT      = 0xE001;
+        static constexpr char32_t KEY_ARROW_UP         = 0xE002;
+        static constexpr char32_t KEY_ARROW_DOWN       = 0xE003;
+        static constexpr char32_t KEY_HOME             = 0xE004;
+        static constexpr char32_t KEY_END              = 0xE005;
+        static constexpr char32_t KEY_CTRL_ARROW_LEFT  = 0xE006;
+        static constexpr char32_t KEY_CTRL_ARROW_RIGHT = 0xE007;
+        static constexpr char32_t KEY_DELETE           = 0xE008;
+    }
+
     //
     // Console state
     //
+#endif
 
     static bool      advanced_display = false;
     static bool      simple_io        = true;
@@ -176,7 +198,18 @@ namespace console {
             if (record.EventType == KEY_EVENT && record.Event.KeyEvent.bKeyDown) {
                 wchar_t wc = record.Event.KeyEvent.uChar.UnicodeChar;
                 if (wc == 0) {
-                    continue;
+                    const DWORD ctrl_mask = LEFT_CTRL_PRESSED | RIGHT_CTRL_PRESSED;
+                    const bool ctrl_pressed = (record.Event.KeyEvent.dwControlKeyState & ctrl_mask) != 0;
+                    switch (record.Event.KeyEvent.wVirtualKeyCode) {
+                        case VK_LEFT:   return ctrl_pressed ? KEY_CTRL_ARROW_LEFT  : KEY_ARROW_LEFT;
+                        case VK_RIGHT:  return ctrl_pressed ? KEY_CTRL_ARROW_RIGHT : KEY_ARROW_RIGHT;
+                        case VK_UP:     return KEY_ARROW_UP;
+                        case VK_DOWN:   return KEY_ARROW_DOWN;
+                        case VK_HOME:   return KEY_HOME;
+                        case VK_END:    return KEY_END;
+                        case VK_DELETE: return KEY_DELETE;
+                        default:        continue;
+                    }
                 }
 
                 if ((wc >= 0xD800) && (wc <= 0xDBFF)) { // Check if wc is a high surrogate
@@ -315,6 +348,52 @@ namespace console {
 #endif
     }
 
+    static char32_t decode_utf8(const std::string & input, size_t pos, size_t & advance) {
+        unsigned char c = static_cast<unsigned char>(input[pos]);
+        if ((c & 0x80u) == 0u) {
+            advance = 1;
+            return c;
+        }
+        if ((c & 0xE0u) == 0xC0u && pos + 1 < input.size()) {
+            unsigned char c1 = static_cast<unsigned char>(input[pos + 1]);
+            if ((c1 & 0xC0u) != 0x80u) {
+                advance = 1;
+                return 0xFFFD;
+            }
+            advance = 2;
+            return ((c & 0x1Fu) << 6) | (static_cast<unsigned char>(input[pos + 1]) & 0x3Fu);
+        }
+        if ((c & 0xF0u) == 0xE0u && pos + 2 < input.size()) {
+            unsigned char c1 = static_cast<unsigned char>(input[pos + 1]);
+            unsigned char c2 = static_cast<unsigned char>(input[pos + 2]);
+            if ((c1 & 0xC0u) != 0x80u || (c2 & 0xC0u) != 0x80u) {
+                advance = 1;
+                return 0xFFFD;
+            }
+            advance = 3;
+            return ((c & 0x0Fu) << 12) |
+                   ((static_cast<unsigned char>(input[pos + 1]) & 0x3Fu) << 6) |
+                   (static_cast<unsigned char>(input[pos + 2]) & 0x3Fu);
+        }
+        if ((c & 0xF8u) == 0xF0u && pos + 3 < input.size()) {
+            unsigned char c1 = static_cast<unsigned char>(input[pos + 1]);
+            unsigned char c2 = static_cast<unsigned char>(input[pos + 2]);
+            unsigned char c3 = static_cast<unsigned char>(input[pos + 3]);
+            if ((c1 & 0xC0u) != 0x80u || (c2 & 0xC0u) != 0x80u || (c3 & 0xC0u) != 0x80u) {
+                advance = 1;
+                return 0xFFFD;
+            }
+            advance = 4;
+            return ((c & 0x07u) << 18) |
+                   ((static_cast<unsigned char>(input[pos + 1]) & 0x3Fu) << 12) |
+                   ((static_cast<unsigned char>(input[pos + 2]) & 0x3Fu) << 6) |
+                   (static_cast<unsigned char>(input[pos + 3]) & 0x3Fu);
+        }
+
+        advance = 1;
+        return 0xFFFD; // replacement character for invalid input
+    }
+
     static void append_utf8(char32_t ch, std::string & out) {
         if (ch <= 0x7F) {
             out.push_back(static_cast<unsigned char>(ch));
@@ -336,22 +415,319 @@ namespace console {
     }
 
     // Helper function to remove the last UTF-8 character from a string
-    static void pop_back_utf8_char(std::string & line) {
-        if (line.empty()) {
+    static size_t prev_utf8_char_pos(const std::string & line, size_t pos) {
+        if (pos == 0) return 0;
+        pos--;
+        while (pos > 0 && (line[pos] & 0xC0) == 0x80) {
+            pos--;
+        }
+        return pos;
+    }
+
+    static size_t next_utf8_char_pos(const std::string & line, size_t pos) {
+        if (pos >= line.length()) return line.length();
+        pos++;
+        while (pos < line.length() && (line[pos] & 0xC0) == 0x80) {
+            pos++;
+        }
+        return pos;
+    }
+
+    static void move_cursor(int delta);
+    static void move_word_left(size_t & char_pos, size_t & byte_pos, const std::vector<int> & widths, const std::string & line);
+    static void move_word_right(size_t & char_pos, size_t & byte_pos, const std::vector<int> & widths, const std::string & line);
+    static void move_to_line_start(size_t & char_pos, size_t & byte_pos, const std::vector<int> & widths);
+    static void move_to_line_end(size_t & char_pos, size_t & byte_pos, const std::vector<int> & widths, const std::string & line);
+
+    static void delete_at_cursor(std::string & line, std::vector<int> & widths, size_t & char_pos, size_t & byte_pos) {
+        if (char_pos >= widths.size()) {
             return;
         }
 
-        size_t pos = line.length() - 1;
+        size_t next_pos = next_utf8_char_pos(line, byte_pos);
+        int w = widths[char_pos];
+        size_t char_len = next_pos - byte_pos;
 
-        // Find the start of the last UTF-8 character (checking up to 4 bytes back)
-        for (size_t i = 0; i < 3 && pos > 0; ++i, --pos) {
-            if ((line[pos] & 0xC0) != 0x80) {
-                break; // Found the start of the character
-            }
+        line.erase(byte_pos, char_len);
+        widths.erase(widths.begin() + char_pos);
+
+        size_t p = byte_pos;
+        int tail_width = 0;
+        for (size_t i = char_pos; i < widths.size(); ++i) {
+            size_t following = next_utf8_char_pos(line, p);
+            put_codepoint(line.c_str() + p, following - p, widths[i]);
+            tail_width += widths[i];
+            p = following;
         }
-        line.erase(pos);
+
+        for (int i = 0; i < w; ++i) {
+            fputc(' ', out);
+        }
+
+        move_cursor(-(tail_width + w));
     }
 
+    static void clear_current_line(const std::vector<int> & widths) {
+        int total_width = 0;
+        for (int w : widths) {
+            total_width += (w > 0 ? w : 1);
+        }
+
+        if (total_width > 0) {
+            std::string spaces(total_width, ' ');
+            fwrite(spaces.c_str(), 1, total_width, out);
+            move_cursor(-total_width);
+        }
+    }
+
+    static void set_line_contents(std::string new_line, std::string & line, std::vector<int> & widths, size_t & char_pos,
+                                  size_t & byte_pos) {
+        move_to_line_start(char_pos, byte_pos, widths);
+        clear_current_line(widths);
+
+        line = std::move(new_line);
+        widths.clear();
+        byte_pos = 0;
+        char_pos = 0;
+
+        size_t idx = 0;
+        while (idx < line.size()) {
+            size_t advance = 0;
+            char32_t cp = decode_utf8(line, idx, advance);
+            int expected_width = estimateWidth(cp);
+            int real_width = put_codepoint(line.c_str() + idx, advance, expected_width);
+            if (real_width < 0) real_width = 0;
+            widths.push_back(real_width);
+            idx += advance;
+            ++char_pos;
+            byte_pos = idx;
+        }
+    }
+
+    static void move_to_line_start(size_t & char_pos, size_t & byte_pos, const std::vector<int> & widths) {
+        int back_width = 0;
+        for (size_t i = 0; i < char_pos; ++i) {
+            back_width += widths[i];
+        }
+        move_cursor(-back_width);
+        char_pos = 0;
+        byte_pos = 0;
+    }
+
+    static void move_to_line_end(size_t & char_pos, size_t & byte_pos, const std::vector<int> & widths, const std::string & line) {
+        int forward_width = 0;
+        for (size_t i = char_pos; i < widths.size(); ++i) {
+            forward_width += widths[i];
+        }
+        move_cursor(forward_width);
+        char_pos = widths.size();
+        byte_pos = line.length();
+    }
+
+    static bool has_ctrl_modifier(const std::string & params) {
+        size_t start = 0;
+        while (start < params.size()) {
+            size_t end = params.find(';', start);
+            size_t len = (end == std::string::npos) ? params.size() - start : end - start;
+            if (len > 0) {
+                int value = 0;
+                for (size_t i = 0; i < len; ++i) {
+                    char ch = params[start + i];
+                    if (!std::isdigit(static_cast<unsigned char>(ch))) {
+                        value = -1;
+                        break;
+                    }
+                    value = value * 10 + (ch - '0');
+                }
+                if (value == 5) {
+                    return true;
+                }
+            }
+
+            if (end == std::string::npos) {
+                break;
+            }
+            start = end + 1;
+        }
+        return false;
+    }
+
+    static bool is_space_codepoint(char32_t cp) {
+        return std::iswspace(static_cast<wint_t>(cp)) != 0;
+    }
+
+    static void move_word_left(size_t & char_pos, size_t & byte_pos, const std::vector<int> & widths, const std::string & line) {
+        if (char_pos == 0) {
+            return;
+        }
+
+        size_t new_char_pos = char_pos;
+        size_t new_byte_pos = byte_pos;
+        int move_width = 0;
+
+        while (new_char_pos > 0) {
+            size_t prev_byte = prev_utf8_char_pos(line, new_byte_pos);
+            size_t advance = 0;
+            char32_t cp = decode_utf8(line, prev_byte, advance);
+            if (!is_space_codepoint(cp)) {
+                break;
+            }
+            move_width += widths[new_char_pos - 1];
+            new_char_pos--;
+            new_byte_pos = prev_byte;
+        }
+
+        while (new_char_pos > 0) {
+            size_t prev_byte = prev_utf8_char_pos(line, new_byte_pos);
+            size_t advance = 0;
+            char32_t cp = decode_utf8(line, prev_byte, advance);
+            if (is_space_codepoint(cp)) {
+                break;
+            }
+            move_width += widths[new_char_pos - 1];
+            new_char_pos--;
+            new_byte_pos = prev_byte;
+        }
+
+        move_cursor(-move_width);
+        char_pos = new_char_pos;
+        byte_pos = new_byte_pos;
+    }
+
+    static void move_word_right(size_t & char_pos, size_t & byte_pos, const std::vector<int> & widths, const std::string & line) {
+        if (char_pos >= widths.size()) {
+            return;
+        }
+
+        size_t new_char_pos = char_pos;
+        size_t new_byte_pos = byte_pos;
+        int move_width = 0;
+
+        while (new_char_pos < widths.size()) {
+            size_t advance = 0;
+            char32_t cp = decode_utf8(line, new_byte_pos, advance);
+            if (!is_space_codepoint(cp)) {
+                break;
+            }
+            move_width += widths[new_char_pos];
+            new_char_pos++;
+            new_byte_pos += advance;
+        }
+
+        while (new_char_pos < widths.size()) {
+            size_t advance = 0;
+            char32_t cp = decode_utf8(line, new_byte_pos, advance);
+            if (is_space_codepoint(cp)) {
+                break;
+            }
+            move_width += widths[new_char_pos];
+            new_char_pos++;
+            new_byte_pos += advance;
+        }
+
+        while (new_char_pos < widths.size()) {
+            size_t advance = 0;
+            char32_t cp = decode_utf8(line, new_byte_pos, advance);
+            if (!is_space_codepoint(cp)) {
+                break;
+            }
+            move_width += widths[new_char_pos];
+            new_char_pos++;
+            new_byte_pos += advance;
+        }
+
+        move_cursor(move_width);
+        char_pos = new_char_pos;
+        byte_pos = new_byte_pos;
+    }
+
+    static void move_cursor(int delta) {
+        if (delta == 0) return;
+#if defined(_WIN32)
+        if (hConsole != NULL) {
+            CONSOLE_SCREEN_BUFFER_INFO bufferInfo;
+            GetConsoleScreenBufferInfo(hConsole, &bufferInfo);
+            COORD newCursorPosition = bufferInfo.dwCursorPosition;
+            int width = bufferInfo.dwSize.X;
+            int newX = newCursorPosition.X + delta;
+            int newY = newCursorPosition.Y;
+
+            while (newX >= width) {
+                newX -= width;
+                newY++;
+            }
+            while (newX < 0) {
+                newX += width;
+                newY--;
+            }
+
+            newCursorPosition.X = newX;
+            newCursorPosition.Y = newY;
+            SetConsoleCursorPosition(hConsole, newCursorPosition);
+        }
+#else
+        if (delta < 0) {
+            for (int i = 0; i < -delta; i++) fprintf(out, "\b");
+        } else {
+            for (int i = 0; i < delta; i++) fprintf(out, "\033[C");
+        }
+#endif
+    }
+
+    struct history_t {
+        std::vector<std::string> entries;
+        size_t viewing_idx = SIZE_MAX;
+        std::string backup_line; // current line before viewing history
+        void add(const std::string & line) {
+            if (line.empty()) {
+                return;
+            }
+            // avoid duplicates with the last entry
+            if (entries.empty() || entries.back() != line) {
+                entries.push_back(line);
+            }
+            // also clear viewing state
+            end_viewing();
+        }
+        bool prev(std::string & cur_line) {
+            if (entries.empty()) {
+                return false;
+            }
+            if (viewing_idx == SIZE_MAX) {
+                return false;
+            }
+            if (viewing_idx > 0) {
+                viewing_idx--;
+            }
+            cur_line = entries[viewing_idx];
+            return true;
+        }
+        bool next(std::string & cur_line) {
+            if (entries.empty() || viewing_idx == SIZE_MAX) {
+                return false;
+            }
+            viewing_idx++;
+            if (viewing_idx >= entries.size()) {
+                cur_line = backup_line;
+                end_viewing();
+            } else {
+                cur_line = entries[viewing_idx];
+            }
+            return true;
+        }
+        void begin_viewing(const std::string & line) {
+            backup_line = line;
+            viewing_idx = entries.size();
+        }
+        void end_viewing() {
+            viewing_idx = SIZE_MAX;
+            backup_line.clear();
+        }
+        bool is_viewing() const {
+            return viewing_idx != SIZE_MAX;
+        }
+    } history;
+
     static bool readline_advanced(std::string & line, bool multiline_input) {
         if (out != stdout) {
             fflush(stdout);
@@ -362,8 +738,33 @@ namespace console {
         bool is_special_char = false;
         bool end_of_stream = false;
 
+        size_t byte_pos = 0; // current byte index
+        size_t char_pos = 0; // current character index (one char can be multiple bytes)
+
         char32_t input_char;
         while (true) {
+            assert(char_pos <= byte_pos);
+            assert(char_pos <= widths.size());
+            auto history_prev = [&]() {
+                if (!history.is_viewing()) {
+                    history.begin_viewing(line);
+                }
+                std::string new_line;
+                if (!history.prev(new_line)) {
+                    return;
+                }
+                set_line_contents(new_line, line, widths, char_pos, byte_pos);
+            };
+            auto history_next = [&]() {
+                if (history.is_viewing()) {
+                    std::string new_line;
+                    if (!history.next(new_line)) {
+                        return;
+                    }
+                    set_line_contents(new_line, line, widths, char_pos, byte_pos);
+                }
+            };
+
             fflush(out); // Ensure all output is displayed before waiting for input
             input_char = getchar32();
 
@@ -371,7 +772,7 @@ namespace console {
                 break;
             }
 
-            if (input_char == (char32_t) WEOF || input_char == 0x04 /* Ctrl+D*/) {
+            if (input_char == (char32_t) WEOF || input_char == 0x04 /* Ctrl+D */) {
                 end_of_stream = true;
                 break;
             }
@@ -384,7 +785,71 @@ namespace console {
 
             if (input_char == '\033') { // Escape sequence
                 char32_t code = getchar32();
-                if (code == '[' || code == 0x1B) {
+                if (code == '[') {
+                    std::string params;
+                    while (true) {
+                        code = getchar32();
+                        if ((code >= 'A' && code <= 'Z') || (code >= 'a' && code <= 'z') || code == '~' || code == (char32_t) WEOF) {
+                            break;
+                        }
+                        params.push_back(static_cast<char>(code));
+                    }
+
+                    const bool ctrl_modifier = has_ctrl_modifier(params);
+
+                    if (code == 'D') { // left
+                        if (ctrl_modifier) {
+                            move_word_left(char_pos, byte_pos, widths, line);
+                        } else if (char_pos > 0) {
+                            int w = widths[char_pos - 1];
+                            move_cursor(-w);
+                            char_pos--;
+                            byte_pos = prev_utf8_char_pos(line, byte_pos);
+                        }
+                    } else if (code == 'C') { // right
+                        if (ctrl_modifier) {
+                            move_word_right(char_pos, byte_pos, widths, line);
+                        } else if (char_pos < widths.size()) {
+                            int w = widths[char_pos];
+                            move_cursor(w);
+                            char_pos++;
+                            byte_pos = next_utf8_char_pos(line, byte_pos);
+                        }
+                    } else if (code == 'H') { // home
+                        move_to_line_start(char_pos, byte_pos, widths);
+                    } else if (code == 'F') { // end
+                        move_to_line_end(char_pos, byte_pos, widths, line);
+                    } else if (code == 'A' || code == 'B') {
+                        // up/down
+                        if (code == 'A') {
+                            history_prev();
+                            is_special_char = false;
+                        } else if (code == 'B') {
+                            history_next();
+                            is_special_char = false;
+                        }
+                    } else if ((code == '~' || (code >= 'A' && code <= 'Z') || (code >= 'a' && code <= 'z')) && !params.empty()) {
+                        std::string digits;
+                        for (char ch : params) {
+                            if (ch == ';') {
+                                break;
+                            }
+                            if (std::isdigit(static_cast<unsigned char>(ch))) {
+                                digits.push_back(ch);
+                            }
+                        }
+
+                        if (code == '~') {
+                            if (digits == "1" || digits == "7") { // home
+                                move_to_line_start(char_pos, byte_pos, widths);
+                            } else if (digits == "4" || digits == "8") { // end
+                                move_to_line_end(char_pos, byte_pos, widths, line);
+                            } else if (digits == "3") { // delete
+                                delete_at_cursor(line, widths, char_pos, byte_pos);
+                            }
+                        }
+                    }
+                } else if (code == 0x1B) {
                     // Discard the rest of the escape sequence
                     while ((code = getchar32()) != (char32_t) WEOF) {
                         if ((code >= 'A' && code <= 'Z') || (code >= 'a' && code <= 'z') || code == '~') {
@@ -392,28 +857,107 @@ namespace console {
                         }
                     }
                 }
+#if defined(_WIN32)
+            } else if (input_char == KEY_ARROW_LEFT) {
+                if (char_pos > 0) {
+                    int w = widths[char_pos - 1];
+                    move_cursor(-w);
+                    char_pos--;
+                    byte_pos = prev_utf8_char_pos(line, byte_pos);
+                }
+            } else if (input_char == KEY_ARROW_RIGHT) {
+                if (char_pos < widths.size()) {
+                    int w = widths[char_pos];
+                    move_cursor(w);
+                    char_pos++;
+                    byte_pos = next_utf8_char_pos(line, byte_pos);
+                }
+            } else if (input_char == KEY_CTRL_ARROW_LEFT) {
+                move_word_left(char_pos, byte_pos, widths, line);
+            } else if (input_char == KEY_CTRL_ARROW_RIGHT) {
+                move_word_right(char_pos, byte_pos, widths, line);
+            } else if (input_char == KEY_HOME) {
+                move_to_line_start(char_pos, byte_pos, widths);
+            } else if (input_char == KEY_END) {
+                move_to_line_end(char_pos, byte_pos, widths, line);
+            } else if (input_char == KEY_DELETE) {
+                delete_at_cursor(line, widths, char_pos, byte_pos);
+            } else if (input_char == KEY_ARROW_UP || input_char == KEY_ARROW_DOWN) {
+                if (input_char == KEY_ARROW_UP) {
+                    history_prev();
+                    is_special_char = false;
+                } else if (input_char == KEY_ARROW_DOWN) {
+                    history_next();
+                    is_special_char = false;
+                }
+#endif
             } else if (input_char == 0x08 || input_char == 0x7F) { // Backspace
-                if (!widths.empty()) {
-                    int count;
-                    do {
-                        count = widths.back();
-                        widths.pop_back();
-                        // Move cursor back, print space, and move cursor back again
-                        for (int i = 0; i < count; i++) {
-                            replace_last(' ');
-                            pop_cursor();
-                        }
-                        pop_back_utf8_char(line);
-                    } while (count == 0 && !widths.empty());
+                if (char_pos > 0) {
+                    int w = widths[char_pos - 1];
+                    move_cursor(-w);
+                    char_pos--;
+                    size_t prev_pos = prev_utf8_char_pos(line, byte_pos);
+                    size_t char_len = byte_pos - prev_pos;
+                    byte_pos = prev_pos;
+
+                    // remove the character
+                    line.erase(byte_pos, char_len);
+                    widths.erase(widths.begin() + char_pos);
+
+                    // redraw tail
+                    size_t p = byte_pos;
+                    int tail_width = 0;
+                    for (size_t i = char_pos; i < widths.size(); ++i) {
+                        size_t next_p = next_utf8_char_pos(line, p);
+                        put_codepoint(line.c_str() + p, next_p - p, widths[i]);
+                        tail_width += widths[i];
+                        p = next_p;
+                    }
+
+                    // clear display
+                    for (int i = 0; i < w; ++i) {
+                        fputc(' ', out);
+                    }
+                    move_cursor(-(tail_width + w));
                 }
             } else {
-                int offset = line.length();
-                append_utf8(input_char, line);
-                int width = put_codepoint(line.c_str() + offset, line.length() - offset, estimateWidth(input_char));
-                if (width < 0) {
-                    width = 0;
+                // insert character
+                std::string new_char_str;
+                append_utf8(input_char, new_char_str);
+                int w = estimateWidth(input_char);
+
+                if (char_pos == widths.size()) {
+                    // insert at the end
+                    line += new_char_str;
+                    int real_w = put_codepoint(new_char_str.c_str(), new_char_str.length(), w);
+                    if (real_w < 0) real_w = 0;
+                    widths.push_back(real_w);
+                    byte_pos += new_char_str.length();
+                    char_pos++;
+                } else {
+                    // insert in middle
+                    line.insert(byte_pos, new_char_str);
+
+                    int real_w = put_codepoint(new_char_str.c_str(), new_char_str.length(), w);
+                    if (real_w < 0) real_w = 0;
+
+                    widths.insert(widths.begin() + char_pos, real_w);
+
+                    // print the tail
+                    size_t p = byte_pos + new_char_str.length();
+                    int tail_width = 0;
+                    for (size_t i = char_pos + 1; i < widths.size(); ++i) {
+                        size_t next_p = next_utf8_char_pos(line, p);
+                        put_codepoint(line.c_str() + p, next_p - p, widths[i]);
+                        tail_width += widths[i];
+                        p = next_p;
+                    }
+
+                    move_cursor(-tail_width);
+
+                    byte_pos += new_char_str.length();
+                    char_pos++;
                 }
-                widths.push_back(width);
             }
 
             if (!line.empty() && (line.back() == '\\' || line.back() == '/')) {
@@ -451,6 +995,15 @@ namespace console {
             }
         }
 
+        if (!end_of_stream && !line.empty()) {
+            // remove the trailing newline for history storage
+            if (!line.empty() && line.back() == '\n') {
+                line.pop_back();
+            }
+            // TODO: maybe support multiline history entries?
+            history.add(line);
+        }
+
         fflush(out);
         return has_more;
     }

From 42b12b560886dc2093b17af11c97ef6d276a3b97 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Tue, 9 Dec 2025 12:15:06 +0100
Subject: [PATCH 032/135] model : nit, DeepSeek V1 MoE is 16B and GigaChat is
 20B (#12652)

* nit, DeepSeek V1 MoE is 16B

* base type on n_ff_exp instead
---
 src/llama-model.cpp | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index e09d59e2c1..04fccc9793 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1606,8 +1606,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,         hparams.n_expert_shared);
                 ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,        hparams.expert_weights_scale);
 
-                switch (hparams.n_layer) {
-                    case 28: type = LLM_TYPE_20B; break;
+                switch (hparams.n_ff_exp) {
+                    case 1408: type = LLM_TYPE_16B; break;
+                    case 1792: type = LLM_TYPE_20B; break;
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;

From 63908b631a2cef6491c2b6ce1c504f046660183f Mon Sep 17 00:00:00 2001
From: Rhys-T <108157737+Rhys-T@users.noreply.github.com>
Date: Tue, 9 Dec 2025 06:17:41 -0500
Subject: [PATCH 033/135] cmake: fix Mach-O current version number (#17877)

PR #17091 set the VERSION of various libraries to 0.0.abcd, where abcd
is the LLAMA_BUILD_NUMBER. That build number is too large to fit in the
Mach-O 'current version' field's 'micro' part, which only goes up to
255. This just sets the Mach-O current version to 0 to get it building
properly again.

Fixes #17258.
---
 src/CMakeLists.txt        | 1 +
 tools/mtmd/CMakeLists.txt | 1 +
 2 files changed, 2 insertions(+)

diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 84a0c2934e..4192af7c0c 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -139,6 +139,7 @@ add_library(llama
 set_target_properties(llama PROPERTIES
     VERSION ${LLAMA_INSTALL_VERSION}
     SOVERSION 0
+    MACHO_CURRENT_VERSION 0 # keep macOS linker from seeing oversized version number
 )
 
 target_include_directories(llama PRIVATE .)
diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt
index f640ae2a6e..13ab7c78f4 100644
--- a/tools/mtmd/CMakeLists.txt
+++ b/tools/mtmd/CMakeLists.txt
@@ -16,6 +16,7 @@ add_library(mtmd
 set_target_properties(mtmd PROPERTIES
     VERSION ${LLAMA_INSTALL_VERSION}
     SOVERSION 0
+    MACHO_CURRENT_VERSION 0 # keep macOS linker from seeing oversized version number
 )
 
 target_link_libraries     (mtmd PUBLIC ggml llama)

From 86a3f0fad8b153ac9396e1ac18e790e4179c53f2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Tue, 9 Dec 2025 12:23:47 +0100
Subject: [PATCH 034/135] ggml : allow fill node alloc inplace (#17870)

---
 ggml/src/ggml-alloc.c      | 1 +
 ggml/src/ggml-cuda/fill.cu | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c
index 218222ece8..a5995fdc2c 100644
--- a/ggml/src/ggml-alloc.c
+++ b/ggml/src/ggml-alloc.c
@@ -25,6 +25,7 @@ static bool ggml_is_view(const struct ggml_tensor * t) {
 // ops that return true for this function must not use restrict pointers for their backend implementations
 bool ggml_op_can_inplace(enum ggml_op op) {
     switch (op) {
+        case GGML_OP_FILL:
         case GGML_OP_SCALE:
         case GGML_OP_DIAG_MASK_ZERO:
         case GGML_OP_DIAG_MASK_INF:
diff --git a/ggml/src/ggml-cuda/fill.cu b/ggml/src/ggml-cuda/fill.cu
index eb8ccb7802..739062c405 100644
--- a/ggml/src/ggml-cuda/fill.cu
+++ b/ggml/src/ggml-cuda/fill.cu
@@ -4,7 +4,7 @@
 #define CUDA_FILL_BLOCK_SIZE 256
 
 template <typename T>
-static __global__ void fill_kernel(T * __restrict__ dst, const int64_t k, const T value) {
+static __global__ void fill_kernel(T * dst, const int64_t k, const T value) {
     const int64_t i = (int64_t)blockDim.x * blockIdx.x + threadIdx.x;
     if (i >= k) {
         return;

From 6b82eb7883d1babf5f6bd4bca70997f229691fed Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 9 Dec 2025 15:25:49 +0200
Subject: [PATCH 035/135] metal : print node names for debugging (#17882)

---
 ggml/src/ggml-metal/ggml-metal-ops.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp
index 9efd51abba..392addb8d1 100644
--- a/ggml/src/ggml-metal/ggml-metal-ops.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp
@@ -221,7 +221,7 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
         }
 
         if (ctx->debug_graph > 0) {
-            GGML_LOG_DEBUG("%s: node[%5d] - %-12s %s\n", __func__, idx, ggml_op_name(node->op), is_concurrent ? "(concurrent)" : "");
+            GGML_LOG_DEBUG("%s: node[%5d] - %-12s %-12s %s\n", __func__, idx, ggml_op_name(node->op), ggml_get_name(node), is_concurrent ? "(concurrent)" : "");
         }
         if (ctx->debug_graph > 1) {
             GGML_TENSOR_LOCALS( int64_t, ne0, node->src[0], ne);

From 02e409a5becd2142c97a843bb63876fb8da7c6f0 Mon Sep 17 00:00:00 2001
From: Gabe Goodhart <ghart@us.ibm.com>
Date: Tue, 9 Dec 2025 09:29:07 -0700
Subject: [PATCH 036/135] ggml : Provide macos-specific backtrace printing to
 avoid terminal death (#17869)

* fix: Provide macos-specific backtrace printing to avoid terminal death

Branch: MacOSSafeBacktrace

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix: Add GGML_BACKTRACE_LLDB env var to enable using lldb for backtrace

Branch: MacOSSafeBacktrace

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

---------

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---
 ggml/src/ggml.c | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 9f5cdc1398..530ff7b953 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -124,6 +124,13 @@ static void ggml_print_backtrace_symbols(void) {
     int nptrs = backtrace(trace, sizeof(trace)/sizeof(trace[0]));
     backtrace_symbols_fd(trace, nptrs, STDERR_FILENO);
 }
+#elif defined(__APPLE__)
+#include <execinfo.h>
+static void ggml_print_backtrace_symbols(void) {
+    void * trace[100];
+    int nptrs = backtrace(trace, sizeof(trace)/sizeof(trace[0]));
+    backtrace_symbols_fd(trace, nptrs, STDERR_FILENO);
+}
 #else
 static void ggml_print_backtrace_symbols(void) {
     // platform not supported
@@ -135,6 +142,20 @@ void ggml_print_backtrace(void) {
     if (GGML_NO_BACKTRACE) {
         return;
     }
+#if defined(__APPLE__)
+    // On macOS, fork+debugger attachment is problematic due to:
+    // 1. libdispatch "poisons" forked child processes
+    // 2. lldb has issues attaching to parent from forked child
+    // Use simple backtrace() instead to avoid Terminal.app crashes
+    const char * GGML_BACKTRACE_LLDB = getenv("GGML_BACKTRACE_LLDB");
+    if (!GGML_BACKTRACE_LLDB) {
+        fprintf(stderr, "WARNING: Using native backtrace. Set GGML_BACKTRACE_LLDB for more info.\n");
+        fprintf(stderr, "WARNING: GGML_BACKTRACE_LLDB may cause native MacOS Terminal.app to crash.\n");
+        fprintf(stderr, "See: https://github.com/ggml-org/llama.cpp/pull/17869\n");
+        ggml_print_backtrace_symbols();
+        return;
+    }
+#endif
 #if defined(__linux__)
     FILE * f = fopen("/proc/self/status", "r");
     size_t size = 0;

From 48f47565a7e6555c92cc506ecfec8d71bdb1838f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Tue, 9 Dec 2025 20:10:36 +0100
Subject: [PATCH 037/135] docs: clarify that CPU support should be first
 (#17886)

---
 CONTRIBUTING.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index e4f05258db..4545ff8f9a 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -15,6 +15,7 @@ The project differentiates between 3 levels of contributors:
     - If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends)
     - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops`
 - Create separate PRs for each feature or fix. Avoid combining unrelated changes in a single PR
+- When adding support for a new model or feature, focus on **CPU support only** in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs
 - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly
 - If your PR becomes stale, rebase it on top of latest `master` to get maintainers attention
 - Maintainers will rely on your insights and approval when making a final decision to approve and merge a PR

From b63509262ac88db248fa70a1364c8a76e339e93f Mon Sep 17 00:00:00 2001
From: "Piotr Wilkin (ilintar)" <piotr.wilkin@syndatis.com>
Date: Tue, 9 Dec 2025 20:28:57 +0100
Subject: [PATCH 038/135] Add DIAG for CUDA (#17873)

* Add DIAG for CUDA

* Refactor parameters
---
 ggml/src/ggml-cuda/diag.cu      | 77 +++++++++++++++++++++++++++++++++
 ggml/src/ggml-cuda/diag.cuh     |  5 +++
 ggml/src/ggml-cuda/ggml-cuda.cu |  5 +++
 tests/test-backend-ops.cpp      | 29 +++++++++++++
 4 files changed, 116 insertions(+)
 create mode 100644 ggml/src/ggml-cuda/diag.cu
 create mode 100644 ggml/src/ggml-cuda/diag.cuh

diff --git a/ggml/src/ggml-cuda/diag.cu b/ggml/src/ggml-cuda/diag.cu
new file mode 100644
index 0000000000..5cea210517
--- /dev/null
+++ b/ggml/src/ggml-cuda/diag.cu
@@ -0,0 +1,77 @@
+#include "convert.cuh"
+#include "diag.cuh"
+#include "ggml.h"
+
+template <typename T>
+static __global__ void diag_kernel(T * __restrict__ dst,
+                                   const T * __restrict__ src,
+                                   const int64_t ne0,
+                                   const int64_t ne1,
+                                   const int64_t ne2,
+                                   const int64_t ne3,
+                                   const int64_t total_elements) {
+    const int64_t global_idx = blockIdx.x * blockDim.x + threadIdx.x;
+
+    if (global_idx >= total_elements) {
+        return;
+    }
+
+    const int64_t i0 = global_idx % ne0;
+    const int64_t i1 = (global_idx / ne0) % ne1;
+    const int64_t i2 = (global_idx / (ne0 * ne1)) % ne2;
+    const int64_t i3 = global_idx / (ne0 * ne1 * ne2);
+
+    const int64_t dst_idx = ((i3 * ne2 + i2) * ne1 + i1) * ne0 + i0;
+
+    if (i0 == i1) {
+        const int64_t batch_idx = i3 * ne2 + i2;
+        const int64_t src_idx   = batch_idx * ne0 + i0;
+        dst[dst_idx]            = src[src_idx];
+    } else {
+        dst[dst_idx] = ggml_cuda_cast<T>(0);
+    }
+    GGML_UNUSED_VARS(ne3);
+}
+
+void ggml_cuda_op_diag(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+
+    void *       dst_d  = dst->data;
+    const void * src0_d = src0->data;
+
+    cudaStream_t stream = ctx.stream();
+
+    GGML_ASSERT(ggml_is_contiguous(dst));
+    GGML_ASSERT(ggml_is_contiguous(src0));
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];
+
+    const int64_t ne0 = dst->ne[0];
+    const int64_t ne1 = dst->ne[1];
+    const int64_t ne2 = dst->ne[2];
+    const int64_t ne3 = dst->ne[3];
+
+    GGML_ASSERT(ne00 == ne0);
+    GGML_ASSERT(ne01 == 1);
+    GGML_ASSERT(ne02 == ne2);
+    GGML_ASSERT(ne03 == ne3);
+
+    const int64_t n_elems    = ggml_nelements(dst);
+    const int64_t num_blocks = (n_elems + CUDA_DIAG_BLOCK_SIZE - 1) / CUDA_DIAG_BLOCK_SIZE;
+
+    switch (dst->type) {
+        case GGML_TYPE_F32:
+            diag_kernel<<<num_blocks, CUDA_DIAG_BLOCK_SIZE, 0, stream>>>((float *) dst_d, (const float *) src0_d, ne0,
+                                                                         ne1, ne2, ne3, n_elems);
+            break;
+        case GGML_TYPE_F16:
+            diag_kernel<<<num_blocks, CUDA_DIAG_BLOCK_SIZE, 0, stream>>>((half *) dst_d, (const half *) src0_d, ne0,
+                                                                         ne1, ne2, ne3, n_elems);
+            break;
+        default:
+            GGML_ABORT("unsupported type");
+    }
+}
diff --git a/ggml/src/ggml-cuda/diag.cuh b/ggml/src/ggml-cuda/diag.cuh
new file mode 100644
index 0000000000..7d73e6a8eb
--- /dev/null
+++ b/ggml/src/ggml-cuda/diag.cuh
@@ -0,0 +1,5 @@
+#include "common.cuh"
+
+#define CUDA_DIAG_BLOCK_SIZE 256
+
+void ggml_cuda_op_diag(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index d0463388c5..279679a4ea 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -20,6 +20,7 @@
 #include "ggml-cuda/cpy.cuh"
 #include "ggml-cuda/cross-entropy-loss.cuh"
 #include "ggml-cuda/diagmask.cuh"
+#include "ggml-cuda/diag.cuh"
 #include "ggml-cuda/fattn.cuh"
 #include "ggml-cuda/getrows.cuh"
 #include "ggml-cuda/im2col.cuh"
@@ -2641,6 +2642,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg
         case GGML_OP_PERMUTE:
         case GGML_OP_TRANSPOSE:
                 break;
+        case GGML_OP_DIAG:
+            ggml_cuda_op_diag(ctx, dst);
+            break;
         case GGML_OP_DIAG_MASK_INF:
             ggml_cuda_op_diag_mask_inf(ctx, dst);
             break;
@@ -4624,6 +4628,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_FILL:
         case GGML_OP_CUMSUM:
         case GGML_OP_TRI:
+        case GGML_OP_DIAG:
             return true;
         case GGML_OP_SOLVE_TRI:
             return op->src[0]->ne[0] <= 64 && op->src[1]->ne[0] <= 32;
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 2e94a53da2..289e2e6d7f 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -6253,6 +6253,31 @@ struct test_solve_tri : public test_case {
     }
 };
 
+// GGML_OP_DIAG
+struct test_diag : public test_case {
+    const ggml_type              type;
+    const std::array<int64_t, 4> ne;
+
+    std::string vars() override { return VARS_TO_STR2(type, ne); }
+
+    test_diag(ggml_type type = GGML_TYPE_F32,
+            std::array<int64_t, 4> ne = { 10, 1, 4, 3 })
+        : type(type), ne(ne) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        GGML_ASSERT(ne[1] == 1);
+        ggml_tensor * a = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], ne[2], ne[3]);
+        ggml_set_param(a);
+        ggml_set_name(a, "a");
+
+        ggml_tensor * out = ggml_diag(ctx, a);
+        ggml_set_name(out, "out");
+
+        return out;
+    }
+};
+
+
 enum llm_norm_type {
     LLM_NORM,
     LLM_NORM_RMS,
@@ -7826,6 +7851,10 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_fill(-152.0f, GGML_TYPE_F32, { 800, 600, 4, 4 }));
     test_cases.emplace_back(new test_fill(3.5f, GGML_TYPE_F32, { 2048, 512, 2, 2 }));
 
+    test_cases.emplace_back(new test_diag());
+    test_cases.emplace_back(new test_diag(GGML_TYPE_F32, { 79, 1, 19, 13 }));
+    test_cases.emplace_back(new test_diag(GGML_TYPE_F32, { 256, 1, 8, 16 }));
+
     test_cases.emplace_back(new test_solve_tri());
     test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 11, 11, 1, 1 }, { 5, 11, 1, 1 }));
     test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 17, 17, 2, 4 }, { 9, 17, 2, 4 }));

From 086a63e3a5d2dbbb7183a74db453459e544eb55a Mon Sep 17 00:00:00 2001
From: Gabe Goodhart <ghart@us.ibm.com>
Date: Tue, 9 Dec 2025 12:30:02 -0700
Subject: [PATCH 039/135] metal: SSM kernel improvements (#17876)

* feat: Add a batched version of ssm_conv

This was done using Claude Code. It found a number of optimizations around
how the threads were organized, resulting in a huge performance boost!

Branch: Mamba2SSD

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: Optimized SSM_SCAN kernel for metal

This used Claude Code and resulted in a modest performance improvement
while maintaining correctness.

Branch: Mamba2SSD

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* test: Add test-backend-ops perf tests for SSM_CONV

Branch: SSMKernelImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* test: Real representitive tests for SSM_CONV

Branch: SSMKernelImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* refactor: Use function constant for ssm_conv batch size

Branch: SSMKernelImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* test: backend op tests for ssm_scan from granite4 1b-h

Branch: SSMKernelImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* style: remove commented out templates

Branch: SSMKernelImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* feat: float4 version of ssm_conv_batched

Branch: SSMKernelImprovements

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

* fix: Add missing ggml_metal_cv_free

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 ggml/src/ggml-metal/ggml-metal-device.cpp |  39 ++++++-
 ggml/src/ggml-metal/ggml-metal-device.h   |   1 +
 ggml/src/ggml-metal/ggml-metal-impl.h     |   1 +
 ggml/src/ggml-metal/ggml-metal-ops.cpp    |  42 +++++--
 ggml/src/ggml-metal/ggml-metal.metal      | 136 ++++++++++++++++++++--
 tests/test-backend-ops.cpp                |   7 ++
 6 files changed, 209 insertions(+), 17 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp
index ba3c342751..680904d132 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-device.cpp
@@ -411,6 +411,38 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv(ggml_me
     return res;
 }
 
+ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv_batched(ggml_metal_library_t lib, const ggml_tensor * op, int ssm_conv_bs) {
+    GGML_ASSERT(op->src[0]->type == GGML_TYPE_F32);
+    GGML_ASSERT(op->src[1]->type == GGML_TYPE_F32);
+
+    GGML_ASSERT(ggml_is_contiguous(op->src[0]));
+    GGML_ASSERT(ggml_is_contiguous(op->src[1]));
+
+    char base[256];
+    char name[256];
+
+    const char * suffix = "";
+    if (op->src[1]->ne[0] % 4 == 0) {
+        suffix = "_4";
+    }
+
+    snprintf(base, 256, "kernel_ssm_conv_%s_%s_batched%s", ggml_type_name(op->src[0]->type), ggml_type_name(op->src[1]->type), suffix);
+    snprintf(name, 256, "%s_ssm_conv_bs=%d", base, ssm_conv_bs);
+
+    ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
+    if (!res.pipeline) {
+        ggml_metal_cv_t cv = ggml_metal_cv_init();
+
+        ggml_metal_cv_set_int16(cv, ssm_conv_bs, FC_SSM_CONV + 0);
+
+        res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
+
+        ggml_metal_cv_free(cv);
+    }
+
+    return res;
+}
+
 ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_scan(ggml_metal_library_t lib, const ggml_tensor * op)  {
     GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
 
@@ -427,7 +459,12 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_scan(ggml_me
         res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
     }
 
-    res.smem = 32*sizeof(float)*nsg;
+    // Shared memory layout:
+    // - sgptg * NW floats for partial sums (nsg * 32)
+    // - sgptg floats for shared_x_dt (nsg)
+    // - sgptg floats for shared_dA (nsg)
+    // Total: nsg * (32 + 2) floats
+    res.smem = (32 + 2)*sizeof(float)*nsg;
 
     return res;
 }
diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h
index 77f2e98cfe..0a8b9211a7 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.h
+++ b/ggml/src/ggml-metal/ggml-metal-device.h
@@ -117,6 +117,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_cumsum_ad
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_tri               (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_soft_max          (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv          (ggml_metal_library_t lib, const struct ggml_tensor * op);
+struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv_batched  (ggml_metal_library_t lib, const struct ggml_tensor * op, int ssm_conv_bs);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_scan          (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_rwkv              (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv_ext        (ggml_metal_library_t lib, enum ggml_type tsrc0, enum ggml_type tsrc1, int nsg, int nxpsg, int r1ptg);
diff --git a/ggml/src/ggml-metal/ggml-metal-impl.h b/ggml/src/ggml-metal/ggml-metal-impl.h
index 30109f83e1..8944b07e90 100644
--- a/ggml/src/ggml-metal/ggml-metal-impl.h
+++ b/ggml/src/ggml-metal/ggml-metal-impl.h
@@ -77,6 +77,7 @@
 #define FC_MUL_MV                      600
 #define FC_MUL_MM                      700
 #define FC_ROPE                        800
+#define FC_SSM_CONV                    900
 
 // op-specific constants
 #define OP_FLASH_ATTN_EXT_NQPTG 8
diff --git a/ggml/src/ggml-metal/ggml-metal-ops.cpp b/ggml/src/ggml-metal/ggml-metal-ops.cpp
index 392addb8d1..e99c1763f6 100644
--- a/ggml/src/ggml-metal/ggml-metal-ops.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-ops.cpp
@@ -1365,15 +1365,43 @@ int ggml_metal_op_ssm_conv(ggml_metal_op_t ctx, int idx) {
         /*.nb2  =*/ nb2,
     };
 
-    auto pipeline = ggml_metal_library_get_pipeline_ssm_conv(lib, op);
+    // Use batched kernel for prefill (ne1 > 1) to reduce threadgroup dispatch overhead
+    const bool use_batched = (ne1 > 1);
 
-    ggml_metal_encoder_set_pipeline(enc, pipeline);
-    ggml_metal_encoder_set_bytes(enc, &args, sizeof(args), 0);
-    ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[0]), 1);
-    ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[1]), 2);
-    ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op),         3);
+    if (use_batched) {
+        // Determine the smallest power of 2 that's >= ne1, but <= 256
+        int BATCH_SIZE;
+        if      (ne1 > 128) BATCH_SIZE = 256;
+        else if (ne1 > 64 ) BATCH_SIZE = 128;
+        else if (ne1 > 32 ) BATCH_SIZE = 64;
+        else if (ne1 > 16 ) BATCH_SIZE = 32;
+        else if (ne1 > 8  ) BATCH_SIZE = 16;
+        else if (ne1 > 4  ) BATCH_SIZE = 8;
+        else                BATCH_SIZE = 2;
 
-    ggml_metal_encoder_dispatch_threadgroups(enc, ne01, ne1, ne02, 1, 1, 1);
+        auto pipeline = ggml_metal_library_get_pipeline_ssm_conv_batched(lib, op, BATCH_SIZE);
+
+        ggml_metal_encoder_set_pipeline(enc, pipeline);
+        ggml_metal_encoder_set_bytes(enc, &args, sizeof(args), 0);
+        ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[0]), 1);
+        ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[1]), 2);
+        ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op),         3);
+
+        // Dispatch: ne01 rows, ceil(ne1/BATCH_SIZE) token batches, ne02 sequences
+        // Each threadgroup has BATCH_SIZE threads, each handling one token
+        const int n_token_batches = (ne1 + BATCH_SIZE - 1) / BATCH_SIZE;
+        ggml_metal_encoder_dispatch_threadgroups(enc, ne01, n_token_batches, ne02, BATCH_SIZE, 1, 1);
+    } else {
+        auto pipeline = ggml_metal_library_get_pipeline_ssm_conv(lib, op);
+
+        ggml_metal_encoder_set_pipeline(enc, pipeline);
+        ggml_metal_encoder_set_bytes(enc, &args, sizeof(args), 0);
+        ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[0]), 1);
+        ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[1]), 2);
+        ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op),         3);
+
+        ggml_metal_encoder_dispatch_threadgroups(enc, ne01, ne1, ne02, 1, 1, 1);
+    }
 
     return 1;
 }
diff --git a/ggml/src/ggml-metal/ggml-metal.metal b/ggml/src/ggml-metal/ggml-metal.metal
index 4b78d5a2ba..51bcbae309 100644
--- a/ggml/src/ggml-metal/ggml-metal.metal
+++ b/ggml/src/ggml-metal/ggml-metal.metal
@@ -2343,7 +2343,102 @@ kernel void kernel_ssm_conv_f32_f32_4(
     x[0] = sumf;
 }
 
+constant short FC_ssm_conv_bs   [[function_constant(FC_SSM_CONV + 0)]];
+
+// Batched version: each threadgroup processes multiple tokens for better efficiency
+// Thread layout: each thread handles one token, threadgroup covers BATCH_SIZE tokens
+kernel void kernel_ssm_conv_f32_f32_batched(
+        constant ggml_metal_kargs_ssm_conv & args,
+        device const  void * src0,
+        device const  void * src1,
+        device       float * dst,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint3 tpitg[[thread_position_in_threadgroup]],
+        uint3   ntg[[threads_per_threadgroup]]) {
+    // tgpig.x = row index (ir)
+    // tgpig.y = batch of tokens (i2_base / BATCH_SIZE)
+    // tgpig.z = sequence index (i3)
+    // tpitg.x = thread within batch (0..BATCH_SIZE-1)
+    const short BATCH_SIZE = FC_ssm_conv_bs;
+
+    const int64_t ir      = tgpig.x;
+    const int64_t i2_base = tgpig.y * BATCH_SIZE;
+    const int64_t i3      = tgpig.z;
+    const int64_t i2_off  = tpitg.x;
+    const int64_t i2      = i2_base + i2_off;
+
+    const int64_t nc  = args.ne10;  // conv kernel size (typically 4)
+    const int64_t n_t = args.ne1;   // number of tokens
+
+    // Bounds check for partial batches at the end
+    if (i2 >= n_t) {
+        return;
+    }
+
+    // Load conv weights (shared across all tokens for this row)
+    device const float * c = (device const float *) ((device const char *) src1 + ir*args.nb11);
+
+    // Load source for this specific token
+    device const float * s = (device const float *) ((device const char *) src0 + ir*args.nb01 + i2*args.nb00 + i3*args.nb02);
+
+    // Output location for this token
+    device float * x = (device float *) ((device char *) dst + ir*args.nb0 + i2*args.nb1 + i3*args.nb2);
+
+    float sumf = 0.0f;
+    for (int64_t i0 = 0; i0 < nc; ++i0) {
+        sumf += s[i0] * c[i0];
+    }
+
+    x[0] = sumf;
+}
+
+kernel void kernel_ssm_conv_f32_f32_batched_4(
+        constant ggml_metal_kargs_ssm_conv & args,
+        device const  void * src0,
+        device const  void * src1,
+        device       float * dst,
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint3 tpitg[[thread_position_in_threadgroup]],
+        uint3   ntg[[threads_per_threadgroup]]) {
+    // tgpig.x = row index (ir)
+    // tgpig.y = batch of tokens (i2_base / BATCH_SIZE)
+    // tgpig.z = sequence index (i3)
+    // tpitg.x = thread within batch (0..BATCH_SIZE-1)
+    const short BATCH_SIZE = FC_ssm_conv_bs;
+
+    const int64_t ir      = tgpig.x;
+    const int64_t i2_base = tgpig.y * BATCH_SIZE;
+    const int64_t i3      = tgpig.z;
+    const int64_t i2_off  = tpitg.x;
+    const int64_t i2      = i2_base + i2_off;
+
+    const int64_t nc  = args.ne10;  // conv kernel size (typically 4)
+    const int64_t n_t = args.ne1;   // number of tokens
+
+    // Bounds check for partial batches at the end
+    if (i2 >= n_t) {
+        return;
+    }
+
+    // Load conv weights (shared across all tokens for this row)
+    device const float4 * c = (device const float4 *) ((device const char *) src1 + ir*args.nb11);
+
+    // Load source for this specific token
+    device const float4 * s = (device const float4 *) ((device const char *) src0 + ir*args.nb01 + i2*args.nb00 + i3*args.nb02);
+
+    // Output location for this token
+    device float * x = (device float *) ((device char *) dst + ir*args.nb0 + i2*args.nb1 + i3*args.nb2);
+
+    float sumf = 0.0f;
+    for (int64_t i0 = 0; i0 < nc/4; ++i0) {
+        sumf += dot(s[i0], c[i0]);
+    }
+
+    x[0] = sumf;
+}
+
 // ref: ggml.c:ggml_compute_forward_ssm_scan_f32, Mamba-2 part
+// Optimized version: reduces redundant memory loads by having one thread load shared values
 kernel void kernel_ssm_scan_f32(
         constant ggml_metal_kargs_ssm_scan & args,
         device const void * src0,
@@ -2363,7 +2458,15 @@ kernel void kernel_ssm_scan_f32(
         uint3    tgpg[[threadgroups_per_grid]]) {
     constexpr short NW = N_SIMDWIDTH;
 
-    shared[tpitg.x] = 0.0f;
+    // Shared memory layout:
+    // [0..sgptg*NW-1]: partial sums for reduction (existing)
+    // [sgptg*NW..sgptg*NW+sgptg-1]: pre-computed x_dt values for each token in batch
+    // [sgptg*NW+sgptg..sgptg*NW+2*sgptg-1]: pre-computed dA values for each token in batch
+    threadgroup float * shared_sums = shared;
+    threadgroup float * shared_x_dt = shared + sgptg * NW;
+    threadgroup float * shared_dA   = shared + sgptg * NW + sgptg;
+
+    shared_sums[tpitg.x] = 0.0f;
 
     const int32_t i0 = tpitg.x;
     const int32_t i1 = tgpig.x;
@@ -2403,32 +2506,47 @@ kernel void kernel_ssm_scan_f32(
     for (int i2 = 0; i2 < n_t; i2 += sgptg) {
         threadgroup_barrier(mem_flags::mem_threadgroup);
 
-        for (int t = 0; t < sgptg && i2 + t < n_t; t++) {
-            const float dt0  = dt[0];
+        // Pre-compute x_dt and dA for this batch of tokens
+        // Only first sgptg threads do the loads and expensive math
+        if (i0 < sgptg && i2 + i0 < n_t) {
+            // ns12 and ns21 are element strides (nb12/nb10, nb21/nb20)
+            device const float * x_t  = x  + i0 * args.ns12;
+            device const float * dt_t = dt + i0 * args.ns21;
+
+            const float dt0  = dt_t[0];
             const float dtsp = dt0 <= 20.0f ? log(1.0f + exp(dt0)) : dt0;
-            const float x_dt = x[0] * dtsp;
-            const float dA   = exp(dtsp * A0);
+            shared_x_dt[i0] = x_t[0] * dtsp;
+            shared_dA[i0]   = dtsp;  // Store dtsp, compute exp(dtsp * A0) per-thread since A0 varies
+        }
+
+        threadgroup_barrier(mem_flags::mem_threadgroup);
+
+        for (int t = 0; t < sgptg && i2 + t < n_t; t++) {
+            const float x_dt = shared_x_dt[t];
+            const float dA   = exp(shared_dA[t] * A0);
 
             s = (s0 * dA) + (B[i0] * x_dt);
 
             const float sumf = simd_sum(s * C[i0]);
 
             if (tiisg == 0) {
-                shared[t*NW + sgitg] = sumf;
+                shared_sums[t*NW + sgitg] = sumf;
             }
 
             // recurse
             s0 = s;
 
-            x  += args.ns12;
-            dt += args.ns21;
             B  += args.ns42;
             C  += args.ns52;
         }
 
+        // Advance pointers for next batch
+        x  += sgptg * args.ns12;
+        dt += sgptg * args.ns21;
+
         threadgroup_barrier(mem_flags::mem_threadgroup);
 
-        const float sumf = simd_sum(shared[sgitg*NW + tiisg]);
+        const float sumf = simd_sum(shared_sums[sgitg*NW + tiisg]);
 
         if (tiisg == 0 && i2 + sgitg < n_t) {
             y[sgitg*nh*nr] = sumf;
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 289e2e6d7f..a6f266601f 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -8193,6 +8193,13 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
         }
     }
 
+    // Examples from granite-4.0-h-1b/ggml-model-Q8_0.gguf
+    test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {515, 3328, 1, 1}, {4, 3328, 1, 1})); // prefill
+    test_cases.emplace_back(new test_ssm_conv(GGML_TYPE_F32, {4,   3328, 1, 1}, {4, 3328, 1, 1})); // generate
+    test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 64, 48, 1, 512, 1)); // prefill
+    test_cases.emplace_back(new test_ssm_scan(GGML_TYPE_F32, 128, 64, 48, 1, 1,   1)); // generate
+
+
     return test_cases;
 }
 

From 63391852b05ceaba6547dbd1c60ac716474e2798 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Tue, 9 Dec 2025 23:31:29 +0100
Subject: [PATCH 040/135] docs : update cpu and cuda ops (#17890)

* update cuda ops

* update CPU as well
---
 docs/ops.md       |  11 +-
 docs/ops/CPU.csv  | 496 +++++++++++++++++++++++++++++++++++++++++--
 docs/ops/CUDA.csv | 524 ++++++++++++++++++++++++++++++++++++++++++----
 3 files changed, 970 insertions(+), 61 deletions(-)

diff --git a/docs/ops.md b/docs/ops.md
index ef1febccae..6ede4a4f64 100644
--- a/docs/ops.md
+++ b/docs/ops.md
@@ -36,14 +36,15 @@ Legend:
 |                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |               CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |          CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                           CUMSUM | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                           CUMSUM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                             DIAG | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
 |                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
-|                             FILL | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                             FILL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
 |                            FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
 |                GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
@@ -102,7 +103,7 @@ Legend:
 |                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
-|                        SOLVE_TRI | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
+|                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
 |                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
 |                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
 |                         SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
@@ -115,8 +116,8 @@ Legend:
 |                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ |
 |                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                            TOP_K | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
-|                              TRI | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                            TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
+|                              TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                            TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
 |                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
 |                            XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
diff --git a/docs/ops/CPU.csv b/docs/ops/CPU.csv
index 7c223e279c..fef3bbce70 100644
--- a/docs/ops/CPU.csv
+++ b/docs/ops/CPU.csv
@@ -4964,6 +4964,7 @@
 "CPU","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","1","yes","CPU"
 "CPU","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","1","yes","CPU"
 "CPU","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","1","yes","CPU"
+"CPU","CONV_TRANSPOSE_2D","ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","1","yes","CPU"
 "CPU","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","1","yes","CPU"
 "CPU","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","1","yes","CPU"
 "CPU","ARGMAX","type=f32,ne=[32,1,1,1]","support","1","yes","CPU"
@@ -5419,17 +5420,45 @@
 "CPU","CPY","type_src=f16,type_dst=f16,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","CPU"
 "CPU","CPY","type_src=f32,type_dst=f32,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","CPU"
 "CPU","CPY","type_src=bf16,type_dst=bf16,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","CPU"
+"CPU","CPY","type_src=i32,type_dst=i32,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","CPU"
+"CPU","CPY","type_src=i32,type_dst=i32,ne=[256,1,4,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","CPU"
 "CPU","CPY","type_src=f32,type_dst=f32,ne=[256,1,4,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","CPU"
-"CPU","CONT","type=f32,ne=[10,10,10,1]","support","1","yes","CPU"
-"CPU","CONT","type=f32,ne=[2,1,1,1]","support","1","yes","CPU"
-"CPU","CONT","type=f32,ne=[2,1,3,5]","support","1","yes","CPU"
-"CPU","CONT","type=f32,ne=[2,3,5,7]","support","1","yes","CPU"
-"CPU","CONT","type=f16,ne=[2,1,1,1]","support","1","yes","CPU"
-"CPU","CONT","type=f16,ne=[2,1,3,5]","support","1","yes","CPU"
-"CPU","CONT","type=f16,ne=[2,3,5,7]","support","1","yes","CPU"
-"CPU","CONT","type=bf16,ne=[2,1,1,1]","support","1","yes","CPU"
-"CPU","CONT","type=bf16,ne=[2,1,3,5]","support","1","yes","CPU"
-"CPU","CONT","type=bf16,ne=[2,3,5,7]","support","1","yes","CPU"
+"CPU","CONT","type=f32,ne=[2,1,1,1],use_view_slice=1","support","1","yes","CPU"
+"CPU","CONT","type=f32,ne=[2,1,3,5],use_view_slice=1","support","1","yes","CPU"
+"CPU","CONT","type=f32,ne=[2,3,5,7],use_view_slice=1","support","1","yes","CPU"
+"CPU","CONT","type=f32,ne=[1,4,4,1],use_view_slice=1","support","1","yes","CPU"
+"CPU","CONT","type=f32,ne=[1,8,17,1],use_view_slice=1","support","1","yes","CPU"
+"CPU","CONT","type=f32,ne=[10,10,10,1],use_view_slice=1","support","1","yes","CPU"
+"CPU","CONT","type=f32,ne=[2,1,1,1],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=f32,ne=[2,1,3,5],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=f32,ne=[2,3,5,7],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=f32,ne=[1,4,4,1],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=f32,ne=[1,8,17,1],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=f32,ne=[10,10,10,1],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=i32,ne=[2,1,1,1],use_view_slice=1","support","1","yes","CPU"
+"CPU","CONT","type=i32,ne=[2,1,3,5],use_view_slice=1","support","1","yes","CPU"
+"CPU","CONT","type=i32,ne=[2,3,5,7],use_view_slice=1","support","1","yes","CPU"
+"CPU","CONT","type=i32,ne=[1,4,4,1],use_view_slice=1","support","1","yes","CPU"
+"CPU","CONT","type=i32,ne=[1,8,17,1],use_view_slice=1","support","1","yes","CPU"
+"CPU","CONT","type=i32,ne=[10,10,10,1],use_view_slice=1","support","1","yes","CPU"
+"CPU","CONT","type=i32,ne=[2,1,1,1],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=i32,ne=[2,1,3,5],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=i32,ne=[2,3,5,7],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=i32,ne=[1,4,4,1],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=i32,ne=[1,8,17,1],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=i32,ne=[10,10,10,1],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=f16,ne=[2,1,1,1],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=f16,ne=[2,1,3,5],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=f16,ne=[2,3,5,7],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=f16,ne=[1,4,4,1],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=f16,ne=[1,8,17,1],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=f16,ne=[10,10,10,1],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=bf16,ne=[2,1,1,1],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=bf16,ne=[2,1,3,5],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=bf16,ne=[2,3,5,7],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=bf16,ne=[1,4,4,1],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=bf16,ne=[1,8,17,1],use_view_slice=0","support","1","yes","CPU"
+"CPU","CONT","type=bf16,ne=[10,10,10,1],use_view_slice=0","support","1","yes","CPU"
 "CPU","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","CPU"
 "CPU","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","CPU"
 "CPU","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","CPU"
@@ -5655,6 +5684,7 @@
 "CPU","MUL","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","CPU"
 "CPU","DIV","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","CPU"
 "CPU","ADD1","type=f32,ne=[10,5,4,3]","support","1","yes","CPU"
+"CPU","ADD1","type=f32,ne=[1024,1024,1,1]","support","1","yes","CPU"
 "CPU","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=0.000000,inplace=0","support","1","yes","CPU"
 "CPU","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=0","support","1","yes","CPU"
 "CPU","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=1","support","1","yes","CPU"
@@ -8644,9 +8674,13 @@
 "CPU","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","CPU"
 "CPU","LEAKY_RELU","type=f16,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","CPU"
 "CPU","FLOOR","type=f16,ne=[7,1,5,3]","support","1","yes","CPU"
+"CPU","FLOOR","type=f16,ne=[1024,1024,1,1]","support","1","yes","CPU"
 "CPU","CEIL","type=f16,ne=[7,1,5,3]","support","1","yes","CPU"
+"CPU","CEIL","type=f16,ne=[1024,1024,1,1]","support","1","yes","CPU"
 "CPU","ROUND","type=f16,ne=[7,1,5,3]","support","1","yes","CPU"
+"CPU","ROUND","type=f16,ne=[1024,1024,1,1]","support","1","yes","CPU"
 "CPU","TRUNC","type=f16,ne=[7,1,5,3]","support","1","yes","CPU"
+"CPU","TRUNC","type=f16,ne=[1024,1024,1,1]","support","1","yes","CPU"
 "CPU","SQR","type=f32,ne=[10,5,4,3]","support","1","yes","CPU"
 "CPU","SQRT","type=f32,ne=[10,3,3,2]","support","1","yes","CPU"
 "CPU","LOG","type=f32,ne=[10,5,4,3]","support","1","yes","CPU"
@@ -8666,9 +8700,13 @@
 "CPU","CLAMP","type=f32,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","CPU"
 "CPU","LEAKY_RELU","type=f32,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","CPU"
 "CPU","FLOOR","type=f32,ne=[7,1,5,3]","support","1","yes","CPU"
+"CPU","FLOOR","type=f32,ne=[1024,1024,1,1]","support","1","yes","CPU"
 "CPU","CEIL","type=f32,ne=[7,1,5,3]","support","1","yes","CPU"
+"CPU","CEIL","type=f32,ne=[1024,1024,1,1]","support","1","yes","CPU"
 "CPU","ROUND","type=f32,ne=[7,1,5,3]","support","1","yes","CPU"
+"CPU","ROUND","type=f32,ne=[1024,1024,1,1]","support","1","yes","CPU"
 "CPU","TRUNC","type=f32,ne=[7,1,5,3]","support","1","yes","CPU"
+"CPU","TRUNC","type=f32,ne=[1024,1024,1,1]","support","1","yes","CPU"
 "CPU","DIAG_MASK_INF","type=f32,ne=[10,10,1,1],n_past=5","support","1","yes","CPU"
 "CPU","DIAG_MASK_INF","type=f32,ne=[10,10,3,1],n_past=5","support","1","yes","CPU"
 "CPU","DIAG_MASK_INF","type=f32,ne=[10,10,3,2],n_past=5","support","1","yes","CPU"
@@ -9411,18 +9449,405 @@
 "CPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","1","yes","CPU"
 "CPU","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","CPU"
 "CPU","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[3,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[4,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[7,1,1,1],order=0","support","1","yes","CPU"
 "CPU","ARGSORT","type=f32,ne=[8,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[15,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[16,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[31,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[32,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[63,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[64,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[127,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[128,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[255,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[256,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[511,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[512,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[1023,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[1024,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[2047,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[2048,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[4095,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[4096,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[8191,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[8192,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[16383,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[16384,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[32767,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[32768,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[65535,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[65536,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[131071,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[131072,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[262143,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[262144,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[524287,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[524288,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[1048575,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[1048576,1,1,1],order=0","support","1","yes","CPU"
 "CPU","ARGSORT","type=f32,ne=[16,10,10,10],order=0","support","1","yes","CPU"
 "CPU","ARGSORT","type=f32,ne=[60,10,10,10],order=0","support","1","yes","CPU"
-"CPU","ARGSORT","type=f32,ne=[1024,1,1,1],order=0","support","1","yes","CPU"
-"CPU","ARGSORT","type=f32,ne=[16384,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[1023,2,1,3],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[1024,2,1,3],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[1025,2,1,3],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[2047,2,1,3],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[2048,2,1,3],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[2049,2,1,3],order=0","support","1","yes","CPU"
 "CPU","ARGSORT","type=f32,ne=[2,8,8192,1],order=0","support","1","yes","CPU"
-"CPU","ARGSORT","type=f32,ne=[8,1,1,1],order=1","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[3,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[4,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[7,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[8,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[15,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[16,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[31,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[32,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[63,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[64,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[127,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[128,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[255,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[256,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[511,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[512,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[1023,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[1024,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[2047,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[2048,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[4095,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[4096,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[8191,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[8192,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[16383,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[16384,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[32767,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[32768,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[65535,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[65536,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[131071,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[131072,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[262143,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[262144,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[524287,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[524288,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[1048575,1,1,1],order=0","support","1","yes","CPU"
+"CPU","ARGSORT","type=f32,ne=[1048576,1,1,1],order=0","support","1","yes","CPU"
 "CPU","ARGSORT","type=f32,ne=[16,10,10,10],order=1","support","1","yes","CPU"
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+"CPU","TOP_K","type=f32,ne=[1023,2,1,3],k=7,ties=0","support","1","yes","CPU"
+"CPU","TOP_K","type=f32,ne=[1024,2,1,3],k=7,ties=0","support","1","yes","CPU"
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+"CPU","TOP_K","type=f32,ne=[2048,2,1,3],k=7,ties=0","support","1","yes","CPU"
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+"CPU","TOP_K","type=f32,ne=[16,10,10,10],k=15,ties=0","support","1","yes","CPU"
+"CPU","TOP_K","type=f32,ne=[60,10,10,10],k=15,ties=0","support","1","yes","CPU"
+"CPU","TOP_K","type=f32,ne=[1023,2,1,3],k=15,ties=0","support","1","yes","CPU"
+"CPU","TOP_K","type=f32,ne=[1024,2,1,3],k=15,ties=0","support","1","yes","CPU"
+"CPU","TOP_K","type=f32,ne=[1025,2,1,3],k=15,ties=0","support","1","yes","CPU"
+"CPU","TOP_K","type=f32,ne=[16384,1,1,1],k=15,ties=0","support","1","yes","CPU"
+"CPU","TOP_K","type=f32,ne=[2047,2,1,3],k=15,ties=0","support","1","yes","CPU"
+"CPU","TOP_K","type=f32,ne=[2048,2,1,3],k=15,ties=0","support","1","yes","CPU"
+"CPU","TOP_K","type=f32,ne=[2049,2,1,3],k=15,ties=0","support","1","yes","CPU"
 "CPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=0","support","1","yes","CPU"
 "CPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=1","support","1","yes","CPU"
 "CPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=nearest,flags=none","support","1","yes","CPU"
@@ -9435,6 +9860,10 @@
 "CPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bicubic,transpose=1","support","1","yes","CPU"
 "CPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic,flags=none","support","1","yes","CPU"
 "CPU","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bicubic,flags=none","support","1","yes","CPU"
+"CPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=513,transpose=0","support","1","yes","CPU"
+"CPU","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=513,transpose=1","support","1","yes","CPU"
+"CPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=none","support","1","yes","CPU"
+"CPU","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear,flags=none","support","1","yes","CPU"
 "CPU","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=align_corners","support","1","yes","CPU"
 "CPU","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bilinear,flags=align_corners","support","1","yes","CPU"
 "CPU","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bilinear,flags=align_corners","support","1","yes","CPU"
@@ -9463,15 +9892,30 @@
 "CPU","GROUP_NORM","type=f32,ne=[64,64,320,1],num_groups=32,eps=0.000001","support","1","yes","CPU"
 "CPU","GROUP_NORM","type=f32,ne=[9,9,1280,1],num_groups=32,eps=0.000001","support","1","yes","CPU"
 "CPU","ACC","type=f32,ne_a=[256,17,1,1],ne_b=[256,16,1,1]","support","1","yes","CPU"
-"CPU","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1","support","1","yes","CPU"
-"CPU","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0","support","1","yes","CPU"
+"CPU","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1,circular=0","support","1","yes","CPU"
+"CPU","PAD","type=f32,ne_a=[33,17,2,1],pad_0=4,pad_1=3,circular=1","support","1","yes","CPU"
+"CPU","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0,circular=0","support","1","yes","CPU"
 "CPU","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","1","yes","CPU"
 "CPU","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","1","yes","CPU"
 "CPU","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","1","yes","CPU"
 "CPU","ARANGE","type=f32,start=0.000000,stop=10.000000,step=1.000000","support","1","yes","CPU"
+"CPU","ARANGE","type=f32,start=0.000000,stop=1048576.000000,step=1.000000","support","1","yes","CPU"
 "CPU","TIMESTEP_EMBEDDING","type=f32,ne_a=[2,1,1,1],dim=320,max_period=10000","support","1","yes","CPU"
 "CPU","LEAKY_RELU","type=f32,ne_a=[10,5,4,3],negative_slope=0.100000","support","1","yes","CPU"
 "CPU","CUMSUM","type=f32,ne=[10,5,4,3]","support","1","yes","CPU"
+"CPU","CUMSUM","type=f32,ne=[127,5,4,3]","support","1","yes","CPU"
+"CPU","CUMSUM","type=f32,ne=[128,5,4,3]","support","1","yes","CPU"
+"CPU","CUMSUM","type=f32,ne=[128,128,4,4]","support","1","yes","CPU"
+"CPU","CUMSUM","type=f32,ne=[255,5,4,3]","support","1","yes","CPU"
+"CPU","CUMSUM","type=f32,ne=[256,5,4,3]","support","1","yes","CPU"
+"CPU","CUMSUM","type=f32,ne=[511,5,4,3]","support","1","yes","CPU"
+"CPU","CUMSUM","type=f32,ne=[512,5,4,3]","support","1","yes","CPU"
+"CPU","CUMSUM","type=f32,ne=[1023,5,4,3]","support","1","yes","CPU"
+"CPU","CUMSUM","type=f32,ne=[1024,5,4,3]","support","1","yes","CPU"
+"CPU","CUMSUM","type=f32,ne=[2047,5,4,3]","support","1","yes","CPU"
+"CPU","CUMSUM","type=f32,ne=[2048,5,4,3]","support","1","yes","CPU"
+"CPU","CUMSUM","type=f32,ne=[242004,1,1,1]","support","1","yes","CPU"
+"CPU","CUMSUM","type=f32,ne=[375960,1,1,1]","support","1","yes","CPU"
 "CPU","XIELU","type=f32,ne=[10,5,4,3]","support","1","yes","CPU"
 "CPU","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","1","yes","CPU"
 "CPU","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","1","yes","CPU"
@@ -9480,6 +9924,10 @@
 "CPU","FILL","type=f32,ne=[10,10,4,3],c=0.000000","support","1","yes","CPU"
 "CPU","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","1","yes","CPU"
 "CPU","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","1","yes","CPU"
+"CPU","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","1","yes","CPU"
+"CPU","DIAG","type=f32,ne=[10,1,4,3]","support","1","yes","CPU"
+"CPU","DIAG","type=f32,ne=[79,1,19,13]","support","1","yes","CPU"
+"CPU","DIAG","type=f32,ne=[256,1,8,16]","support","1","yes","CPU"
 "CPU","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","1","yes","CPU"
 "CPU","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","1","yes","CPU"
 "CPU","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","1","yes","CPU"
@@ -9487,10 +9935,16 @@
 "CPU","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","1","yes","CPU"
 "CPU","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","1","yes","CPU"
 "CPU","SOLVE_TRI","type=f32,ne_lhs=[100,100,4,4],ne_rhs=[41,100,4,4]","support","1","yes","CPU"
-"CPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0","support","1","yes","CPU"
-"CPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0","support","1","yes","CPU"
-"CPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1","support","1","yes","CPU"
-"CPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1","support","1","yes","CPU"
+"CPU","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[31,128,4,4]","support","1","yes","CPU"
+"CPU","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[300,64,4,4]","support","1","yes","CPU"
+"CPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=0","support","1","yes","CPU"
+"CPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=0","support","1","yes","CPU"
+"CPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=1","support","1","yes","CPU"
+"CPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=1","support","1","yes","CPU"
+"CPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=0","support","1","yes","CPU"
+"CPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=0","support","1","yes","CPU"
+"CPU","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=1","support","1","yes","CPU"
+"CPU","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=1","support","1","yes","CPU"
 "CPU","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CPU"
 "CPU","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CPU"
 "CPU","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","CPU"
diff --git a/docs/ops/CUDA.csv b/docs/ops/CUDA.csv
index 56ba32273a..22c84dd143 100644
--- a/docs/ops/CUDA.csv
+++ b/docs/ops/CUDA.csv
@@ -4964,6 +4964,7 @@
 "CUDA0","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","1","yes","CUDA"
 "CUDA0","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","1","yes","CUDA"
 "CUDA0","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","1","yes","CUDA"
+"CUDA0","CONV_TRANSPOSE_2D","ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","1","yes","CUDA"
 "CUDA0","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","1","yes","CUDA"
 "CUDA0","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","1","yes","CUDA"
 "CUDA0","ARGMAX","type=f32,ne=[32,1,1,1]","support","1","yes","CUDA"
@@ -5419,17 +5420,45 @@
 "CUDA0","CPY","type_src=f16,type_dst=f16,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","CUDA"
 "CUDA0","CPY","type_src=f32,type_dst=f32,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","CUDA"
 "CUDA0","CPY","type_src=bf16,type_dst=bf16,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","CUDA"
+"CUDA0","CPY","type_src=i32,type_dst=i32,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","CUDA"
+"CUDA0","CPY","type_src=i32,type_dst=i32,ne=[256,1,4,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","CUDA"
 "CUDA0","CPY","type_src=f32,type_dst=f32,ne=[256,1,4,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","CUDA"
-"CUDA0","CONT","type=f32,ne=[10,10,10,1]","support","1","yes","CUDA"
-"CUDA0","CONT","type=f32,ne=[2,1,1,1]","support","1","yes","CUDA"
-"CUDA0","CONT","type=f32,ne=[2,1,3,5]","support","1","yes","CUDA"
-"CUDA0","CONT","type=f32,ne=[2,3,5,7]","support","1","yes","CUDA"
-"CUDA0","CONT","type=f16,ne=[2,1,1,1]","support","1","yes","CUDA"
-"CUDA0","CONT","type=f16,ne=[2,1,3,5]","support","1","yes","CUDA"
-"CUDA0","CONT","type=f16,ne=[2,3,5,7]","support","1","yes","CUDA"
-"CUDA0","CONT","type=bf16,ne=[2,1,1,1]","support","1","yes","CUDA"
-"CUDA0","CONT","type=bf16,ne=[2,1,3,5]","support","1","yes","CUDA"
-"CUDA0","CONT","type=bf16,ne=[2,3,5,7]","support","1","yes","CUDA"
+"CUDA0","CONT","type=f32,ne=[2,1,1,1],use_view_slice=1","support","1","yes","CUDA"
+"CUDA0","CONT","type=f32,ne=[2,1,3,5],use_view_slice=1","support","1","yes","CUDA"
+"CUDA0","CONT","type=f32,ne=[2,3,5,7],use_view_slice=1","support","1","yes","CUDA"
+"CUDA0","CONT","type=f32,ne=[1,4,4,1],use_view_slice=1","support","1","yes","CUDA"
+"CUDA0","CONT","type=f32,ne=[1,8,17,1],use_view_slice=1","support","1","yes","CUDA"
+"CUDA0","CONT","type=f32,ne=[10,10,10,1],use_view_slice=1","support","1","yes","CUDA"
+"CUDA0","CONT","type=f32,ne=[2,1,1,1],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=f32,ne=[2,1,3,5],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=f32,ne=[2,3,5,7],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=f32,ne=[1,4,4,1],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=f32,ne=[1,8,17,1],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=f32,ne=[10,10,10,1],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=i32,ne=[2,1,1,1],use_view_slice=1","support","1","yes","CUDA"
+"CUDA0","CONT","type=i32,ne=[2,1,3,5],use_view_slice=1","support","1","yes","CUDA"
+"CUDA0","CONT","type=i32,ne=[2,3,5,7],use_view_slice=1","support","1","yes","CUDA"
+"CUDA0","CONT","type=i32,ne=[1,4,4,1],use_view_slice=1","support","1","yes","CUDA"
+"CUDA0","CONT","type=i32,ne=[1,8,17,1],use_view_slice=1","support","1","yes","CUDA"
+"CUDA0","CONT","type=i32,ne=[10,10,10,1],use_view_slice=1","support","1","yes","CUDA"
+"CUDA0","CONT","type=i32,ne=[2,1,1,1],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=i32,ne=[2,1,3,5],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=i32,ne=[2,3,5,7],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=i32,ne=[1,4,4,1],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=i32,ne=[1,8,17,1],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=i32,ne=[10,10,10,1],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=f16,ne=[2,1,1,1],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=f16,ne=[2,1,3,5],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=f16,ne=[2,3,5,7],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=f16,ne=[1,4,4,1],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=f16,ne=[1,8,17,1],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=f16,ne=[10,10,10,1],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=bf16,ne=[2,1,1,1],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=bf16,ne=[2,1,3,5],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=bf16,ne=[2,3,5,7],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=bf16,ne=[1,4,4,1],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=bf16,ne=[1,8,17,1],use_view_slice=0","support","1","yes","CUDA"
+"CUDA0","CONT","type=bf16,ne=[10,10,10,1],use_view_slice=0","support","1","yes","CUDA"
 "CUDA0","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","CUDA"
 "CUDA0","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","CUDA"
 "CUDA0","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","CUDA"
@@ -5655,6 +5684,7 @@
 "CUDA0","MUL","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","CUDA"
 "CUDA0","DIV","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","CUDA"
 "CUDA0","ADD1","type=f32,ne=[10,5,4,3]","support","1","yes","CUDA"
+"CUDA0","ADD1","type=f32,ne=[1024,1024,1,1]","support","1","yes","CUDA"
 "CUDA0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=0.000000,inplace=0","support","1","yes","CUDA"
 "CUDA0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=0","support","1","yes","CUDA"
 "CUDA0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=1","support","1","yes","CUDA"
@@ -8644,9 +8674,13 @@
 "CUDA0","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","CUDA"
 "CUDA0","LEAKY_RELU","type=f16,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","CUDA"
 "CUDA0","FLOOR","type=f16,ne=[7,1,5,3]","support","1","yes","CUDA"
+"CUDA0","FLOOR","type=f16,ne=[1024,1024,1,1]","support","1","yes","CUDA"
 "CUDA0","CEIL","type=f16,ne=[7,1,5,3]","support","1","yes","CUDA"
+"CUDA0","CEIL","type=f16,ne=[1024,1024,1,1]","support","1","yes","CUDA"
 "CUDA0","ROUND","type=f16,ne=[7,1,5,3]","support","1","yes","CUDA"
+"CUDA0","ROUND","type=f16,ne=[1024,1024,1,1]","support","1","yes","CUDA"
 "CUDA0","TRUNC","type=f16,ne=[7,1,5,3]","support","1","yes","CUDA"
+"CUDA0","TRUNC","type=f16,ne=[1024,1024,1,1]","support","1","yes","CUDA"
 "CUDA0","SQR","type=f32,ne=[10,5,4,3]","support","1","yes","CUDA"
 "CUDA0","SQRT","type=f32,ne=[10,3,3,2]","support","1","yes","CUDA"
 "CUDA0","LOG","type=f32,ne=[10,5,4,3]","support","1","yes","CUDA"
@@ -8666,9 +8700,13 @@
 "CUDA0","CLAMP","type=f32,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","CUDA"
 "CUDA0","LEAKY_RELU","type=f32,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","CUDA"
 "CUDA0","FLOOR","type=f32,ne=[7,1,5,3]","support","1","yes","CUDA"
+"CUDA0","FLOOR","type=f32,ne=[1024,1024,1,1]","support","1","yes","CUDA"
 "CUDA0","CEIL","type=f32,ne=[7,1,5,3]","support","1","yes","CUDA"
+"CUDA0","CEIL","type=f32,ne=[1024,1024,1,1]","support","1","yes","CUDA"
 "CUDA0","ROUND","type=f32,ne=[7,1,5,3]","support","1","yes","CUDA"
+"CUDA0","ROUND","type=f32,ne=[1024,1024,1,1]","support","1","yes","CUDA"
 "CUDA0","TRUNC","type=f32,ne=[7,1,5,3]","support","1","yes","CUDA"
+"CUDA0","TRUNC","type=f32,ne=[1024,1024,1,1]","support","1","yes","CUDA"
 "CUDA0","DIAG_MASK_INF","type=f32,ne=[10,10,1,1],n_past=5","support","1","yes","CUDA"
 "CUDA0","DIAG_MASK_INF","type=f32,ne=[10,10,3,1],n_past=5","support","1","yes","CUDA"
 "CUDA0","DIAG_MASK_INF","type=f32,ne=[10,10,3,2],n_past=5","support","1","yes","CUDA"
@@ -9411,18 +9449,405 @@
 "CUDA0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","0","no","CUDA"
 "CUDA0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","CUDA"
 "CUDA0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","0","no","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[3,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[4,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[7,1,1,1],order=0","support","1","yes","CUDA"
 "CUDA0","ARGSORT","type=f32,ne=[8,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[15,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[16,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[31,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[32,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[63,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[64,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[127,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[128,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[255,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[256,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[511,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[512,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[1023,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[1024,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[2047,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[2048,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[4095,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[4096,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[8191,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[8192,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[16383,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[16384,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[32767,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[32768,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[65535,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[65536,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[131071,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[131072,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[262143,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[262144,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[524287,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[524288,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[1048575,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[1048576,1,1,1],order=0","support","1","yes","CUDA"
 "CUDA0","ARGSORT","type=f32,ne=[16,10,10,10],order=0","support","1","yes","CUDA"
 "CUDA0","ARGSORT","type=f32,ne=[60,10,10,10],order=0","support","1","yes","CUDA"
-"CUDA0","ARGSORT","type=f32,ne=[1024,1,1,1],order=0","support","1","yes","CUDA"
-"CUDA0","ARGSORT","type=f32,ne=[16384,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[1023,2,1,3],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[1024,2,1,3],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[1025,2,1,3],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[2047,2,1,3],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[2048,2,1,3],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[2049,2,1,3],order=0","support","1","yes","CUDA"
 "CUDA0","ARGSORT","type=f32,ne=[2,8,8192,1],order=0","support","1","yes","CUDA"
-"CUDA0","ARGSORT","type=f32,ne=[8,1,1,1],order=1","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[3,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[4,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[7,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[8,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[15,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[16,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[31,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[32,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[63,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[64,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[127,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[128,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[255,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[256,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[511,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[512,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[1023,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[1024,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[2047,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[2048,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[4095,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[4096,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[8191,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[8192,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[16383,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[16384,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[32767,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[32768,1,1,1],order=0","support","1","yes","CUDA"
+"CUDA0","ARGSORT","type=f32,ne=[65535,1,1,1],order=0","support","1","yes","CUDA"
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+"CUDA0","TOP_K","type=f32,ne=[1023,2,1,3],k=15,ties=0","support","0","no","CUDA"
+"CUDA0","TOP_K","type=f32,ne=[1024,2,1,3],k=15,ties=0","support","0","no","CUDA"
+"CUDA0","TOP_K","type=f32,ne=[1025,2,1,3],k=15,ties=0","support","0","no","CUDA"
+"CUDA0","TOP_K","type=f32,ne=[16384,1,1,1],k=15,ties=0","support","0","no","CUDA"
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+"CUDA0","TOP_K","type=f32,ne=[2048,2,1,3],k=15,ties=0","support","0","no","CUDA"
+"CUDA0","TOP_K","type=f32,ne=[2049,2,1,3],k=15,ties=0","support","0","no","CUDA"
 "CUDA0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=0","support","1","yes","CUDA"
 "CUDA0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=1","support","1","yes","CUDA"
 "CUDA0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=nearest,flags=none","support","1","yes","CUDA"
@@ -9435,6 +9860,10 @@
 "CUDA0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bicubic,transpose=1","support","1","yes","CUDA"
 "CUDA0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic,flags=none","support","1","yes","CUDA"
 "CUDA0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bicubic,flags=none","support","1","yes","CUDA"
+"CUDA0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=513,transpose=0","support","1","yes","CUDA"
+"CUDA0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=513,transpose=1","support","1","yes","CUDA"
+"CUDA0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=none","support","1","yes","CUDA"
+"CUDA0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear,flags=none","support","1","yes","CUDA"
 "CUDA0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=align_corners","support","1","yes","CUDA"
 "CUDA0","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bilinear,flags=align_corners","support","1","yes","CUDA"
 "CUDA0","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bilinear,flags=align_corners","support","1","yes","CUDA"
@@ -9463,34 +9892,59 @@
 "CUDA0","GROUP_NORM","type=f32,ne=[64,64,320,1],num_groups=32,eps=0.000001","support","1","yes","CUDA"
 "CUDA0","GROUP_NORM","type=f32,ne=[9,9,1280,1],num_groups=32,eps=0.000001","support","1","yes","CUDA"
 "CUDA0","ACC","type=f32,ne_a=[256,17,1,1],ne_b=[256,16,1,1]","support","1","yes","CUDA"
-"CUDA0","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1","support","1","yes","CUDA"
-"CUDA0","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0","support","1","yes","CUDA"
+"CUDA0","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1,circular=0","support","1","yes","CUDA"
+"CUDA0","PAD","type=f32,ne_a=[33,17,2,1],pad_0=4,pad_1=3,circular=1","support","1","yes","CUDA"
+"CUDA0","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0,circular=0","support","1","yes","CUDA"
 "CUDA0","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","1","yes","CUDA"
 "CUDA0","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","1","yes","CUDA"
 "CUDA0","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","1","yes","CUDA"
 "CUDA0","ARANGE","type=f32,start=0.000000,stop=10.000000,step=1.000000","support","1","yes","CUDA"
+"CUDA0","ARANGE","type=f32,start=0.000000,stop=1048576.000000,step=1.000000","support","1","yes","CUDA"
 "CUDA0","TIMESTEP_EMBEDDING","type=f32,ne_a=[2,1,1,1],dim=320,max_period=10000","support","1","yes","CUDA"
 "CUDA0","LEAKY_RELU","type=f32,ne_a=[10,5,4,3],negative_slope=0.100000","support","1","yes","CUDA"
-"CUDA0","CUMSUM","type=f32,ne=[10,5,4,3]","support","0","no","CUDA"
+"CUDA0","CUMSUM","type=f32,ne=[10,5,4,3]","support","1","yes","CUDA"
+"CUDA0","CUMSUM","type=f32,ne=[127,5,4,3]","support","1","yes","CUDA"
+"CUDA0","CUMSUM","type=f32,ne=[128,5,4,3]","support","1","yes","CUDA"
+"CUDA0","CUMSUM","type=f32,ne=[128,128,4,4]","support","1","yes","CUDA"
+"CUDA0","CUMSUM","type=f32,ne=[255,5,4,3]","support","1","yes","CUDA"
+"CUDA0","CUMSUM","type=f32,ne=[256,5,4,3]","support","1","yes","CUDA"
+"CUDA0","CUMSUM","type=f32,ne=[511,5,4,3]","support","1","yes","CUDA"
+"CUDA0","CUMSUM","type=f32,ne=[512,5,4,3]","support","1","yes","CUDA"
+"CUDA0","CUMSUM","type=f32,ne=[1023,5,4,3]","support","1","yes","CUDA"
+"CUDA0","CUMSUM","type=f32,ne=[1024,5,4,3]","support","1","yes","CUDA"
+"CUDA0","CUMSUM","type=f32,ne=[2047,5,4,3]","support","1","yes","CUDA"
+"CUDA0","CUMSUM","type=f32,ne=[2048,5,4,3]","support","1","yes","CUDA"
+"CUDA0","CUMSUM","type=f32,ne=[242004,1,1,1]","support","1","yes","CUDA"
+"CUDA0","CUMSUM","type=f32,ne=[375960,1,1,1]","support","1","yes","CUDA"
 "CUDA0","XIELU","type=f32,ne=[10,5,4,3]","support","0","no","CUDA"
-"CUDA0","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","0","no","CUDA"
-"CUDA0","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","0","no","CUDA"
-"CUDA0","TRI","type=f32,ne=[10,10,4,3],tri_type=1","support","0","no","CUDA"
-"CUDA0","TRI","type=f32,ne=[10,10,4,3],tri_type=0","support","0","no","CUDA"
-"CUDA0","FILL","type=f32,ne=[10,10,4,3],c=0.000000","support","0","no","CUDA"
-"CUDA0","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","0","no","CUDA"
-"CUDA0","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","0","no","CUDA"
-"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","0","no","CUDA"
-"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","0","no","CUDA"
-"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","0","no","CUDA"
-"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[30,30,7,1],ne_rhs=[8,30,7,1]","support","0","no","CUDA"
-"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","0","no","CUDA"
-"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","0","no","CUDA"
+"CUDA0","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","1","yes","CUDA"
+"CUDA0","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","1","yes","CUDA"
+"CUDA0","TRI","type=f32,ne=[10,10,4,3],tri_type=1","support","1","yes","CUDA"
+"CUDA0","TRI","type=f32,ne=[10,10,4,3],tri_type=0","support","1","yes","CUDA"
+"CUDA0","FILL","type=f32,ne=[10,10,4,3],c=0.000000","support","1","yes","CUDA"
+"CUDA0","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","1","yes","CUDA"
+"CUDA0","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","1","yes","CUDA"
+"CUDA0","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","1","yes","CUDA"
+"CUDA0","DIAG","type=f32,ne=[10,1,4,3]","support","1","yes","CUDA"
+"CUDA0","DIAG","type=f32,ne=[79,1,19,13]","support","1","yes","CUDA"
+"CUDA0","DIAG","type=f32,ne=[256,1,8,16]","support","1","yes","CUDA"
+"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","1","yes","CUDA"
+"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","1","yes","CUDA"
+"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","1","yes","CUDA"
+"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[30,30,7,1],ne_rhs=[8,30,7,1]","support","1","yes","CUDA"
+"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","1","yes","CUDA"
+"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","1","yes","CUDA"
 "CUDA0","SOLVE_TRI","type=f32,ne_lhs=[100,100,4,4],ne_rhs=[41,100,4,4]","support","0","no","CUDA"
-"CUDA0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0","support","1","yes","CUDA"
-"CUDA0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0","support","1","yes","CUDA"
-"CUDA0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1","support","0","no","CUDA"
-"CUDA0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1","support","0","no","CUDA"
+"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[31,128,4,4]","support","0","no","CUDA"
+"CUDA0","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[300,64,4,4]","support","0","no","CUDA"
+"CUDA0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=0","support","1","yes","CUDA"
+"CUDA0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=0","support","1","yes","CUDA"
+"CUDA0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=1","support","1","yes","CUDA"
+"CUDA0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=1","support","1","yes","CUDA"
+"CUDA0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=0","support","0","no","CUDA"
+"CUDA0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=0","support","0","no","CUDA"
+"CUDA0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=1","support","0","no","CUDA"
+"CUDA0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=1","support","0","no","CUDA"
 "CUDA0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","CUDA"
 "CUDA0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","CUDA"
 "CUDA0","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","CUDA"

From 2fbe3b7bb7814621477d658a81f0d0cca08761cf Mon Sep 17 00:00:00 2001
From: Aldehir Rojas <hello@alde.dev>
Date: Tue, 9 Dec 2025 17:31:04 -0600
Subject: [PATCH 041/135] common : add parser for ministral/mistral large
 3/devstral 2 (#17713)

---
 common/chat.cpp                               | 132 +++++++++++++++
 ...ralai-Ministral-3-14B-Reasoning-2512.jinja | 126 ++++++++++++++
 tests/test-chat.cpp                           | 154 ++++++++++++++++++
 tools/server/server-common.cpp                |   3 +
 4 files changed, 415 insertions(+)
 create mode 100644 models/templates/mistralai-Ministral-3-14B-Reasoning-2512.jinja

diff --git a/common/chat.cpp b/common/chat.cpp
index 41a5bb42d5..c371edaa5a 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -1,5 +1,6 @@
 #include "chat.h"
 #include "chat-parser.h"
+#include "chat-peg-parser.h"
 #include "common.h"
 #include "json-partial.h"
 #include "json-schema-to-grammar.h"
@@ -150,6 +151,7 @@ struct templates_params {
     common_chat_tool_choice tool_choice;
     json json_schema;
     bool parallel_tool_calls;
+    common_reasoning_format reasoning_format;
     bool stream;
     std::string grammar;
     bool add_generation_prompt = true;
@@ -589,6 +591,16 @@ common_chat_templates_ptr common_chat_templates_init(
             "{%- if false %}");
     }
 
+    // TODO @aldehir : this is a temporary fix, pending Minja changes
+    // Ref: https://github.com/ggml-org/llama.cpp/pull/17713#issuecomment-3631342664
+    if (default_template_src.find("[TOOL_CALLS]") != std::string::npos
+            // search for the error message and patch it
+            && default_template_src.find("if (message['content'] is none or") != std::string::npos) {
+        string_replace_all(default_template_src,
+            "{%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %}",
+            "{%- if false %}");
+    }
+
     std::string token_bos = bos_token_override;
     std::string token_eos = eos_token_override;
     bool add_bos = false;
@@ -987,6 +999,118 @@ static common_chat_params common_chat_params_init_lfm2(const common_chat_templat
     return data;
 }
 
+static common_chat_params common_chat_params_init_ministral_3(const common_chat_template & tmpl, const struct templates_params & inputs) {
+    common_chat_params data;
+
+    // Build up messages to follow the format: https://huggingface.co/mistralai/Ministral-3-14B-Reasoning-2512/blob/main/chat_template.jinja
+    auto adjusted_messages = json::array();
+    for (const auto & msg : inputs.messages) {
+        auto role = msg.value("role", "");
+        if (role != "system" && role != "assistant") {
+            // Only adjust system and assistant messages. Interestingly, the system message may contain thinking.
+            adjusted_messages.push_back(msg);
+            continue;
+        }
+
+        auto content = json::array();
+
+        // If message contains `reasoning_content`, add it as a block of type `thinking`
+        if (msg.contains("reasoning_content") && msg.at("reasoning_content").is_string()) {
+            content.push_back({
+                {"type", "thinking"},
+                {"thinking", msg.at("reasoning_content").get<std::string>()},
+            });
+        }
+
+        // If message contains `content`, add it as a block of type `text`
+        if (msg.contains("content")) {
+            if (msg.at("content").is_string()) {
+                content.push_back({
+                    {"type", "text"},
+                    {"text", msg.at("content").get<std::string>()},
+                });
+            } else if (msg.at("content").is_array()) {
+                auto blocks = msg.at("content");
+                content.insert(content.end(), blocks.begin(), blocks.end());
+            }
+        }
+
+        auto adjusted = msg;
+        adjusted["content"] = content;
+        adjusted.erase("reasoning_content");
+        adjusted_messages.push_back(adjusted);
+    }
+
+    auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
+    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar = true;
+
+    data.prompt = apply(tmpl, inputs, /* messages_override = */ adjusted_messages);
+    data.format = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.preserved_tokens = {
+        "[THINK]",
+        "[/THINK]",
+        "[TOOL_CALLS]",
+        "[ARGS]",
+    };
+
+    auto parser = build_chat_peg_native_parser([&](common_chat_peg_native_builder & p) {
+        auto reasoning = extract_reasoning ? p.optional("[THINK]" + p.reasoning(p.until("[/THINK]")) + "[/THINK]") : p.eps();
+
+        // Response format parser
+        if (inputs.json_schema.is_object() && !inputs.json_schema.empty()) {
+            // Ministral wants to emit json surrounded by code fences
+            return reasoning << "```json" << p.content(p.schema(p.json(), "response-format", inputs.json_schema)) << "```";
+        }
+
+        // Tool call parser
+        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
+            auto tool_choice = p.choice();
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                std::string name = function.at("name");
+                const auto & schema = function.at("parameters");
+
+                tool_choice |= p.rule("tool-" + name,
+                    p.tool_open(p.tool_name(p.literal(name)) + "[ARGS]")
+                    + p.tool_args(p.schema(p.json(), "tool-" + name + "-schema", schema))
+                );
+            });
+
+            auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
+            auto max_calls = inputs.parallel_tool_calls ? -1 : 1;
+            auto tool_calls = p.trigger_rule("tool-call", p.repeat("[TOOL_CALLS]" + tool_choice, min_calls, max_calls));
+
+            return reasoning << p.content(p.until("[TOOL_CALLS]")) << tool_calls;
+        }
+
+        // Content only parser
+        include_grammar = false;
+        return reasoning << p.content(p.rest());
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
+
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto schema = function.at("parameters");
+                builder.resolve_refs(schema);
+            });
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            {COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "[TOOL_CALLS]"}
+        };
+    }
+
+    return data;
+}
+
 static common_chat_params common_chat_params_init_magistral(const common_chat_template & tmpl, const struct templates_params & inputs) {
     common_chat_params data;
     data.prompt = apply(tmpl, inputs);
@@ -2341,6 +2465,7 @@ static common_chat_params common_chat_templates_apply_jinja(
     params.messages = common_chat_msgs_to_json_oaicompat<json>(inputs.messages, /* concat_text= */ !tmpl.original_caps().requires_typed_content);
     params.add_generation_prompt = inputs.add_generation_prompt;
     params.tool_choice = inputs.tool_choice;
+    params.reasoning_format = inputs.reasoning_format;
     params.enable_thinking = inputs.enable_thinking;
     params.grammar = inputs.grammar;
     params.now = inputs.now;
@@ -2504,6 +2629,13 @@ static common_chat_params common_chat_templates_apply_jinja(
         return common_chat_params_init_llama_3_x(tmpl, params, allow_python_tag_builtin_tools);
     }
 
+    // Ministral/Mistral Large 3
+    if (src.find("[SYSTEM_PROMPT]") != std::string::npos &&
+        src.find("[TOOL_CALLS]") != std::string::npos &&
+        src.find("[ARGS]") != std::string::npos) {
+        return common_chat_params_init_ministral_3(tmpl, params);
+    }
+
     if (src.find("[THINK]") != std::string::npos && src.find("[/THINK]") != std::string::npos) {
         return common_chat_params_init_magistral(tmpl, params);
     }
diff --git a/models/templates/mistralai-Ministral-3-14B-Reasoning-2512.jinja b/models/templates/mistralai-Ministral-3-14B-Reasoning-2512.jinja
new file mode 100644
index 0000000000..beb4d612c7
--- /dev/null
+++ b/models/templates/mistralai-Ministral-3-14B-Reasoning-2512.jinja
@@ -0,0 +1,126 @@
+{#- Default system message if no system prompt is passed. #}
+{%- set default_system_message = '# HOW YOU SHOULD THINK AND ANSWER\n\nFirst draft your thinking process (inner monologue) until you arrive at a response. Format your response using Markdown, and use LaTeX for any mathematical equations. Write both your thoughts and the response in the same language as the input.\n\nYour thinking process must follow the template below:[THINK]Your thoughts or/and draft, like working through an exercise on scratch paper. Be as casual and as long as you want until you are confident to generate the response to the user.[/THINK]Here, provide a self-contained response.' %}
+
+{#- Begin of sequence token. #}
+{{- bos_token }}
+
+{#- Handle system prompt if it exists. #}
+{#- System prompt supports text content or text and thinking chunks. #}
+{%- if messages[0]['role'] == 'system' %}
+    {{- '[SYSTEM_PROMPT]' -}}
+    {%- if messages[0]['content'] is string %}
+        {{- messages[0]['content'] -}}
+    {%- else %}        
+        {%- for block in messages[0]['content'] %}
+            {%- if block['type'] == 'text' %}
+                {{- block['text'] }}
+            {%- elif block['type'] == 'thinking' %}
+                {{- '[THINK]' + block['thinking'] + '[/THINK]' }}
+            {%- else %}
+                {{- raise_exception('Only text and thinking chunks are supported in system message contents.') }}
+            {%- endif %}
+        {%- endfor %}
+    {%- endif %}
+    {{- '[/SYSTEM_PROMPT]' -}}
+    {%- set loop_messages = messages[1:] %}
+{%- else %}
+    {%- set loop_messages = messages %}
+    {%- if default_system_message != '' %}
+        {{- '[SYSTEM_PROMPT]' + default_system_message + '[/SYSTEM_PROMPT]' }}
+    {%- endif %}
+{%- endif %}
+
+
+{#- Tools definition #}
+{%- set tools_definition = '' %}
+{%- set has_tools = false %}
+{%- if tools is defined and tools is not none and tools|length > 0 %}
+    {%- set has_tools = true %}
+    {%- set tools_definition = '[AVAILABLE_TOOLS]' + (tools| tojson) + '[/AVAILABLE_TOOLS]' %}
+    {{- tools_definition }}
+{%- endif %}
+
+{#- Checks for alternating user/assistant messages. #}
+{%- set ns = namespace(index=0) %}
+{%- for message in loop_messages %}
+    {%- if message.role == 'user' or (message.role == 'assistant' and (message.tool_calls is not defined or message.tool_calls is none or message.tool_calls | length == 0)) %}
+        {%- if (message['role'] == 'user') != (ns.index % 2 == 0) %}
+            {{- raise_exception('After the optional system message, conversation roles must alternate user and assistant roles except for tool calls and results.') }}
+        {%- endif %}
+        {%- set ns.index = ns.index + 1 %}
+    {%- endif %}
+{%- endfor %}
+
+{#- Handle conversation messages. #}
+{%- for message in loop_messages %}
+
+    {#- User messages supports text content or text and image chunks. #}
+    {%- if message['role'] == 'user' %}
+        {%- if message['content'] is string %}
+            {{- '[INST]' + message['content'] + '[/INST]' }}
+        {%- elif message['content'] | length > 0 %}
+            {{- '[INST]' }}
+            {%- if message['content'] | length == 2 %}
+                {%- set blocks = message['content'] | sort(attribute='type') %}
+            {%- else %}
+                {%- set blocks = message['content'] %}
+            {%- endif %}
+            {%- for block in blocks %}
+                {%- if block['type'] == 'text' %}
+                    {{- block['text'] }}
+                {%- elif block['type'] in ['image', 'image_url'] %}
+                    {{- '[IMG]' }}
+                {%- else %}
+                    {{- raise_exception('Only text, image and image_url chunks are supported in user message content.') }}
+                {%- endif %}
+            {%- endfor %}
+            {{- '[/INST]' }}
+        {%- else %}
+            {{- raise_exception('User message must have a string or a list of chunks in content') }}
+        {%- endif %}
+
+    {#- Assistant messages supports text content or text, image and thinking chunks. #}
+    {%- elif message['role'] == 'assistant' %}
+        {%- if (message['content'] is none or message['content'] == '' or message['content']|length == 0) and (message['tool_calls'] is not defined or message['tool_calls'] is none or message['tool_calls']|length == 0) %}
+            {{- raise_exception('Assistant message must have a string or a list of chunks in content or a list of tool calls.') }}
+        {%- endif %}
+
+        {%- if message['content'] is string and message['content'] != '' %}
+            {{- message['content'] }}
+        {%- elif message['content'] | length > 0 %}
+            {%- for block in message['content'] %}
+                {%- if block['type'] == 'text' %}
+                    {{- block['text'] }}
+                {%- elif block['type'] == 'thinking' %}
+                    {{- '[THINK]' + block['thinking'] + '[/THINK]' }}
+                {%- else %}
+                    {{- raise_exception('Only text and thinking chunks are supported in assistant message contents.') }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+        
+        {%- if message['tool_calls'] is defined and message['tool_calls'] is not none and message['tool_calls']|length > 0 %}
+            {%- for tool in message['tool_calls'] %}
+                {{- '[TOOL_CALLS]' }}
+                {%- set name = tool['function']['name'] %}
+                {%- set arguments = tool['function']['arguments'] %}
+                {%- if arguments is not string %}
+                    {%- set arguments = arguments|tojson|safe %}
+                {%- elif arguments == '' %}
+                    {%- set arguments = '{}' %}
+                {%- endif %}
+                {{- name + '[ARGS]' + arguments }}
+            {%- endfor %}
+        {%- endif %}
+
+        {{- eos_token }}
+
+    {#- Tool messages only supports text content. #}
+    {%- elif message['role'] == 'tool' %}
+        {{- '[TOOL_RESULTS]' + message['content']|string + '[/TOOL_RESULTS]' }}
+
+    {#- Raise exception for unsupported roles. #}
+    {%- else %}
+        {{- raise_exception('Only user, assistant and tool roles are supported, got ' + message['role'] + '.') }}
+    {%- endif %}
+{%- endfor %}
diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp
index f765bda62c..007929f517 100644
--- a/tests/test-chat.cpp
+++ b/tests/test-chat.cpp
@@ -539,6 +539,71 @@ const common_chat_msg message_assist_call_python_lines           = simple_assist
 const common_chat_msg message_assist_call_python_lines_unclosed  = simple_assist_msg("", "", "python", "{\"code\":\"# This is a program:\\nprint('hey')");
 const common_chat_msg message_assist_call_code_interpreter       = simple_assist_msg("", "", "code_interpreter", "{\"code\":\"print('hey')\"}");
 
+// Use for PEG parser implementations
+struct peg_test_case {
+    common_chat_templates_inputs params;
+    std::string input;
+    common_chat_msg expect;
+};
+
+struct make_peg_parser {
+    common_chat_params params_;
+    common_peg_arena arena_;
+
+    make_peg_parser(common_chat_templates * tmpls, const common_chat_templates_inputs & inputs) {
+        params_ = common_chat_templates_apply(tmpls, inputs);
+        arena_.load(params_.parser);
+    }
+
+    common_chat_msg parse(const std::string & msg, bool is_partial) {
+        return common_chat_peg_parse(arena_, msg, is_partial, /* syntax = */ {params_.format});
+    }
+};
+
+static void test_peg_parser(common_chat_templates * tmpls, const std::function<void(peg_test_case &)> & init) {
+    peg_test_case tc;
+    init(tc);
+    if (tc.params.messages.empty()) {
+        tc.params.messages = {message_user};
+    }
+    if (tc.expect.role.empty()) {
+        tc.expect.role = "assistant";
+    }
+
+    auto parser = make_peg_parser(tmpls, tc.params);
+
+    common_chat_msg msg_accum;
+    common_chat_msg msg_prev;
+    msg_accum.role = msg_prev.role = "assistant";
+
+    for (size_t i = 1; i <= tc.input.size(); ++i) {
+        auto is_partial = i < tc.input.size();
+        common_chat_msg msg_current = parser.parse(tc.input.substr(0, i), is_partial);
+
+        for (const auto & diff : common_chat_msg_diff::compute_diffs(msg_prev, msg_current)) {
+            if (!diff.reasoning_content_delta.empty()) {
+                msg_accum.reasoning_content += diff.reasoning_content_delta;
+            }
+            if (!diff.content_delta.empty()) {
+                msg_accum.content += diff.content_delta;
+            }
+            if (diff.tool_call_index != std::string::npos) {
+                if (!diff.tool_call_delta.name.empty()) {
+                    msg_accum.tool_calls.push_back({diff.tool_call_delta.name, "", ""});
+                }
+                if (!diff.tool_call_delta.arguments.empty()) {
+                    msg_accum.tool_calls.back().arguments += diff.tool_call_delta.arguments;
+                }
+            }
+        }
+        assert_msg_equals(msg_current, msg_accum, true);
+        msg_prev = msg_current;
+    }
+
+    assert_msg_equals(tc.expect, parser.parse(tc.input, false), true);
+    assert_msg_equals(tc.expect, msg_accum, true);
+}
+
 static void test_msgs_oaicompat_json_conversion() {
     printf("[%s]\n", __func__);
     std::vector<common_chat_msg> msgs{
@@ -3434,7 +3499,95 @@ Hey there!<|im_end|>
         auto grammar = build_grammar(params.grammar);
         GGML_ASSERT(grammar && "Failed to build Qwen3-Coder grammar with union types");
     }
+}
 
+static void test_template_output_peg_parsers() {
+    printf("[%s]\n", __func__);
+
+    // JSON schemas
+    const char * invoice_schema = R"({
+        "type": "object",
+        "properties": {
+            "amount": {"type": "number"},
+            "date": {"type": "string"}
+        }
+    })";
+
+    {
+        // Ministral-3-14B-Reasoning-2512
+        auto tmpls = read_templates("models/templates/mistralai-Ministral-3-14B-Reasoning-2512.jinja");
+
+        // Test basic message
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input = "Hello, world!\nWhat's up?";
+            t.expect = message_assist;
+        });
+
+        // Test basic message and reasoning with reasoning_format = none
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input = "[THINK]I'm\nthinking[/THINK]Hello, world!\nWhat's up?";
+            t.expect.content = "[THINK]I'm\nthinking[/THINK]Hello, world!\nWhat's up?";
+        });
+
+        // Test basic message and reasoning with reasoning_format = auto
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input = "[THINK]I'm\nthinking[/THINK]Hello, world!\nWhat's up?";
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+
+            t.expect = message_assist_thoughts;
+        });
+
+        // Test tool call
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input = R"([TOOL_CALLS]special_function[ARGS]{"arg1":1})";
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+            t.params.tools = {special_function_tool};
+
+            t.expect = message_assist_call;
+        });
+
+        // Test tool call with reasoning
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input = "[THINK]I'm\nthinking[/THINK]"
+                      R"([TOOL_CALLS]special_function[ARGS]{"arg1":1})";
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+            t.params.tools = {special_function_tool};
+
+            t.expect = message_assist_call_thoughts;
+        });
+
+        // Test parallel tool calls
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input = R"([TOOL_CALLS]special_function[ARGS]{"arg1": 1})"
+                      R"([TOOL_CALLS]special_function_with_opt[ARGS]{"arg1": 1, "arg2": 2})";
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+            t.params.parallel_tool_calls = true;
+            t.params.tools = {special_function_tool, special_function_tool_with_optional_param};
+
+            t.expect.tool_calls = {{
+                /* .name = */      "special_function",
+                /* .arguments = */ R"({"arg1": 1})",
+                /* .id = */        {},
+            }, {
+                /* .name = */      "special_function_with_opt",
+                /* .arguments = */ R"({"arg1": 1, "arg2": 2})",
+                /* .id = */        {},
+            }};
+        });
+
+        // Test response format
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input = "[THINK]I need to output the invoice details in JSON[/THINK]"
+                      "```json\n"
+                      R"({"amount": 123.45, "date": "2025-12-03"})"
+                      "\n```";
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+            t.params.json_schema = invoice_schema;
+
+            t.expect.reasoning_content = "I need to output the invoice details in JSON";
+            t.expect.content =R"({"amount": 123.45, "date": "2025-12-03"})";
+        });
+    }
 }
 
 static void test_msg_diffs_compute() {
@@ -3560,6 +3713,7 @@ int main(int argc, char ** argv) {
             test_msgs_oaicompat_json_conversion();
             test_tools_oaicompat_json_conversion();
             test_template_output_parsers();
+            test_template_output_peg_parsers();
             std::cout << "\n[chat] All tests passed!" << '\n';
         }
         return 0;
diff --git a/tools/server/server-common.cpp b/tools/server/server-common.cpp
index b403864e0e..ab6b3aa7ce 100644
--- a/tools/server/server-common.cpp
+++ b/tools/server/server-common.cpp
@@ -972,6 +972,9 @@ json oaicompat_chat_params_parse(
     inputs.parallel_tool_calls   = json_value(body, "parallel_tool_calls", false);
     inputs.add_generation_prompt = json_value(body, "add_generation_prompt", true);
     inputs.reasoning_format      = opt.reasoning_format;
+    if (body.contains("reasoning_format")) {
+        inputs.reasoning_format = common_reasoning_format_from_name(body.at("reasoning_format").get<std::string>());
+    }
     inputs.enable_thinking       = opt.enable_thinking;
     if (!inputs.tools.empty() && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
         if (body.contains("grammar")) {

From 2e9eab80c26d4b7e64f27c48b5af683c35c28742 Mon Sep 17 00:00:00 2001
From: Neo Zhang Jianyu <jianyu.zhang@intel.com>
Date: Wed, 10 Dec 2025 16:59:57 +0800
Subject: [PATCH 042/135] fix softmax for iGPU (#17838)

---
 ggml/src/ggml-sycl/softmax.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-sycl/softmax.cpp b/ggml/src/ggml-sycl/softmax.cpp
index 83b7c71b66..b41124acc1 100644
--- a/ggml/src/ggml-sycl/softmax.cpp
+++ b/ggml/src/ggml-sycl/softmax.cpp
@@ -277,7 +277,7 @@ static void soft_max_f32_sycl(const float *x, const T *mask,
     const int id       = get_current_device_id();
     const size_t smpbo = ggml_sycl_info().devices[id].smpbo;
 
-    if (nbytes_shared <= smpbo) {
+    if (nbytes_shared <= smpbo && ncols_x <= max_block_size) {
         launch_soft_max_kernels<32, 64, 128, 256, 512, 1024, 2048, 4096>(
             x, mask, sinks, dst, params, stream, block_dims, block_nums,
             nbytes_shared);

From 9e79b0116ebb6ff4a1ef1b42a7f2f64182ec4f10 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Wed, 10 Dec 2025 10:26:22 +0100
Subject: [PATCH 043/135] convert: allow using quantized Mistral weight
 (#17889)

* convert: allow using quantized Mistral weight

* data_torch.ndim

* update dequant fn

Co-authored-by: compilade <compilade@users.noreply.github.com>

---------

Co-authored-by: compilade <compilade@users.noreply.github.com>
---
 convert_hf_to_gguf.py | 28 ++++++++++++++++++++++++----
 1 file changed, 24 insertions(+), 4 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 2cc2a38823..867bc90531 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -383,6 +383,17 @@ class ModelBase:
                         s = self.model_tensors[name]
                         self.model_tensors[weight_name] = lambda w=w, s=s, bs=block_size: dequant_simple(w(), s(), bs)
                         tensors_to_remove.append(name)
+                    if name.endswith(".activation_scale"):  # unused
+                        tensors_to_remove.append(name)
+                    # mistral format
+                    if name.endswith(".qscale_weight"):
+                        weight_name = name.removesuffix("qscale_weight") + "weight"
+                        w = self.model_tensors[weight_name]
+                        s = self.model_tensors[name]
+                        self.model_tensors[weight_name] = lambda w=w, s=s, bs=block_size: dequant_simple(w(), s(), bs)
+                        tensors_to_remove.append(name)
+                    if name.endswith(".qscale_act"):
+                        tensors_to_remove.append(name)
             elif quant_method == "gptq":
                 for name in self.model_tensors.keys():
                     if name.endswith(".qweight"):
@@ -2854,13 +2865,10 @@ class Mistral3Model(LlamaModel):
             self.gguf_writer.add_attn_temperature_scale(rope_params["llama_4_scaling_beta"])
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
-        # TODO: probably not worth supporting quantized weight, as official BF16 is also available
-        if name.endswith("weight_scale_inv"):
-            raise ValueError("This is a quantized weight, please use BF16 weight instead")
-
         name = name.replace("language_model.", "")
         if "multi_modal_projector" in name or "vision_tower" in name:
             return []
+
         return super().modify_tensors(data_torch, name, bid)
 
 
@@ -9898,6 +9906,18 @@ class MistralModel(LlamaModel):
             self.gguf_writer.add_architecture()
             self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
 
+    def dequant_model(self):
+        # transform quantization config into HF format
+        quant_config = self.hparams.get("quantization")
+        if quant_config is not None:
+            assert quant_config["qformat_weight"] == "fp8_e4m3"
+            self.hparams["quantization_config"] = {
+                "activation_scheme": "static",
+                "quant_method": "fp8",
+                "weight_block_size": None,
+            }
+        return super().dequant_model()
+
     @staticmethod
     def get_community_chat_template(vocab: MistralVocab, templates_dir: Path, is_mistral_format: bool):
         assert TokenizerVersion is not None and Tekkenizer is not None and SentencePieceTokenizer is not None, _mistral_import_error_msg

From 17f7f4baad8b3a716ee139da7bb56ae984e8c0fa Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Wed, 10 Dec 2025 12:39:56 +0100
Subject: [PATCH 044/135] CUDA: fix unpadded strides in MMA FA kernel (#17891)

---
 ggml/src/ggml-cuda/fattn-mma-f16.cuh | 37 +++++++++++++---------------
 ggml/src/ggml-cuda/fattn.cu          | 16 +++++++++++-
 2 files changed, 32 insertions(+), 21 deletions(-)

diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
index ade0773dad..d51537f7d0 100644
--- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh
+++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
@@ -955,22 +955,11 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
             (K_h2 + int64_t(kb0)*nbatch_fa*stride_K, tile_K, nbatch_K2, stride_K, k_VKQ_sup);
     }
 
-    for (; kb0 < kb0_stop-1; ++kb0) {
-        constexpr bool last_iter = false;
-        constexpr bool oob_check = false;
-        constexpr int  k_VKQ_sup = nbatch_fa;
-        flash_attn_ext_f16_iter
-            <DKQ, DV, ncols1, ncols2, nwarps, use_logit_softcap, mla, needs_fixup, is_fixup, last_iter, oob_check,
-             T_A_KQ, T_B_KQ, T_C_KQ, T_A_VKQ, T_B_VKQ, T_C_VKQ>
-            (Q_f2, K_h2, V_h2, mask_h, dstk, dstk_fixup, scale, slope, logit_softcap,
-             ne01, ne02, stride_K, stride_V, stride_mask, tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C,
-             KQ_max, KQ_rowsum, jt, kb0, k_VKQ_sup);
-    }
     // kb0_start is always < kb0_stop so the last iter can be executed unconditionally.
     if constexpr (ncols2 == 1) {
-        if (ne11 % nbatch_fa == 0) {
-            constexpr bool last_iter = true;
-            constexpr bool oob_check = false;
+        constexpr bool oob_check = true;
+        for (; kb0 < kb0_stop-1; ++kb0) {
+            constexpr bool last_iter = false;
             constexpr int  k_VKQ_sup = nbatch_fa;
             flash_attn_ext_f16_iter
                 <DKQ, DV, ncols1, ncols2, nwarps, use_logit_softcap, mla, needs_fixup, is_fixup, last_iter, oob_check,
@@ -978,10 +967,20 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
                 (Q_f2, K_h2, V_h2, mask_h, dstk, dstk_fixup, scale, slope, logit_softcap,
                  ne01, ne02, stride_K, stride_V, stride_mask, tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C,
                  KQ_max, KQ_rowsum, jt, kb0, k_VKQ_sup);
-        } else {
-            constexpr bool last_iter = true;
-            constexpr bool oob_check = true;
-            const     int  k_VKQ_sup = ne11 - kb0*nbatch_fa;
+        }
+        constexpr bool last_iter = true;
+        const     int  k_VKQ_sup = ne11 - kb0*nbatch_fa;
+        flash_attn_ext_f16_iter
+            <DKQ, DV, ncols1, ncols2, nwarps, use_logit_softcap, mla, needs_fixup, is_fixup, last_iter, oob_check,
+              T_A_KQ, T_B_KQ, T_C_KQ, T_A_VKQ, T_B_VKQ, T_C_VKQ>
+            (Q_f2, K_h2, V_h2, mask_h, dstk, dstk_fixup, scale, slope, logit_softcap,
+             ne01, ne02, stride_K, stride_V, stride_mask, tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C,
+             KQ_max, KQ_rowsum, jt, kb0, k_VKQ_sup);
+    } else {
+        constexpr bool oob_check = false;
+        for (; kb0 < kb0_stop-1; ++kb0) {
+            constexpr bool last_iter = false;
+            constexpr int  k_VKQ_sup = nbatch_fa;
             flash_attn_ext_f16_iter
                 <DKQ, DV, ncols1, ncols2, nwarps, use_logit_softcap, mla, needs_fixup, is_fixup, last_iter, oob_check,
                  T_A_KQ, T_B_KQ, T_C_KQ, T_A_VKQ, T_B_VKQ, T_C_VKQ>
@@ -989,9 +988,7 @@ static __device__ __forceinline__ void flash_attn_ext_f16_process_tile(
                  ne01, ne02, stride_K, stride_V, stride_mask, tile_Q, tile_K, tile_V, tile_mask, Q_B, VKQ_C,
                  KQ_max, KQ_rowsum, jt, kb0, k_VKQ_sup);
         }
-    } else {
         constexpr bool last_iter = true;
-        constexpr bool oob_check = false;
         constexpr int  k_VKQ_sup = nbatch_fa;
         flash_attn_ext_f16_iter
             <DKQ, DV, ncols1, ncols2, nwarps, use_logit_softcap, mla, needs_fixup, is_fixup, last_iter, oob_check,
diff --git a/ggml/src/ggml-cuda/fattn.cu b/ggml/src/ggml-cuda/fattn.cu
index dec01ff8ad..0155406665 100644
--- a/ggml/src/ggml-cuda/fattn.cu
+++ b/ggml/src/ggml-cuda/fattn.cu
@@ -36,12 +36,26 @@ static void ggml_cuda_flash_attn_ext_mma_f16_switch_ncols2(ggml_backend_cuda_con
     const ggml_tensor * KQV  = dst;
     const ggml_tensor * Q    = dst->src[0];
     const ggml_tensor * K    = dst->src[1];
+    const ggml_tensor * V    = dst->src[2];
     const ggml_tensor * mask = dst->src[3];
 
     float max_bias = 0.0f;
     memcpy(&max_bias, (const float *) KQV->op_params + 1, sizeof(float));
 
-    const bool use_gqa_opt = mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0;
+    // Edge cases like no mask, ALiBi, unpadded K/V, or misaligned addresses for large data transfers
+    //     are put into the template specialization without GQA optimizations.
+    bool use_gqa_opt = mask && max_bias == 0.0f && K->ne[1] % FATTN_KQ_STRIDE == 0;
+    for (const ggml_tensor * t : {Q, K, V, mask}) {
+        if (t == nullptr) {
+            continue;
+        }
+        for (size_t i = 1; i < GGML_MAX_DIMS; ++i) {
+            if (t->nb[i] % 16 != 0) {
+                use_gqa_opt = false;
+                break;
+            }
+        }
+    }
 
     GGML_ASSERT(Q->ne[2] % K->ne[2] == 0);
     const int gqa_ratio = Q->ne[2] / K->ne[2];

From 2d2e1030e3183f7d86f0f194297cd9a75aa707cf Mon Sep 17 00:00:00 2001
From: lhez <lih@qti.qualcomm.com>
Date: Wed, 10 Dec 2025 06:20:00 -0800
Subject: [PATCH 045/135] docs : update opencl ops (#17904)

---
 docs/ops.md         |    32 +-
 docs/ops/OpenCL.csv | 19640 ++++++++++++++++++++++++++++++++----------
 2 files changed, 15146 insertions(+), 4526 deletions(-)

diff --git a/docs/ops.md b/docs/ops.md
index 6ede4a4f64..43163b39ac 100644
--- a/docs/ops.md
+++ b/docs/ops.md
@@ -16,12 +16,12 @@ Legend:
 |-----------|------|------|------|------|------|------|------|------|------|------|------|
 |                              ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
 |                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
@@ -39,13 +39,13 @@ Legend:
 |                           CUMSUM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                             DIAG | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                    DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ |
 |                              EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                            EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                             FILL | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
+|                   FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | ❌ | 🟡 | ❌ | ❌ | ❌ |
 |                            FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
 |                GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                            GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
@@ -65,8 +65,8 @@ Legend:
 |                          L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
 |                              LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
-|                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
 |                       MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                              NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
@@ -75,7 +75,7 @@ Legend:
 |                   OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                     OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                         OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | ❌ |
-|                              PAD | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | ❌ |
+|                              PAD | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                   PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ |
 |                          POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
@@ -84,7 +84,7 @@ Legend:
 |                      REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                         RMS_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                 RMS_NORM_MUL_ADD | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ |
+|                 RMS_NORM_MUL_ADD | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                             ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                        ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
@@ -104,20 +104,20 @@ Legend:
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
-|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                         SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                         SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
 |                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | ❌ |
+|                              SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                              SUM | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                         SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | ❌ |
+|                         SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ |
+|                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ❌ | ❌ |
 |                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
 |                              TRI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                            TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                          UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ |
 |                            XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ |
diff --git a/docs/ops/OpenCL.csv b/docs/ops/OpenCL.csv
index df352e1ea1..997d8a4206 100644
--- a/docs/ops/OpenCL.csv
+++ b/docs/ops/OpenCL.csv
@@ -27,8 +27,20 @@
 "OpenCL","HARDSIGMOID","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
 "OpenCL","EXP","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL"
 "OpenCL","EXP","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
+"OpenCL","EXPM1","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL"
+"OpenCL","EXPM1","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
+"OpenCL","SOFTPLUS","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL"
+"OpenCL","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
 "OpenCL","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL"
 "OpenCL","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
+"OpenCL","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL"
+"OpenCL","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
+"OpenCL","CEIL","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL"
+"OpenCL","CEIL","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
+"OpenCL","ROUND","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL"
+"OpenCL","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
+"OpenCL","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL"
+"OpenCL","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
 "OpenCL","ABS","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
 "OpenCL","ABS","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
 "OpenCL","SGN","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
@@ -57,8 +69,20 @@
 "OpenCL","HARDSIGMOID","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
 "OpenCL","EXP","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
 "OpenCL","EXP","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
+"OpenCL","EXPM1","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
+"OpenCL","EXPM1","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
+"OpenCL","SOFTPLUS","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
+"OpenCL","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
 "OpenCL","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
 "OpenCL","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
+"OpenCL","FLOOR","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
+"OpenCL","FLOOR","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
+"OpenCL","CEIL","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
+"OpenCL","CEIL","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
+"OpenCL","ROUND","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
+"OpenCL","ROUND","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
+"OpenCL","TRUNC","type=f16,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
+"OpenCL","TRUNC","type=f16,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
 "OpenCL","ABS","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL"
 "OpenCL","ABS","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
 "OpenCL","SGN","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL"
@@ -87,8 +111,20 @@
 "OpenCL","HARDSIGMOID","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
 "OpenCL","EXP","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL"
 "OpenCL","EXP","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
+"OpenCL","EXPM1","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL"
+"OpenCL","EXPM1","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
+"OpenCL","SOFTPLUS","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL"
+"OpenCL","SOFTPLUS","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
 "OpenCL","GELU_ERF","type=f32,ne_a=[128,2,2,2],v=0","support","1","yes","OpenCL"
 "OpenCL","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=0","support","1","yes","OpenCL"
+"OpenCL","FLOOR","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL"
+"OpenCL","FLOOR","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
+"OpenCL","CEIL","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL"
+"OpenCL","CEIL","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
+"OpenCL","ROUND","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL"
+"OpenCL","ROUND","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
+"OpenCL","TRUNC","type=f32,ne_a=[128,2,2,2],v=0","support","0","no","OpenCL"
+"OpenCL","TRUNC","type=f32,ne_a=[5,7,11,13],v=0","support","0","no","OpenCL"
 "OpenCL","ABS","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
 "OpenCL","ABS","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
 "OpenCL","SGN","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
@@ -117,8 +153,20 @@
 "OpenCL","HARDSIGMOID","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
 "OpenCL","EXP","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
 "OpenCL","EXP","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
+"OpenCL","EXPM1","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
+"OpenCL","EXPM1","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
+"OpenCL","SOFTPLUS","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
+"OpenCL","SOFTPLUS","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
 "OpenCL","GELU_ERF","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
 "OpenCL","GELU_ERF","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
+"OpenCL","FLOOR","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
+"OpenCL","FLOOR","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
+"OpenCL","CEIL","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
+"OpenCL","CEIL","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
+"OpenCL","ROUND","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
+"OpenCL","ROUND","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
+"OpenCL","TRUNC","type=f32,ne_a=[128,2,2,2],v=1","support","0","no","OpenCL"
+"OpenCL","TRUNC","type=f32,ne_a=[5,7,11,13],v=1","support","0","no","OpenCL"
 "OpenCL","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=0","support","1","yes","OpenCL"
 "OpenCL","REGLU","type=f16,ne_a=[5,7,11,13],v=0,swapped=0","support","1","yes","OpenCL"
 "OpenCL","REGLU","type=f16,ne_a=[128,2,2,2],v=0,swapped=1","support","1","yes","OpenCL"
@@ -239,99 +287,117 @@
 "OpenCL","GEGLU_QUICK","type=f32,ne_a=[5,7,11,13],v=1,swapped=1","support","1","yes","OpenCL"
 "OpenCL","GEGLU_QUICK","type=f32,ne_a=[128,2,2,2],v=1,split","support","1","yes","OpenCL"
 "OpenCL","GEGLU_QUICK","type=f32,ne_a=[5,7,11,13],v=1,split","support","1","yes","OpenCL"
-"OpenCL","GET_ROWS","type=f32,n=1,m=8,r=2,b=1,v=0","support","1","yes","OpenCL"
-"OpenCL","GET_ROWS","type=f32,n=256,m=5,r=4,b=1,v=0","support","1","yes","OpenCL"
-"OpenCL","GET_ROWS","type=f32,n=256,m=5,r=4,b=1,v=1","support","1","yes","OpenCL"
-"OpenCL","GET_ROWS","type=f32,n=256,m=5,r=4,b=7,v=0","support","1","yes","OpenCL"
-"OpenCL","GET_ROWS","type=f32,n=256,m=5,r=4,b=7,v=1","support","1","yes","OpenCL"
-"OpenCL","GET_ROWS","type=f16,n=256,m=5,r=4,b=1,v=0","support","1","yes","OpenCL"
-"OpenCL","GET_ROWS","type=f16,n=256,m=5,r=4,b=1,v=1","support","1","yes","OpenCL"
-"OpenCL","GET_ROWS","type=f16,n=256,m=5,r=4,b=7,v=0","support","1","yes","OpenCL"
-"OpenCL","GET_ROWS","type=f16,n=256,m=5,r=4,b=7,v=1","support","1","yes","OpenCL"
-"OpenCL","GET_ROWS","type=bf16,n=256,m=5,r=4,b=1,v=0","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=bf16,n=256,m=5,r=4,b=1,v=1","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=bf16,n=256,m=5,r=4,b=7,v=0","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=bf16,n=256,m=5,r=4,b=7,v=1","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q4_0,n=256,m=5,r=4,b=1,v=0","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q4_0,n=256,m=5,r=4,b=1,v=1","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q4_0,n=256,m=5,r=4,b=7,v=0","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q4_0,n=256,m=5,r=4,b=7,v=1","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q4_1,n=256,m=5,r=4,b=1,v=0","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q4_1,n=256,m=5,r=4,b=1,v=1","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q4_1,n=256,m=5,r=4,b=7,v=0","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q4_1,n=256,m=5,r=4,b=7,v=1","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q5_0,n=256,m=5,r=4,b=1,v=0","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q5_0,n=256,m=5,r=4,b=1,v=1","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q5_0,n=256,m=5,r=4,b=7,v=0","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q5_0,n=256,m=5,r=4,b=7,v=1","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q5_1,n=256,m=5,r=4,b=1,v=0","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q5_1,n=256,m=5,r=4,b=1,v=1","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q5_1,n=256,m=5,r=4,b=7,v=0","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q5_1,n=256,m=5,r=4,b=7,v=1","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q8_0,n=256,m=5,r=4,b=1,v=0","support","0","no","OpenCL"
-"OpenCL","GET_ROWS","type=q8_0,n=256,m=5,r=4,b=1,v=1","support","0","no","OpenCL"
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+"OpenCL","GET_ROWS","type=iq4_nl,n=256,m=5,r=4,be1=7,be2=1,v=0","support","0","no","OpenCL"
+"OpenCL","GET_ROWS","type=iq4_nl,n=256,m=5,r=4,be1=7,be2=1,v=1","support","0","no","OpenCL"
+"OpenCL","GET_ROWS","type=iq3_s,n=256,m=5,r=4,be1=1,be2=1,v=0","support","0","no","OpenCL"
+"OpenCL","GET_ROWS","type=iq3_s,n=256,m=5,r=4,be1=1,be2=1,v=1","support","0","no","OpenCL"
+"OpenCL","GET_ROWS","type=iq3_s,n=256,m=5,r=4,be1=7,be2=1,v=0","support","0","no","OpenCL"
+"OpenCL","GET_ROWS","type=iq3_s,n=256,m=5,r=4,be1=7,be2=1,v=1","support","0","no","OpenCL"
+"OpenCL","GET_ROWS","type=iq4_xs,n=256,m=5,r=4,be1=1,be2=1,v=0","support","0","no","OpenCL"
+"OpenCL","GET_ROWS","type=iq4_xs,n=256,m=5,r=4,be1=1,be2=1,v=1","support","0","no","OpenCL"
+"OpenCL","GET_ROWS","type=iq4_xs,n=256,m=5,r=4,be1=7,be2=1,v=0","support","0","no","OpenCL"
+"OpenCL","GET_ROWS","type=iq4_xs,n=256,m=5,r=4,be1=7,be2=1,v=1","support","0","no","OpenCL"
+"OpenCL","GET_ROWS","type=i32,n=256,m=5,r=4,be1=1,be2=1,v=0","support","0","no","OpenCL"
+"OpenCL","GET_ROWS","type=i32,n=256,m=5,r=4,be1=1,be2=1,v=1","support","0","no","OpenCL"
+"OpenCL","GET_ROWS","type=i32,n=256,m=5,r=4,be1=7,be2=1,v=0","support","0","no","OpenCL"
+"OpenCL","GET_ROWS","type=i32,n=256,m=5,r=4,be1=7,be2=1,v=1","support","0","no","OpenCL"
 "OpenCL","GET_ROWS_BACK","type=f32,n=1,m=8,r=2,b=1,v=0","support","0","no","OpenCL"
 "OpenCL","GET_ROWS_BACK","type=f32,n=256,m=5,r=4,b=1,v=0","support","0","no","OpenCL"
 "OpenCL","GET_ROWS_BACK","type=f32,n=256,m=5,r=4,b=1,v=1","support","0","no","OpenCL"
@@ -349,6 +415,8 @@
 "OpenCL","GET_ROWS_BACK","type=q5_1,n=256,m=5,r=4,b=1,v=1","support","0","no","OpenCL"
 "OpenCL","GET_ROWS_BACK","type=q8_0,n=256,m=5,r=4,b=1,v=0","support","0","no","OpenCL"
 "OpenCL","GET_ROWS_BACK","type=q8_0,n=256,m=5,r=4,b=1,v=1","support","0","no","OpenCL"
+"OpenCL","GET_ROWS_BACK","type=mxfp4,n=256,m=5,r=4,b=1,v=0","support","0","no","OpenCL"
+"OpenCL","GET_ROWS_BACK","type=mxfp4,n=256,m=5,r=4,b=1,v=1","support","0","no","OpenCL"
 "OpenCL","GET_ROWS_BACK","type=q2_K,n=256,m=5,r=4,b=1,v=0","support","0","no","OpenCL"
 "OpenCL","GET_ROWS_BACK","type=q2_K,n=256,m=5,r=4,b=1,v=1","support","0","no","OpenCL"
 "OpenCL","GET_ROWS_BACK","type=q3_K,n=256,m=5,r=4,b=1,v=0","support","0","no","OpenCL"
@@ -379,295 +447,309 @@
 "OpenCL","GET_ROWS_BACK","type=iq4_xs,n=256,m=5,r=4,b=1,v=1","support","0","no","OpenCL"
 "OpenCL","GET_ROWS_BACK","type=i32,n=256,m=5,r=4,b=1,v=0","support","0","no","OpenCL"
 "OpenCL","GET_ROWS_BACK","type=i32,n=256,m=5,r=4,b=1,v=1","support","0","no","OpenCL"
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-"OpenCL","SET_ROWS","type=f32,ne=[3,3,1,1],nr23=[2,3],r=2,v=0","support","1","yes","OpenCL"
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@@ -883,6 +965,2057 @@
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+"OpenCL","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[20,20,10,3],ne_kernel=[3,3,3,3],IC=3,s0=3,s1=3,s2=3,p0=3,p1=3,p2=3,d0=1,d1=3,d2=1,v=0","support","0","no","OpenCL"
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@@ -2455,6 +4588,264 @@
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+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","OpenCL"
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+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f32","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f32","support","0","no","OpenCL"
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+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=1,ID=18,IH=22,IW=20,OC=1,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","OpenCL"
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+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=0,p1=0,p2=0,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=3,KW=3,s0=2,s1=2,s2=2,p0=1,p1=1,p2=1,d0=2,d1=2,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=2,IC=3,ID=18,IH=22,IW=20,OC=4,KD=3,KH=1,KW=5,s0=2,s1=1,s2=1,p0=2,p1=0,p2=1,d0=1,d1=1,d2=2,type_kernel=f16","support","0","no","OpenCL"
+"OpenCL","CONV_3D","N=1,IC=4,ID=8,IH=8,IW=8,OC=8,KD=1,KH=1,KW=1,s0=1,s1=1,s2=1,p0=0,p1=0,p2=0,d0=1,d1=1,d2=1,type_kernel=f16","support","0","no","OpenCL"
 "OpenCL","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=1,p0=0,d0=1","support","0","no","OpenCL"
 "OpenCL","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=2,p0=0,d0=1","support","0","no","OpenCL"
 "OpenCL","CONV_TRANSPOSE_1D","ne_input=[1,1,1,1],ne_kernel=[1,1,1,1],s0=3,p0=0,d0=1","support","0","no","OpenCL"
@@ -2573,9 +4964,11 @@
 "OpenCL","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","0","no","OpenCL"
 "OpenCL","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","OpenCL"
 "OpenCL","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","OpenCL"
+"OpenCL","CONV_TRANSPOSE_2D","ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","OpenCL"
 "OpenCL","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","0","no","OpenCL"
 "OpenCL","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","0","no","OpenCL"
 "OpenCL","ARGMAX","type=f32,ne=[32,1,1,1]","support","0","no","OpenCL"
+"OpenCL","ARGMAX","type=f32,ne=[32,513,1,1]","support","0","no","OpenCL"
 "OpenCL","ARGMAX","type=f32,ne=[100,10,1,1]","support","0","no","OpenCL"
 "OpenCL","ARGMAX","type=f32,ne=[1024,10,1,1]","support","0","no","OpenCL"
 "OpenCL","ARGMAX","type=f32,ne=[1024,12,1,1]","support","0","no","OpenCL"
@@ -2621,498 +5014,563 @@
 "OpenCL","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=1","support","0","no","OpenCL"
 "OpenCL","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=2","support","0","no","OpenCL"
 "OpenCL","SET","type_src=i32,type_dst=i32,ne=[6,5,4,3],dim=3","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=f32,ne=[1,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=f32,ne=[2,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=f32,ne=[2,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=f32,ne=[2,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=f32,ne=[3,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=f32,ne=[3,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=f32,ne=[3,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=f16,ne=[1,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=f16,ne=[1,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=f16,ne=[1,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=f16,ne=[2,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=f16,ne=[2,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=f16,ne=[2,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=f16,ne=[3,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=f16,ne=[3,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=f16,ne=[3,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=bf16,ne=[1,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=bf16,ne=[1,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=bf16,ne=[1,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=bf16,ne=[2,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=bf16,ne=[2,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=bf16,ne=[2,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=bf16,ne=[3,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=bf16,ne=[3,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=bf16,ne=[3,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_0,type_dst=q4_0,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_0,type_dst=q4_0,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_0,type_dst=q4_0,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_0,type_dst=q4_0,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_0,type_dst=q4_0,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_0,type_dst=q4_0,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_0,type_dst=q4_0,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_0,type_dst=q4_0,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_0,type_dst=q4_0,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_1,type_dst=q4_1,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_1,type_dst=q4_1,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_1,type_dst=q4_1,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_1,type_dst=q4_1,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_1,type_dst=q4_1,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_1,type_dst=q4_1,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_1,type_dst=q4_1,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_1,type_dst=q4_1,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_1,type_dst=q4_1,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_0,type_dst=q5_0,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_0,type_dst=q5_0,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_0,type_dst=q5_0,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_0,type_dst=q5_0,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_0,type_dst=q5_0,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_0,type_dst=q5_0,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_0,type_dst=q5_0,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_0,type_dst=q5_0,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_0,type_dst=q5_0,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_1,type_dst=q5_1,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_1,type_dst=q5_1,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_1,type_dst=q5_1,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_1,type_dst=q5_1,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_1,type_dst=q5_1,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_1,type_dst=q5_1,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_1,type_dst=q5_1,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_1,type_dst=q5_1,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_1,type_dst=q5_1,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q8_0,type_dst=q8_0,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q8_0,type_dst=q8_0,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q8_0,type_dst=q8_0,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q8_0,type_dst=q8_0,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q8_0,type_dst=q8_0,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q8_0,type_dst=q8_0,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q8_0,type_dst=q8_0,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q2_K,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q2_K,type_dst=q2_K,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q2_K,type_dst=q2_K,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q2_K,type_dst=q2_K,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q2_K,type_dst=q2_K,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q2_K,type_dst=q2_K,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q2_K,type_dst=q2_K,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q3_K,type_dst=q3_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q3_K,type_dst=q3_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q3_K,type_dst=q3_K,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q3_K,type_dst=q3_K,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q3_K,type_dst=q3_K,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q3_K,type_dst=q3_K,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q3_K,type_dst=q3_K,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q3_K,type_dst=q3_K,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q3_K,type_dst=q3_K,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_K,type_dst=q4_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_K,type_dst=q4_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_K,type_dst=q4_K,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_K,type_dst=q4_K,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_K,type_dst=q4_K,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_K,type_dst=q4_K,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_K,type_dst=q4_K,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_K,type_dst=q4_K,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_K,type_dst=q4_K,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_K,type_dst=q5_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_K,type_dst=q5_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_K,type_dst=q5_K,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_K,type_dst=q5_K,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_K,type_dst=q5_K,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_K,type_dst=q5_K,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_K,type_dst=q5_K,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_K,type_dst=q5_K,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_K,type_dst=q5_K,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q6_K,type_dst=q6_K,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q6_K,type_dst=q6_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q6_K,type_dst=q6_K,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q6_K,type_dst=q6_K,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q6_K,type_dst=q6_K,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q6_K,type_dst=q6_K,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q6_K,type_dst=q6_K,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q6_K,type_dst=q6_K,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q6_K,type_dst=q6_K,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xxs,type_dst=iq2_xxs,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xs,type_dst=iq2_xs,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_s,type_dst=iq2_s,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_xxs,type_dst=iq3_xxs,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_s,type_dst=iq1_s,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_m,type_dst=iq1_m,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[32,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[32,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[32,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[64,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[64,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[64,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[96,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[96,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_nl,type_dst=iq4_nl,ne=[96,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_s,type_dst=iq3_s,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[256,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[256,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[512,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[512,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[512,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[768,2,3,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[768,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_xs,type_dst=iq4_xs,ne=[768,2,3,4],permute_src=[0,3,1,2],permute_dst=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=f16,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=f16,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=bf16,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=bf16,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q4_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q4_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q4_1,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q4_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q5_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q5_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q5_1,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q3_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q4_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q4_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q5_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q5_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q6_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=q6_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq2_xxs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq2_xxs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq2_xs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq2_xs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq2_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq2_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq3_xxs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq3_xxs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq1_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq1_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq1_m,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq1_m,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq4_nl,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq4_nl,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq3_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq3_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq4_xs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=iq4_xs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=f16,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=f16,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=bf16,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=bf16,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q4_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q4_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q4_1,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q4_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q5_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q5_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q5_1,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q3_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q4_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q4_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q5_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q5_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q6_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=q6_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq2_xxs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq2_xxs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq2_xs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq2_xs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq2_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq2_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq3_xxs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq3_xxs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq1_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq1_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq1_m,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq1_m,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq4_nl,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq4_nl,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq3_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq3_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq4_xs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=iq4_xs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=f16,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=f16,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=bf16,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=bf16,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q4_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q4_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q4_1,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q4_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q5_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q5_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q5_1,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q5_1,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q8_0,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q8_0,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q2_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q2_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q3_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q3_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q4_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q4_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q5_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q5_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q6_K,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=q6_K,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq2_xxs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq2_xxs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq2_xs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq2_xs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq2_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq2_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq3_xxs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq3_xxs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq1_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq1_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq1_m,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq1_m,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq4_nl,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq4_nl,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq3_s,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq3_s,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq4_xs,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=iq4_xs,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=bf16,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_0,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_0,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_1,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_1,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_0,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_0,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_1,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_1,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q8_0,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q8_0,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q2_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q2_K,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q3_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q3_K,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q4_K,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q5_K,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q6_K,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=q6_K,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xxs,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xxs,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xs,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_xs,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_s,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq2_s,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_xxs,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_xxs,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_s,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_s,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_m,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq1_m,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_nl,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_nl,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_s,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq3_s,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_xs,type_dst=f32,ne=[256,4,4,4],permute_src=[0,0,0,0],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=iq4_xs,type_dst=f32,ne=[256,2,3,4],permute_src=[0,2,1,3],permute_dst=[0,0,0,0]","support","0","no","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=f16,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f16,type_dst=f32,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=f16,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CPY","type_src=f32,type_dst=f32,ne=[256,2,3,4],permute_src=[1,0,2,3],permute_dst=[0,0,0,0]","support","1","yes","OpenCL"
-"OpenCL","CONT","type=f32,ne=[10,10,10,1]","support","1","yes","OpenCL"
-"OpenCL","CONT","type=f32,ne=[2,1,1,1]","support","1","yes","OpenCL"
-"OpenCL","CONT","type=f32,ne=[2,1,3,5]","support","1","yes","OpenCL"
-"OpenCL","CONT","type=f32,ne=[2,3,5,7]","support","1","yes","OpenCL"
-"OpenCL","CONT","type=f16,ne=[2,1,1,1]","support","1","yes","OpenCL"
-"OpenCL","CONT","type=f16,ne=[2,1,3,5]","support","1","yes","OpenCL"
-"OpenCL","CONT","type=f16,ne=[2,3,5,7]","support","1","yes","OpenCL"
-"OpenCL","CONT","type=bf16,ne=[2,1,1,1]","support","0","no","OpenCL"
-"OpenCL","CONT","type=bf16,ne=[2,1,3,5]","support","0","no","OpenCL"
-"OpenCL","CONT","type=bf16,ne=[2,3,5,7]","support","0","no","OpenCL"
-"OpenCL","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","DIV","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","ADD","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","DIV","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","ADD","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","DIV","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","ADD","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","DIV","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","ADD","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","DIV","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","ADD","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","DIV","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","0","no","OpenCL"
-"OpenCL","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","0","no","OpenCL"
-"OpenCL","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","0","no","OpenCL"
-"OpenCL","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","0","no","OpenCL"
-"OpenCL","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","0","no","OpenCL"
-"OpenCL","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","0","no","OpenCL"
-"OpenCL","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","0","no","OpenCL"
-"OpenCL","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","0","no","OpenCL"
-"OpenCL","ADD","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","0","no","OpenCL"
-"OpenCL","DIV","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","0","no","OpenCL"
-"OpenCL","ADD","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","DIV","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","ADD","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","0","no","OpenCL"
-"OpenCL","DIV","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","0","no","OpenCL"
-"OpenCL","ADD","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","DIV","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","ADD","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","0","no","OpenCL"
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-"OpenCL","SUB","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","0","no","OpenCL"
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-"OpenCL","SUB","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
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-"OpenCL","ADD","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","0","no","OpenCL"
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-"OpenCL","ADD","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","0","no","OpenCL"
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-"OpenCL","ADD","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","0","no","OpenCL"
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-"OpenCL","ADD","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","0","no","OpenCL"
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-"OpenCL","ADD","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","0","no","OpenCL"
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-"OpenCL","ADD","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","SUB","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","0","no","OpenCL"
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-"OpenCL","SUB","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","MUL","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
-"OpenCL","DIV","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","0","no","OpenCL"
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+"OpenCL","CONT","type=i32,ne=[2,3,5,7],use_view_slice=0","support","0","no","OpenCL"
+"OpenCL","CONT","type=i32,ne=[1,4,4,1],use_view_slice=0","support","0","no","OpenCL"
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+"OpenCL","CONT","type=i32,ne=[10,10,10,1],use_view_slice=0","support","0","no","OpenCL"
+"OpenCL","CONT","type=f16,ne=[2,1,1,1],use_view_slice=0","support","1","yes","OpenCL"
+"OpenCL","CONT","type=f16,ne=[2,1,3,5],use_view_slice=0","support","1","yes","OpenCL"
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+"OpenCL","CONT","type=f16,ne=[1,4,4,1],use_view_slice=0","support","1","yes","OpenCL"
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+"OpenCL","CONT","type=f16,ne=[10,10,10,1],use_view_slice=0","support","1","yes","OpenCL"
+"OpenCL","CONT","type=bf16,ne=[2,1,1,1],use_view_slice=0","support","0","no","OpenCL"
+"OpenCL","CONT","type=bf16,ne=[2,1,3,5],use_view_slice=0","support","0","no","OpenCL"
+"OpenCL","CONT","type=bf16,ne=[2,3,5,7],use_view_slice=0","support","0","no","OpenCL"
+"OpenCL","CONT","type=bf16,ne=[1,4,4,1],use_view_slice=0","support","0","no","OpenCL"
+"OpenCL","CONT","type=bf16,ne=[1,8,17,1],use_view_slice=0","support","0","no","OpenCL"
+"OpenCL","CONT","type=bf16,ne=[10,10,10,1],use_view_slice=0","support","0","no","OpenCL"
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+"OpenCL","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[1,1,1,1],nr=[32,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[1,1,320,320],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[10,5,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[10,5,4,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[10,5,4,3],nr=[2,1,1,1],nf=1","support","1","yes","OpenCL"
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+"OpenCL","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","OpenCL"
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+"OpenCL","DIV","type=f16,ne=[10,5,4,3],nr=[1,2,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","OpenCL"
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+"OpenCL","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,2,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[10,5,4,3],nr=[1,1,1,2],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[10,5,4,3],nr=[1,1,2,2],nf=1","support","1","yes","OpenCL"
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+"OpenCL","ADD","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[10,5,4,3],nr=[1,2,2,2],nf=1","support","1","yes","OpenCL"
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+"OpenCL","ADD","type=f16,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","OpenCL"
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+"OpenCL","ADD","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[1280,1,1,1],nr=[1,16,16,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[1280,16,16,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[1280,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[1,1,1280,1],nr=[16,16,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[16,16,1280,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[1,1,1920,1],nr=[16,16,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[1,1,2560,1],nr=[16,16,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[1,1,1280,1],nr=[32,32,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[1,1,1920,1],nr=[32,32,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[1,1,640,1],nr=[32,32,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[5120,1,1,1],nr=[1,256,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f16,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
 "OpenCL","ADD","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
 "OpenCL","SUB","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
 "OpenCL","MUL","type=f32,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
@@ -3165,6 +5623,10 @@
 "OpenCL","SUB","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","OpenCL"
 "OpenCL","MUL","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","OpenCL"
 "OpenCL","DIV","type=f32,ne=[10,5,4,3],nr=[2,2,2,2],nf=1","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f32,ne=[1,1,65536,1],nr=[256,1,1,1],nf=1","support","1","yes","OpenCL"
 "OpenCL","ADD","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
 "OpenCL","SUB","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
 "OpenCL","MUL","type=f32,ne=[1280,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
@@ -3217,70 +5679,61 @@
 "OpenCL","SUB","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
 "OpenCL","MUL","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
 "OpenCL","DIV","type=f32,ne=[640,1,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
-"OpenCL","ADD","type=f32,ne=[10,5,4,3],nr=[2,1,1,1],nf=2","support","1","yes","OpenCL"
-"OpenCL","ADD","type=f32,ne=[16,5,4,3],nr=[1,2,1,1],nf=3","support","1","yes","OpenCL"
-"OpenCL","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,1],nf=4","support","1","yes","OpenCL"
-"OpenCL","ADD","type=f32,ne=[16,5,4,3],nr=[1,1,1,2],nf=5","support","1","yes","OpenCL"
-"OpenCL","ADD","type=f32,ne=[10,5,4,3],nr=[1,1,2,2],nf=6","support","1","yes","OpenCL"
-"OpenCL","ADD","type=f32,ne=[10,5,4,3],nr=[1,2,2,2],nf=7","support","1","yes","OpenCL"
-"OpenCL","ADD","type=f32,ne=[16,5,4,3],nr=[2,2,2,2],nf=8","support","1","yes","OpenCL"
+"OpenCL","ADD","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","SUB","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","MUL","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
+"OpenCL","DIV","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","OpenCL"
 "OpenCL","ADD1","type=f32,ne=[10,5,4,3]","support","0","no","OpenCL"
-"OpenCL","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=0.000000","support","1","yes","OpenCL"
-"OpenCL","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000","support","1","yes","OpenCL"
+"OpenCL","ADD1","type=f32,ne=[1024,1024,1,1]","support","0","no","OpenCL"
+"OpenCL","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=0.000000,inplace=0","support","1","yes","OpenCL"
+"OpenCL","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=0","support","1","yes","OpenCL"
+"OpenCL","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=1","support","1","yes","OpenCL"
+"OpenCL","SCALE","type=f32,ne=[100,10,10,10],scale=2.000000,bias=1.000000,inplace=0","support","1","yes","OpenCL"
 "OpenCL","SILU_BACK","type=f32,ne=[64,5,4,3],eps=0.000001","support","0","no","OpenCL"
 "OpenCL","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000000","support","1","yes","OpenCL"
-"OpenCL","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000000","support","1","yes","OpenCL"
+"OpenCL","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000000,inplace=0","support","1","yes","OpenCL"
 "OpenCL","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000","support","1","yes","OpenCL"
-"OpenCL","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000","support","1","yes","OpenCL"
+"OpenCL","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000000,inplace=0","support","1","yes","OpenCL"
 "OpenCL","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000000","support","0","no","OpenCL"
 "OpenCL","L2_NORM","type=f32,ne=[64,5,4,3]","support","0","no","OpenCL"
 "OpenCL","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001","support","1","yes","OpenCL"
-"OpenCL","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001","support","1","yes","OpenCL"
+"OpenCL","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=0","support","1","yes","OpenCL"
 "OpenCL","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001","support","1","yes","OpenCL"
-"OpenCL","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001","support","1","yes","OpenCL"
+"OpenCL","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000001,inplace=0","support","1","yes","OpenCL"
 "OpenCL","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000001","support","0","no","OpenCL"
 "OpenCL","L2_NORM","type=f32,ne=[64,5,4,3]","support","0","no","OpenCL"
 "OpenCL","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100","support","1","yes","OpenCL"
-"OpenCL","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100","support","1","yes","OpenCL"
+"OpenCL","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000100,inplace=0","support","1","yes","OpenCL"
 "OpenCL","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100","support","1","yes","OpenCL"
-"OpenCL","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100","support","1","yes","OpenCL"
+"OpenCL","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.000100,inplace=0","support","1","yes","OpenCL"
 "OpenCL","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.000100","support","0","no","OpenCL"
 "OpenCL","L2_NORM","type=f32,ne=[64,5,4,3]","support","0","no","OpenCL"
 "OpenCL","NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000","support","1","yes","OpenCL"
-"OpenCL","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000","support","1","yes","OpenCL"
+"OpenCL","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.100000,inplace=0","support","1","yes","OpenCL"
 "OpenCL","NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000","support","1","yes","OpenCL"
-"OpenCL","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000","support","1","yes","OpenCL"
+"OpenCL","RMS_NORM","type=f32,ne=[64,5,4,3],v=1,eps=0.100000,inplace=0","support","1","yes","OpenCL"
 "OpenCL","RMS_NORM_BACK","type=f32,ne=[64,5,4,3],eps=0.100000","support","0","no","OpenCL"
 "OpenCL","L2_NORM","type=f32,ne=[64,5,4,3]","support","0","no","OpenCL"
-"OpenCL","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000000,broadcast=0","support","1","yes","OpenCL"
-"OpenCL","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000000,broadcast=1","support","1","yes","OpenCL"
-"OpenCL","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000001,broadcast=0","support","1","yes","OpenCL"
-"OpenCL","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000001,broadcast=1","support","1","yes","OpenCL"
-"OpenCL","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000100,broadcast=0","support","1","yes","OpenCL"
-"OpenCL","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.000100,broadcast=1","support","1","yes","OpenCL"
-"OpenCL","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.100000,broadcast=0","support","1","yes","OpenCL"
-"OpenCL","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=0.100000,broadcast=1","support","1","yes","OpenCL"
-"OpenCL","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=1.000000,broadcast=0","support","1","yes","OpenCL"
-"OpenCL","RMS_NORM_MUL_ADD","type=f32,ne=[64,5,4,3],eps=1.000000,broadcast=1","support","1","yes","OpenCL"
+"OpenCL","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=1","support","1","yes","OpenCL"
 "OpenCL","L2_NORM","type=f32,ne=[64,5,4,3]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[3,1024,1,1]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[3,1024,1,1]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[3,1536,1,1]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[3,1536,1,1]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[3,1536,1,1]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[3,2048,1,1]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[3,2048,1,1]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[3,2048,1,1]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[4,1024,1,1]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[4,1024,1,1]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[4,1024,1,1]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[4,1536,1,1]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[4,1536,1,1]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[4,1536,1,1]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[4,2048,1,1]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[4,2048,1,1]","support","0","no","OpenCL"
-"OpenCL","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[4,2048,1,1]","support","0","no","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[3,1024,1,1]","support","1","yes","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[3,1536,1,1]","support","1","yes","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[3,2048,1,1]","support","1","yes","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[4,1024,1,1]","support","1","yes","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[4,1536,1,1]","support","1","yes","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[4,1536,1,1]","support","1","yes","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[4,1536,1,1]","support","1","yes","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","OpenCL"
+"OpenCL","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[4,2048,1,1]","support","1","yes","OpenCL"
 "OpenCL","SSM_SCAN","type=f32,d_state=16,head_dim=1,n_head=1024,n_group=1,n_seq_tokens=32,n_seqs=4","support","0","no","OpenCL"
 "OpenCL","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=16,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","OpenCL"
 "OpenCL","SSM_SCAN","type=f32,d_state=256,head_dim=64,n_head=8,n_group=2,n_seq_tokens=32,n_seqs=4","support","0","no","OpenCL"
@@ -3296,1207 +5749,1816 @@
 "OpenCL","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=1","support","0","no","OpenCL"
 "OpenCL","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=32,n_seqs=4","support","0","no","OpenCL"
 "OpenCL","GATED_LINEAR_ATTN","type=f32,head_count=32,head_size=64,n_seq_tokens=128,n_seqs=4","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q3_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q5_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=q6_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_xs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=1,k=32,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=1,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=64,n=2,k=128,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=83,n=2,k=128,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=64,n=2,k=64,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=83,n=2,k=64,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=64,n=45,k=128,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=128,n=45,k=64,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=193,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=67,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[1,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[1,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[2,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[2,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[4,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[4,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[1,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[1,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","0","no","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,1],nr=[4,1],per=[0,2,1,3],v=0","support","1","yes","OpenCL"
-"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[8,1],nr=[4,1],per=[0,1,2,3],v=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=bf16,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=q5_0,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
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+"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
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+"OpenCL","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
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+"OpenCL","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
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+"OpenCL","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq2_s,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_xxs,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_s,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq1_m,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq4_nl,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq3_s,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
+"OpenCL","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
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+"OpenCL","MUL_MAT","type_a=iq4_xs,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","OpenCL"
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+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","OpenCL"
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+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","OpenCL"
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+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","OpenCL"
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+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","OpenCL"
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+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
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+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f32,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=f16,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
+"OpenCL","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","OpenCL"
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@@ -4505,8 +7567,8 @@
 "OpenCL","MUL_MAT_ID","type_a=q5_0,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","0","no","OpenCL"
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@@ -5301,6 +8363,134 @@
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 "OpenCL","OUT_PROD","type_a=iq2_xxs,type_b=f32,m=256,n=1,k=1,bs=[1,1],nr=[1,1],trans_b=0","support","0","no","OpenCL"
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@@ -5429,548 +8619,804 @@
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-"OpenCL","SOFT_MAX","type=f32,ne=[15,1023,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[16,1024,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[15,1023,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1024,16,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1023,15,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1024,16,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1023,15,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1024,1024,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1023,1023,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1024,1024,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1023,1023,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[16,16,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[15,15,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[16,16,1,3],mask=1,m_prec=f32,nr23=[3,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[15,15,1,1],mask=1,m_prec=f32,nr23=[2,3],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[16,16,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[15,15,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[16,16,1,3],mask=1,m_prec=f16,nr23=[3,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[15,15,1,1],mask=1,m_prec=f16,nr23=[2,3],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[16,1024,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[15,1023,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[16,1024,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[15,1023,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1024,16,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1023,15,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1024,16,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1023,15,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1024,1024,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1023,1023,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1024,1024,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1023,1023,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[16,16,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[15,15,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[16,16,1,3],mask=1,m_prec=f32,nr23=[3,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[15,15,1,1],mask=1,m_prec=f32,nr23=[2,3],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[16,16,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[15,15,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[16,16,1,3],mask=1,m_prec=f16,nr23=[3,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[15,15,1,1],mask=1,m_prec=f16,nr23=[2,3],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[16,1024,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[15,1023,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[16,1024,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[15,1023,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1024,16,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1023,15,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1024,16,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1023,15,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1024,1024,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1023,1023,1,1],mask=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1024,1024,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[1023,1023,1,1],mask=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[16,2,32,1],mask=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[16,2,32,1],mask=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[16,2,32,1],mask=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
-"OpenCL","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000","support","1","yes","OpenCL"
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+"OpenCL","SOFT_MAX","type=f32,ne=[15,15,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","1","yes","OpenCL"
+"OpenCL","SOFT_MAX","type=f32,ne=[16,1024,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","1","yes","OpenCL"
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+"OpenCL","SOFT_MAX","type=f32,ne=[1024,16,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","1","yes","OpenCL"
+"OpenCL","SOFT_MAX","type=f32,ne=[1023,15,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","1","yes","OpenCL"
+"OpenCL","SOFT_MAX","type=f32,ne=[1024,1024,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","1","yes","OpenCL"
+"OpenCL","SOFT_MAX","type=f32,ne=[1023,1023,1,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=1.000000,max_bias=0.000000,inplace=0","support","1","yes","OpenCL"
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+"OpenCL","SOFT_MAX","type=f32,ne=[16,2,32,1],mask=0,sinks=0,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000,inplace=1","support","1","yes","OpenCL"
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+"OpenCL","SOFT_MAX","type=f32,ne=[1023,1023,1,1],mask=0,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000,inplace=0","support","1","yes","OpenCL"
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+"OpenCL","SOFT_MAX","type=f32,ne=[1023,15,1,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000,inplace=0","support","1","yes","OpenCL"
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+"OpenCL","SOFT_MAX","type=f32,ne=[1023,1023,1,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=1.000000,max_bias=8.000000,inplace=0","support","1","yes","OpenCL"
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+"OpenCL","SOFT_MAX","type=f32,ne=[15,15,1,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","OpenCL"
+"OpenCL","SOFT_MAX","type=f32,ne=[16,16,1,3],mask=1,sinks=1,m_prec=f16,nr23=[3,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","OpenCL"
+"OpenCL","SOFT_MAX","type=f32,ne=[15,15,1,1],mask=1,sinks=1,m_prec=f16,nr23=[2,3],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","OpenCL"
+"OpenCL","SOFT_MAX","type=f32,ne=[16,1024,1,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","OpenCL"
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+"OpenCL","SOFT_MAX","type=f32,ne=[15,1023,1,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","OpenCL"
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+"OpenCL","SOFT_MAX","type=f32,ne=[16,2,32,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=0.000000,inplace=0","support","1","yes","OpenCL"
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-"OpenCL","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
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-"OpenCL","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1","support","1","yes","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1","support","0","no","OpenCL"
-"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1","support","0","no","OpenCL"
+"OpenCL","SOFT_MAX_BACK","type=f32,ne=[1024,1024,2,3],scale=0.100000,max_bias=8.000000","support","0","no","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,40,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,52,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,64,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,1,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,71,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,8,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=20,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,2,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,32,4,1],n_dims=32,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=8,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,12,2,1],n_dims=20,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,28,2,1],n_dims=32,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[80,16,2,1],n_dims=80,mode=24,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,16,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.000000,af=1.424500,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.000000,ef=0.746500,af=1.424500,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=0,v=1,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=0,inplace=0","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.000000,ff=1,v=1,inplace=0","support","1","yes","OpenCL"
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+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.000000,af=1.424500,ff=0,v=0,inplace=0","support","1","yes","OpenCL"
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+"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=0,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=0,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=1,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=1,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=0,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=0,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=1,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=1,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=0,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=0,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=1,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=0,v=1,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=0,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=0,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=1,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.000000,ff=1,v=1,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f32,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=1,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE_BACK","type=f16,ne_a=[64,128,2,1],n_dims=64,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=1,inplace=0","support","0","no","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f32,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=0,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=2,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=8,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=40,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=0,v=0,inplace=1","support","1","yes","OpenCL"
+"OpenCL","ROPE","type=f16,ne_a=[128,32,2,1],n_dims=128,mode=24,n_ctx=512,fs=1.424500,ef=0.746500,af=1.424500,ff=1,v=0,inplace=1","support","1","yes","OpenCL"
 "OpenCL","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","1","yes","OpenCL"
 "OpenCL","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=0,v=0","support","0","no","OpenCL"
 "OpenCL","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=1,v=0","support","1","yes","OpenCL"
@@ -6003,2131 +9449,9305 @@
 "OpenCL","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","0","no","OpenCL"
 "OpenCL","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","OpenCL"
 "OpenCL","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","0","no","OpenCL"
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 "OpenCL","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","0","no","OpenCL"
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 "OpenCL","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","0","no","OpenCL"
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 "OpenCL","TIMESTEP_EMBEDDING","type=f32,ne_a=[2,1,1,1],dim=320,max_period=10000","support","1","yes","OpenCL"
 "OpenCL","LEAKY_RELU","type=f32,ne_a=[10,5,4,3],negative_slope=0.100000","support","0","no","OpenCL"
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-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
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-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
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-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[1,3],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=64,hsv=64,nh=4,nr23=[4,3],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=80,hsv=80,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=128,hsv=128,nh=4,nr23=[16,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=10.000000,prec=def,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=128,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=192,hsv=192,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=256,hsv=256,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,2,1,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=1,max_bias=8.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=1,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=3,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=32,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
-"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=512,nb=35,mask=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
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+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[1,1],kv=1024,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","OpenCL"
+"OpenCL","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","OpenCL"
 "OpenCL","CROSS_ENTROPY_LOSS","type=f32,ne=[10,5,4,3]","support","0","no","OpenCL"
 "OpenCL","CROSS_ENTROPY_LOSS","type=f32,ne=[30000,1,1,1]","support","0","no","OpenCL"
 "OpenCL","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[10,5,4,3]","support","0","no","OpenCL"
 "OpenCL","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[30000,1,1,1]","support","0","no","OpenCL"
 "OpenCL","OPT_STEP_ADAMW","type=f32,ne=[10,5,4,3]","support","0","no","OpenCL"
+"OpenCL","OPT_STEP_SGD","type=f32,ne=[10,5,4,3]","support","0","no","OpenCL"

From b6777218198888ed4b18d839e805f887562a9be4 Mon Sep 17 00:00:00 2001
From: Eric Zhang <34133756+EZForever@users.noreply.github.com>
Date: Wed, 10 Dec 2025 22:22:40 +0800
Subject: [PATCH 046/135] model : Qwen3-Next-80B-A3B has 48 layers (#17898)

* model : Qwen3-Next-80B-A3B has 48 layers

* model : Add 80B-A3B type name
---
 src/llama-model.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 04fccc9793..fc337b045e 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -120,6 +120,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_16B_A1B:       return "16B.A1B";
         case LLM_TYPE_21B_A3B:       return "21B.A3B";
         case LLM_TYPE_30B_A3B:       return "30B.A3B";
+        case LLM_TYPE_80B_A3B:       return "80B.A3B";
         case LLM_TYPE_100B_A6B:      return "100B.A6B";
         case LLM_TYPE_106B_A12B:     return "106B.A12B";
         case LLM_TYPE_230B_A10B:     return "230B.A10B";
@@ -2257,7 +2258,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 }
 
                 switch (hparams.n_layer) {
-                    case 80: type = LLM_TYPE_80B_A3B; break;
+                    case 48: type = LLM_TYPE_80B_A3B; break;
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;

From 6c2131773c5975c719863a21a8195a66dea4a4d0 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Wed, 10 Dec 2025 15:28:59 +0100
Subject: [PATCH 047/135] cli: new CLI experience (#17824)

* wip

* wip

* fix logging, add display info

* handle commands

* add args

* wip

* move old cli to llama-completion

* rm deprecation notice

* move server to a shared library

* move ci to llama-completion

* add loading animation

* add --show-timings arg

* add /read command, improve LOG_ERR

* add args for speculative decoding, enable show timings by default

* add arg --image and --audio

* fix windows build

* support reasoning_content

* fix llama2c workflow

* color default is auto

* fix merge conflicts

* properly fix color problem

Co-authored-by: bandoti <bandoti@users.noreply.github.com>

* better loading spinner

* make sure to clean color on force-exit

* also clear input files on "/clear"

* simplify common_log_flush

* add warning in mtmd-cli

* implement console writter

* fix data race

* add attribute

* fix llama-completion and mtmd-cli

* add some notes about console::log

* fix compilation

---------

Co-authored-by: bandoti <bandoti@users.noreply.github.com>
---
 .github/workflows/build.yml                   |   4 +-
 README.md                                     |  13 -
 ci/run.sh                                     |  24 +-
 common/arg.cpp                                | 114 ++---
 common/common.h                               |   4 +-
 common/console.cpp                            | 116 ++++-
 common/console.h                              |  38 +-
 common/log.cpp                                |   5 +
 common/log.h                                  |   1 +
 examples/gen-docs/gen-docs.cpp                |   2 +-
 scripts/snapdragon/adb/run-cli.sh             |   2 +-
 tests/test-lora-conversion-inference.sh       |  12 +-
 tools/CMakeLists.txt                          |   3 +-
 tools/cli/CMakeLists.txt                      |  10 +
 tools/cli/cli.cpp                             | 395 ++++++++++++++++++
 tools/{main => completion}/CMakeLists.txt     |   4 +-
 tools/{main => completion}/README.md          |   0
 .../main.cpp => completion/completion.cpp}    |  20 +-
 tools/gguf-split/tests.sh                     |   2 +-
 tools/mtmd/mtmd-cli.cpp                       |   7 +-
 tools/quantize/tests.sh                       |   2 +-
 tools/server/CMakeLists.txt                   |  40 +-
 tools/server/server-context.cpp               |  52 ++-
 tools/server/server-context.h                 |  11 +
 tools/server/server-queue.h                   |   5 +-
 tools/server/server-task.h                    |   4 +
 26 files changed, 742 insertions(+), 148 deletions(-)
 create mode 100644 tools/cli/CMakeLists.txt
 create mode 100644 tools/cli/cli.cpp
 rename tools/{main => completion}/CMakeLists.txt (74%)
 rename tools/{main => completion}/README.md (100%)
 rename tools/{main/main.cpp => completion/completion.cpp} (98%)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index ad205f3ec9..182d433b1b 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -243,7 +243,7 @@ jobs:
           echo "Fetch llama2c model"
           wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/stories260K.bin
           ./bin/llama-convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf
-          ./bin/llama-cli -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256
+          ./bin/llama-completion -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256
 
       - name: Test llama2c (s390x)
         id: llama2c_test_s390x
@@ -252,7 +252,7 @@ jobs:
           cd build
           echo "Fetch llama2c big-endian model"
           wget https://huggingface.co/ggml-org/models/resolve/main/tinyllamas/stories260K-be.gguf
-          ./bin/llama-cli -m stories260K-be.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256
+          ./bin/llama-completion -m stories260K-be.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256
 
   ubuntu-latest-cmake-sanitizer:
     runs-on: ubuntu-latest
diff --git a/README.md b/README.md
index 7dd2bfd8a1..b7d24c9dd7 100644
--- a/README.md
+++ b/README.md
@@ -347,19 +347,6 @@ To learn more about model quantization, [read this documentation](tools/quantize
 
     </details>
 
-- <details>
-    <summary>Run simple text completion</summary>
-
-    To disable conversation mode explicitly, use `-no-cnv`
-
-    ```bash
-    llama-cli -m model.gguf -p "I believe the meaning of life is" -n 128 -no-cnv
-
-    # I believe the meaning of life is to find your own truth and to live in accordance with it. For me, this means being true to myself and following my passions, even if they don't align with societal expectations. I think that's what I love about yoga – it's not just a physical practice, but a spiritual one too. It's about connecting with yourself, listening to your inner voice, and honoring your own unique journey.
-    ```
-
-    </details>
-
 - <details>
     <summary>Constrain the output with a custom grammar</summary>
 
diff --git a/ci/run.sh b/ci/run.sh
index 83b2603e82..0676504b3e 100755
--- a/ci/run.sh
+++ b/ci/run.sh
@@ -398,18 +398,18 @@ function gg_run_qwen3_0_6b {
     ./bin/llama-quantize ${model_bf16} ${model_q5_k} q5_k $(nproc)
     ./bin/llama-quantize ${model_bf16} ${model_q6_k} q6_k $(nproc)
 
-    (time ./bin/llama-cli -no-cnv --model ${model_f16}  -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-cli -no-cnv --model ${model_bf16} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-bf16.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q8_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_1} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_1} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q2_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q3_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q4_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q5_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/llama-cli -no-cnv --model ${model_q6_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+    (time ./bin/llama-completion -no-cnv --model ${model_f16}  -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/llama-completion -no-cnv --model ${model_bf16} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-bf16.log
+    (time ./bin/llama-completion -no-cnv --model ${model_q8_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/llama-completion -no-cnv --model ${model_q4_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/llama-completion -no-cnv --model ${model_q4_1} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/llama-completion -no-cnv --model ${model_q5_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/llama-completion -no-cnv --model ${model_q5_1} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/llama-completion -no-cnv --model ${model_q2_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/llama-completion -no-cnv --model ${model_q3_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/llama-completion -no-cnv --model ${model_q4_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/llama-completion -no-cnv --model ${model_q5_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/llama-completion -no-cnv --model ${model_q6_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
     (time ./bin/llama-perplexity --model ${model_f16}  -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
     if [ -z ${GG_BUILD_NO_BF16} ]; then
diff --git a/common/arg.cpp b/common/arg.cpp
index 4203da4a0a..210ef8d621 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -51,6 +51,7 @@ using json = nlohmann::ordered_json;
 static std::initializer_list<enum llama_example> mmproj_examples = {
     LLAMA_EXAMPLE_MTMD,
     LLAMA_EXAMPLE_SERVER,
+    LLAMA_EXAMPLE_CLI,
 };
 
 static std::string read_file(const std::string & fname) {
@@ -468,6 +469,8 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         ));
     }
 
+    common_log_set_verbosity_thold(params.verbosity);
+
     return true;
 }
 
@@ -790,7 +793,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.display_prompt = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
         {"-co", "--color"}, "[on|off|auto]",
         "Colorize output to distinguish prompt and user input from generations ('on', 'off', or 'auto', default: 'auto')\n"
@@ -807,7 +810,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                     string_format("error: unknown value for --color: '%s'\n", value.c_str()));
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP}));
     add_opt(common_arg(
         {"-t", "--threads"}, "N",
         string_format("number of CPU threads to use during generation (default: %d)", params.cpuparams.n_threads),
@@ -940,7 +943,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     add_opt(common_arg(
         {"-n", "--predict", "--n-predict"}, "N",
         string_format(
-            ex == LLAMA_EXAMPLE_MAIN
+            ex == LLAMA_EXAMPLE_COMPLETION
                 ? "number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)"
                 : "number of tokens to predict (default: %d, -1 = infinity)",
             params.n_predict),
@@ -984,7 +987,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, int value) {
             params.n_ctx_checkpoints = value;
         }
-    ).set_env("LLAMA_ARG_CTX_CHECKPOINTS").set_examples({LLAMA_EXAMPLE_SERVER}));
+    ).set_env("LLAMA_ARG_CTX_CHECKPOINTS").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
         {"--cache-ram", "-cram"}, "N",
         string_format("set the maximum cache size in MiB (default: %d, -1 - no limit, 0 - disable)\n"
@@ -992,7 +995,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, int value) {
             params.cache_ram_mib = value;
         }
-    ).set_env("LLAMA_ARG_CACHE_RAM").set_examples({LLAMA_EXAMPLE_SERVER}));
+    ).set_env("LLAMA_ARG_CACHE_RAM").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
         {"--kv-unified", "-kvu"},
         string_format("use single unified KV buffer for the KV cache of all sequences (default: %s)\n"
@@ -1007,14 +1010,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.ctx_shift = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT"));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT"));
     add_opt(common_arg(
         {"--context-shift"},
         string_format("enables context shift on infinite text generation (default: %s)", params.ctx_shift ? "enabled" : "disabled"),
         [](common_params & params) {
             params.ctx_shift = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_CONTEXT_SHIFT"));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_CONTEXT_SHIFT"));
     add_opt(common_arg(
         {"--chunks"}, "N",
         string_format("max number of chunks to process (default: %d, -1 = all)", params.n_chunks),
@@ -1050,7 +1053,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.system_prompt = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_DIFFUSION}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_DIFFUSION}));
     add_opt(common_arg(
         {"--no-perf"},
         string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),
@@ -1059,6 +1062,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.no_perf = true;
         }
     ).set_env("LLAMA_ARG_NO_PERF"));
+    add_opt(common_arg(
+        {"--no-show-timings"},
+        string_format("disable timing information after each response (default: %s)", params.show_timings ? "true" : "false"),
+        [](common_params & params) {
+            params.show_timings = false;
+        }
+    ).set_examples({LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_NO_SHOW_TIMINGS"));
     add_opt(common_arg(
         {"-f", "--file"}, "FNAME",
         "a file containing the prompt (default: none)",
@@ -1080,7 +1090,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 params.system_prompt.pop_back();
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_DIFFUSION}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_DIFFUSION}));
     add_opt(common_arg(
         {"--in-file"}, "FNAME",
         "an input file (repeat to specify multiple files)",
@@ -1128,42 +1138,42 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, int value) {
             params.n_print = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION}));
     add_opt(common_arg(
         {"--prompt-cache"}, "FNAME",
         "file to cache prompt state for faster startup (default: none)",
         [](common_params & params, const std::string & value) {
             params.path_prompt_cache = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION}));
     add_opt(common_arg(
         {"--prompt-cache-all"},
         "if specified, saves user input and generations to cache as well\n",
         [](common_params & params) {
             params.prompt_cache_all = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION}));
     add_opt(common_arg(
         {"--prompt-cache-ro"},
         "if specified, uses the prompt cache but does not update it",
         [](common_params & params) {
             params.prompt_cache_ro = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION}));
     add_opt(common_arg(
         {"-r", "--reverse-prompt"}, "PROMPT",
         "halt generation at PROMPT, return control in interactive mode\n",
         [](common_params & params, const std::string & value) {
             params.antiprompt.emplace_back(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-sp", "--special"},
         string_format("special tokens output enabled (default: %s)", params.special ? "true" : "false"),
         [](common_params & params) {
             params.special = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-cnv", "--conversation"},
         "run in conversation mode:\n"
@@ -1173,14 +1183,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION}));
     add_opt(common_arg(
         {"-no-cnv", "--no-conversation"},
         "force disable conversation mode (default: false)",
         [](common_params & params) {
             params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
         {"-st", "--single-turn"},
         "run conversation for a single turn only, then exit when done\n"
@@ -1189,28 +1199,28 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.single_turn = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
         {"-i", "--interactive"},
         string_format("run in interactive mode (default: %s)", params.interactive ? "true" : "false"),
         [](common_params & params) {
             params.interactive = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION}));
     add_opt(common_arg(
         {"-if", "--interactive-first"},
         string_format("run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false"),
         [](common_params & params) {
             params.interactive_first = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION}));
     add_opt(common_arg(
         {"-mli", "--multiline-input"},
         "allows you to write or paste multiple lines without ending each in '\\'",
         [](common_params & params) {
             params.multiline_input = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
         {"--in-prefix-bos"},
         "prefix BOS to user inputs, preceding the `--in-prefix` string",
@@ -1218,7 +1228,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.input_prefix_bos = true;
             params.enable_chat_template = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION}));
     add_opt(common_arg(
         {"--in-prefix"}, "STRING",
         "string to prefix user inputs with (default: empty)",
@@ -1226,7 +1236,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.input_prefix = value;
             params.enable_chat_template = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION}));
     add_opt(common_arg(
         {"--in-suffix"}, "STRING",
         "string to suffix after user inputs with (default: empty)",
@@ -1234,14 +1244,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.input_suffix = value;
             params.enable_chat_template = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION}));
     add_opt(common_arg(
         {"--no-warmup"},
         "skip warming up the model with an empty run",
         [](common_params & params) {
             params.warmup = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_PERPLEXITY}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_PERPLEXITY}));
     add_opt(common_arg(
         {"--spm-infill"},
         string_format(
@@ -1632,14 +1642,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, int value) {
             params.grp_attn_n = value;
         }
-    ).set_env("LLAMA_ARG_GRP_ATTN_N").set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_PASSKEY}));
+    ).set_env("LLAMA_ARG_GRP_ATTN_N").set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_PASSKEY}));
     add_opt(common_arg(
         {"-gaw", "--grp-attn-w"}, "N",
         string_format("group-attention width (default: %d)", params.grp_attn_w),
         [](common_params & params, int value) {
             params.grp_attn_w = value;
         }
-    ).set_env("LLAMA_ARG_GRP_ATTN_W").set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_env("LLAMA_ARG_GRP_ATTN_W").set_examples({LLAMA_EXAMPLE_COMPLETION}));
     add_opt(common_arg(
         {"-nkvo", "--no-kv-offload"},
         "disable KV offload",
@@ -1829,7 +1839,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.image.emplace_back(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_MTMD}));
+    ).set_examples({LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
         {"--image-min-tokens"}, "N",
         "minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)",
@@ -1922,7 +1932,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         "override tensor buffer type for draft model", [](common_params & params, const std::string & value) {
             parse_tensor_buffer_overrides(value, params.speculative.tensor_buft_overrides);
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
         {"--cpu-moe", "-cmoe"},
         "keep all Mixture of Experts (MoE) weights in the CPU",
@@ -1951,7 +1961,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.speculative.tensor_buft_overrides.push_back(llm_ffn_exps_cpu_override());
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CPU_MOE_DRAFT"));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_CPU_MOE_DRAFT"));
     add_opt(common_arg(
         {"--n-cpu-moe-draft", "-ncmoed"}, "N",
         "keep the Mixture of Experts (MoE) weights of the first N layers in the CPU for the draft model",
@@ -1965,7 +1975,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 params.speculative.tensor_buft_overrides.push_back({buft_overrides_draft.back().c_str(), ggml_backend_cpu_buffer_type()});
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_N_CPU_MOE_DRAFT"));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_N_CPU_MOE_DRAFT"));
     add_opt(common_arg(
         {"-ngl", "--gpu-layers", "--n-gpu-layers"}, "N",
         string_format("max. number of layers to store in VRAM (default: %d)", params.n_gpu_layers),
@@ -2444,7 +2454,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 params.default_template_kwargs[item.key()] = item.value().dump();
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_CHAT_TEMPLATE_KWARGS"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_CHAT_TEMPLATE_KWARGS"));
     add_opt(common_arg(
         {"-to", "--timeout"}, "N",
         string_format("server read/write timeout in seconds (default: %d)", params.timeout_read),
@@ -2553,14 +2563,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.use_jinja = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_JINJA"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_JINJA"));
     add_opt(common_arg(
         {"--no-jinja"},
         string_format("disable jinja template for chat (default: %s)\n", params.use_jinja ? "enabled" : "disabled"),
         [](common_params & params) {
             params.use_jinja = false;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_NO_JINJA"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_NO_JINJA"));
     add_opt(common_arg(
         {"--reasoning-format"}, "FORMAT",
         "controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n"
@@ -2571,7 +2581,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.reasoning_format = common_reasoning_format_from_name(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_THINK"));
     add_opt(common_arg(
         {"--reasoning-budget"}, "N",
         "controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)",
@@ -2579,7 +2589,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             if (value != 0 && value != -1) { throw std::invalid_argument("invalid value"); }
             params.reasoning_budget = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MAIN}).set_env("LLAMA_ARG_THINK_BUDGET"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_THINK_BUDGET"));
     add_opt(common_arg(
         {"--chat-template"}, "JINJA_TEMPLATE",
         string_format(
@@ -2591,7 +2601,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.chat_template = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_CHAT_TEMPLATE"));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_CHAT_TEMPLATE"));
     add_opt(common_arg(
         {"--chat-template-file"}, "JINJA_TEMPLATE_FILE",
         string_format(
@@ -2603,7 +2613,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.chat_template = read_file(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE"));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE"));
     add_opt(common_arg(
         {"--no-prefill-assistant"},
         string_format(
@@ -2634,7 +2644,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params) {
             params.simple_io = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
         {"--positive-file"}, "FNAME",
         string_format("positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str()),
@@ -2717,7 +2727,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         "Set verbosity level to infinity (i.e. log all messages, useful for debugging)",
         [](common_params & params) {
             params.verbosity = INT_MAX;
-            common_log_set_verbosity_thold(INT_MAX);
         }
     ));
     add_opt(common_arg(
@@ -2738,7 +2747,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             "(default: %d)\n", params.verbosity),
         [](common_params & params, int value) {
             params.verbosity = value;
-            common_log_set_verbosity_thold(value);
         }
     ).set_env("LLAMA_LOG_VERBOSITY"));
     add_opt(common_arg(
@@ -2871,14 +2879,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, int value) {
             params.speculative.n_max = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_MAX"));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_DRAFT_MAX"));
     add_opt(common_arg(
         {"--draft-min", "--draft-n-min"}, "N",
         string_format("minimum number of draft tokens to use for speculative decoding (default: %d)", params.speculative.n_min),
         [](common_params & params, int value) {
             params.speculative.n_min = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_MIN"));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_DRAFT_MIN"));
     add_opt(common_arg(
         {"--draft-p-split"}, "P",
         string_format("speculative decoding split probability (default: %.1f)", (double)params.speculative.p_split),
@@ -2892,14 +2900,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.speculative.p_min = std::stof(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_DRAFT_P_MIN"));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_DRAFT_P_MIN"));
     add_opt(common_arg(
         {"-cd", "--ctx-size-draft"}, "N",
         string_format("size of the prompt context for the draft model (default: %d, 0 = loaded from model)", params.speculative.n_ctx),
         [](common_params & params, int value) {
             params.speculative.n_ctx = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CTX_SIZE_DRAFT"));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_CTX_SIZE_DRAFT"));
     add_opt(common_arg(
         {"-devd", "--device-draft"}, "<dev1,dev2,..>",
         "comma-separated list of devices to use for offloading the draft model (none = don't offload)\n"
@@ -2907,7 +2915,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         [](common_params & params, const std::string & value) {
             params.speculative.devices = parse_device_list(value);
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
         {"-ngld", "--gpu-layers-draft", "--n-gpu-layers-draft"}, "N",
         "number of layers to store in VRAM for the draft model",
@@ -2919,21 +2927,21 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 fprintf(stderr, "warning: consult docs/build.md for compilation instructions\n");
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_N_GPU_LAYERS_DRAFT"));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_N_GPU_LAYERS_DRAFT"));
     add_opt(common_arg(
         {"-md", "--model-draft"}, "FNAME",
         "draft model for speculative decoding (default: unused)",
         [](common_params & params, const std::string & value) {
             params.speculative.model.path = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODEL_DRAFT"));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_MODEL_DRAFT"));
     add_opt(common_arg(
         {"--spec-replace"}, "TARGET", "DRAFT",
         "translate the string in TARGET into DRAFT if the draft model and main model are not compatible",
         [](common_params & params, const std::string & tgt, const std::string & dft) {
             params.speculative.replacements.push_back({ tgt, dft });
         }
-    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
         {"-ctkd", "--cache-type-k-draft"}, "TYPE",
         string_format(
@@ -3197,7 +3205,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.use_jinja = true;
             //params.default_template_kwargs["reasoning_effort"] = "\"high\"";
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
 
     add_opt(common_arg(
         {"--gpt-oss-120b-default"},
@@ -3216,7 +3224,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.use_jinja = true;
             //params.default_template_kwargs["reasoning_effort"] = "\"high\"";
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
 
     add_opt(common_arg(
         {"--vision-gemma-4b-default"},
@@ -3227,7 +3235,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.n_ctx = 0;
             params.use_jinja = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
 
     add_opt(common_arg(
         {"--vision-gemma-12b-default"},
@@ -3238,7 +3246,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.n_ctx = 0;
             params.use_jinja = true;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
 
     return ctx_arg;
 }
diff --git a/common/common.h b/common/common.h
index d28e48991c..ad79f5b425 100644
--- a/common/common.h
+++ b/common/common.h
@@ -82,7 +82,8 @@ int32_t cpu_get_num_math();
 enum llama_example {
     LLAMA_EXAMPLE_COMMON,
     LLAMA_EXAMPLE_SPECULATIVE,
-    LLAMA_EXAMPLE_MAIN,
+    LLAMA_EXAMPLE_COMPLETION,
+    LLAMA_EXAMPLE_CLI,
     LLAMA_EXAMPLE_EMBEDDING,
     LLAMA_EXAMPLE_PERPLEXITY,
     LLAMA_EXAMPLE_RETRIEVAL,
@@ -406,6 +407,7 @@ struct common_params {
     bool simple_io         = false; // improves compatibility with subprocesses and limited consoles
     bool cont_batching     = true;  // insert new sequences for decoding on-the-fly
     bool no_perf           = false; // disable performance metrics
+    bool show_timings      = true;  // show timing information on CLI
     bool ctx_shift         = false; // context shift on infinite text generation
     bool swa_full          = false; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
     bool kv_unified        = false; // enable unified KV cache
diff --git a/common/console.cpp b/common/console.cpp
index 5e9901e4a2..2ea178f81e 100644
--- a/common/console.cpp
+++ b/common/console.cpp
@@ -1,4 +1,5 @@
 #include "console.h"
+#include "log.h"
 #include <vector>
 #include <iostream>
 #include <cassert>
@@ -6,6 +7,10 @@
 #include <cctype>
 #include <cwctype>
 #include <cstdint>
+#include <condition_variable>
+#include <mutex>
+#include <thread>
+#include <stdarg.h>
 
 #if defined(_WIN32)
 #define WIN32_LEAN_AND_MEAN
@@ -35,6 +40,7 @@
 #define ANSI_COLOR_BLUE    "\x1b[34m"
 #define ANSI_COLOR_MAGENTA "\x1b[35m"
 #define ANSI_COLOR_CYAN    "\x1b[36m"
+#define ANSI_COLOR_GRAY    "\x1b[90m"
 #define ANSI_COLOR_RESET   "\x1b[0m"
 #define ANSI_BOLD          "\x1b[1m"
 
@@ -61,17 +67,17 @@ namespace console {
     //
 #endif
 
-    static bool      advanced_display = false;
-    static bool      simple_io        = true;
-    static display_t current_display  = reset;
+    static bool         advanced_display = false;
+    static bool         simple_io        = true;
+    static display_type current_display  = DISPLAY_TYPE_RESET;
 
-    static FILE*     out              = stdout;
+    static FILE*        out              = stdout;
 
 #if defined (_WIN32)
-    static void*     hConsole;
+    static void*        hConsole;
 #else
-    static FILE*     tty              = nullptr;
-    static termios   initial_state;
+    static FILE*        tty              = nullptr;
+    static termios      initial_state;
 #endif
 
     //
@@ -142,7 +148,7 @@ namespace console {
 
     void cleanup() {
         // Reset console display
-        set_display(reset);
+        set_display(DISPLAY_TYPE_RESET);
 
 #if !defined(_WIN32)
         // Restore settings on POSIX systems
@@ -162,20 +168,26 @@ namespace console {
     //
 
     // Keep track of current display and only emit ANSI code if it changes
-    void set_display(display_t display) {
+    void set_display(display_type display) {
         if (advanced_display && current_display != display) {
-            fflush(stdout);
+            common_log_flush(common_log_main());
             switch(display) {
-                case reset:
+                case DISPLAY_TYPE_RESET:
                     fprintf(out, ANSI_COLOR_RESET);
                     break;
-                case prompt:
+                case DISPLAY_TYPE_INFO:
+                    fprintf(out, ANSI_COLOR_MAGENTA);
+                    break;
+                case DISPLAY_TYPE_PROMPT:
                     fprintf(out, ANSI_COLOR_YELLOW);
                     break;
-                case user_input:
+                case DISPLAY_TYPE_REASONING:
+                    fprintf(out, ANSI_COLOR_GRAY);
+                    break;
+                case DISPLAY_TYPE_USER_INPUT:
                     fprintf(out, ANSI_BOLD ANSI_COLOR_GREEN);
                     break;
-                case error:
+                case DISPLAY_TYPE_ERROR:
                     fprintf(out, ANSI_BOLD ANSI_COLOR_RED);
             }
             current_display = display;
@@ -778,7 +790,6 @@ namespace console {
             }
 
             if (is_special_char) {
-                set_display(user_input);
                 replace_last(line.back());
                 is_special_char = false;
             }
@@ -961,7 +972,6 @@ namespace console {
             }
 
             if (!line.empty() && (line.back() == '\\' || line.back() == '/')) {
-                set_display(prompt);
                 replace_last(line.back());
                 is_special_char = true;
             }
@@ -1046,12 +1056,82 @@ namespace console {
     }
 
     bool readline(std::string & line, bool multiline_input) {
-        set_display(user_input);
-
         if (simple_io) {
             return readline_simple(line, multiline_input);
         }
         return readline_advanced(line, multiline_input);
     }
 
+    namespace spinner {
+        static const char LOADING_CHARS[] = {'|', '/', '-', '\\'};
+        static std::condition_variable cv_stop;
+        static std::thread th;
+        static size_t frame = 0; // only modified by one thread
+        static bool running = false;
+        static std::mutex mtx;
+        static auto wait_time = std::chrono::milliseconds(100);
+        static void draw_next_frame() {
+            // don't need lock because only one thread modifies running
+            frame = (frame + 1) % sizeof(LOADING_CHARS);
+            replace_last(LOADING_CHARS[frame]);
+            fflush(out);
+        }
+        void start() {
+            std::unique_lock<std::mutex> lock(mtx);
+            if (simple_io || running) {
+                return;
+            }
+            common_log_flush(common_log_main());
+            fprintf(out, "%c", LOADING_CHARS[0]);
+            fflush(out);
+            frame = 1;
+            running = true;
+            th = std::thread([]() {
+                std::unique_lock<std::mutex> lock(mtx);
+                while (true) {
+                    if (cv_stop.wait_for(lock, wait_time, []{ return !running; })) {
+                        break;
+                    }
+                    draw_next_frame();
+                }
+            });
+        }
+        void stop() {
+            {
+                std::unique_lock<std::mutex> lock(mtx);
+                if (simple_io || !running) {
+                    return;
+                }
+                running = false;
+                cv_stop.notify_all();
+            }
+            if (th.joinable()) {
+                th.join();
+            }
+            replace_last(' ');
+            pop_cursor();
+            fflush(out);
+        }
+    }
+
+    void log(const char * fmt, ...) {
+        va_list args;
+        va_start(args, fmt);
+        vfprintf(out, fmt, args);
+        va_end(args);
+    }
+
+    void error(const char * fmt, ...) {
+        va_list args;
+        va_start(args, fmt);
+        display_type cur = current_display;
+        set_display(DISPLAY_TYPE_ERROR);
+        vfprintf(out, fmt, args);
+        set_display(cur); // restore previous color
+        va_end(args);
+    }
+
+    void flush() {
+        fflush(out);
+    }
 }
diff --git a/common/console.h b/common/console.h
index ec175269b9..fad6d39531 100644
--- a/common/console.h
+++ b/common/console.h
@@ -2,18 +2,40 @@
 
 #pragma once
 
+#include "common.h"
+
 #include <string>
 
-namespace console {
-    enum display_t {
-        reset = 0,
-        prompt,
-        user_input,
-        error
-    };
+enum display_type {
+    DISPLAY_TYPE_RESET = 0,
+    DISPLAY_TYPE_INFO,
+    DISPLAY_TYPE_PROMPT,
+    DISPLAY_TYPE_REASONING,
+    DISPLAY_TYPE_USER_INPUT,
+    DISPLAY_TYPE_ERROR
+};
 
+namespace console {
     void init(bool use_simple_io, bool use_advanced_display);
     void cleanup();
-    void set_display(display_t display);
+    void set_display(display_type display);
     bool readline(std::string & line, bool multiline_input);
+
+    namespace spinner {
+        void start();
+        void stop();
+    }
+
+    // note: the logging API below output directly to stdout
+    // it can negatively impact performance if used on inference thread
+    // only use in in a dedicated CLI thread
+    // for logging in inference thread, use log.h instead
+
+    LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2)
+    void log(const char * fmt, ...);
+
+    LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2)
+    void error(const char * fmt, ...);
+
+    void flush();
 }
diff --git a/common/log.cpp b/common/log.cpp
index 00a03f158d..b17d2b62c3 100644
--- a/common/log.cpp
+++ b/common/log.cpp
@@ -420,6 +420,11 @@ void common_log_set_timestamps(struct common_log * log, bool timestamps) {
     log->set_timestamps(timestamps);
 }
 
+void common_log_flush(struct common_log * log) {
+    log->pause();
+    log->resume();
+}
+
 static int common_get_verbosity(enum ggml_log_level level) {
     switch (level) {
         case GGML_LOG_LEVEL_DEBUG: return LOG_LEVEL_DEBUG;
diff --git a/common/log.h b/common/log.h
index b24f5f000a..f0f8471b5f 100644
--- a/common/log.h
+++ b/common/log.h
@@ -84,6 +84,7 @@ void common_log_set_file      (struct common_log * log, const char * file); // n
 void common_log_set_colors    (struct common_log * log, log_colors colors); // not thread-safe
 void common_log_set_prefix    (struct common_log * log, bool prefix);       // whether to output prefix to each log
 void common_log_set_timestamps(struct common_log * log, bool timestamps);   // whether to output timestamps in the prefix
+void common_log_flush         (struct common_log * log);                    // flush all pending log messages
 
 // helper macros for logging
 // use these to avoid computing log arguments if the verbosity of the log is higher than the threshold
diff --git a/examples/gen-docs/gen-docs.cpp b/examples/gen-docs/gen-docs.cpp
index 77c59a836e..420195f198 100644
--- a/examples/gen-docs/gen-docs.cpp
+++ b/examples/gen-docs/gen-docs.cpp
@@ -76,7 +76,7 @@ static void export_md(std::string fname, llama_example ex) {
 }
 
 int main(int, char **) {
-    export_md("autogen-main.md", LLAMA_EXAMPLE_MAIN);
+    export_md("autogen-main.md", LLAMA_EXAMPLE_COMPLETION);
     export_md("autogen-server.md", LLAMA_EXAMPLE_SERVER);
 
     return 0;
diff --git a/scripts/snapdragon/adb/run-cli.sh b/scripts/snapdragon/adb/run-cli.sh
index ab8d6d49a2..cc5e47c2d6 100755
--- a/scripts/snapdragon/adb/run-cli.sh
+++ b/scripts/snapdragon/adb/run-cli.sh
@@ -46,7 +46,7 @@ adb $adbserial shell " \
     LD_LIBRARY_PATH=$basedir/$branch/lib   \
     ADSP_LIBRARY_PATH=$basedir/$branch/lib \
     $verbose $experimental $sched $opmask $profile $nhvx $ndev       \
-      ./$branch/bin/llama-cli --no-mmap -m $basedir/../gguf/$model   \
+      ./$branch/bin/llama-completion --no-mmap -m $basedir/../gguf/$model   \
          --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1             \
          --ctx-size 8192 --batch-size 128 -ctk q8_0 -ctv q8_0 -fa on \
          -ngl 99 --device $device $cli_opts $@ \
diff --git a/tests/test-lora-conversion-inference.sh b/tests/test-lora-conversion-inference.sh
index 0255494b82..e7d6776933 100755
--- a/tests/test-lora-conversion-inference.sh
+++ b/tests/test-lora-conversion-inference.sh
@@ -79,19 +79,19 @@ run_conversion_and_inference_lora() {
 
     # Run inference
     echo -e "\n\n---------------------------\n\n"
-    echo "Running llama-cli without lora for $model_name with hidden_size $hidden_size..."
-    OUTPUT_BASE=$(./llama-cli -no-cnv -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32.gguf \
+    echo "Running llama-completion without lora for $model_name with hidden_size $hidden_size..."
+    OUTPUT_BASE=$(./llama-completion -no-cnv -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32.gguf \
         -p "$EXPECTED_BASE_FIRST_WORD" -n 50 --seed 42 --temp 0)
 
     echo -e "\n\n---------------------------\n\n"
-    echo "Running llama-cli with hot lora for $model_name with hidden_size $hidden_size..."
-    OUTPUT_LORA_HOT=$(./llama-cli -no-cnv -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32.gguf \
+    echo "Running llama-completion with hot lora for $model_name with hidden_size $hidden_size..."
+    OUTPUT_LORA_HOT=$(./llama-completion -no-cnv -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32.gguf \
         --lora $MODELS_REPO/$model_name/hidden_size=$hidden_size/lora/Lora-F32-LoRA.gguf \
         -p "$EXPECTED_LORA_FIRST_WORD" -n 50 --seed 42 --temp 0)
 
     echo -e "\n\n---------------------------\n\n"
-    echo "Running llama-cli with merged lora for $model_name with hidden_size $hidden_size..."
-    OUTPUT_LORA_MERGED=$(./llama-cli -no-cnv -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32-lora-merged.gguf \
+    echo "Running llama-completion with merged lora for $model_name with hidden_size $hidden_size..."
+    OUTPUT_LORA_MERGED=$(./llama-completion -no-cnv -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32-lora-merged.gguf \
         -p "$EXPECTED_LORA_FIRST_WORD" -n 50 --seed 42 --temp 0)
 
     # Remove any initial white space
diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
index d64956b843..43a0e81949 100644
--- a/tools/CMakeLists.txt
+++ b/tools/CMakeLists.txt
@@ -18,7 +18,8 @@ else()
     add_subdirectory(gguf-split)
     add_subdirectory(imatrix)
     add_subdirectory(llama-bench)
-    add_subdirectory(main)
+    add_subdirectory(cli)
+    add_subdirectory(completion)
     add_subdirectory(perplexity)
     add_subdirectory(quantize)
     if (LLAMA_BUILD_SERVER)
diff --git a/tools/cli/CMakeLists.txt b/tools/cli/CMakeLists.txt
new file mode 100644
index 0000000000..b08fff4c28
--- /dev/null
+++ b/tools/cli/CMakeLists.txt
@@ -0,0 +1,10 @@
+set(TARGET llama-cli)
+add_executable(${TARGET} cli.cpp)
+target_link_libraries(${TARGET} PRIVATE server-context PUBLIC common ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
+
+include_directories(../server)
+
+if(LLAMA_TOOLS_INSTALL)
+    install(TARGETS ${TARGET} RUNTIME)
+endif()
diff --git a/tools/cli/cli.cpp b/tools/cli/cli.cpp
new file mode 100644
index 0000000000..8a8639207b
--- /dev/null
+++ b/tools/cli/cli.cpp
@@ -0,0 +1,395 @@
+#include "common.h"
+#include "arg.h"
+#include "console.h"
+// #include "log.h"
+
+#include "server-context.h"
+#include "server-task.h"
+
+#include <atomic>
+#include <fstream>
+#include <thread>
+#include <signal.h>
+
+#if defined(_WIN32)
+#define WIN32_LEAN_AND_MEAN
+#ifndef NOMINMAX
+#   define NOMINMAX
+#endif
+#include <windows.h>
+#endif
+
+const char * LLAMA_ASCII_LOGO = R"(
+▄▄ ▄▄
+██ ██
+██ ██  ▀▀█▄ ███▄███▄  ▀▀█▄    ▄████ ████▄ ████▄
+██ ██ ▄█▀██ ██ ██ ██ ▄█▀██    ██    ██ ██ ██ ██
+██ ██ ▀█▄██ ██ ██ ██ ▀█▄██ ██ ▀████ ████▀ ████▀
+                                    ██    ██
+                                    ▀▀    ▀▀
+)";
+
+static std::atomic<bool> g_is_interrupted = false;
+static bool should_stop() {
+    return g_is_interrupted.load();
+}
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
+static void signal_handler(int) {
+    if (g_is_interrupted.load()) {
+        // second Ctrl+C - exit immediately
+        // make sure to clear colors before exiting (not using LOG or console.cpp here to avoid deadlock)
+        fprintf(stdout, "\033[0m\n");
+        fflush(stdout);
+        std::exit(130);
+    }
+    g_is_interrupted.store(true);
+}
+#endif
+
+struct cli_context {
+    server_context ctx_server;
+    json messages = json::array();
+    std::vector<raw_buffer> input_files;
+    task_params defaults;
+
+    // thread for showing "loading" animation
+    std::atomic<bool> loading_show;
+
+    cli_context(const common_params & params) {
+        defaults.sampling    = params.sampling;
+        defaults.speculative = params.speculative;
+        defaults.n_keep      = params.n_keep;
+        defaults.n_predict   = params.n_predict;
+        defaults.antiprompt  = params.antiprompt;
+
+        defaults.stream = true; // make sure we always use streaming mode
+        defaults.timings_per_token = true; // in order to get timings even when we cancel mid-way
+        // defaults.return_progress = true; // TODO: show progress
+        defaults.oaicompat_chat_syntax.reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
+    }
+
+    std::string generate_completion(result_timings & out_timings) {
+        server_response_reader rd = ctx_server.get_response_reader();
+        {
+            // TODO: reduce some copies here in the future
+            server_task task = server_task(SERVER_TASK_TYPE_COMPLETION);
+            task.id        = rd.get_new_id();
+            task.index     = 0;
+            task.params    = defaults;    // copy
+            task.cli_input = messages;    // copy
+            task.cli_files = input_files; // copy
+            rd.post_task({std::move(task)});
+        }
+
+        // wait for first result
+        console::spinner::start();
+        server_task_result_ptr result = rd.next(should_stop);
+
+        console::spinner::stop();
+        std::string curr_content;
+        bool is_thinking = false;
+
+        while (result) {
+            if (should_stop()) {
+                break;
+            }
+            if (result->is_error()) {
+                json err_data = result->to_json();
+                if (err_data.contains("message")) {
+                    console::error("Error: %s\n", err_data["message"].get<std::string>().c_str());
+                } else {
+                    console::error("Error: %s\n", err_data.dump().c_str());
+                }
+                return curr_content;
+            }
+            auto res_partial = dynamic_cast<server_task_result_cmpl_partial *>(result.get());
+            if (res_partial) {
+                out_timings = std::move(res_partial->timings);
+                for (const auto & diff : res_partial->oaicompat_msg_diffs) {
+                    if (!diff.content_delta.empty()) {
+                        if (is_thinking) {
+                            console::log("\n[End thinking]\n\n");
+                            console::set_display(DISPLAY_TYPE_RESET);
+                            is_thinking = false;
+                        }
+                        curr_content += diff.content_delta;
+                        console::log("%s", diff.content_delta.c_str());
+                        console::flush();
+                    }
+                    if (!diff.reasoning_content_delta.empty()) {
+                        console::set_display(DISPLAY_TYPE_REASONING);
+                        if (!is_thinking) {
+                            console::log("[Start thinking]\n");
+                        }
+                        is_thinking = true;
+                        console::log("%s", diff.reasoning_content_delta.c_str());
+                        console::flush();
+                    }
+                }
+            }
+            auto res_final = dynamic_cast<server_task_result_cmpl_final *>(result.get());
+            if (res_final) {
+                out_timings = std::move(res_final->timings);
+                break;
+            }
+            result = rd.next(should_stop);
+        }
+        g_is_interrupted.store(false);
+        // server_response_reader automatically cancels pending tasks upon destruction
+        return curr_content;
+    }
+
+    // TODO: support remote files in the future (http, https, etc)
+    std::string load_input_file(const std::string & fname, bool is_media) {
+        std::ifstream file(fname, std::ios::binary);
+        if (!file) {
+            return "";
+        }
+        if (is_media) {
+            raw_buffer buf;
+            buf.assign((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
+            input_files.push_back(std::move(buf));
+            return mtmd_default_marker();
+        } else {
+            std::string content((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
+            return content;
+        }
+    }
+};
+
+int main(int argc, char ** argv) {
+    common_params params;
+
+    params.verbosity = LOG_LEVEL_ERROR; // by default, less verbose logs
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_CLI)) {
+        return 1;
+    }
+
+    // TODO: maybe support it later?
+    if (params.conversation_mode == COMMON_CONVERSATION_MODE_DISABLED) {
+        console::error("--no-conversation is not supported by llama-cli\n");
+        console::error("please use llama-completion instead\n");
+    }
+
+    common_init();
+
+    // struct that contains llama context and inference
+    cli_context ctx_cli(params);
+
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    // TODO: avoid using atexit() here by making `console` a singleton
+    console::init(params.simple_io, params.use_color);
+    atexit([]() { console::cleanup(); });
+
+    console::set_display(DISPLAY_TYPE_RESET);
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
+    struct sigaction sigint_action;
+    sigint_action.sa_handler = signal_handler;
+    sigemptyset (&sigint_action.sa_mask);
+    sigint_action.sa_flags = 0;
+    sigaction(SIGINT, &sigint_action, NULL);
+    sigaction(SIGTERM, &sigint_action, NULL);
+#elif defined (_WIN32)
+    auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
+        return (ctrl_type == CTRL_C_EVENT) ? (signal_handler(SIGINT), true) : false;
+    };
+    SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
+#endif
+
+    console::log("\nLoading model... "); // followed by loading animation
+    console::spinner::start();
+    if (!ctx_cli.ctx_server.load_model(params)) {
+        console::spinner::stop();
+        console::error("\nFailed to load the model\n");
+        return 1;
+    }
+
+    ctx_cli.ctx_server.init();
+
+    console::spinner::stop();
+    console::log("\n");
+
+    std::thread inference_thread([&ctx_cli]() {
+        ctx_cli.ctx_server.start_loop();
+    });
+
+    auto inf = ctx_cli.ctx_server.get_info();
+    std::string modalities = "text";
+    if (inf.has_inp_image) {
+        modalities += ", vision";
+    }
+    if (inf.has_inp_audio) {
+        modalities += ", audio";
+    }
+
+    if (!params.system_prompt.empty()) {
+        ctx_cli.messages.push_back({
+            {"role",    "system"},
+            {"content", params.system_prompt}
+        });
+    }
+
+    console::log("\n");
+    console::log("%s\n", LLAMA_ASCII_LOGO);
+    console::log("build      : %s\n", inf.build_info.c_str());
+    console::log("model      : %s\n", inf.model_name.c_str());
+    console::log("modalities : %s\n", modalities.c_str());
+    if (!params.system_prompt.empty()) {
+        console::log("using custom system prompt\n");
+    }
+    console::log("\n");
+    console::log("available commands:\n");
+    console::log("  /exit or Ctrl+C     stop or exit\n");
+    console::log("  /regen              regenerate the last response\n");
+    console::log("  /clear              clear the chat history\n");
+    console::log("  /read               add a text file\n");
+    if (inf.has_inp_image) {
+        console::log("  /image <file>       add an image file\n");
+    }
+    if (inf.has_inp_audio) {
+        console::log("  /audio <file>       add an audio file\n");
+    }
+    console::log("\n");
+
+    // interactive loop
+    std::string cur_msg;
+    while (true) {
+        std::string buffer;
+        console::set_display(DISPLAY_TYPE_USER_INPUT);
+        if (params.prompt.empty()) {
+            console::log("\n> ");
+            std::string line;
+            bool another_line = true;
+            do {
+                another_line = console::readline(line, params.multiline_input);
+                buffer += line;
+            } while (another_line);
+        } else {
+            // process input prompt from args
+            for (auto & fname : params.image) {
+                std::string marker = ctx_cli.load_input_file(fname, true);
+                if (marker.empty()) {
+                    console::error("file does not exist or cannot be opened: '%s'\n", fname.c_str());
+                    break;
+                }
+                console::log("Loaded media from '%s'\n", fname.c_str());
+                cur_msg += marker;
+            }
+            buffer = params.prompt;
+            if (buffer.size() > 500) {
+                console::log("\n> %s ... (truncated)\n", buffer.substr(0, 500).c_str());
+            } else {
+                console::log("\n> %s\n", buffer.c_str());
+            }
+            params.prompt.clear(); // only use it once
+        }
+        console::set_display(DISPLAY_TYPE_RESET);
+        console::log("\n");
+
+        if (should_stop()) {
+            g_is_interrupted.store(false);
+            break;
+        }
+
+        // remove trailing newline
+        if (!buffer.empty() &&buffer.back() == '\n') {
+            buffer.pop_back();
+        }
+
+        // skip empty messages
+        if (buffer.empty()) {
+            continue;
+        }
+
+        bool add_user_msg = true;
+
+        // process commands
+        if (string_starts_with(buffer, "/exit")) {
+            break;
+        } else if (string_starts_with(buffer, "/regen")) {
+            if (ctx_cli.messages.size() >= 2) {
+                size_t last_idx = ctx_cli.messages.size() - 1;
+                ctx_cli.messages.erase(last_idx);
+                add_user_msg = false;
+            } else {
+                console::error("No message to regenerate.\n");
+                continue;
+            }
+        } else if (string_starts_with(buffer, "/clear")) {
+            ctx_cli.messages.clear();
+            ctx_cli.input_files.clear();
+            console::log("Chat history cleared.\n");
+            continue;
+        } else if (
+                (string_starts_with(buffer, "/image ") && inf.has_inp_image) ||
+                (string_starts_with(buffer, "/audio ") && inf.has_inp_audio)) {
+            // just in case (bad copy-paste for example), we strip all trailing/leading spaces
+            std::string fname = string_strip(buffer.substr(7));
+            std::string marker = ctx_cli.load_input_file(fname, true);
+            if (marker.empty()) {
+                console::error("file does not exist or cannot be opened: '%s'\n", fname.c_str());
+                continue;
+            }
+            cur_msg += marker;
+            console::log("Loaded media from '%s'\n", fname.c_str());
+            continue;
+        } else if (string_starts_with(buffer, "/read ")) {
+            std::string fname = string_strip(buffer.substr(6));
+            std::string marker = ctx_cli.load_input_file(fname, false);
+            if (marker.empty()) {
+                console::error("file does not exist or cannot be opened: '%s'\n", fname.c_str());
+                continue;
+            }
+            cur_msg += marker;
+            console::log("Loaded text from '%s'\n", fname.c_str());
+            continue;
+        } else {
+            // not a command
+            cur_msg += buffer;
+        }
+
+        // generate response
+        if (add_user_msg) {
+            ctx_cli.messages.push_back({
+                {"role",    "user"},
+                {"content", cur_msg}
+            });
+            cur_msg.clear();
+        }
+        result_timings timings;
+        std::string assistant_content = ctx_cli.generate_completion(timings);
+        ctx_cli.messages.push_back({
+            {"role",    "assistant"},
+            {"content", assistant_content}
+        });
+        console::log("\n");
+
+        if (params.show_timings) {
+            console::set_display(DISPLAY_TYPE_INFO);
+            console::log("\n");
+            console::log("[ Prompt: %.1f t/s | Generation: %.1f t/s ]\n", timings.prompt_per_second, timings.predicted_per_second);
+            console::set_display(DISPLAY_TYPE_RESET);
+        }
+
+        if (params.single_turn) {
+            break;
+        }
+    }
+
+    console::set_display(DISPLAY_TYPE_RESET);
+
+    console::log("\nExiting...\n");
+    ctx_cli.ctx_server.terminate();
+    inference_thread.join();
+
+    // bump the log level to display timings
+    common_log_set_verbosity_thold(LOG_LEVEL_INFO);
+    llama_memory_breakdown_print(ctx_cli.ctx_server.get_llama_context());
+
+    return 0;
+}
diff --git a/tools/main/CMakeLists.txt b/tools/completion/CMakeLists.txt
similarity index 74%
rename from tools/main/CMakeLists.txt
rename to tools/completion/CMakeLists.txt
index 8f8e9d444c..126ae6ab3d 100644
--- a/tools/main/CMakeLists.txt
+++ b/tools/completion/CMakeLists.txt
@@ -1,5 +1,5 @@
-set(TARGET llama-cli)
-add_executable(${TARGET} main.cpp)
+set(TARGET llama-completion)
+add_executable(${TARGET} completion.cpp)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
 
diff --git a/tools/main/README.md b/tools/completion/README.md
similarity index 100%
rename from tools/main/README.md
rename to tools/completion/README.md
diff --git a/tools/main/main.cpp b/tools/completion/completion.cpp
similarity index 98%
rename from tools/main/main.cpp
rename to tools/completion/completion.cpp
index 960ddbe391..79581eacb5 100644
--- a/tools/main/main.cpp
+++ b/tools/completion/completion.cpp
@@ -86,7 +86,7 @@ static void sigint_handler(int signo) {
 int main(int argc, char ** argv) {
     common_params params;
     g_params = &params;
-    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_MAIN, print_usage)) {
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMPLETION, print_usage)) {
         return 1;
     }
 
@@ -521,12 +521,6 @@ int main(int argc, char ** argv) {
         is_interacting = params.interactive_first;
     }
 
-    LOG_WRN("*****************************\n");
-    LOG_WRN("IMPORTANT: The current llama-cli will be moved to llama-completion in the near future\n");
-    LOG_WRN("  New llama-cli will have enhanced features and improved user experience\n");
-    LOG_WRN("  More info: https://github.com/ggml-org/llama.cpp/discussions/17618\n");
-    LOG_WRN("*****************************\n");
-
     bool is_antiprompt        = false;
     bool input_echo           = true;
     bool display              = true;
@@ -543,7 +537,7 @@ int main(int argc, char ** argv) {
     std::ostringstream assistant_ss; // for storing current assistant message, used in conversation mode
 
     // the first thing we will do is to output the prompt, so set color accordingly
-    console::set_display(console::prompt);
+    console::set_display(DISPLAY_TYPE_PROMPT);
     display = params.display_prompt;
 
     std::vector<llama_token> embd;
@@ -588,9 +582,9 @@ int main(int argc, char ** argv) {
                 const int skipped_tokens = (int) embd.size() - max_embd_size;
                 embd.resize(max_embd_size);
 
-                console::set_display(console::error);
+                console::set_display(DISPLAY_TYPE_ERROR);
                 LOG_WRN("<<input too long: skipped %d token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
-                console::set_display(console::reset);
+                console::set_display(DISPLAY_TYPE_RESET);
             }
 
             if (ga_n == 1) {
@@ -772,7 +766,7 @@ int main(int argc, char ** argv) {
 
         // reset color to default if there is no pending user input
         if (input_echo && (int) embd_inp.size() == n_consumed) {
-            console::set_display(console::reset);
+            console::set_display(DISPLAY_TYPE_RESET);
             display = true;
         }
 
@@ -868,7 +862,7 @@ int main(int argc, char ** argv) {
                 }
 
                 // color user input only
-                console::set_display(console::user_input);
+                console::set_display(DISPLAY_TYPE_USER_INPUT);
                 display = params.display_prompt;
 
                 std::string line;
@@ -879,7 +873,7 @@ int main(int argc, char ** argv) {
                 } while (another_line);
 
                 // done taking input, reset color
-                console::set_display(console::reset);
+                console::set_display(DISPLAY_TYPE_RESET);
                 display = true;
 
                 if (buffer.empty()) { // Ctrl+D on empty line exits
diff --git a/tools/gguf-split/tests.sh b/tools/gguf-split/tests.sh
index e8677018f5..c8dd0b0079 100755
--- a/tools/gguf-split/tests.sh
+++ b/tools/gguf-split/tests.sh
@@ -19,7 +19,7 @@ fi
 set -x
 
 SPLIT=$1/llama-gguf-split
-MAIN=$1/llama-cli
+MAIN=$1/llama-completion
 WORK_PATH=$TMP_DIR/gguf-split
 ROOT_DIR=$(realpath $(dirname $0)/../../)
 
diff --git a/tools/mtmd/mtmd-cli.cpp b/tools/mtmd/mtmd-cli.cpp
index b5bbc6536b..a75af406cd 100644
--- a/tools/mtmd/mtmd-cli.cpp
+++ b/tools/mtmd/mtmd-cli.cpp
@@ -310,6 +310,9 @@ int main(int argc, char ** argv) {
 
     if (g_is_interrupted) return 130;
 
+    LOG_WRN("WARN: This is an experimental CLI for testing multimodal capability.\n");
+    LOG_WRN("      For normal use cases, please use the standard llama-cli\n");
+
     if (is_single_turn) {
         g_is_generating = true;
         if (params.prompt.find(mtmd_default_marker()) == std::string::npos) {
@@ -349,11 +352,11 @@ int main(int argc, char ** argv) {
         while (!g_is_interrupted) {
             g_is_generating = false;
             LOG("\n> ");
-            console::set_display(console::user_input);
+            console::set_display(DISPLAY_TYPE_USER_INPUT);
             std::string line;
             console::readline(line, false);
             if (g_is_interrupted) break;
-            console::set_display(console::reset);
+            console::set_display(DISPLAY_TYPE_RESET);
             line = string_strip(line);
             if (line.empty()) {
                 continue;
diff --git a/tools/quantize/tests.sh b/tools/quantize/tests.sh
index acc54fd9b1..2cae588e9e 100644
--- a/tools/quantize/tests.sh
+++ b/tools/quantize/tests.sh
@@ -20,7 +20,7 @@ set -x
 
 SPLIT=$1/llama-gguf-split
 QUANTIZE=$1/llama-quantize
-MAIN=$1/llama-cli
+MAIN=$1/llama-completion
 WORK_PATH=$TMP_DIR/quantize
 ROOT_DIR=$(realpath $(dirname $0)/../../)
 
diff --git a/tools/server/CMakeLists.txt b/tools/server/CMakeLists.txt
index 1aa659a906..a39b4c5b35 100644
--- a/tools/server/CMakeLists.txt
+++ b/tools/server/CMakeLists.txt
@@ -1,7 +1,33 @@
-set(TARGET llama-server)
-
 include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR})
 
+# server-context containing the core server logic, used by llama-server and CLI
+
+set(TARGET server-context)
+
+add_library(${TARGET} STATIC
+    server-task.cpp
+    server-task.h
+    server-queue.cpp
+    server-queue.h
+    server-common.cpp
+    server-common.h
+    server-context.cpp
+    server-context.h
+)
+
+if (BUILD_SHARED_LIBS)
+    set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
+endif()
+
+target_include_directories(${TARGET} PRIVATE ../mtmd)
+target_include_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR})
+target_link_libraries(${TARGET} PUBLIC common mtmd ${CMAKE_THREAD_LIBS_INIT})
+
+
+# llama-server executable
+
+set(TARGET llama-server)
+
 if (NOT LLAMA_HTTPLIB)
     message(FATAL_ERROR "LLAMA_HTTPLIB is OFF, cannot build llama-server. Hint: to skip building server, set -DLLAMA_BUILD_SERVER=OFF")
 endif()
@@ -12,14 +38,6 @@ set(TARGET_SRCS
     server-http.h
     server-models.cpp
     server-models.h
-    server-task.cpp
-    server-task.h
-    server-queue.cpp
-    server-queue.h
-    server-common.cpp
-    server-common.h
-    server-context.cpp
-    server-context.h
 )
 set(PUBLIC_ASSETS
     index.html.gz
@@ -43,7 +61,7 @@ install(TARGETS ${TARGET} RUNTIME)
 
 target_include_directories(${TARGET} PRIVATE ../mtmd)
 target_include_directories(${TARGET} PRIVATE ${CMAKE_SOURCE_DIR})
-target_link_libraries(${TARGET} PRIVATE common mtmd cpp-httplib ${CMAKE_THREAD_LIBS_INIT})
+target_link_libraries(${TARGET} PRIVATE server-context PUBLIC common cpp-httplib ${CMAKE_THREAD_LIBS_INIT})
 
 if (WIN32)
     TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 4578f8d7a9..5a67f508df 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -1474,6 +1474,44 @@ struct server_context_impl {
     // Functions to process the task
     //
 
+    // tokenize the input if it's set by CLI, return false on error
+    bool tokenize_cli_input(server_task & task) {
+        if (task.cli_input == nullptr) {
+            return true; // nothing to do
+        }
+        try {
+            auto & opt = oai_parser_opt;
+            common_chat_templates_inputs inputs;
+            inputs.messages              = common_chat_msgs_parse_oaicompat(task.cli_input);
+            inputs.tools                 = {}; // TODO
+            inputs.tool_choice           = COMMON_CHAT_TOOL_CHOICE_NONE;
+            inputs.json_schema           = ""; // TODO
+            inputs.grammar               = ""; // TODO
+            inputs.use_jinja             = opt.use_jinja;
+            inputs.parallel_tool_calls   = false;
+            inputs.add_generation_prompt = true;
+            inputs.reasoning_format      = opt.reasoning_format;
+            inputs.enable_thinking       = opt.enable_thinking;
+
+            // Apply chat template to the list of messages
+            auto chat_params = common_chat_templates_apply(opt.tmpls, inputs);
+
+            // tokenize the resulting prompt
+            auto & prompt = chat_params.prompt;
+            if (mctx != nullptr) {
+                task.tokens = process_mtmd_prompt(mctx, prompt, task.cli_files);
+            } else {
+                task.tokens = std::move(tokenize_input_prompts(vocab, mctx, prompt, true, true)[0]);
+            }
+            task.cli_input.clear();
+            task.cli_files.clear();
+        } catch (const std::exception & e) {
+            send_error(task, std::string("Failed to format input: ") + e.what(), ERROR_TYPE_INVALID_REQUEST);
+            return false;
+        }
+        return true;
+    }
+
     void process_single_task(server_task && task) {
         switch (task.type) {
             case SERVER_TASK_TYPE_COMPLETION:
@@ -1481,6 +1519,10 @@ struct server_context_impl {
             case SERVER_TASK_TYPE_EMBEDDING:
             case SERVER_TASK_TYPE_RERANK:
                 {
+                    if (!tokenize_cli_input(task)) {
+                        break;
+                    }
+
                     const int id_slot = task.id_slot;
 
                     server_slot * slot = id_slot != -1 ? get_slot_by_id(id_slot) : get_available_slot(task);
@@ -1690,7 +1732,6 @@ struct server_context_impl {
                     res->id = task.id;
                     queue_results.send(std::move(res));
                 } break;
-
         }
     }
 
@@ -2626,6 +2667,15 @@ server_response_reader server_context::get_response_reader() {
     return impl->get_response_reader();
 }
 
+server_context_info server_context::get_info() const {
+    return server_context_info {
+        /* build_info    */ build_info,
+        /* model_name    */ impl->model_name,
+        /* has_inp_image */ impl->oai_parser_opt.allow_image,
+        /* has_inp_audio */ impl->oai_parser_opt.allow_audio,
+    };
+}
+
 
 
 // generator-like API for HTTP response generation
diff --git a/tools/server/server-context.h b/tools/server/server-context.h
index eaa1380877..230b25952e 100644
--- a/tools/server/server-context.h
+++ b/tools/server/server-context.h
@@ -9,6 +9,13 @@
 
 struct server_context_impl; // private implementation
 
+struct server_context_info {
+    std::string build_info;
+    std::string model_name;
+    bool has_inp_image;
+    bool has_inp_audio;
+};
+
 struct server_context {
     std::unique_ptr<server_context_impl> impl;
 
@@ -33,6 +40,10 @@ struct server_context {
 
     // get a new response reader, used by CLI application
     server_response_reader get_response_reader();
+
+    // get server info
+    // used by CLI application
+    server_context_info get_info() const;
 };
 
 
diff --git a/tools/server/server-queue.h b/tools/server/server-queue.h
index 726eadf4ef..8780d7fe12 100644
--- a/tools/server/server-queue.h
+++ b/tools/server/server-queue.h
@@ -135,7 +135,10 @@ struct server_response_reader {
         stop();
     }
 
-    void post_task(server_task && tasks);
+    int get_new_id() {
+        return queue_tasks.get_new_id();
+    }
+    void post_task(server_task && task);
     void post_tasks(std::vector<server_task> && tasks);
     bool has_next() const;
 
diff --git a/tools/server/server-task.h b/tools/server/server-task.h
index 9011ff944b..0759094a01 100644
--- a/tools/server/server-task.h
+++ b/tools/server/server-task.h
@@ -120,6 +120,10 @@ struct server_task {
     task_params   params;
     server_tokens tokens;
 
+    // only used by CLI, this delegates the tokenization to the server
+    json                    cli_input = nullptr;
+    std::vector<raw_buffer> cli_files;
+
     server_task_type type;
 
     // used by SERVER_TASK_TYPE_SLOT_SAVE, SERVER_TASK_TYPE_SLOT_RESTORE, SERVER_TASK_TYPE_SLOT_ERASE

From 4df6e859e92dac52536f735785bb9e0a3bc63e2b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Wed, 10 Dec 2025 16:16:20 +0100
Subject: [PATCH 048/135] cuda : add missing support check for xielu (#17895)

---
 ggml/src/ggml-cuda/ggml-cuda.cu | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 279679a4ea..8d17bc669a 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -4313,6 +4313,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                 case GGML_UNARY_OP_EXPM1:
                 case GGML_UNARY_OP_SOFTPLUS:
                 case GGML_UNARY_OP_ELU:
+                case GGML_UNARY_OP_XIELU:
                 case GGML_UNARY_OP_FLOOR:
                 case GGML_UNARY_OP_CEIL:
                 case GGML_UNARY_OP_ROUND:

From 4dff236a522bd0ed949331d6cb1ee2a1b3615c35 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Wed, 10 Dec 2025 20:53:16 +0200
Subject: [PATCH 049/135] ggml : remove GGML_KQ_MASK_PAD constant (#17910)

* ggml : remove GGML_KQ_MASK_PAD constant

* cont : remove comment
---
 ggml/include/ggml.h        | 12 +++++-------
 ggml/src/ggml.c            |  2 --
 src/llama-context.cpp      |  8 --------
 src/llama-graph.cpp        | 20 ++++++++++----------
 src/llama-kv-cache.cpp     |  5 ++---
 tests/test-backend-ops.cpp |  2 +-
 tools/mtmd/clip.cpp        |  6 +-----
 7 files changed, 19 insertions(+), 36 deletions(-)

diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index 6bc762c069..686da3dbd1 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -2305,13 +2305,11 @@ extern "C" {
             float                 stop,
             float                 step);
 
-#define GGML_KQ_MASK_PAD 1
-
-    // q:    [n_embd_k, n_batch,     n_head,    ne3 ]
-    // k:    [n_embd_k, n_kv,        n_head_kv, ne3 ]
-    // v:    [n_embd_v, n_kv,        n_head_kv, ne3 ] !! not transposed !!
-    // mask: [n_kv,     n_batch_pad, ne32,      ne33] !! n_batch_pad = GGML_PAD(n_batch, GGML_KQ_MASK_PAD) !!
-    // res:  [n_embd_v, n_head,      n_batch,   ne3 ] !! permuted !!
+    // q:    [n_embd_k, n_batch, n_head,    ne3 ]
+    // k:    [n_embd_k, n_kv,    n_head_kv, ne3 ]
+    // v:    [n_embd_v, n_kv,    n_head_kv, ne3 ] !! not transposed !!
+    // mask: [n_kv,     n_batch, ne32,      ne33]
+    // res:  [n_embd_v, n_head,  n_batch,   ne3 ] !! permuted !!
     //
     // broadcast:
     //   n_head % n_head_kv == 0
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 530ff7b953..f0913cd359 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -5260,8 +5260,6 @@ struct ggml_tensor * ggml_flash_attn_ext(
 
     if (mask) {
         GGML_ASSERT(ggml_is_contiguous(mask));
-        GGML_ASSERT(mask->ne[1] >= GGML_PAD(q->ne[1], GGML_KQ_MASK_PAD) &&
-                "the Flash-Attention kernel requires the mask to be padded to GGML_KQ_MASK_PAD and at least n_queries big");
         //GGML_ASSERT(ggml_can_repeat_rows(mask, qk));
 
         GGML_ASSERT(q->ne[2] % mask->ne[2] == 0);
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 4171400713..2692297dca 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -93,14 +93,6 @@ llama_context::llama_context(
     // with causal attention, the batch size is limited by the context size
     cparams.n_batch = cparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch;
 
-    // the batch has to be at least GGML_KQ_MASK_PAD because we will be padding the KQ_mask
-    // this is required by GPU kernels in order to avoid out-of-bounds accesses (e.g. ggml_flash_attn_ext)
-    // ref: https://github.com/ggerganov/llama.cpp/pull/5021
-    // TODO: this padding is not needed for the cache-less context so we should probably move it to llama_memory
-    if (cparams.n_batch < GGML_KQ_MASK_PAD) {
-        LLAMA_LOG_WARN("%s: n_batch is less than GGML_KQ_MASK_PAD - increasing to %d\n", __func__, GGML_KQ_MASK_PAD);
-        cparams.n_batch = GGML_KQ_MASK_PAD;
-    }
     cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
 
     cparams.op_offload = params.op_offload;
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 43620df780..6cf9a883a6 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -385,7 +385,7 @@ bool llm_graph_input_attn_kv::can_reuse(const llm_graph_params & params) {
   //res &= self_v_idxs->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
 
     res &= self_kq_mask->ne[0] == mctx->get_n_kv();
-    res &= self_kq_mask->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD);
+    res &= self_kq_mask->ne[1] == params.ubatch.n_tokens;
 
     return res;
 }
@@ -416,10 +416,10 @@ bool llm_graph_input_attn_kv_iswa::can_reuse(const llm_graph_params & params) {
   //res &= self_v_idxs_swa->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
 
     res &= self_kq_mask->ne[0] == mctx->get_base()->get_n_kv();
-    res &= self_kq_mask->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD);
+    res &= self_kq_mask->ne[1] == params.ubatch.n_tokens;
 
     res &= self_kq_mask_swa->ne[0] == mctx->get_swa()->get_n_kv();
-    res &= self_kq_mask_swa->ne[1] == GGML_PAD(params.ubatch.n_tokens, GGML_KQ_MASK_PAD);
+    res &= self_kq_mask_swa->ne[1] == params.ubatch.n_tokens;
 
     return res;
 }
@@ -452,7 +452,7 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
             }
         }
 
-        for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
+        for (int i = n_tokens; i < n_tokens; ++i) {
             for (int j = 0; j < n_enc; ++j) {
                 data[h*(n_enc*n_tokens) + i*n_enc + j] = -INFINITY;
             }
@@ -1470,13 +1470,13 @@ llm_graph_input_attn_no_cache * llm_graph_context::build_attn_inp_no_cache() con
     auto inp = std::make_unique<llm_graph_input_attn_no_cache>(hparams, cparams);
 
     // note: there is no KV cache, so the number of KV values is equal to the number of tokens in the batch
-    inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1);
+    inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens, 1, 1);
     ggml_set_input(inp->self_kq_mask);
 
     inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask;
 
     if (hparams.swa_type != LLAMA_SWA_TYPE_NONE) {
-        inp->self_kq_mask_swa = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1);
+        inp->self_kq_mask_swa = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens, 1, 1);
         ggml_set_input(inp->self_kq_mask_swa);
 
         inp->self_kq_mask_swa_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask_swa, GGML_TYPE_F16) : inp->self_kq_mask_swa;
@@ -1558,7 +1558,7 @@ static std::unique_ptr<llm_graph_input_attn_kv> build_attn_inp_kv_impl(
         inp->self_k_idxs = mctx_cur->build_input_k_idxs(ctx0, ubatch);
         inp->self_v_idxs = mctx_cur->build_input_v_idxs(ctx0, ubatch);
 
-        inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens/n_stream, GGML_KQ_MASK_PAD), 1, n_stream);
+        inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, n_tokens/n_stream, 1, n_stream);
         ggml_set_input(inp->self_kq_mask);
 
         inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask;
@@ -1701,7 +1701,7 @@ llm_graph_input_attn_cross * llm_graph_context::build_attn_inp_cross() const {
 
     const int32_t n_enc = !cross->v_embd.empty() ? cross->n_enc : hparams.n_ctx_train;
 
-    inp->cross_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_enc, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1, 1);
+    inp->cross_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_enc, n_tokens, 1, 1);
     ggml_set_input(inp->cross_kq_mask);
 
     inp->cross_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->cross_kq_mask, GGML_TYPE_F16) : inp->cross_kq_mask;
@@ -1767,7 +1767,7 @@ llm_graph_input_attn_kv_iswa * llm_graph_context::build_attn_inp_kv_iswa() const
         inp->self_k_idxs = mctx_cur->get_base()->build_input_k_idxs(ctx0, ubatch);
         inp->self_v_idxs = mctx_cur->get_base()->build_input_v_idxs(ctx0, ubatch);
 
-        inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens/n_stream, GGML_KQ_MASK_PAD), 1, n_stream);
+        inp->self_kq_mask = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, n_tokens/n_stream, 1, n_stream);
         ggml_set_input(inp->self_kq_mask);
 
         inp->self_kq_mask_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask, GGML_TYPE_F16) : inp->self_kq_mask;
@@ -1781,7 +1781,7 @@ llm_graph_input_attn_kv_iswa * llm_graph_context::build_attn_inp_kv_iswa() const
         inp->self_k_idxs_swa = mctx_cur->get_swa()->build_input_k_idxs(ctx0, ubatch);
         inp->self_v_idxs_swa = mctx_cur->get_swa()->build_input_v_idxs(ctx0, ubatch);
 
-        inp->self_kq_mask_swa = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens/n_stream, GGML_KQ_MASK_PAD), 1, n_stream);
+        inp->self_kq_mask_swa = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_kv, n_tokens/n_stream, 1, n_stream);
         ggml_set_input(inp->self_kq_mask_swa);
 
         inp->self_kq_mask_swa_cnv = cparams.flash_attn ? ggml_cast(ctx0, inp->self_kq_mask_swa, GGML_TYPE_F16) : inp->self_kq_mask_swa;
diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index e26385a1fe..3e02bd6297 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -1232,8 +1232,7 @@ void llama_kv_cache::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * u
     GGML_ASSERT(n_tokens%n_stream == 0);
 
     // n_tps == n_tokens_per_stream
-    const int64_t n_tps     = n_tokens/n_stream;
-    const int64_t n_tps_pad = GGML_PAD(n_tps, GGML_KQ_MASK_PAD);
+    const int64_t n_tps = n_tokens/n_stream;
 
     std::fill(data, data + ggml_nelements(dst), -INFINITY);
 
@@ -1266,7 +1265,7 @@ void llama_kv_cache::set_input_kq_mask(ggml_tensor * dst, const llama_ubatch * u
                 const llama_pos p1_x = is_2d ? ubatch->pos[i + ubatch->n_tokens*2] : 0;
                 const llama_pos p1_y = is_2d ? ubatch->pos[i + ubatch->n_tokens]   : 0;
 
-                const uint64_t idst = n_kv*(h*n_stream*n_tps_pad + s*n_tps_pad + ii);
+                const uint64_t idst = n_kv*(h*n_stream*n_tps + s*n_tps + ii);
 
                 for (uint32_t j = 0; j < n_kv; ++j) {
                     if (cells.is_empty(j)) {
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index a6f266601f..7be1f66038 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -5875,7 +5875,7 @@ struct test_flash_attn_ext : public test_case {
 
         ggml_tensor * m = nullptr;
         if (mask) {
-            m = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), 1, nr23[1]);
+            m = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, nb, 1, nr23[1]);
             ggml_set_name(m, "m");
         }
 
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index 3ed08a0fec..e5f7117dbf 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -775,10 +775,6 @@ struct clip_graph {
 
             // if flash attn is used, we need to pad the mask and cast to f16
             if (ctx->flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) {
-                int n_pad = GGML_PAD(window_mask->ne[1], GGML_KQ_MASK_PAD) - window_mask->ne[1];
-                if (n_pad > 0) {
-                    window_mask = ggml_pad(ctx0, window_mask, 0, n_pad, 0, 0);
-                }
                 window_mask = ggml_cast(ctx0, window_mask, GGML_TYPE_F16);
             }
 
@@ -791,7 +787,7 @@ struct clip_graph {
 
         // loop over layers
         for (int il = 0; il < n_layer; il++) {
-            auto & layer = model.layers[il];
+            const auto & layer = model.layers[il];
             const bool full_attn = use_window_attn ? (il + 1) % n_wa_pattern == 0 : true;
 
             ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states

From e1f4921980444b30d3b4fa1c6e18cdecd85b0690 Mon Sep 17 00:00:00 2001
From: Max Krasnyansky <maxk@qti.qualcomm.com>
Date: Wed, 10 Dec 2025 12:32:23 -0800
Subject: [PATCH 050/135] Fix race conditions in threadpool when dealing with
 dynamic/frequent n_threads changes (#17748)

* tests: update barrier test to check for race condition in active threads

* cpu: combine n_graph and n_threads into a single atomic update

* tests: add multi-graph test for test_barrier
---
 ggml/src/ggml-cpu/ggml-cpu.c |  73 +++++++--------
 tests/test-barrier.cpp       | 170 ++++++++++++++++++++++++++++++++---
 2 files changed, 190 insertions(+), 53 deletions(-)

diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index b468b115a1..c47511adcb 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -187,6 +187,9 @@ typedef void * thread_ret_t;
 
 typedef pthread_t ggml_thread_t;
 
+#define GGML_THREADPOOL_N_THREADS_MASK (0xffffU)
+#define GGML_THREADPOOL_N_THREADS_BITS (16)
+
 #if defined(__APPLE__)
 #include <unistd.h>
 #include <mach/mach.h>
@@ -449,7 +452,7 @@ struct ggml_threadpool {
     struct ggml_cplan  * cplan;
 
     // synchronization primitives
-    atomic_int n_graph;       // incremented when there is work to be done (i.e each graph)
+    atomic_int n_graph;       // updated when there is work to be done (i.e each graph) holds graph and active thread counts.
     atomic_int GGML_CACHE_ALIGN n_barrier;
     atomic_int GGML_CACHE_ALIGN n_barrier_passed;
     atomic_int GGML_CACHE_ALIGN current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads.
@@ -457,12 +460,10 @@ struct ggml_threadpool {
     // these are atomic as an annotation for thread-sanitizer
     atomic_bool stop;         // Used for stopping the threadpool altogether
     atomic_bool pause;        // Used for pausing the threadpool or individual threads
-    atomic_int abort;         // Used for aborting processing of a graph
+    atomic_int  abort;        // Used for aborting processing of a graph
 
     struct ggml_compute_state * workers;   // per thread state
-    int          n_threads_max; // number of threads in the pool
-    atomic_int   n_threads_cur; // number of threads used in the current graph
-
+    int          n_threads;   // Number of threads in the pool
     int32_t      prio;        // Scheduling priority
     uint32_t     poll;        // Polling level (0 - no polling)
 
@@ -539,7 +540,7 @@ struct ggml_state {
 static struct ggml_state g_state = {0};
 
 void ggml_barrier(struct ggml_threadpool * tp) {
-    int n_threads = atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed);
+    int n_threads = atomic_load_explicit(&tp->n_graph, memory_order_relaxed) & GGML_THREADPOOL_N_THREADS_MASK;
     if (n_threads == 1) {
         return;
     }
@@ -556,7 +557,7 @@ void ggml_barrier(struct ggml_threadpool * tp) {
         // last thread
         atomic_store_explicit(&tp->n_barrier, 0, memory_order_relaxed);
 
-        // exit barrier (fill seq-cst fence)
+        // exit barrier (full seq-cst fence)
         atomic_fetch_add_explicit(&tp->n_barrier_passed, 1, memory_order_seq_cst);
         return;
     }
@@ -2628,7 +2629,7 @@ static void ggml_thread_cpumask_next(const bool * global_mask, bool * local_mask
 void ggml_threadpool_free(struct ggml_threadpool* threadpool) {
     if (!threadpool) return;
 
-    const int n_threads = threadpool->n_threads_max;
+    const int n_threads = threadpool->n_threads;
 
 #ifndef GGML_USE_OPENMP
     struct ggml_compute_state* workers = threadpool->workers;
@@ -2704,7 +2705,7 @@ struct ggml_cplan ggml_graph_plan(
         //GGML_PRINT_DEBUG("Threadpool is not specified. Will create a disposable threadpool : n_threads %d\n", n_threads);
     }
     if (n_threads <= 0) {
-        n_threads = threadpool ? threadpool->n_threads_max : GGML_DEFAULT_N_THREADS;
+        n_threads = threadpool ? threadpool->n_threads : GGML_DEFAULT_N_THREADS;
     }
 
 #if defined(__EMSCRIPTEN__) && !defined(__EMSCRIPTEN_PTHREADS__)
@@ -2912,12 +2913,14 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
 
     struct ggml_compute_params params = {
         /*.ith       =*/ state->ith,
-        /*.nth       =*/ atomic_load_explicit(&tp->n_threads_cur, memory_order_relaxed),
+        /*.nth       =*/ atomic_load_explicit(&tp->n_graph, memory_order_relaxed) & GGML_THREADPOOL_N_THREADS_MASK,
         /*.wsize     =*/ cplan->work_size,
         /*.wdata     =*/ cplan->work_data,
         /*.threadpool=*/ tp,
     };
 
+    GGML_PRINT_DEBUG("thread #%d compute-start cplan %p last-graph %d \n", state->ith, cplan, state->last_graph);
+
     for (int node_n = 0; node_n < cgraph->n_nodes && atomic_load_explicit(&tp->abort, memory_order_relaxed) != node_n; node_n++) {
         struct ggml_tensor * node = cgraph->nodes[node_n];
 
@@ -2939,6 +2942,8 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
         }
     }
 
+    GGML_PRINT_DEBUG("thread #%d compute-done cplan %p last-graph %d \n", state->ith, cplan, state->last_graph);
+
     ggml_barrier(state->threadpool);
 
     return 0;
@@ -2946,27 +2951,23 @@ static thread_ret_t ggml_graph_compute_thread(void * data) {
 
 #ifndef GGML_USE_OPENMP
 
-// check if thread is active
-static inline bool ggml_graph_compute_thread_active(struct ggml_compute_state * state) {
-    struct ggml_threadpool * threadpool = state->threadpool;
-    int n_threads = atomic_load_explicit(&threadpool->n_threads_cur, memory_order_relaxed);
-    return (state->ith < n_threads);
-}
-
 // check if thread is ready to proceed (exit from polling or sleeping)
+// returns true if loops should exit, sets state->pending to indicate new work
 static inline bool ggml_graph_compute_thread_ready(struct ggml_compute_state * state) {
     struct ggml_threadpool * threadpool = state->threadpool;
 
     if (state->pending || threadpool->stop || threadpool->pause) { return true; }
 
     // check for new graph/work
-    int new_graph = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed);
-    if (new_graph != state->last_graph) {
-        state->pending    = ggml_graph_compute_thread_active(state);
-        state->last_graph = new_graph;
+    int n_graph   = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed);
+    int n_threads = n_graph & GGML_THREADPOOL_N_THREADS_MASK;
+    if (n_graph != state->last_graph) {
+        state->pending    = (state->ith < n_threads);
+        state->last_graph = n_graph;
+        return true;
     }
 
-    return state->pending;
+    return false;
 }
 
 // sync thread state after polling
@@ -2983,11 +2984,6 @@ static inline void ggml_graph_compute_thread_sync(struct ggml_compute_state * st
 static inline bool ggml_graph_compute_poll_for_work(struct ggml_compute_state * state) {
     struct ggml_threadpool * threadpool = state->threadpool;
 
-    // Skip polling for unused threads
-    if (!ggml_graph_compute_thread_active(state)) {
-        return state->pending;
-    }
-
     // This seems to make 0 ... 100 a decent range for polling level across modern processors.
     // Perhaps, we can adjust it dynamically based on load and things.
     const uint64_t n_rounds = 1024UL * 128 * threadpool->poll;
@@ -3049,7 +3045,6 @@ static thread_ret_t ggml_graph_compute_secondary_thread(void* data) {
         ggml_graph_compute_check_for_work(state);
         if (state->pending) {
             state->pending = false;
-
             ggml_graph_compute_thread(state);
         }
     }
@@ -3064,14 +3059,15 @@ static void ggml_graph_compute_kickoff(struct ggml_threadpool * threadpool, int
 
     ggml_mutex_lock(&threadpool->mutex);
 
-    GGML_PRINT_DEBUG("threadpool: n_threads_cur %d n_threads %d\n", threadpool->n_threads_cur, n_threads);
+    // Update the number of active threads and the graph count
+    int n_graph = atomic_load_explicit(&threadpool->n_graph, memory_order_relaxed) >> GGML_THREADPOOL_N_THREADS_BITS;
+    n_graph = ((n_graph + 1) << GGML_THREADPOOL_N_THREADS_BITS) | (n_threads & GGML_THREADPOOL_N_THREADS_MASK);
 
-    // Update the number of active threads
-    atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed);
+    GGML_PRINT_DEBUG("compute-kickoff: n_threads %d n_graph %d\n", n_threads, n_graph);
 
     // Indicate the graph is ready to be processed
     // We need the full seq-cst fence here because of the polling threads (used in thread_sync)
-    atomic_fetch_add_explicit(&threadpool->n_graph, 1, memory_order_seq_cst);
+    atomic_store_explicit(&threadpool->n_graph, n_graph, memory_order_seq_cst);
 
     if (threadpool->pause) {
        // Update main thread prio and affinity to match the threadpool settings
@@ -3109,8 +3105,7 @@ static struct ggml_threadpool * ggml_threadpool_new_impl(
         threadpool->pause            = tpp->paused;
         threadpool->abort            = -1;
         threadpool->workers          = NULL;
-        threadpool->n_threads_max    = tpp->n_threads;
-        threadpool->n_threads_cur    = tpp->n_threads;
+        threadpool->n_threads        = tpp->n_threads;
         threadpool->poll             = tpp->poll;
         threadpool->prio             = tpp->prio;
         threadpool->ec               = GGML_STATUS_SUCCESS;
@@ -3205,7 +3200,7 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl
             {
                 // update the number of threads from the actual number of threads that we got from OpenMP
                 n_threads = omp_get_num_threads();
-                atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed);
+                atomic_store_explicit(&threadpool->n_graph, n_threads, memory_order_relaxed);
             }
 
             // Apply thread CPU mask and priority
@@ -3218,13 +3213,13 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl
             ggml_graph_compute_thread(&threadpool->workers[ith]);
         }
     } else {
-        atomic_store_explicit(&threadpool->n_threads_cur, 1, memory_order_relaxed);
+        atomic_store_explicit(&threadpool->n_graph, 1, memory_order_relaxed);
         ggml_graph_compute_thread(&threadpool->workers[0]);
     }
 #else
-    if (n_threads > threadpool->n_threads_max) {
-        GGML_LOG_WARN("cplan requested more threads (%d) than available (%d)\n", n_threads, threadpool->n_threads_max);
-        n_threads = threadpool->n_threads_max;
+    if (n_threads > threadpool->n_threads) {
+        GGML_LOG_WARN("cplan requested more threads (%d) than available (%d)\n", n_threads, threadpool->n_threads);
+        n_threads = threadpool->n_threads;
     }
 
     // Kick all threads to start the new graph
diff --git a/tests/test-barrier.cpp b/tests/test-barrier.cpp
index 04c27761dc..61f73adfd2 100644
--- a/tests/test-barrier.cpp
+++ b/tests/test-barrier.cpp
@@ -11,19 +11,7 @@
 
 #define MAX_NARGS 2
 
-int main(int argc, char *argv[]) {
-
-    int n_threads = std::max(1, std::min(4, (int) std::thread::hardware_concurrency()));
-    int n_rounds  = 100;
-
-    if (argc > 1) {
-        n_threads = std::atoi(argv[1]);
-    }
-
-    if (argc > 2) {
-        n_rounds  = std::atoi(argv[2]);
-    }
-
+static void test_barrier(int n_threads, int n_rounds) {
     struct ggml_init_params params = {
         /* .mem_size   = */ 1024*1024*1024,
         /* .mem_buffer = */ NULL,
@@ -56,7 +44,7 @@ int main(int argc, char *argv[]) {
         exit(1);
     }
 
-    // Create compute plan
+    // The test runs with constant number of threads
     struct ggml_cplan cplan = ggml_graph_plan(gf, n_threads, threadpool);
 
     std::vector<uint8_t> work_data(cplan.work_size);
@@ -89,6 +77,160 @@ int main(int argc, char *argv[]) {
 
     ggml_threadpool_free(threadpool);
     ggml_free(ctx);
+}
+
+static void test_active(int n_threads, int n_rounds) {
+    struct ggml_init_params params = {
+        /* .mem_size   = */ 1024*1024*1024,
+        /* .mem_buffer = */ NULL,
+        /* .no_alloc   = */ false,
+    };
+
+    struct ggml_context * ctx = ggml_init(params);
+
+    // Create graph
+    struct ggml_cgraph * gf = ggml_new_graph(ctx);
+
+    // Small graph with, parallel ops with barriers
+    struct ggml_tensor * out = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,  64);
+    for (int i = 0; i < 2; i++) {
+        struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 64, 128);
+        out = ggml_mul_mat(ctx, a, out);
+
+        struct ggml_tensor * d = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 128, 64);
+        out = ggml_mul_mat(ctx, d, out);
+    }
+
+    ggml_build_forward_expand(gf, out);
+    int n_nodes = ggml_graph_n_nodes(gf);
+
+    // Create threadpool
+    struct ggml_threadpool_params tpp  = ggml_threadpool_params_default(n_threads);
+    struct ggml_threadpool* threadpool = ggml_threadpool_new(&tpp);
+    if (!threadpool) {
+        fprintf(stderr, "threadpool create failed : n_threads %d\n", n_threads);
+        exit(1);
+    }
+
+    std::cerr << "graph-compute with"
+              << "\n n_threads: " << n_threads
+              << "\n   n_nodes: " << n_nodes
+              << "\n  n_rounds: " << n_rounds
+              << "\n";
+    // ggml_graph_print(gf);
+
+    // In this test we keep changing the number of threads every 4th iteration
+    // to test for race conditions in that path
+
+    for (int i=0; i < n_rounds; i++) {
+        struct ggml_cplan cplan = ggml_graph_plan(gf, (i % 4) == 0 ? 1 : n_threads, threadpool);
+
+        std::vector<uint8_t> work_data(cplan.work_size);
+        cplan.work_data = work_data.data();
+
+        ggml_graph_compute(gf, &cplan);
+    }
+
+    ggml_threadpool_free(threadpool);
+    ggml_free(ctx);
+}
+
+static void test_multi_graph(int n_threads, int n_rounds) {
+    struct ggml_init_params params = {
+        /* .mem_size   = */ 1024*1024*1024,
+        /* .mem_buffer = */ NULL,
+        /* .no_alloc   = */ false,
+    };
+
+    struct ggml_context * ctx = ggml_init(params);
+
+    // Create graphs
+    struct ggml_cgraph * gf0 = ggml_new_graph(ctx);
+    {
+        // Small graph with parallel ops with barriers
+        struct ggml_tensor * out = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,  64);
+        for (int i = 0; i < 2; i++) {
+            struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 64, 128);
+            out = ggml_mul_mat(ctx, a, out);
+
+            struct ggml_tensor * d = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 128, 64);
+            out = ggml_mul_mat(ctx, d, out);
+        }
+
+        ggml_build_forward_expand(gf0, out);
+    }
+
+    struct ggml_cgraph * gf1 = ggml_new_graph(ctx);
+    {
+        // Small graph with parallel ops with barriers
+        // Use larger tensors to make sure work_data size is larger than gf0
+        struct ggml_tensor * out = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,  256);
+        for (int i = 0; i < 4; i++) {
+            struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 256, 128);
+            out = ggml_mul_mat(ctx, a, out);
+
+            struct ggml_tensor * d = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 128, 256);
+            out = ggml_mul_mat(ctx, d, out);
+        }
+
+        ggml_build_forward_expand(gf1, out);
+    }
+
+
+    // Create threadpool
+    struct ggml_threadpool_params tpp  = ggml_threadpool_params_default(n_threads);
+    struct ggml_threadpool* threadpool = ggml_threadpool_new(&tpp);
+    if (!threadpool) {
+        fprintf(stderr, "threadpool create failed : n_threads %d\n", n_threads);
+        exit(1);
+    }
+
+    std::cerr << "graph-compute with"
+              << "\n gf0 n_nodes: " << ggml_graph_n_nodes(gf0)
+              << "\n gf1 n_nodes: " << ggml_graph_n_nodes(gf1)
+              << "\n   n_threads: " << n_threads
+              << "\n    n_rounds: " << n_rounds
+              << "\n";
+
+    // In this test we keep changing the number of threads every 4th iteration
+    // and we compute two graphs back to back to test graph frequent graph switching
+
+    for (int i=0; i < n_rounds; i++) {
+        struct ggml_cplan cplan0 = ggml_graph_plan(gf0, (i % 4) == 0 ? 1 : n_threads, threadpool);
+        std::vector<uint8_t> work_data0(cplan0.work_size);
+        cplan0.work_data = work_data0.data();
+
+        struct ggml_cplan cplan1 = ggml_graph_plan(gf1, (i % 4) == 0 ? 1 : n_threads, threadpool);
+        std::vector<uint8_t> work_data1(cplan1.work_size);
+        cplan1.work_data = work_data1.data();
+
+        ggml_graph_compute(gf0, &cplan0);
+        ggml_graph_compute(gf1, &cplan1);
+    }
+
+    ggml_threadpool_free(threadpool);
+    ggml_free(ctx);
+}
+
+
+int main(int argc, char *argv[]) {
+
+    int n_threads = std::max(1, std::min(4, (int) std::thread::hardware_concurrency()));
+    int n_rounds  = 100;
+
+    if (argc > 1) {
+        n_threads = std::atoi(argv[1]);
+    }
+
+    if (argc > 2) {
+        n_rounds  = std::atoi(argv[2]);
+    }
+
+    test_barrier(n_threads, n_rounds);
+
+    test_active(n_threads,  n_rounds * 100);
+
+    test_multi_graph(n_threads,  n_rounds * 10);
 
     return 0;
 }

From f32ca51bfeb74b8d1b735c2744c0e4a6224f6b7c Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Wed, 10 Dec 2025 22:18:21 +0100
Subject: [PATCH 051/135] server: add presets (config) when using multiple
 models (#17859)

* llama-server: recursive GGUF loading

Replace flat directory scan with recursive traversal using
std::filesystem::recursive_directory_iterator. Support for
nested vendor/model layouts (e.g. vendor/model/*.gguf).
Model name now reflects the relative path within --models-dir
instead of just the filename. Aggregate files by parent
directory via std::map before constructing local_model

* server : router config POC (INI-based per-model settings)

* server: address review feedback from @aldehir and @ngxson

PEG parser usage improvements:
- Simplify parser instantiation (remove arena indirection)
- Optimize grammar usage (ws instead of zero_or_more, remove optional wrapping)
- Fix last line without newline bug (+ operator instead of <<)
- Remove redundant end position check

Feature scope:
- Remove auto-reload feature (will be separate PR per @ngxson)
- Keep config.ini auto-creation and template generation
- Preserve per-model customization logic

Co-authored-by: aldehir <aldehir@users.noreply.github.com>
Co-authored-by: ngxson <ngxson@users.noreply.github.com>

* server: adopt aldehir's line-oriented PEG parser

Complete rewrite of INI parser grammar and visitor:
- Use p.chars(), p.negate(), p.any() instead of p.until()
- Support end-of-line comments (key=value # comment)
- Handle EOF without trailing newline correctly
- Strict identifier validation ([a-zA-Z_][a-zA-Z0-9_.-]*)
- Simplified visitor (no pending state, no trim needed)
- Grammar handles whitespace natively via eol rule

Business validation preserved:
- Reject section names starting with LLAMA_ARG_*
- Accept only keys starting with LLAMA_ARG_*
- Require explicit section before key-value pairs

Co-authored-by: aldehir <aldehir@users.noreply.github.com>

* server: fix CLI/env duplication in child processes

Children now receive minimal CLI args (executable, model, port, alias)
instead of inheriting all router args. Global settings pass through
LLAMA_ARG_* environment variables only, eliminating duplicate config
warnings.

Fixes: Router args like -ngl, -fa were passed both via CLI and env,
causing 'will be overwritten' warnings on every child spawn

* add common/preset.cpp

* fix compile

* cont

* allow custom-path models

* add falsey check

* server: fix router model discovery and child process spawning

- Sanitize model names: replace / and \ with _ for display
- Recursive directory scan with relative path storage
- Convert relative paths to absolute when spawning children
- Filter router control args from child processes
- Refresh args after port assignment for correct port value
- Fallback preset lookup for compatibility
- Fix missing argv[0]: store server binary path before base_args parsing

* Revert "server: fix router model discovery and child process spawning"

This reverts commit e3832b42eeea7fcb108995966c7584479f745857.

* clarify about "no-" prefix

* correct render_args() to include binary path

* also remove arg LLAMA_ARG_MODELS_PRESET for child

* add co-author for ini parser code

Co-authored-by: aldehir <hello@alde.dev>

* also set LLAMA_ARG_HOST

* add CHILD_ADDR

* Remove dead code

---------

Co-authored-by: aldehir <aldehir@users.noreply.github.com>
Co-authored-by: ngxson <ngxson@users.noreply.github.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: aldehir <hello@alde.dev>
---
 common/CMakeLists.txt          |   2 +
 common/arg.cpp                 |  72 ++++++++-
 common/arg.h                   |  32 +++-
 common/common.h                |   7 +-
 common/preset.cpp              | 180 ++++++++++++++++++++++
 common/preset.h                |  32 ++++
 tools/server/CMakeLists.txt    |   8 +
 tools/server/README.md         |  50 ++++++
 tools/server/server-models.cpp | 267 ++++++++++++++++++++++-----------
 tools/server/server-models.h   |  27 +++-
 10 files changed, 580 insertions(+), 97 deletions(-)
 create mode 100644 common/preset.cpp
 create mode 100644 common/preset.h

diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt
index 377b26846b..0182767c2b 100644
--- a/common/CMakeLists.txt
+++ b/common/CMakeLists.txt
@@ -73,6 +73,8 @@ add_library(${TARGET} STATIC
     ngram-cache.h
     peg-parser.cpp
     peg-parser.h
+    preset.cpp
+    preset.h
     regex-partial.cpp
     regex-partial.h
     sampling.cpp
diff --git a/common/arg.cpp b/common/arg.cpp
index 210ef8d621..b333f45c96 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -47,6 +47,7 @@
 #define LLAMA_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
 
 using json = nlohmann::ordered_json;
+using namespace common_arg_utils;
 
 static std::initializer_list<enum llama_example> mmproj_examples = {
     LLAMA_EXAMPLE_MTMD,
@@ -64,6 +65,15 @@ static std::string read_file(const std::string & fname) {
     return content;
 }
 
+static const std::vector<common_arg> & get_common_arg_defs() {
+    static const std::vector<common_arg> options = [] {
+        common_params params;
+        auto ctx = common_params_parser_init(params, LLAMA_EXAMPLE_SERVER, nullptr);
+        return ctx.options;
+    }();
+    return options;
+}
+
 common_arg & common_arg::set_examples(std::initializer_list<enum llama_example> examples) {
     this->examples = examples;
     return *this;
@@ -134,7 +144,7 @@ static std::vector<std::string> break_str_into_lines(std::string input, size_t m
     return result;
 }
 
-std::string common_arg::to_string() {
+std::string common_arg::to_string() const {
     // params for printing to console
     const static int n_leading_spaces = 40;
     const static int n_char_per_line_help = 70; // TODO: detect this based on current console
@@ -647,6 +657,53 @@ static void add_rpc_devices(const std::string & servers) {
     }
 }
 
+bool common_params_parse(int argc, char ** argv, llama_example ex, std::map<common_arg, std::string> & out_map) {
+    common_params dummy_params;
+    common_params_context ctx_arg = common_params_parser_init(dummy_params, ex, nullptr);
+
+    std::unordered_map<std::string, common_arg *> arg_to_options;
+    for (auto & opt : ctx_arg.options) {
+        for (const auto & arg : opt.args) {
+            arg_to_options[arg] = &opt;
+        }
+    }
+
+    // TODO @ngxson : find a way to deduplicate this code
+
+    // handle command line arguments
+    auto check_arg = [&](int i) {
+        if (i+1 >= argc) {
+            throw std::invalid_argument("expected value for argument");
+        }
+    };
+
+    for (int i = 1; i < argc; i++) {
+        const std::string arg_prefix = "--";
+
+        std::string arg = argv[i];
+        if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) {
+            std::replace(arg.begin(), arg.end(), '_', '-');
+        }
+        if (arg_to_options.find(arg) == arg_to_options.end()) {
+            throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str()));
+        }
+        auto opt = *arg_to_options[arg];
+        std::string val;
+        if (opt.value_hint != nullptr) {
+            // arg with single value
+            check_arg(i);
+            val = argv[++i];
+        }
+        if (opt.value_hint_2 != nullptr) {
+            // TODO: support arg with 2 values
+            throw std::invalid_argument("error: argument with 2 values is not yet supported\n");
+        }
+        out_map[opt] = val;
+    }
+
+    return true;
+}
+
 bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
     auto ctx_arg = common_params_parser_init(params, ex, print_usage);
     const common_params params_org = ctx_arg.params; // the example can modify the default params
@@ -692,15 +749,15 @@ static std::string list_builtin_chat_templates() {
     return msg.str();
 }
 
-static bool is_truthy(const std::string & value) {
+bool common_arg_utils::is_truthy(const std::string & value) {
     return value == "on" || value == "enabled" || value == "1";
 }
 
-static bool is_falsey(const std::string & value) {
+bool common_arg_utils::is_falsey(const std::string & value) {
     return value == "off" || value == "disabled" || value == "0";
 }
 
-static bool is_autoy(const std::string & value) {
+bool common_arg_utils::is_autoy(const std::string & value) {
     return value == "auto" || value == "-1";
 }
 
@@ -2543,6 +2600,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.models_dir = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_DIR"));
+    add_opt(common_arg(
+        {"--models-preset"}, "PATH",
+        "path to INI file containing model presets for the router server (default: disabled)",
+        [](common_params & params, const std::string & value) {
+            params.models_preset = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_PRESET"));
     add_opt(common_arg(
         {"--models-max"}, "N",
         string_format("for router server, maximum number of models to load simultaneously (default: %d, 0 = unlimited)", params.models_max),
diff --git a/common/arg.h b/common/arg.h
index 7ab7e2cea4..219c115e63 100644
--- a/common/arg.h
+++ b/common/arg.h
@@ -3,8 +3,10 @@
 #include "common.h"
 
 #include <set>
+#include <map>
 #include <string>
 #include <vector>
+#include <cstring>
 
 //
 // CLI argument parsing
@@ -24,6 +26,8 @@ struct common_arg {
     void (*handler_str_str)(common_params & params, const std::string &, const std::string &) = nullptr;
     void (*handler_int)    (common_params & params, int) = nullptr;
 
+    common_arg() = default;
+
     common_arg(
         const std::initializer_list<const char *> & args,
         const char * value_hint,
@@ -61,9 +65,29 @@ struct common_arg {
     bool is_exclude(enum llama_example ex);
     bool get_value_from_env(std::string & output) const;
     bool has_value_from_env() const;
-    std::string to_string();
+    std::string to_string() const;
+
+    // for using as key in std::map
+    bool operator<(const common_arg& other) const {
+        if (args.empty() || other.args.empty()) {
+            return false;
+        }
+        return strcmp(args[0], other.args[0]) < 0;
+    }
+    bool operator==(const common_arg& other) const {
+        if (args.empty() || other.args.empty()) {
+            return false;
+        }
+        return strcmp(args[0], other.args[0]) == 0;
+    }
 };
 
+namespace common_arg_utils {
+    bool is_truthy(const std::string & value);
+    bool is_falsey(const std::string & value);
+    bool is_autoy(const std::string & value);
+}
+
 struct common_params_context {
     enum llama_example ex = LLAMA_EXAMPLE_COMMON;
     common_params & params;
@@ -76,7 +100,11 @@ struct common_params_context {
 // if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message)
 bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
 
-// function to be used by test-arg-parser
+// parse input arguments from CLI into a map
+// TODO: support repeated args in the future
+bool common_params_parse(int argc, char ** argv, llama_example ex, std::map<common_arg, std::string> & out_map);
+
+// initialize argument parser context - used by test-arg-parser and preset
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
 
 struct common_remote_params {
diff --git a/common/common.h b/common/common.h
index ad79f5b425..6119adcc0f 100644
--- a/common/common.h
+++ b/common/common.h
@@ -484,9 +484,10 @@ struct common_params {
     bool endpoint_metrics = false;
 
     // router server configs
-    std::string models_dir = ""; // directory containing models for the router server
-    int models_max = 4;          // maximum number of models to load simultaneously
-    bool models_autoload = true; // automatically load models when requested via the router server
+    std::string models_dir    = ""; // directory containing models for the router server
+    std::string models_preset = ""; // directory containing model presets for the router server
+    int models_max = 4;             // maximum number of models to load simultaneously
+    bool models_autoload = true;    // automatically load models when requested via the router server
 
     bool log_json = false;
 
diff --git a/common/preset.cpp b/common/preset.cpp
new file mode 100644
index 0000000000..09ac171b72
--- /dev/null
+++ b/common/preset.cpp
@@ -0,0 +1,180 @@
+#include "arg.h"
+#include "preset.h"
+#include "peg-parser.h"
+#include "log.h"
+
+#include <fstream>
+#include <sstream>
+#include <filesystem>
+
+static std::string rm_leading_dashes(const std::string & str) {
+    size_t pos = 0;
+    while (pos < str.size() && str[pos] == '-') {
+        ++pos;
+    }
+    return str.substr(pos);
+}
+
+std::vector<std::string> common_preset::to_args() const {
+    std::vector<std::string> args;
+
+    for (const auto & [opt, value] : options) {
+        args.push_back(opt.args.back()); // use the last arg as the main arg
+        if (opt.value_hint == nullptr && opt.value_hint_2 == nullptr) {
+            // flag option, no value
+            if (common_arg_utils::is_falsey(value)) {
+                // skip the flag
+                args.pop_back();
+            }
+        }
+        if (opt.value_hint != nullptr) {
+            // single value
+            args.push_back(value);
+        }
+        if (opt.value_hint != nullptr && opt.value_hint_2 != nullptr) {
+            throw std::runtime_error(string_format(
+                "common_preset::to_args(): option '%s' has two values, which is not supported yet",
+                opt.args.back()
+            ));
+        }
+    }
+
+    return args;
+}
+
+std::string common_preset::to_ini() const {
+    std::ostringstream ss;
+
+    ss << "[" << name << "]\n";
+    for (const auto & [opt, value] : options) {
+        auto espaced_value = value;
+        string_replace_all(espaced_value, "\n", "\\\n");
+        ss << rm_leading_dashes(opt.args.back()) << " = ";
+        ss << espaced_value << "\n";
+    }
+    ss << "\n";
+
+    return ss.str();
+}
+
+static std::map<std::string, std::map<std::string, std::string>> parse_ini_from_file(const std::string & path) {
+    std::map<std::string, std::map<std::string, std::string>> parsed;
+
+    if (!std::filesystem::exists(path)) {
+        throw std::runtime_error("preset file does not exist: " + path);
+    }
+
+    std::ifstream file(path);
+    if (!file.good()) {
+        throw std::runtime_error("failed to open server preset file: " + path);
+    }
+
+    std::string contents((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
+
+    static const auto parser = build_peg_parser([](auto & p) {
+        // newline ::= "\r\n" / "\n" / "\r"
+        auto newline = p.rule("newline", p.literal("\r\n") | p.literal("\n") | p.literal("\r"));
+
+        // ws ::= [ \t]*
+        auto ws = p.rule("ws", p.chars("[ \t]", 0, -1));
+
+        // comment ::= [;#] (!newline .)*
+        auto comment = p.rule("comment", p.chars("[;#]", 1, 1) + p.zero_or_more(p.negate(newline) + p.any()));
+
+        // eol ::= ws comment? (newline / EOF)
+        auto eol = p.rule("eol", ws + p.optional(comment) + (newline | p.end()));
+
+        // ident ::= [a-zA-Z_] [a-zA-Z0-9_.-]*
+        auto ident = p.rule("ident", p.chars("[a-zA-Z_]", 1, 1) + p.chars("[a-zA-Z0-9_.-]", 0, -1));
+
+        // value ::= (!eol-start .)*
+        auto eol_start = p.rule("eol-start", ws + (p.chars("[;#]", 1, 1) | newline | p.end()));
+        auto value = p.rule("value", p.zero_or_more(p.negate(eol_start) + p.any()));
+
+        // header-line ::= "[" ws ident ws "]" eol
+        auto header_line = p.rule("header-line", "[" + ws + p.tag("section-name", p.chars("[^]]")) + ws + "]" + eol);
+
+        // kv-line ::= ident ws "=" ws value eol
+        auto kv_line = p.rule("kv-line", p.tag("key", ident) + ws + "=" + ws + p.tag("value", value) + eol);
+
+        // comment-line ::= ws comment (newline / EOF)
+        auto comment_line = p.rule("comment-line", ws + comment + (newline | p.end()));
+
+        // blank-line ::= ws (newline / EOF)
+        auto blank_line = p.rule("blank-line", ws + (newline | p.end()));
+
+        // line ::= header-line / kv-line / comment-line / blank-line
+        auto line = p.rule("line", header_line | kv_line | comment_line | blank_line);
+
+        // ini ::= line* EOF
+        auto ini = p.rule("ini", p.zero_or_more(line) + p.end());
+
+        return ini;
+    });
+
+    common_peg_parse_context ctx(contents);
+    const auto result = parser.parse(ctx);
+    if (!result.success()) {
+        throw std::runtime_error("failed to parse server config file: " + path);
+    }
+
+    std::string current_section = COMMON_PRESET_DEFAULT_NAME;
+    std::string current_key;
+
+    ctx.ast.visit(result, [&](const auto & node) {
+        if (node.tag == "section-name") {
+            const std::string section = std::string(node.text);
+            current_section = section;
+            parsed[current_section] = {};
+        } else if (node.tag == "key") {
+            const std::string key = std::string(node.text);
+            current_key = key;
+        } else if (node.tag == "value" && !current_key.empty() && !current_section.empty()) {
+            parsed[current_section][current_key] = std::string(node.text);
+            current_key.clear();
+        }
+    });
+
+    return parsed;
+}
+
+static std::map<std::string, common_arg> get_map_key_opt(common_params_context & ctx_params) {
+    std::map<std::string, common_arg> mapping;
+    for (const auto & opt : ctx_params.options) {
+        if (opt.env != nullptr) {
+            mapping[opt.env] = opt;
+        }
+        for (const auto & arg : opt.args) {
+            mapping[rm_leading_dashes(arg)] = opt;
+        }
+    }
+    return mapping;
+}
+
+common_presets common_presets_load(const std::string & path, common_params_context & ctx_params) {
+    common_presets out;
+    auto key_to_opt = get_map_key_opt(ctx_params);
+    auto ini_data = parse_ini_from_file(path);
+
+    for (auto section : ini_data) {
+        common_preset preset;
+        if (section.first.empty()) {
+            preset.name = COMMON_PRESET_DEFAULT_NAME;
+        } else {
+            preset.name = section.first;
+        }
+        LOG_DBG("loading preset: %s\n", preset.name.c_str());
+        for (const auto & [key, value] : section.second) {
+            LOG_DBG("option: %s = %s\n", key.c_str(), value.c_str());
+            if (key_to_opt.find(key) != key_to_opt.end()) {
+                preset.options[key_to_opt[key]] = value;
+                LOG_DBG("accepted option: %s = %s\n", key.c_str(), value.c_str());
+            } else {
+                // TODO: maybe warn about unknown key?
+            }
+        }
+        out[preset.name] = preset;
+    }
+
+    return out;
+}
diff --git a/common/preset.h b/common/preset.h
new file mode 100644
index 0000000000..dceb849eb8
--- /dev/null
+++ b/common/preset.h
@@ -0,0 +1,32 @@
+#pragma once
+
+#include "common.h"
+#include "arg.h"
+
+#include <string>
+#include <vector>
+#include <map>
+
+//
+// INI preset parser and writer
+//
+
+constexpr const char * COMMON_PRESET_DEFAULT_NAME = "default";
+
+struct common_preset {
+    std::string name;
+    // TODO: support repeated args in the future
+    std::map<common_arg, std::string> options;
+
+    // convert preset to CLI argument list
+    std::vector<std::string> to_args() const;
+
+    // convert preset to INI format string
+    std::string to_ini() const;
+
+    // TODO: maybe implement to_env() if needed
+};
+
+// interface for multiple presets in one file
+using common_presets = std::map<std::string, common_preset>;
+common_presets common_presets_load(const std::string & path, common_params_context & ctx_params);
diff --git a/tools/server/CMakeLists.txt b/tools/server/CMakeLists.txt
index a39b4c5b35..ae1a497be6 100644
--- a/tools/server/CMakeLists.txt
+++ b/tools/server/CMakeLists.txt
@@ -38,6 +38,14 @@ set(TARGET_SRCS
     server-http.h
     server-models.cpp
     server-models.h
+    server-task.cpp
+    server-task.h
+    server-queue.cpp
+    server-queue.h
+    server-common.cpp
+    server-common.h
+    server-context.cpp
+    server-context.h
 )
 set(PUBLIC_ASSETS
     index.html.gz
diff --git a/tools/server/README.md b/tools/server/README.md
index f98fb44c7b..d6b9b87dcf 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -1369,6 +1369,11 @@ llama-server
 
 ### Model sources
 
+There are 3 possible sources for model files:
+1. Cached models (controlled by the `LLAMA_CACHE` environment variable)
+2. Custom model directory (set via the `--models-dir` argument)
+3. Custom preset (set via the `--models-preset` argument)
+
 By default, the router looks for models in the cache. You can add Hugging Face models to the cache with:
 
 ```sh
@@ -1413,6 +1418,51 @@ llama-server -ctx 8192 -n 1024 -np 2
 
 Note: model instances inherit both command line arguments and environment variables from the router server.
 
+Alternatively, you can also add GGUF based preset (see next section)
+
+### Model presets
+
+Model presets allow advanced users to define custom configurations using an `.ini` file:
+
+```sh
+llama-server --models-preset ./my-models.ini
+```
+
+Each section in the file defines a new preset. Keys within a section correspond to command-line arguments (without leading dashes). For example, the argument `--n-gpu-layer 123` is written as `n-gpu-layer = 123`.
+
+Short argument forms (e.g., `c`, `ngl`) and environment variable names (e.g., `LLAMA_ARG_N_GPU_LAYERS`) are also supported as keys.
+
+Example:
+
+```ini
+version = 1
+
+; If the key corresponds to an existing model on the server,
+; this will be used as the default config for that model
+[ggml-org/MY-MODEL-GGUF:Q8_0]
+; string value
+chat-template = chatml
+; numeric value
+n-gpu-layer = 123
+; flag value (for certain flags, you need to use the "no-" prefix for negation)
+jinja = true
+; shorthand argument (for example, context size)
+c = 4096
+; environment variable name
+LLAMA_ARG_CACHE_RAM = 0
+; file paths are relative to server's CWD
+model-draft = ./my-models/draft.gguf
+; but it's RECOMMENDED to use absolute path
+model-draft = /Users/abc/my-models/draft.gguf
+
+; If the key does NOT correspond to an existing model,
+; you need to specify at least the model path
+[custom_model]
+model = /Users/abc/my-awesome-model-Q4_K_M.gguf
+```
+
+Note: some arguments are controlled by router (e.g., host, port, API key, HF repo, model alias). They will be removed or overwritten upload loading.
+
 ### Routing requests
 
 Requests are routed according to the requested model name.
diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp
index 6f88e93c4b..6c618a673c 100644
--- a/tools/server/server-models.cpp
+++ b/tools/server/server-models.cpp
@@ -1,6 +1,7 @@
 #include "server-common.h"
 #include "server-models.h"
 
+#include "preset.h"
 #include "download.h"
 
 #include <cpp-httplib/httplib.h> // TODO: remove this once we use HTTP client from download.h
@@ -33,6 +34,10 @@
 
 #define CMD_EXIT "exit"
 
+// address for child process, this is needed because router may run on 0.0.0.0
+// ref: https://github.com/ggml-org/llama.cpp/issues/17862
+#define CHILD_ADDR "127.0.0.1"
+
 static std::filesystem::path get_server_exec_path() {
 #if defined(_WIN32)
     wchar_t buf[32768] = { 0 };  // Large buffer to handle long paths
@@ -132,6 +137,93 @@ static std::vector<local_model> list_local_models(const std::string & dir) {
     return models;
 }
 
+//
+// server_presets
+//
+
+
+server_presets::server_presets(int argc, char ** argv, common_params & base_params, const std::string & presets_path)
+        : ctx_params(common_params_parser_init(base_params, LLAMA_EXAMPLE_SERVER)) {
+    if (!presets_path.empty()) {
+        presets = common_presets_load(presets_path, ctx_params);
+        SRV_INF("Loaded %zu presets from %s\n", presets.size(), presets_path.c_str());
+    }
+
+    // populate reserved args (will be appended by the router)
+    for (auto & opt : ctx_params.options) {
+        if (opt.env == nullptr) {
+            continue;
+        }
+        std::string env = opt.env;
+        if (env == "LLAMA_ARG_PORT" ||
+            env == "LLAMA_ARG_HOST" ||
+            env == "LLAMA_ARG_ALIAS" ||
+            env == "LLAMA_ARG_API_KEY" ||
+            env == "LLAMA_ARG_MODELS_DIR" ||
+            env == "LLAMA_ARG_MODELS_MAX" ||
+            env == "LLAMA_ARG_MODELS_PRESET" ||
+            env == "LLAMA_ARG_MODEL" ||
+            env == "LLAMA_ARG_MMPROJ" ||
+            env == "LLAMA_ARG_HF_REPO" ||
+            env == "LLAMA_ARG_NO_MODELS_AUTOLOAD") {
+            control_args[env] = opt;
+        }
+    }
+
+    // read base args from router's argv
+    common_params_parse(argc, argv, LLAMA_EXAMPLE_SERVER, base_args);
+
+    // remove any router-controlled args from base_args
+    for (const auto & cargs : control_args) {
+        auto it = base_args.find(cargs.second);
+        if (it != base_args.end()) {
+            base_args.erase(it);
+        }
+    }
+}
+
+common_preset server_presets::get_preset(const std::string & name) {
+    auto it = presets.find(name);
+    if (it != presets.end()) {
+        return it->second;
+    }
+    return common_preset();
+}
+
+void server_presets::render_args(server_model_meta & meta) {
+    common_preset preset = meta.preset; // copy
+    // merging 3 kinds of args:
+    // 1. model-specific args (from preset)
+    // force removing control args if any
+    for (auto & cargs : control_args) {
+        if (preset.options.find(cargs.second) != preset.options.end()) {
+            SRV_WRN("Preset '%s' contains reserved arg '%s', removing it\n", preset.name.c_str(), cargs.second.args[0]);
+            preset.options.erase(cargs.second);
+        }
+    }
+    // 2. base args (from router)
+    // inherit from base args
+    for (const auto & [arg, value] : base_args) {
+        preset.options[arg] = value;
+    }
+    // 3. control args (from router)
+    // set control values
+    preset.options[control_args["LLAMA_ARG_HOST"]] = CHILD_ADDR;
+    preset.options[control_args["LLAMA_ARG_PORT"]] = std::to_string(meta.port);
+    preset.options[control_args["LLAMA_ARG_ALIAS"]] = meta.name;
+    if (meta.in_cache) {
+        preset.options[control_args["LLAMA_ARG_HF_REPO"]] = meta.name;
+    } else {
+        preset.options[control_args["LLAMA_ARG_MODEL"]] = meta.path;
+        if (!meta.path_mmproj.empty()) {
+            preset.options[control_args["LLAMA_ARG_MMPROJ"]] = meta.path_mmproj;
+        }
+    }
+    meta.args = preset.to_args();
+    // add back the binary path at the front
+    meta.args.insert(meta.args.begin(), get_server_exec_path().string());
+}
+
 //
 // server_models
 //
@@ -140,7 +232,7 @@ server_models::server_models(
         const common_params & params,
         int argc,
         char ** argv,
-        char ** envp) : base_params(params) {
+        char ** envp) : base_params(params), presets(argc, argv, base_params, params.models_preset) {
     for (int i = 0; i < argc; i++) {
         base_args.push_back(std::string(argv[i]));
     }
@@ -155,11 +247,58 @@ server_models::server_models(
         LOG_WRN("failed to get server executable path: %s\n", e.what());
         LOG_WRN("using original argv[0] as fallback: %s\n", base_args[0].c_str());
     }
-    // TODO: allow refreshing cached model list
-    // add cached models
+    load_models();
+}
+
+void server_models::add_model(server_model_meta && meta) {
+    if (mapping.find(meta.name) != mapping.end()) {
+        throw std::runtime_error(string_format("model '%s' appears multiple times", meta.name.c_str()));
+    }
+    presets.render_args(meta); // populate meta.args
+    std::string name = meta.name;
+    mapping[name] = instance_t{
+        /* subproc */ std::make_shared<subprocess_s>(),
+        /* th      */ std::thread(),
+        /* meta    */ std::move(meta)
+    };
+}
+
+static std::vector<local_model> list_custom_path_models(server_presets & presets) {
+    // detect any custom-path models in presets
+    std::vector<local_model> custom_models;
+    for (auto & [model_name, preset] : presets.presets) {
+        local_model model;
+        model.name = model_name;
+        std::vector<common_arg> to_erase;
+        for (auto & [arg, value] : preset.options) {
+            std::string env(arg.env ? arg.env : "");
+            if (env == "LLAMA_ARG_MODEL") {
+                model.path = value;
+                to_erase.push_back(arg);
+            }
+            if (env == "LLAMA_ARG_MMPROJ") {
+                model.path_mmproj = value;
+                to_erase.push_back(arg);
+            }
+        }
+        for (auto & arg : to_erase) {
+            preset.options.erase(arg);
+        }
+        if (!model.name.empty() && !model.path.empty()) {
+            custom_models.push_back(model);
+        }
+    }
+    return custom_models;
+}
+
+// TODO: allow refreshing cached model list
+void server_models::load_models() {
+    // loading models from 3 sources:
+    // 1. cached models
     auto cached_models = common_list_cached_models();
     for (const auto & model : cached_models) {
         server_model_meta meta{
+            /* preset      */ presets.get_preset(model.to_string()),
             /* name        */ model.to_string(),
             /* path        */ model.manifest_path,
             /* path_mmproj */ "", // auto-detected when loading
@@ -170,21 +309,18 @@ server_models::server_models(
             /* args        */ std::vector<std::string>(),
             /* exit_code   */ 0
         };
-        mapping[meta.name] = instance_t{
-            /* subproc */ std::make_shared<subprocess_s>(),
-            /* th      */ std::thread(),
-            /* meta    */ meta
-        };
+        add_model(std::move(meta));
     }
-    // add local models specificed via --models-dir
-    if (!params.models_dir.empty()) {
-        auto local_models = list_local_models(params.models_dir);
+    // 2. local models specificed via --models-dir
+    if (!base_params.models_dir.empty()) {
+        auto local_models = list_local_models(base_params.models_dir);
         for (const auto & model : local_models) {
             if (mapping.find(model.name) != mapping.end()) {
                 // already exists in cached models, skip
                 continue;
             }
             server_model_meta meta{
+                /* preset      */ presets.get_preset(model.name),
                 /* name        */ model.name,
                 /* path        */ model.path,
                 /* path_mmproj */ model.path_mmproj,
@@ -195,13 +331,31 @@ server_models::server_models(
                 /* args        */ std::vector<std::string>(),
                 /* exit_code   */ 0
             };
-            mapping[meta.name] = instance_t{
-                /* subproc */ std::make_shared<subprocess_s>(),
-                /* th      */ std::thread(),
-                /* meta    */ meta
-            };
+            add_model(std::move(meta));
         }
     }
+    // 3. custom-path models specified in presets
+    auto custom_models = list_custom_path_models(presets);
+    for (const auto & model : custom_models) {
+        server_model_meta meta{
+            /* preset      */ presets.get_preset(model.name),
+            /* name        */ model.name,
+            /* path        */ model.path,
+            /* path_mmproj */ model.path_mmproj,
+            /* in_cache    */ false,
+            /* port        */ 0,
+            /* status      */ SERVER_MODEL_STATUS_UNLOADED,
+            /* last_used   */ 0,
+            /* args        */ std::vector<std::string>(),
+            /* exit_code   */ 0
+        };
+        add_model(std::move(meta));
+    }
+    // log available models
+    SRV_INF("Available models (%zu) (*: custom preset)\n", mapping.size());
+    for (const auto & [name, inst] : mapping) {
+        SRV_INF("  %c %s\n", inst.meta.preset.name.empty() ? ' ' : '*', name.c_str());
+    }
 }
 
 void server_models::update_meta(const std::string & name, const server_model_meta & meta) {
@@ -335,19 +489,7 @@ void server_models::unload_lru() {
     }
 }
 
-static void add_or_replace_arg(std::vector<std::string> & args, const std::string & key, const std::string & value) {
-    for (size_t i = 0; i < args.size(); i++) {
-        if (args[i] == key && i + 1 < args.size()) {
-            args[i + 1] = value;
-            return;
-        }
-    }
-    // not found, append
-    args.push_back(key);
-    args.push_back(value);
-}
-
-void server_models::load(const std::string & name, bool auto_load) {
+void server_models::load(const std::string & name) {
     if (!has_model(name)) {
         throw std::runtime_error("model name=" + name + " is not found");
     }
@@ -376,26 +518,10 @@ void server_models::load(const std::string & name, bool auto_load) {
     {
         SRV_INF("spawning server instance with name=%s on port %d\n", inst.meta.name.c_str(), inst.meta.port);
 
-        std::vector<std::string> child_args;
-        if (auto_load && !meta.args.empty()) {
-            child_args = meta.args; // copy previous args
-        } else {
-            child_args = base_args; // copy
-            if (inst.meta.in_cache) {
-                add_or_replace_arg(child_args, "-hf", inst.meta.name);
-            } else {
-                add_or_replace_arg(child_args, "-m", inst.meta.path);
-                if (!inst.meta.path_mmproj.empty()) {
-                    add_or_replace_arg(child_args, "--mmproj", inst.meta.path_mmproj);
-                }
-            }
-        }
+        presets.render_args(inst.meta); // update meta.args
 
-        // set model args
-        add_or_replace_arg(child_args, "--port", std::to_string(inst.meta.port));
-        add_or_replace_arg(child_args, "--alias", inst.meta.name);
-
-        std::vector<std::string> child_env = base_env; // copy
+        std::vector<std::string> child_args = inst.meta.args; // copy
+        std::vector<std::string> child_env  = base_env; // copy
         child_env.push_back("LLAMA_SERVER_ROUTER_PORT=" + std::to_string(base_params.port));
 
         SRV_INF("%s", "spawning server instance with args:\n");
@@ -541,7 +667,7 @@ bool server_models::ensure_model_loaded(const std::string & name) {
     }
     if (meta->status == SERVER_MODEL_STATUS_UNLOADED) {
         SRV_INF("model name=%s is not loaded, loading...\n", name.c_str());
-        load(name, true);
+        load(name);
     }
 
     SRV_INF("waiting until model name=%s is fully loaded...\n", name.c_str());
@@ -571,7 +697,7 @@ server_http_res_ptr server_models::proxy_request(const server_http_req & req, co
     SRV_INF("proxying request to model %s on port %d\n", name.c_str(), meta->port);
     auto proxy = std::make_unique<server_http_proxy>(
             method,
-            base_params.hostname,
+            CHILD_ADDR,
             meta->port,
             req.path,
             req.headers,
@@ -724,38 +850,6 @@ void server_models_routes::init_routes() {
         return models.proxy_request(req, method, name, true); // update last usage for POST request only
     };
 
-    this->get_router_models = [this](const server_http_req &) {
-        auto res = std::make_unique<server_http_res>();
-        json models_json = json::array();
-        auto all_models = models.get_all_meta();
-        std::time_t t = std::time(0);
-        for (const auto & meta : all_models) {
-            json status {
-                {"value", server_model_status_to_string(meta.status)},
-                {"args",  meta.args},
-            };
-            if (meta.is_failed()) {
-                status["exit_code"] = meta.exit_code;
-                status["failed"]    = true;
-            }
-            models_json.push_back(json {
-                {"id",       meta.name},
-                {"object",   "model"},    // for OAI-compat
-                {"owned_by", "llamacpp"}, // for OAI-compat
-                {"created",  t},          // for OAI-compat
-                {"in_cache", meta.in_cache},
-                {"path",     meta.path},
-                {"status",   status},
-                // TODO: add other fields, may require reading GGUF metadata
-            });
-        }
-        res_ok(res, {
-            {"data", models_json},
-            {"object", "list"},
-        });
-        return res;
-    };
-
     this->post_router_models_load = [this](const server_http_req & req) {
         auto res = std::make_unique<server_http_res>();
         json body = json::parse(req.body);
@@ -769,7 +863,7 @@ void server_models_routes::init_routes() {
             res_err(res, format_error_response("model is already loaded", ERROR_TYPE_INVALID_REQUEST));
             return res;
         }
-        models.load(name, false);
+        models.load(name);
         res_ok(res, {{"success", true}});
         return res;
     };
@@ -793,9 +887,12 @@ void server_models_routes::init_routes() {
         std::time_t t = std::time(0);
         for (const auto & meta : all_models) {
             json status {
-                {"value", server_model_status_to_string(meta.status)},
-                {"args",  meta.args},
+                {"value",  server_model_status_to_string(meta.status)},
+                {"args",   meta.args},
             };
+            if (!meta.preset.name.empty()) {
+                status["preset"] = meta.preset.to_ini();
+            }
             if (meta.is_failed()) {
                 status["exit_code"] = meta.exit_code;
                 status["failed"]    = true;
diff --git a/tools/server/server-models.h b/tools/server/server-models.h
index 526e7488dc..9cdbbad9b6 100644
--- a/tools/server/server-models.h
+++ b/tools/server/server-models.h
@@ -1,6 +1,7 @@
 #pragma once
 
 #include "common.h"
+#include "preset.h"
 #include "server-http.h"
 
 #include <mutex>
@@ -47,6 +48,7 @@ static std::string server_model_status_to_string(server_model_status status) {
 }
 
 struct server_model_meta {
+    common_preset preset;
     std::string name;
     std::string path;
     std::string path_mmproj; // only available if in_cache=false
@@ -54,7 +56,7 @@ struct server_model_meta {
     int port = 0;
     server_model_status status = SERVER_MODEL_STATUS_UNLOADED;
     int64_t last_used = 0; // for LRU unloading
-    std::vector<std::string> args; // additional args passed to the model instance (used for debugging)
+    std::vector<std::string> args; // args passed to the model instance, will be populated by render_args()
     int exit_code = 0; // exit code of the model instance process (only valid if status == FAILED)
 
     bool is_active() const {
@@ -66,6 +68,19 @@ struct server_model_meta {
     }
 };
 
+// the server_presets struct holds the presets read from presets.ini
+// as well as base args from the router server
+struct server_presets {
+    common_presets presets;
+    common_params_context ctx_params;
+    std::map<common_arg, std::string> base_args;
+    std::map<std::string, common_arg> control_args; // args reserved for server control
+
+    server_presets(int argc, char ** argv, common_params & base_params, const std::string & models_dir);
+    common_preset get_preset(const std::string & name);
+    void render_args(server_model_meta & meta);
+};
+
 struct subprocess_s;
 
 struct server_models {
@@ -85,14 +100,21 @@ private:
     std::vector<std::string> base_args;
     std::vector<std::string> base_env;
 
+    server_presets presets;
+
     void update_meta(const std::string & name, const server_model_meta & meta);
 
     // unload least recently used models if the limit is reached
     void unload_lru();
 
+    // not thread-safe, caller must hold mutex
+    void add_model(server_model_meta && meta);
+
 public:
     server_models(const common_params & params, int argc, char ** argv, char ** envp);
 
+    void load_models();
+
     // check if a model instance exists
     bool has_model(const std::string & name);
 
@@ -102,8 +124,7 @@ public:
     // return a copy of all model metadata
     std::vector<server_model_meta> get_all_meta();
 
-    // if auto_load is true, load the model with previous args if any
-    void load(const std::string & name, bool auto_load);
+    void load(const std::string & name);
     void unload(const std::string & name);
     void unload_all();
 

From 34a6d86982b54314516fd40ef5110525247528b8 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Wed, 10 Dec 2025 22:19:42 +0100
Subject: [PATCH 052/135] cli: enable jinja by default (#17911)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* cli: enable jinja by default

* Update common/arg.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---
 common/arg.cpp                  | 10 ++--------
 common/common.h                 |  2 +-
 tools/completion/completion.cpp |  4 ++++
 tools/mtmd/mtmd-cli.cpp         |  1 +
 4 files changed, 8 insertions(+), 9 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index b333f45c96..a31dcbc689 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -762,12 +762,6 @@ bool common_arg_utils::is_autoy(const std::string & value) {
 }
 
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
-    // default values specific to example
-    // note: we place it here instead of inside server.cpp to allow llama-gen-docs to pick it up
-    if (ex == LLAMA_EXAMPLE_SERVER) {
-        params.use_jinja = true;
-    }
-
     params.use_color = tty_can_use_colors();
 
     // load dynamic backends
@@ -2623,14 +2617,14 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_MODELS_AUTOLOAD"));
     add_opt(common_arg(
         {"--jinja"},
-        string_format("use jinja template for chat (default: %s)\n", params.use_jinja ? "enabled" : "disabled"),
+        string_format("use jinja template for chat (default: %s)", params.use_jinja ? "enabled" : "disabled"),
         [](common_params & params) {
             params.use_jinja = true;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_JINJA"));
     add_opt(common_arg(
         {"--no-jinja"},
-        string_format("disable jinja template for chat (default: %s)\n", params.use_jinja ? "enabled" : "disabled"),
+        string_format("disable jinja template for chat (default: %s)", params.use_jinja ? "disabled" : "enabled"),
         [](common_params & params) {
             params.use_jinja = false;
         }
diff --git a/common/common.h b/common/common.h
index 6119adcc0f..2fd83f0cf9 100644
--- a/common/common.h
+++ b/common/common.h
@@ -464,7 +464,7 @@ struct common_params {
     std::string public_path   = "";                                                                         // NOLINT
     std::string api_prefix    = "";                                                                         // NOLINT
     std::string chat_template = "";                                                                         // NOLINT
-    bool use_jinja = false;                                                                                 // NOLINT
+    bool use_jinja = true;                                                                                  // NOLINT
     bool enable_chat_template = true;
     common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
     int reasoning_budget = -1;
diff --git a/tools/completion/completion.cpp b/tools/completion/completion.cpp
index 79581eacb5..cb2641ae0a 100644
--- a/tools/completion/completion.cpp
+++ b/tools/completion/completion.cpp
@@ -86,6 +86,10 @@ static void sigint_handler(int signo) {
 int main(int argc, char ** argv) {
     common_params params;
     g_params = &params;
+
+    // disable jinja by default
+    params.use_jinja = false;
+
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMPLETION, print_usage)) {
         return 1;
     }
diff --git a/tools/mtmd/mtmd-cli.cpp b/tools/mtmd/mtmd-cli.cpp
index a75af406cd..ab7203d170 100644
--- a/tools/mtmd/mtmd-cli.cpp
+++ b/tools/mtmd/mtmd-cli.cpp
@@ -270,6 +270,7 @@ int main(int argc, char ** argv) {
     ggml_time_init();
 
     common_params params;
+    params.use_jinja = false;   // disable jinja by default
     params.sampling.temp = 0.2; // lower temp by default for better quality
 
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_MTMD, show_additional_info)) {

From c6b2c9310cc53e5cd4f65bbb8d0cb498caf2ed1e Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Wed, 10 Dec 2025 22:20:06 +0100
Subject: [PATCH 053/135] mtmd: some small clean up (#17909)

* clip: add support for fused qkv in build_vit

* use bulid_ffn whenever possible

* fix internvl

* mtmd-cli: move image to beginning

* test script: support custom args
---
 tools/mtmd/clip.cpp     | 152 ++++++++++++++++++++++++----------------
 tools/mtmd/mtmd-cli.cpp |   4 +-
 tools/mtmd/tests.sh     |  51 +++++++++-----
 3 files changed, 126 insertions(+), 81 deletions(-)

diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index e5f7117dbf..7360e8e09d 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -595,11 +595,12 @@ struct clip_graph {
             cur = ggml_mul(ctx0, cur, model.mm_input_norm_w);
             cur = ggml_add(ctx0, cur, model.mm_input_norm_b);
 
-            cur = ggml_mul_mat(ctx0, model.mm_1_w, cur);
-            cur = ggml_add(ctx0, cur, model.mm_1_b);
-            cur = ggml_gelu(ctx0, cur);
-            cur = ggml_mul_mat(ctx0, model.mm_2_w, cur);
-            cur = ggml_add(ctx0, cur, model.mm_2_b);
+            cur = build_ffn(cur,
+                model.mm_1_w, model.mm_1_b,
+                nullptr, nullptr,
+                model.mm_2_w, model.mm_2_b,
+                FFN_GELU,
+                -1);
 
         } else if (ctx->proj_type() == PROJECTOR_TYPE_JANUS_PRO) {
             cur = build_ffn(cur,
@@ -667,16 +668,12 @@ struct clip_graph {
 
         // LlavaMultiModalProjector (always using GELU activation)
         {
-            cur = ggml_mul_mat(ctx0, model.mm_1_w, cur);
-            if (model.mm_1_b) {
-                cur = ggml_add(ctx0, cur, model.mm_1_b);
-            }
-
-            cur = ggml_gelu(ctx0, cur);
-            cur = ggml_mul_mat(ctx0, model.mm_2_w, cur);
-            if (model.mm_2_b) {
-                cur = ggml_add(ctx0, cur, model.mm_2_b);
-            }
+            cur = build_ffn(cur,
+                model.mm_1_w, model.mm_1_b,
+                nullptr, nullptr,
+                model.mm_2_w, model.mm_2_b,
+                FFN_GELU,
+                -1);
         }
 
         // arrangement of the [IMG_BREAK] token
@@ -866,16 +863,12 @@ struct clip_graph {
         // multimodal projection
         ggml_tensor * embeddings = inpL;
         embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size);
-
-        embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
-        embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
-
-        // GELU activation
-        embeddings = ggml_gelu(ctx0, embeddings);
-
-        // Second linear layer
-        embeddings = ggml_mul_mat(ctx0, model.mm_1_w, embeddings);
-        embeddings = ggml_add(ctx0, embeddings, model.mm_1_b);
+        embeddings = build_ffn(embeddings,
+                            model.mm_0_w, model.mm_0_b,
+                            nullptr, nullptr,
+                            model.mm_1_w, model.mm_1_b,
+                            FFN_GELU,
+                            -1);
 
         if (use_window_attn) {
             window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4);
@@ -1253,11 +1246,12 @@ struct clip_graph {
             // projector LayerNorm uses pytorch's default eps = 1e-5
             // ref: https://huggingface.co/OpenGVLab/InternVL3-8B-Instruct/blob/a34d3e4e129a5856abfd6aa6de79776484caa14e/modeling_internvl_chat.py#L79
             cur = build_norm(cur, model.mm_0_w, model.mm_0_b, NORM_TYPE_NORMAL, 1e-5, -1);
-            cur = ggml_mul_mat(ctx0, model.mm_1_w, cur);
-            cur = ggml_add(ctx0, cur, model.mm_1_b);
-            cur = ggml_gelu(ctx0, cur);
-            cur = ggml_mul_mat(ctx0, model.mm_3_w, cur);
-            cur = ggml_add(ctx0, cur, model.mm_3_b);
+            cur = build_ffn(cur,
+                model.mm_1_w, model.mm_1_b,
+                nullptr, nullptr,
+                model.mm_3_w, model.mm_3_b,
+                FFN_GELU,
+                -1);
         }
 
         // build the graph
@@ -1408,11 +1402,12 @@ struct clip_graph {
             cb(cur, "proj_inp_normed", -1);
 
             // projection mlp
-            cur = ggml_mul_mat(ctx0, model.mm_1_w, cur);
-            cur = ggml_add(ctx0, cur, model.mm_1_b);
-            cur = ggml_gelu(ctx0, cur);
-            cur = ggml_mul_mat(ctx0, model.mm_2_w, cur);
-            cur = ggml_add(ctx0, cur, model.mm_2_b);
+            cur = build_ffn(cur,
+                model.mm_1_w, model.mm_1_b,
+                nullptr, nullptr,
+                model.mm_2_w, model.mm_2_b,
+                FFN_GELU,
+                -1);
             cb(cur, "proj_out", -1);
         }
 
@@ -1883,9 +1878,12 @@ struct clip_graph {
 
         } else if (ctx->proj_type() == PROJECTOR_TYPE_VOXTRAL) {
             // projector
-            cur = ggml_mul_mat(ctx0, model.mm_1_w, cur);
-            cur = ggml_gelu_erf(ctx0, cur);
-            cur = ggml_mul_mat(ctx0, model.mm_2_w, cur);
+            cur = build_ffn(cur,
+                model.mm_1_w, model.mm_1_b,
+                nullptr, nullptr,
+                model.mm_2_w, model.mm_2_b,
+                FFN_GELU_ERF,
+                -1);
 
         } else {
             GGML_ABORT("%s: unknown projector type", __func__);
@@ -2070,34 +2068,66 @@ private:
 
             // self-attention
             {
-                ggml_tensor * Qcur = ggml_mul_mat(ctx0, layer.q_w, cur);
-                if (layer.q_b) {
-                    Qcur = ggml_add(ctx0, Qcur, layer.q_b);
-                }
+                ggml_tensor * Qcur = nullptr;
+                ggml_tensor * Kcur = nullptr;
+                ggml_tensor * Vcur = nullptr;
+                if (layer.qkv_w != nullptr) {
+                    // fused qkv
+                    cur = ggml_mul_mat(ctx0, layer.qkv_w, cur);
+                    if (layer.qkv_b != nullptr) {
+                        cur = ggml_add(ctx0, cur, layer.qkv_b);
+                    }
 
-                ggml_tensor * Kcur = ggml_mul_mat(ctx0, layer.k_w, cur);
-                if (layer.k_b) {
-                    Kcur = ggml_add(ctx0, Kcur, layer.k_b);
-                }
+                    Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
+                        /* nb1    */ ggml_row_size(cur->type, d_head),
+                        /* nb2    */ cur->nb[1],
+                        /* offset */ 0);
 
-                ggml_tensor * Vcur = ggml_mul_mat(ctx0, layer.v_w, cur);
-                if (layer.v_b) {
-                    Vcur = ggml_add(ctx0, Vcur, layer.v_b);
-                }
+                    Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
+                        /* nb1    */ ggml_row_size(cur->type, d_head),
+                        /* nb2    */ cur->nb[1],
+                        /* offset */ ggml_row_size(cur->type, n_embd));
 
-                if (layer.q_norm) {
-                    Qcur = build_norm(Qcur, layer.q_norm, NULL, norm_t, eps, il);
-                    cb(Qcur, "Qcur_norm", il);
-                }
+                    Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
+                        /* nb1    */ ggml_row_size(cur->type, d_head),
+                        /* nb2    */ cur->nb[1],
+                        /* offset */ ggml_row_size(cur->type, 2 * n_embd));
 
-                if (layer.k_norm) {
-                    Kcur = build_norm(Kcur, layer.k_norm, NULL, norm_t, eps, il);
-                    cb(Kcur, "Kcur_norm", il);
-                }
+                    // TODO: q/k norm requires row size == n_embd, while here it's d_head
+                    // we can add support in the future if needed
+                    GGML_ASSERT(layer.q_norm == nullptr && layer.k_norm == nullptr);
 
-                Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos);
-                Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos);
+                } else {
+                    // separate q, k, v
+                    Qcur = ggml_mul_mat(ctx0, layer.q_w, cur);
+                    if (layer.q_b) {
+                        Qcur = ggml_add(ctx0, Qcur, layer.q_b);
+                    }
+
+                    Kcur = ggml_mul_mat(ctx0, layer.k_w, cur);
+                    if (layer.k_b) {
+                        Kcur = ggml_add(ctx0, Kcur, layer.k_b);
+                    }
+
+                    Vcur = ggml_mul_mat(ctx0, layer.v_w, cur);
+                    if (layer.v_b) {
+                        Vcur = ggml_add(ctx0, Vcur, layer.v_b);
+                    }
+
+                    if (layer.q_norm) {
+                        Qcur = build_norm(Qcur, layer.q_norm, NULL, norm_t, eps, il);
+                        cb(Qcur, "Qcur_norm", il);
+                    }
+
+                    if (layer.k_norm) {
+                        Kcur = build_norm(Kcur, layer.k_norm, NULL, norm_t, eps, il);
+                        cb(Kcur, "Kcur_norm", il);
+                    }
+
+                    Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos);
+                    Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos);
+                    Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos);
+                }
 
                 cb(Qcur, "Qcur", il);
                 cb(Kcur, "Kcur", il);
diff --git a/tools/mtmd/mtmd-cli.cpp b/tools/mtmd/mtmd-cli.cpp
index ab7203d170..25d24603db 100644
--- a/tools/mtmd/mtmd-cli.cpp
+++ b/tools/mtmd/mtmd-cli.cpp
@@ -318,7 +318,9 @@ int main(int argc, char ** argv) {
         g_is_generating = true;
         if (params.prompt.find(mtmd_default_marker()) == std::string::npos) {
             for (size_t i = 0; i < params.image.size(); i++) {
-                params.prompt += mtmd_default_marker();
+                // most models require the marker before each image
+                // ref: https://github.com/ggml-org/llama.cpp/pull/17616
+                params.prompt = mtmd_default_marker() + params.prompt;
             }
         }
         common_chat_msg msg;
diff --git a/tools/mtmd/tests.sh b/tools/mtmd/tests.sh
index 472f7d821c..82b486ec93 100755
--- a/tools/mtmd/tests.sh
+++ b/tools/mtmd/tests.sh
@@ -32,23 +32,32 @@ fi
 
 arr_prefix=()
 arr_hf=()
-arr_tmpl=() # chat template
+arr_extra_args=()
 arr_file=()
 
 add_test_vision() {
     local hf=$1
-    local tmpl=${2:-""} # default to empty string if not provided
+    shift
+    local extra_args=""
+    if [ $# -gt 0 ]; then
+        extra_args=$(printf " %q" "$@")
+    fi
     arr_prefix+=("[vision]")
     arr_hf+=("$hf")
-    arr_tmpl+=("$tmpl")
+    arr_extra_args+=("$extra_args")
     arr_file+=("test-1.jpeg")
 }
 
 add_test_audio() {
     local hf=$1
+    shift
+    local extra_args=""
+    if [ $# -gt 0 ]; then
+        extra_args=$(printf " %q" "$@")
+    fi
     arr_prefix+=("[audio] ")
     arr_hf+=("$hf")
-    arr_tmpl+=("") # no need for chat tmpl
+    arr_extra_args+=("$extra_args")
     arr_file+=("test-2.mp3")
 }
 
@@ -56,9 +65,9 @@ add_test_vision "ggml-org/SmolVLM-500M-Instruct-GGUF:Q8_0"
 add_test_vision "ggml-org/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M"
 add_test_vision "ggml-org/SmolVLM2-500M-Video-Instruct-GGUF:Q8_0"
 add_test_vision "ggml-org/gemma-3-4b-it-GGUF:Q4_K_M"
-add_test_vision "THUDM/glm-edge-v-5b-gguf:Q4_K_M"
-add_test_vision "second-state/Llava-v1.5-7B-GGUF:Q2_K"            "vicuna"
-add_test_vision "cjpais/llava-1.6-mistral-7b-gguf:Q3_K_M"         "vicuna"
+add_test_vision "THUDM/glm-edge-v-5b-gguf:Q4_K_M" -p "name of the newspaper?<__media__>"
+add_test_vision "second-state/Llava-v1.5-7B-GGUF:Q2_K" --chat-template vicuna
+add_test_vision "cjpais/llava-1.6-mistral-7b-gguf:Q3_K_M" --chat-template vicuna
 add_test_vision "ibm-research/granite-vision-3.2-2b-GGUF:Q4_K_M"
 add_test_vision "second-state/MiniCPM-Llama3-V-2_5-GGUF:Q2_K"  # model from openbmb is corrupted
 add_test_vision "openbmb/MiniCPM-V-2_6-gguf:Q2_K"
@@ -79,7 +88,7 @@ add_test_audio  "ggml-org/Voxtral-Mini-3B-2507-GGUF:Q4_K_M"
 # to test the big models, run: ./tests.sh big
 if [ "$RUN_BIG_TESTS" = true ]; then
     add_test_vision "ggml-org/pixtral-12b-GGUF:Q4_K_M"
-    add_test_vision "ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF" "mistral-v7"
+    add_test_vision "ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF" --chat-template mistral-v7
     add_test_vision "ggml-org/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M"
     add_test_vision "ggml-org/Qwen2-VL-7B-Instruct-GGUF:Q4_K_M"
     add_test_vision "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M"
@@ -89,7 +98,7 @@ if [ "$RUN_BIG_TESTS" = true ]; then
     add_test_vision "ggml-org/InternVL3-14B-Instruct-GGUF:Q4_K_M"
     add_test_vision "ggml-org/Qwen2.5-Omni-7B-GGUF:Q4_K_M"
     # add_test_vision "ggml-org/Qwen2.5-VL-32B-Instruct-GGUF:Q4_K_M" # does not work on my mac M3 Ultra
-    add_test_vision "ggml-org/Kimi-VL-A3B-Thinking-2506-GGUF:Q4_K_M"
+    # add_test_vision "ggml-org/Kimi-VL-A3B-Thinking-2506-GGUF:Q4_K_M" # not always working
 
     add_test_audio  "ggml-org/ultravox-v0_5-llama-3_1-8b-GGUF:Q4_K_M"
     add_test_audio  "ggml-org/Qwen2.5-Omni-7B-GGUF:Q4_K_M"
@@ -122,21 +131,25 @@ for i in "${!arr_hf[@]}"; do
     bin="llama-mtmd-cli"
     prefix="${arr_prefix[$i]}"
     hf="${arr_hf[$i]}"
-    tmpl="${arr_tmpl[$i]}"
+    extra_args="${arr_extra_args[$i]}"
     inp_file="${arr_file[$i]}"
 
     echo "Running test with binary: $bin and HF model: $hf"
     echo ""
     echo ""
 
-    output=$(\
-        "$PROJ_ROOT/build/bin/$bin" \
-        -hf "$hf" \
-        --image $SCRIPT_DIR/$inp_file \
-        -p "what is the publisher name of the newspaper?" \
+    cmd="$(printf %q "$PROJ_ROOT/build/bin/$bin") \
+        -hf $(printf %q "$hf") \
+        --image $(printf %q "$SCRIPT_DIR/$inp_file") \
         --temp 0 -n 128 \
-        ${tmpl:+--chat-template "$tmpl"} \
-        2>&1 | tee /dev/tty)
+        ${extra_args}"
+
+    # if extra_args does not contain -p, we add a default prompt
+    if ! [[ "$extra_args" =~ "-p" ]]; then
+        cmd+=" -p \"what is the publisher name of the newspaper?\""
+    fi
+
+    output=$(eval "$cmd" 2>&1 | tee /dev/tty)
 
     echo "$output" > $SCRIPT_DIR/output/$bin-$(echo "$hf" | tr '/' '-').log
 
@@ -144,9 +157,9 @@ for i in "${!arr_hf[@]}"; do
     if echo "$output" | grep -iq "new york" \
             || (echo "$output" | grep -iq "men" && echo "$output" | grep -iq "walk")
     then
-        result="$prefix \033[32mOK\033[0m:   $bin $hf"
+        result="$prefix \033[32mOK\033[0m:   $hf"
     else
-        result="$prefix \033[31mFAIL\033[0m: $bin $hf"
+        result="$prefix \033[31mFAIL\033[0m: $hf"
     fi
     echo -e "$result"
     arr_res+=("$result")

From 45e350e3d3a663714d7d3d2397e14c2904534338 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Wed, 10 Dec 2025 23:24:31 +0100
Subject: [PATCH 054/135] ci: fix riscv64-native build (#17916)

---
 .github/workflows/build.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 182d433b1b..383427f36f 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -1770,7 +1770,7 @@ jobs:
           echo "Fetch llama2c model"
           wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/stories260K.bin
           ./bin/llama-convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf
-          ./bin/llama-cli -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256
+          ./bin/llama-completion -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256
 
   ubuntu-cmake-sanitizer-riscv64-native:
     runs-on: RISCV64

From 34ce48d97a8cd5497ee418224da5bf422ed96673 Mon Sep 17 00:00:00 2001
From: nullname <chraac@gmail.com>
Date: Thu, 11 Dec 2025 06:45:43 +0800
Subject: [PATCH 055/135] ggml-hexagon: fix `rope` failure at
 `test-backend-ops` (#17565)

* fix test failure

* fix: correct scaling calculations in rope_cache_init

* fix: optimize element copying in rope_hex_f32 using memcpy

* fix: optimize loop boundaries in rope_hex_f32 for better performance

* feat: add profiling macros for performance measurement in operations
---
 ggml/src/ggml-hexagon/htp/rope-ops.c | 78 +++++++++++++---------------
 1 file changed, 37 insertions(+), 41 deletions(-)

diff --git a/ggml/src/ggml-hexagon/htp/rope-ops.c b/ggml/src/ggml-hexagon/htp/rope-ops.c
index 00419bcba6..a4399704fc 100644
--- a/ggml/src/ggml-hexagon/htp/rope-ops.c
+++ b/ggml/src/ggml-hexagon/htp/rope-ops.c
@@ -73,15 +73,15 @@ static float rope_yarn_ramp(const float low, const float high, const int i0) {
     return (1 - MIN(1, MAX(0, y)));
 }
 
-static void rope_cache_init(const float   theta_base,
-                            float         freq_scale,
-                            const float * freq_factors,
-                            float *       corr_dims,
-                            uint32_t      ne0,
-                            float         ext_factor,
-                            float         mscale,
-                            float *       cache,
-                            float         theta_scale) {
+static void rope_cache_init(const float    theta_base,
+                            const float    freq_scale,
+                            const float *  freq_factors,
+                            float *        corr_dims,
+                            const uint32_t ne0,
+                            const float    ext_factor,
+                            const float    mscale,
+                            float *        cache,
+                            const float    theta_scale) {
     // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py
     float theta = theta_base;
 
@@ -92,18 +92,19 @@ static void rope_cache_init(const float   theta_base,
 
         // Get n-d rotational scaling corrected for extrapolation
         float theta_interp = freq_scale * theta_extrap;
-        float theta2       = theta_interp;
+        float theta_final  = theta_interp;
+        float mscale_final = mscale;
 
         if (ext_factor != 0.0f) {
             float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor;
-            theta2         = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+            theta_final    = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
 
             // Get n-d magnitude scaling corrected for interpolation
-            mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
+            mscale_final *= 1.0f + 0.1f * logf(1.0f / freq_scale);
         }
 
-        cache[i0 + 0] = cosf(theta2) * mscale;
-        cache[i0 + 1] = sinf(theta2) * mscale;
+        cache[i0 + 0] = cosf(theta_final) * mscale_final;
+        cache[i0 + 1] = sinf(theta_final) * mscale_final;
 
         theta *= theta_scale;
     }
@@ -151,9 +152,9 @@ static void init_rope_ctx(struct rope_th_ctx * rope_ctx, struct htp_ops_context
 }
 
 static void hvx_calc_rope_neox_f32(const float * restrict src0,
-                              float * restrict dst,
-                              const int num_elems,
-                              const float * restrict theta_cache) {
+                                   float * restrict dst,
+                                   const int num_elems,
+                                   const float * restrict theta_cache) {
     // for (int i = 0; i < num_elems; i += 2) {
     //const float cos_theta = theta_cache[i + 0];
     //const float sin_theta = theta_cache[i + 1];
@@ -192,7 +193,7 @@ static void hvx_calc_rope_neox_f32(const float * restrict src0,
         HVX_Vector v4 = Q6_Vqf32_vsub_Vqf32Vqf32(vx0_c, vx1_s);
         HVX_Vector v5 = Q6_Vqf32_vadd_Vqf32Vqf32(vx0_s, vx1_c);
 
-        *(HVX_Vector *) dst_curr          = Q6_Vsf_equals_Vqf32(v4);
+        *(HVX_Vector *) dst_curr               = Q6_Vsf_equals_Vqf32(v4);
         *(HVX_Vector *) (dst_curr + half_size) = Q6_Vsf_equals_Vqf32(v5);
 
         src0_curr += VLEN;
@@ -259,7 +260,7 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx,
                          const uint32_t       ir1,
                          int                  nth,
                          int                  ith,
-                         int                  opt_path) {
+                         const int            opt_path) {
     struct htp_ops_context * octx = rope_ctx->octx;
 
     const struct htp_tensor * src0 = &octx->src0;
@@ -267,8 +268,8 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx,
     const struct htp_tensor * src2 = &octx->src2;
     struct htp_tensor *       dst  = &octx->dst;
 
-    const int32_t mode  = rope_ctx->mode;
-    const bool is_neox  = mode & HTP_ROPE_TYPE_NEOX;
+    const int32_t mode    = rope_ctx->mode;
+    const bool    is_neox = mode & HTP_ROPE_TYPE_NEOX;
 
     htp_rope_preamble;
 
@@ -281,8 +282,9 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx,
         freq_factors = (const float *) src2->data;
     }
 
-    int ir = 0;
-
+    const uint32_t i1_end       = MIN(ir1, ne1);
+    const int32_t  half_dims    = rope_ctx->n_dims / 2;
+    const size_t   remain_bytes = (ne0 - rope_ctx->n_dims) * sizeof(float);
     for (uint32_t i3 = 0; i3 < ne3; i3++) {      // batch
         for (uint32_t i2 = 0; i2 < ne2; i2++) {  // seq-len
             const int32_t p = pos[i2];
@@ -290,14 +292,7 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx,
             rope_cache_init(p, rope_ctx->freq_scale, freq_factors, rope_ctx->corr_dims, ne0, rope_ctx->ext_factor,
                             rope_ctx->attn_factor, wp0, rope_ctx->theta_scale);
 
-            for (uint32_t i1 = 0; i1 < ne1; i1++) {  // attn-heads
-                if (ir++ < ir0) {
-                    continue;
-                }
-                if (ir > ir1) {
-                    break;
-                }
-
+            for (uint32_t i1 = ir0; i1 < i1_end; i1++) {  // attn-heads
                 const float * src      = (float *) ((char *) src0->data + i3 * nb03 + i2 * nb02 + i1 * nb01);
                 float *       dst_data = (float *) ((char *) dst->data + i3 * nb3 + i2 * nb2 + i1 * nb1);
 
@@ -310,6 +305,9 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx,
                     } else {
                         hvx_calc_rope_f32(src_loc, dst_data_loc, rope_ctx->n_dims, wp0);
                     }
+
+                    src_loc += rope_ctx->n_dims;
+                    dst_data_loc += rope_ctx->n_dims;
                 } else {
                     for (uint32_t i0 = 0; i0 < rope_ctx->n_dims; i0 += 2) {
                         const float cos_theta = wp0[i0 + 0];
@@ -317,10 +315,10 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx,
 
                         if (is_neox) {
                             const float x0 = src_loc[0];
-                            const float x1 = src_loc[rope_ctx->n_dims/2];
+                            const float x1 = src_loc[half_dims];
 
-                            dst_data_loc[0] = x0 * cos_theta - x1 * sin_theta;
-                            dst_data_loc[rope_ctx->n_dims/2] = x0 * sin_theta + x1 * cos_theta;
+                            dst_data_loc[0]         = x0 * cos_theta - x1 * sin_theta;
+                            dst_data_loc[half_dims] = x0 * sin_theta + x1 * cos_theta;
 
                             src_loc += 1;
                             dst_data_loc += 1;
@@ -335,15 +333,13 @@ static void rope_hex_f32(struct rope_th_ctx * rope_ctx,
                             dst_data_loc += 2;
                         }
                     }
+
+                    src_loc += (is_neox ? half_dims : 0);
+                    dst_data_loc += (is_neox ? half_dims : 0);
                 }
 
-                for (uint32_t i0 = rope_ctx->n_dims; i0 < ne0; i0 += 2) {
-                    dst_data_loc[0] = src_loc[0];
-                    dst_data_loc[1] = src_loc[1];
-
-                    src_loc += 2;
-                    dst_data_loc += 2;
-                }
+                // TODO: use simd to speed up the remaining elements copy
+                memcpy(dst_data_loc, src_loc, remain_bytes);
             }
         }
     }

From e4ae38331702aeb43b6ecc3f912d626171c9862a Mon Sep 17 00:00:00 2001
From: Yuichiro Utsumi <81412151+utsumi-fj@users.noreply.github.com>
Date: Thu, 11 Dec 2025 18:12:07 +0900
Subject: [PATCH 056/135] docs: use port 8080 in Docker examples (#17903)

---
 docs/docker.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/docker.md b/docs/docker.md
index 98502a0c50..b9e5015396 100644
--- a/docs/docker.md
+++ b/docs/docker.md
@@ -56,7 +56,7 @@ docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:light -m /model
 or with a server image:
 
 ```bash
-docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggml-org/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512
+docker run -v /path/to/models:/models -p 8080:8080 ghcr.io/ggml-org/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8080 --host 0.0.0.0 -n 512
 ```
 
 ## Docker With CUDA
@@ -91,7 +91,7 @@ After building locally, Usage is similar to the non-CUDA examples, but you'll ne
 ```bash
 docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
 docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
-docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1
+docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8080 --host 0.0.0.0 -n 512 --n-gpu-layers 1
 ```
 
 ## Docker With MUSA
@@ -125,5 +125,5 @@ After building locally, Usage is similar to the non-MUSA examples, but you'll ne
 ```bash
 docker run -v /path/to/models:/models local/llama.cpp:full-musa --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
 docker run -v /path/to/models:/models local/llama.cpp:light-musa -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
-docker run -v /path/to/models:/models local/llama.cpp:server-musa -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1
+docker run -v /path/to/models:/models local/llama.cpp:server-musa -m /models/7B/ggml-model-q4_0.gguf --port 8080 --host 0.0.0.0 -n 512 --n-gpu-layers 1
 ```

From d9f8f60618a1df2797cb7df4ad1272f71d6bd7b2 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 11 Dec 2025 14:29:47 +0200
Subject: [PATCH 057/135] batch : fix sequence id ownership (#17915)

* batch : fix sequence id ownage

* cont : reduce allocations
---
 src/llama-batch.cpp | 14 ++++++++++++--
 src/llama-batch.h   |  6 ++++--
 2 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/src/llama-batch.cpp b/src/llama-batch.cpp
index 86a1a4ba18..386fab04ac 100644
--- a/src/llama-batch.cpp
+++ b/src/llama-batch.cpp
@@ -695,6 +695,8 @@ llama_ubatch llama_batch_allocr::ubatch_add(const std::vector<int32_t> & idxs, u
     udata->seq_idx   .resize(LLAMA_MAX_SEQ, -1);
     udata->output    .resize(n_tokens);
 
+    udata->seq_id_data.reserve(n_tokens);
+
     seq_set_t seq_set_unq;
 
     for (size_t i = 0; i < idxs.size(); ++i) {
@@ -716,11 +718,13 @@ llama_ubatch llama_batch_allocr::ubatch_add(const std::vector<int32_t> & idxs, u
         }
 
         udata->n_seq_id[i] = batch.n_seq_id[idxs[i]];
-        udata->seq_id[i]   = batch.seq_id[idxs[i]];
         udata->output[i]   = batch.logits[idxs[i]];
 
         for (int s = 0; s < udata->n_seq_id[i]; ++s) {
-            seq_set_unq.set(udata->seq_id[i][s]);
+            const llama_seq_id seq_id = batch.seq_id[idxs[i]][s];
+
+            udata->seq_id_data.push_back(seq_id);
+            seq_set_unq.set(seq_id);
         }
 
         if (udata->output[i]) {
@@ -728,6 +732,12 @@ llama_ubatch llama_batch_allocr::ubatch_add(const std::vector<int32_t> & idxs, u
         }
     }
 
+    llama_seq_id * seq_id_ptr = udata->seq_id_data.data();
+    for (size_t i = 0; i < idxs.size(); ++i) {
+        udata->seq_id[i] = seq_id_ptr;
+        seq_id_ptr += udata->n_seq_id[i];
+    }
+
     for (uint32_t s = 0; s < n_seq_max; ++s) {
         if (seq_set_unq.test(s)) {
             udata->seq_idx[s] = udata->seq_id_unq.size();
diff --git a/src/llama-batch.h b/src/llama-batch.h
index 209cf3699d..8e6fac0efa 100644
--- a/src/llama-batch.h
+++ b/src/llama-batch.h
@@ -56,13 +56,15 @@ struct llama_ubatch {
         std::vector<float>          embd;
         std::vector<llama_pos>      pos;
         std::vector<int32_t>        n_seq_id;
-        std::vector<llama_seq_id *> seq_id;
+        std::vector<llama_seq_id *> seq_id;      // these point into the seq_id_data below
         std::vector<llama_seq_id>   seq_id_unq;
         std::vector<int32_t>        seq_idx;
         std::vector<int8_t>         output;
+
+        std::vector<llama_seq_id> seq_id_data;
     };
 
-    // the llama_ubatch pointers above point to this data if set. otherwise - points to non-owning data
+    // the llama_ubatch pointers above point to this data if set. otherwise - point to external non-owning data
     std::shared_ptr<data_t> data;
 };
 

From c6f6e4f96a7f7bce49f5c21d19ee69fb8b72f84d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 11 Dec 2025 14:30:10 +0200
Subject: [PATCH 058/135] ggml-alloc : fix reuse-parent logic for misaligned
 sizes (#17884)

---
 ggml/src/ggml-alloc.c | 28 +++++++++++++++-------------
 1 file changed, 15 insertions(+), 13 deletions(-)

diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c
index a5995fdc2c..ec16cbda9f 100644
--- a/ggml/src/ggml-alloc.c
+++ b/ggml/src/ggml-alloc.c
@@ -312,16 +312,9 @@ static struct buffer_address ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * al
 }
 
 // this is a very naive implementation, but for our case the number of free blocks should be very small
-static void ggml_dyn_tallocr_free_tensor(struct ggml_dyn_tallocr * alloc, struct buffer_address addr, size_t size, const struct ggml_tensor * tensor) {
+static void ggml_dyn_tallocr_free_bytes(struct ggml_dyn_tallocr * alloc, struct buffer_address addr, size_t size) {
     size = aligned_offset(NULL, size, alloc->alignment);
 
-    AT_PRINTF("%s: freeing %s at {chunk=%d, offset=%zu} (%zu bytes) - n_free_blocks = %d\n",
-        __func__, tensor->name, addr.chunk, addr.offset, size, alloc->chunks[addr.chunk]->n_free_blocks);
-
-#ifdef GGML_ALLOCATOR_DEBUG
-    remove_allocated_tensor(alloc, addr, tensor);
-#endif
-
     struct tallocr_chunk * chunk = alloc->chunks[addr.chunk];
 
     // see if we can merge with an existing block
@@ -357,8 +350,6 @@ static void ggml_dyn_tallocr_free_tensor(struct ggml_dyn_tallocr * alloc, struct
     }
     // otherwise, add a new block
     ggml_dyn_tallocr_insert_block(chunk, addr.offset, size);
-
-    GGML_UNUSED(tensor);
 }
 
 static void ggml_dyn_tallocr_reset(struct ggml_dyn_tallocr * alloc) {
@@ -616,13 +607,17 @@ static void ggml_gallocr_free_extra_space(ggml_gallocr_t galloc, struct ggml_ten
 
     GGML_ASSERT(parent_size >= node_size);
 
+    // note: we want after the freeing the chunks to continue to be aligned
+    struct ggml_dyn_tallocr * p_alloc = galloc->buf_tallocs[p_hn->buffer_id];
+    parent_size = aligned_offset(NULL, parent_size, p_alloc->alignment);
+    node_size = aligned_offset(NULL, node_size, p_alloc->alignment);
+
     if (parent_size > node_size) {
-        struct ggml_dyn_tallocr * p_alloc = galloc->buf_tallocs[p_hn->buffer_id];
         struct buffer_address p_addr = p_hn->addr;
         p_addr.offset += node_size;
         size_t extra_size = parent_size - node_size;
         AT_PRINTF("freeing extra %zu bytes from parent %s for %s\n", extra_size, parent->name, node->name);
-        ggml_dyn_tallocr_free_tensor(p_alloc, p_addr, extra_size, parent);
+        ggml_dyn_tallocr_free_bytes(p_alloc, p_addr, extra_size);
     }
 }
 
@@ -706,7 +701,14 @@ static void ggml_gallocr_free_node(ggml_gallocr_t galloc, struct ggml_tensor * n
     struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id];
     ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id];
     size_t size = ggml_backend_buft_get_alloc_size(buft, node);
-    ggml_dyn_tallocr_free_tensor(alloc, hn->addr, size, node);
+
+    AT_PRINTF("%s: freeing %s at {chunk=%d, offset=%zu} (%zu bytes) - n_free_blocks = %d\n",
+        __func__, node->name, hn->addr.chunk, hn->addr.offset, size, alloc->chunks[hn->addr.chunk]->n_free_blocks);
+#ifdef GGML_ALLOCATOR_DEBUG
+    remove_allocated_tensor(alloc, hn->addr, node);
+#endif
+
+    ggml_dyn_tallocr_free_bytes(alloc, hn->addr, size);
     hn->allocated = false;
 }
 

From 53ecd4fdb923dcb53d311ed42798cae7198aa742 Mon Sep 17 00:00:00 2001
From: "Piotr Wilkin (ilintar)" <piotr.wilkin@syndatis.com>
Date: Thu, 11 Dec 2025 17:20:43 +0100
Subject: [PATCH 059/135] SOLVE_TRI extension to more dimensions (#17793)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Extended TRI

* Fix whitespace

* chore: update webui build output

* Just use cuBLAS for everything...

* Merge both versions

* Remove incorrect imports causing failures for CI

* Still failing... remove all direct cublas imports and rely on common imports from "common.cuh"

* Defines for hipBlas

* Aaaand MUSA defines...

* I hate this job...

* Stupid typo...

* Update ggml/src/ggml-cuda/solve_tri.cu

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---
 ggml/src/ggml-cuda/ggml-cuda.cu   |   4 +-
 ggml/src/ggml-cuda/solve_tri.cu   | 110 ++++++++++++++++++++++++++----
 ggml/src/ggml-cuda/vendors/hip.h  |   4 ++
 ggml/src/ggml-cuda/vendors/musa.h |   5 ++
 tests/test-backend-ops.cpp        |  22 +++++-
 tools/server/public/index.html.gz | Bin 1442044 -> 1444504 bytes
 6 files changed, 125 insertions(+), 20 deletions(-)

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index 8d17bc669a..ab0f6fe9ce 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -4630,9 +4630,9 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_CUMSUM:
         case GGML_OP_TRI:
         case GGML_OP_DIAG:
-            return true;
         case GGML_OP_SOLVE_TRI:
-            return op->src[0]->ne[0] <= 64 && op->src[1]->ne[0] <= 32;
+            return true;
+
         default:
             return false;
     }
diff --git a/ggml/src/ggml-cuda/solve_tri.cu b/ggml/src/ggml-cuda/solve_tri.cu
index e161d4dc43..177ffc268f 100644
--- a/ggml/src/ggml-cuda/solve_tri.cu
+++ b/ggml/src/ggml-cuda/solve_tri.cu
@@ -3,6 +3,80 @@
 #include "solve_tri.cuh"
 
 #define MAX_N_FAST 64
+#define MAX_K_FAST 32
+
+static __global__ void get_batch_pointers(const float *  A,
+                                          float *        X,
+                                          const float ** A_ptrs,
+                                          float **       X_ptrs,
+                                          int64_t        ne02,
+                                          int64_t        total_batches,
+                                          size_t         s02,
+                                          size_t         s03,
+                                          size_t         s2,
+                                          size_t         s3) {
+    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
+    if (idx >= total_batches) {
+        return;
+    }
+
+    const int64_t i3 = idx / ne02;
+    const int64_t i2 = idx % ne02;
+
+    A_ptrs[idx] = A + i3 * s03 + i2 * s02;
+    X_ptrs[idx] = X + i3 * s3 + i2 * s2;
+}
+
+static void solve_tri_f32_cublas(ggml_backend_cuda_context & ctx,
+                                 const float *               A,
+                                 const float *               B,
+                                 float *                     X,
+                                 int                         n,
+                                 int                         k,
+                                 int64_t                     ne02,
+                                 int64_t                     ne03,
+                                 size_t                      s02,
+                                 size_t                      s03,
+                                 size_t                      s12,
+                                 size_t                      s13,
+                                 size_t                      s2,
+                                 size_t                      s3,
+                                 cudaStream_t                stream) {
+    const float   alpha         = 1.0f;
+    const int64_t total_batches = ne02 * ne03;
+    if (total_batches == 0) {
+        return;
+    }
+
+    // Bulk copy B -> X (contiguous tensors)
+    if (X != B) {
+        const int64_t total_elements_BX = n * k * total_batches;
+        CUDA_CHECK(cudaMemcpyAsync(X, B, total_elements_BX * sizeof(float), cudaMemcpyDeviceToDevice, stream));
+    }
+
+    const int id = ggml_cuda_get_device();
+
+    ggml_cuda_pool_alloc<const float *> A_ptrs_alloc(ctx.pool(id), total_batches);
+    ggml_cuda_pool_alloc<float *>       X_ptrs_alloc(ctx.pool(id), total_batches);
+
+    const float ** A_ptrs_dev = A_ptrs_alloc.get();
+    float **       X_ptrs_dev = X_ptrs_alloc.get();
+
+    get_batch_pointers<<<(total_batches + 255) / 256, 256, 0, stream>>>(A, X, A_ptrs_dev, X_ptrs_dev, ne02,
+                                                                        total_batches, s02, s03, s2, s3);
+
+    CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream));
+
+    // Yes, this is necessary, without this we get RMSE errors
+    CUBLAS_CHECK(cublasSetMathMode(ctx.cublas_handle(id), CUBLAS_DEFAULT_MATH));
+    CUBLAS_CHECK(cublasStrsmBatched(ctx.cublas_handle(id), CUBLAS_SIDE_RIGHT, CUBLAS_FILL_MODE_UPPER, CUBLAS_OP_N,
+                                    CUBLAS_DIAG_NON_UNIT, k, n, &alpha, A_ptrs_dev, n, X_ptrs_dev, k, total_batches));
+
+    // revert to standard mode from common.cuh
+    CUBLAS_CHECK(cublasSetMathMode(ctx.cublas_handle(id), CUBLAS_TF32_TENSOR_OP_MATH));
+
+    GGML_UNUSED_VARS(s12, s13);
+}
 
 // ======================
 // Fast Kernel (n <= 64, k <= 32) - Warp-based parallel reduction
@@ -63,7 +137,7 @@ static __global__ void solve_tri_f32_fast(const float * __restrict__ A,
     float x_low  = (lane < n) ? B_batch[lane * k + col_idx] : 0.0f;
     float x_high = (WARP_SIZE + lane < n) ? B_batch[(WARP_SIZE + lane) * k + col_idx] : 0.0f;
 
-    const int half = WARP_SIZE;
+    const int half      = WARP_SIZE;
     const int nrows_low = (n < half) ? n : half;
 
 #pragma unroll
@@ -81,8 +155,8 @@ static __global__ void solve_tri_f32_fast(const float * __restrict__ A,
 
 #pragma unroll
     for (int row = half; row < n; ++row) {
-        float sum = sA[row * n + lane] * x_low;
-        const int j = half + lane;
+        float     sum = sA[row * n + lane] * x_low;
+        const int j   = half + lane;
         if (j < row) {
             sum += sA[row * n + j] * x_high;
         }
@@ -97,7 +171,7 @@ static __global__ void solve_tri_f32_fast(const float * __restrict__ A,
     for (int rr = 0; rr < 2; ++rr) {
         const int row = rr * WARP_SIZE + lane;
         if (row < n) {
-            const float val = (row < half) ? x_low : x_high;
+            const float val            = (row < half) ? x_low : x_high;
             X_batch[row * k + col_idx] = val;
         }
     }
@@ -176,20 +250,26 @@ static void solve_tri_f32_cuda(const float * A,
 }
 
 void ggml_cuda_op_solve_tri(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * src0 = dst->src[0];  // A (triangular n x x matrix)
-    const ggml_tensor * src1 = dst->src[1];  // B (right hand side of n x k equation columns)
+    const ggml_tensor * src0 = dst->src[0];  // A (n×n, lower triangular)
+    const ggml_tensor * src1 = dst->src[1];  // B (n×k)
 
     ggml_is_contiguous(src0);
     ggml_is_contiguous(src1);
 
-    const int64_t n = src0->ne[0];
-    const int64_t k = src1->ne[0];
+    const int64_t n    = src0->ne[0];
+    const int64_t k    = src1->ne[0];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t ne03 = src0->ne[3];
 
-    GGML_ASSERT(n <= 64);
-    GGML_ASSERT(k <= 32);
-
-    solve_tri_f32_cuda((const float *) src0->data, (const float *) src1->data, (float *) dst->data, n, k, src0->ne[2],
-                       src0->ne[3], src0->nb[2] / sizeof(float), src0->nb[3] / sizeof(float),
-                       src1->nb[2] / sizeof(float), src1->nb[3] / sizeof(float), dst->nb[2] / sizeof(float),
-                       dst->nb[3] / sizeof(float), ctx.stream());
+    if (n <= MAX_N_FAST && k <= MAX_K_FAST) {
+        solve_tri_f32_cuda((const float *) src0->data, (const float *) src1->data, (float *) dst->data, n, k,
+                           src0->ne[2], src0->ne[3], src0->nb[2] / sizeof(float), src0->nb[3] / sizeof(float),
+                           src1->nb[2] / sizeof(float), src1->nb[3] / sizeof(float), dst->nb[2] / sizeof(float),
+                           dst->nb[3] / sizeof(float), ctx.stream());
+    } else {
+        solve_tri_f32_cublas(ctx, (const float *) src0->data, (const float *) src1->data, (float *) dst->data, n, k,
+                             ne02, ne03, src0->nb[2] / sizeof(float), src0->nb[3] / sizeof(float),
+                             src1->nb[2] / sizeof(float), src1->nb[3] / sizeof(float), dst->nb[2] / sizeof(float),
+                             dst->nb[3] / sizeof(float), ctx.stream());
+    }
 }
diff --git a/ggml/src/ggml-cuda/vendors/hip.h b/ggml/src/ggml-cuda/vendors/hip.h
index b7d6edf7fc..951a88d567 100644
--- a/ggml/src/ggml-cuda/vendors/hip.h
+++ b/ggml/src/ggml-cuda/vendors/hip.h
@@ -19,6 +19,9 @@
 #define CUDA_R_16F  HIPBLAS_R_16F
 #define CUDA_R_16BF HIPBLAS_R_16B
 #define CUDA_R_32F  HIPBLAS_R_32F
+#define CUBLAS_SIDE_RIGHT HIPBLAS_SIDE_RIGHT
+#define CUBLAS_FILL_MODE_UPPER HIPBLAS_FILL_MODE_UPPER
+#define CUBLAS_DIAG_NON_UNIT HIPBLAS_DIAG_NON_UNIT
 #define CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED hipDeviceAttributeVirtualMemoryManagementSupported
 #define CU_MEM_ALLOC_GRANULARITY_RECOMMENDED hipMemAllocationGranularityRecommended
 #define CU_MEM_ALLOCATION_TYPE_PINNED hipMemAllocationTypePinned
@@ -30,6 +33,7 @@
 #define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
 #define __all_sync(mask, var) __all(var)
 #define __any_sync(mask, var) __any(var)
+#define cublasStrsmBatched hipblasStrsmBatched
 #define cublasCreate hipblasCreate
 #define cublasDestroy hipblasDestroy
 #define cublasGemmEx hipblasGemmEx
diff --git a/ggml/src/ggml-cuda/vendors/musa.h b/ggml/src/ggml-cuda/vendors/musa.h
index 8c55a2e4e5..221e67f96a 100644
--- a/ggml/src/ggml-cuda/vendors/musa.h
+++ b/ggml/src/ggml-cuda/vendors/musa.h
@@ -12,11 +12,16 @@
 #define CUBLAS_GEMM_DEFAULT_TENSOR_OP MUBLAS_GEMM_DEFAULT
 #define CUBLAS_OP_N MUBLAS_OP_N
 #define CUBLAS_OP_T MUBLAS_OP_T
+#define CUBLAS_DEFAULT_MATH MUBLAS_DEFAULT_MATH
+#define CUBLAS_SIDE_RIGHT MUBLAS_SIDE_RIGHT
+#define CUBLAS_FILL_MODE_UPPER MUBLAS_FILL_MODE_UPPER
+#define CUBLAS_DIAG_NON_UNIT MUBLAS_DIAG_NON_UNIT
 #define CUBLAS_STATUS_SUCCESS MUBLAS_STATUS_SUCCESS
 #define CUBLAS_TF32_TENSOR_OP_MATH MUBLAS_TENSOR_OP_MATH
 #define CUDA_R_16F  MUSA_R_16F
 #define CUDA_R_16BF MUSA_R_16BF
 #define CUDA_R_32F  MUSA_R_32F
+#define cublasStrsmBatched mublasStrsmBatched
 #define cublasComputeType_t cudaDataType_t
 #define cublasCreate mublasCreate
 #define cublasDestroy mublasDestroy
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 7be1f66038..308e752b1d 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -7861,9 +7861,24 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 30, 30, 7, 1 }, { 8, 30, 7, 1 }));
     test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 42, 42, 5, 2 }, { 10, 42, 5, 2 }));
     test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 2, 2 }, { 10, 64, 2, 2 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 2, 2 }, { 64, 64, 2, 2 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 79, 79, 5, 3 }, { 417, 79, 5, 3 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 2 }, { 32, 128, 4, 2 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 2, 8 }, { 80, 80, 2, 8 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 2, 8 }, { 79, 80, 2, 8 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 2, 8 }, { 81, 80, 2, 8 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 8, 8 }, { 80, 80, 8, 8 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 8, 8 }, { 79, 80, 8, 8 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 80, 80, 8, 8 }, { 81, 80, 8, 8 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 84, 84, 4, 4 }, { 32, 84, 4, 4 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 95, 95, 8, 8 }, { 40, 95, 8, 8 }));
     test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 100, 100, 4, 4 }, { 41, 100, 4, 4 }));
     test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 4 }, { 31, 128, 4, 4 }));
-    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 300, 64, 4, 4 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 4 }, { 32, 128, 4, 4 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 3, 4 }, { 32, 128, 3, 4 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 1 }, { 32, 128, 4, 1 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 200, 64, 4, 4 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 384, 64, 4, 4 }));
 
     for (bool v : {false, true}) {
         for (bool circular : {false, true}) {
@@ -8064,12 +8079,13 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 16416, 1, 128, {8,  1}, {4, 1}, {0, 2, 1, 3}));
     test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F16, GGML_TYPE_F32, 128, 1, 16416, {8,  1}, {4, 1}, {0, 1, 2, 3}, 2*16416));
 
-    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 2 }, { 6, 64, 4, 2 }));
-    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 1 }, { 8, 128, 4, 1 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 32, 64, 4, 4 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 2 }, { 32, 128, 4, 2 }));
     // qwen3next with CHUNK_SIZE 64
     test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 8, 32 }, { 64, 64, 8, 32 }));
     // qwen3next with CHUNK_SIZE 128
     test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 32 }, { 128, 128, 4, 32 }));
+    test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 256, 256, 4, 2 }, { 128, 256, 4, 2 }));
 
     test_cases.emplace_back(new test_tri(GGML_TRI_TYPE_LOWER, GGML_TYPE_F32, { 256, 256, 4, 4 }));
     test_cases.emplace_back(new test_tri(GGML_TRI_TYPE_UPPER_DIAG, GGML_TYPE_F32, { 1024, 1024, 8, 4 }));
diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index 2db04e9522742e0d7bb36034054bcbb58a958520..dda053498a73d759f8695743cf9e0db816ba7255 100644
GIT binary patch
delta 1170954
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From a81a569577cc38b32558958b048228150be63eae Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Thu, 11 Dec 2025 18:21:21 +0100
Subject: [PATCH 060/135] Add a search field on model selector / improve mobile
 display (#17765)

* webui: add search field to model selector and fixes mobile viewport overflow

* webui: simplify model search style and code

* refacor: Search Input component & consistent UI for Models Selector search

* feat: Use Popover component + improve interactions

* fix: Fetching props for only loaded models in ROUTER mode

* webui: prevent models selector popover from overflowing viewport

Use Floating UI's auto-positioning with 50dvh height limit and proper
collision detection instead of forcing top positioning. Fixes overflow
on desktop and mobile keyboard issues

* webui: keep search field near trigger in models selector

Place search at the 'near end' (closest to trigger) by swapping layout
with CSS flexbox order based on popover direction. Prevents input from
moving during typing as list shrinks

* chore: update webui build output

---------

Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
---
 tools/server/public/index.html.gz             | Bin 1444504 -> 1445117 bytes
 tools/server/webui/package-lock.json          |  36 +-
 tools/server/webui/package.json               |   2 +-
 .../app/chat/ChatForm/ChatForm.svelte         |   1 +
 .../chat/ChatSidebar/ChatSidebarSearch.svelte |  18 +-
 .../webui/src/lib/components/app/index.ts     |   1 +
 .../components/app/misc/SearchInput.svelte    |  73 ++++
 .../app/models/ModelsSelector.svelte          | 335 ++++++++----------
 .../src/lib/components/ui/popover/index.ts    |  19 +
 .../ui/popover/popover-close.svelte           |   7 +
 .../ui/popover/popover-content.svelte         |  37 ++
 .../ui/popover/popover-portal.svelte          |   7 +
 .../ui/popover/popover-trigger.svelte         |  17 +
 .../lib/components/ui/popover/popover.svelte  |   7 +
 .../lib/constants/floating-ui-constraints.ts  |   1 -
 .../webui/src/lib/stores/models.svelte.ts     |   9 +-
 16 files changed, 342 insertions(+), 228 deletions(-)
 create mode 100644 tools/server/webui/src/lib/components/app/misc/SearchInput.svelte
 create mode 100644 tools/server/webui/src/lib/components/ui/popover/index.ts
 create mode 100644 tools/server/webui/src/lib/components/ui/popover/popover-close.svelte
 create mode 100644 tools/server/webui/src/lib/components/ui/popover/popover-content.svelte
 create mode 100644 tools/server/webui/src/lib/components/ui/popover/popover-portal.svelte
 create mode 100644 tools/server/webui/src/lib/components/ui/popover/popover-trigger.svelte
 create mode 100644 tools/server/webui/src/lib/components/ui/popover/popover.svelte

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index dda053498a73d759f8695743cf9e0db816ba7255..4cff76429e17e3278492ced5821f896eaf94decb 100644
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z>@?|*mxCeiw#fRTpD`pwjUX?W;T4ChW`LeBzIPo31s!1R{46?V1k|~Q>s!M)>X1PV
z<bxogPm)$6sV$`-Asz5WC8W<AU4z-N;h5f=wvs|NLZO6r_marb?dgBky$%6LK4b0C
z9>Pj*yY^8g{1uw(LgS=C#$H{fbz;U+&qmYMP@b^{N>jve#TpJZFHPIWp0)-W|M*QX
zNR?mF-z<qiR$EmvYoBSU2<ELcjdYBEQoD+RjMP(%?u`z1KC`{TR><Vmj^5yIk91*6
zd=~v6Y!t>5>P6s>LoR>q!J|p-L2*LWaXN>pP8>my9Merm*4~-B!!@`ICi?#d*Qt&<
zS5;7pjl_4sKpe%d5OE}&VzuW8f{i7ELlS$L-Pe}I<ZpAG#{H`%C(6|x{L;{umY2U@
zRj)43kEi2c_ITYpt;`DAf%bceu=ixNFC=Tp{_L>AU&5vK|NCm&$|OPc!o&iI&)epU
z*{$kEO^9oSsqi<y`OW|0|9<&p4sXE4@cn4<%kJ{SSEpb9osDX9n{ei$n1kXhyW)Qz
s-NzMqbg_7hACK-2Mj!CUqx;9x!R6!MJi1R8pU1!Xf4IV(i~Z9;0Ih3L`~Uy|

diff --git a/tools/server/webui/package-lock.json b/tools/server/webui/package-lock.json
index 9c1c2499cf..4f37b308b1 100644
--- a/tools/server/webui/package-lock.json
+++ b/tools/server/webui/package-lock.json
@@ -41,7 +41,7 @@
 				"@tailwindcss/vite": "^4.0.0",
 				"@types/node": "^22",
 				"@vitest/browser": "^3.2.3",
-				"bits-ui": "^2.8.11",
+				"bits-ui": "^2.14.4",
 				"clsx": "^2.1.1",
 				"dexie": "^4.0.11",
 				"eslint": "^9.18.0",
@@ -3343,17 +3343,17 @@
 			}
 		},
 		"node_modules/bits-ui": {
-			"version": "2.8.11",
-			"resolved": "https://registry.npmjs.org/bits-ui/-/bits-ui-2.8.11.tgz",
-			"integrity": "sha512-lKN9rAk69my6j7H1D4B87r8LrHuEtfEsf1xCixBj9yViql2BdI3f04HyyyT7T1GOCpgb9+8b0B+nm3LN81Konw==",
+			"version": "2.14.4",
+			"resolved": "https://registry.npmjs.org/bits-ui/-/bits-ui-2.14.4.tgz",
+			"integrity": "sha512-W6kenhnbd/YVvur+DKkaVJ6GldE53eLewur5AhUCqslYQ0vjZr8eWlOfwZnMiPB+PF5HMVqf61vXBvmyrAmPWg==",
 			"dev": true,
 			"license": "MIT",
 			"dependencies": {
 				"@floating-ui/core": "^1.7.1",
 				"@floating-ui/dom": "^1.7.1",
 				"esm-env": "^1.1.2",
-				"runed": "^0.29.1",
-				"svelte-toolbelt": "^0.9.3",
+				"runed": "^0.35.1",
+				"svelte-toolbelt": "^0.10.6",
 				"tabbable": "^6.2.0"
 			},
 			"engines": {
@@ -3368,9 +3368,9 @@
 			}
 		},
 		"node_modules/bits-ui/node_modules/runed": {
-			"version": "0.29.2",
-			"resolved": "https://registry.npmjs.org/runed/-/runed-0.29.2.tgz",
-			"integrity": "sha512-0cq6cA6sYGZwl/FvVqjx9YN+1xEBu9sDDyuWdDW1yWX7JF2wmvmVKfH+hVCZs+csW+P3ARH92MjI3H9QTagOQA==",
+			"version": "0.35.1",
+			"resolved": "https://registry.npmjs.org/runed/-/runed-0.35.1.tgz",
+			"integrity": "sha512-2F4Q/FZzbeJTFdIS/PuOoPRSm92sA2LhzTnv6FXhCoENb3huf5+fDuNOg1LNvGOouy3u/225qxmuJvcV3IZK5Q==",
 			"dev": true,
 			"funding": [
 				"https://github.com/sponsors/huntabyte",
@@ -3378,23 +3378,31 @@
 			],
 			"license": "MIT",
 			"dependencies": {
-				"esm-env": "^1.0.0"
+				"dequal": "^2.0.3",
+				"esm-env": "^1.0.0",
+				"lz-string": "^1.5.0"
 			},
 			"peerDependencies": {
+				"@sveltejs/kit": "^2.21.0",
 				"svelte": "^5.7.0"
+			},
+			"peerDependenciesMeta": {
+				"@sveltejs/kit": {
+					"optional": true
+				}
 			}
 		},
 		"node_modules/bits-ui/node_modules/svelte-toolbelt": {
-			"version": "0.9.3",
-			"resolved": "https://registry.npmjs.org/svelte-toolbelt/-/svelte-toolbelt-0.9.3.tgz",
-			"integrity": "sha512-HCSWxCtVmv+c6g1ACb8LTwHVbDqLKJvHpo6J8TaqwUme2hj9ATJCpjCPNISR1OCq2Q4U1KT41if9ON0isINQZw==",
+			"version": "0.10.6",
+			"resolved": "https://registry.npmjs.org/svelte-toolbelt/-/svelte-toolbelt-0.10.6.tgz",
+			"integrity": "sha512-YWuX+RE+CnWYx09yseAe4ZVMM7e7GRFZM6OYWpBKOb++s+SQ8RBIMMe+Bs/CznBMc0QPLjr+vDBxTAkozXsFXQ==",
 			"dev": true,
 			"funding": [
 				"https://github.com/sponsors/huntabyte"
 			],
 			"dependencies": {
 				"clsx": "^2.1.1",
-				"runed": "^0.29.0",
+				"runed": "^0.35.1",
 				"style-to-object": "^1.0.8"
 			},
 			"engines": {
diff --git a/tools/server/webui/package.json b/tools/server/webui/package.json
index 987a7239ed..c20ab3cfde 100644
--- a/tools/server/webui/package.json
+++ b/tools/server/webui/package.json
@@ -43,7 +43,7 @@
 		"@tailwindcss/vite": "^4.0.0",
 		"@types/node": "^22",
 		"@vitest/browser": "^3.2.3",
-		"bits-ui": "^2.8.11",
+		"bits-ui": "^2.14.4",
 		"clsx": "^2.1.1",
 		"dexie": "^4.0.11",
 		"eslint": "^9.18.0",
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatForm.svelte b/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
index 7f8e38286d..78cc1c47da 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
@@ -331,6 +331,7 @@
 	class="{INPUT_CLASSES} border-radius-bottom-none mx-auto max-w-[48rem] overflow-hidden rounded-3xl backdrop-blur-md {disabled
 		? 'cursor-not-allowed opacity-60'
 		: ''} {className}"
+	data-slot="chat-form"
 >
 	<ChatAttachmentsList
 		bind:uploadedFiles
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarSearch.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarSearch.svelte
index c9e6c6616a..afc9847028 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarSearch.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarSearch.svelte
@@ -1,6 +1,5 @@
 <script lang="ts">
-	import { Input } from '$lib/components/ui/input';
-	import { Search } from '@lucide/svelte';
+	import { SearchInput } from '$lib/components/app';
 
 	interface Props {
 		value?: string;
@@ -15,19 +14,6 @@
 		onInput,
 		class: className
 	}: Props = $props();
-
-	function handleInput(event: Event) {
-		const target = event.target as HTMLInputElement;
-
-		value = target.value;
-		onInput?.(target.value);
-	}
 </script>
 
-<div class="relative mb-4 {className}">
-	<Search
-		class="absolute top-1/2 left-3 h-4 w-4 -translate-y-1/2 transform text-muted-foreground"
-	/>
-
-	<Input bind:value class="pl-10" oninput={handleInput} {placeholder} type="search" />
-</div>
+<SearchInput bind:value {placeholder} {onInput} class="mb-4 {className}" />
diff --git a/tools/server/webui/src/lib/components/app/index.ts b/tools/server/webui/src/lib/components/app/index.ts
index 87b24598b7..8631d4fb3b 100644
--- a/tools/server/webui/src/lib/components/app/index.ts
+++ b/tools/server/webui/src/lib/components/app/index.ts
@@ -64,6 +64,7 @@ export { default as CopyToClipboardIcon } from './misc/CopyToClipboardIcon.svelt
 export { default as KeyboardShortcutInfo } from './misc/KeyboardShortcutInfo.svelte';
 export { default as MarkdownContent } from './misc/MarkdownContent.svelte';
 export { default as RemoveButton } from './misc/RemoveButton.svelte';
+export { default as SearchInput } from './misc/SearchInput.svelte';
 export { default as SyntaxHighlightedCode } from './misc/SyntaxHighlightedCode.svelte';
 export { default as ModelsSelector } from './models/ModelsSelector.svelte';
 
diff --git a/tools/server/webui/src/lib/components/app/misc/SearchInput.svelte b/tools/server/webui/src/lib/components/app/misc/SearchInput.svelte
new file mode 100644
index 0000000000..15cd6abaa9
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/misc/SearchInput.svelte
@@ -0,0 +1,73 @@
+<script lang="ts">
+	import { Input } from '$lib/components/ui/input';
+	import { Search, X } from '@lucide/svelte';
+
+	interface Props {
+		value?: string;
+		placeholder?: string;
+		onInput?: (value: string) => void;
+		onClose?: () => void;
+		onKeyDown?: (event: KeyboardEvent) => void;
+		class?: string;
+		id?: string;
+		ref?: HTMLInputElement | null;
+	}
+
+	let {
+		value = $bindable(''),
+		placeholder = 'Search...',
+		onInput,
+		onClose,
+		onKeyDown,
+		class: className,
+		id,
+		ref = $bindable(null)
+	}: Props = $props();
+
+	let showClearButton = $derived(!!value || !!onClose);
+
+	function handleInput(event: Event) {
+		const target = event.target as HTMLInputElement;
+
+		value = target.value;
+		onInput?.(target.value);
+	}
+
+	function handleClear() {
+		if (value) {
+			value = '';
+			onInput?.('');
+			ref?.focus();
+		} else {
+			onClose?.();
+		}
+	}
+</script>
+
+<div class="relative {className}">
+	<Search
+		class="absolute top-1/2 left-3 h-4 w-4 -translate-y-1/2 transform text-muted-foreground"
+	/>
+
+	<Input
+		{id}
+		bind:value
+		bind:ref
+		class="pl-9 {showClearButton ? 'pr-9' : ''}"
+		oninput={handleInput}
+		onkeydown={onKeyDown}
+		{placeholder}
+		type="search"
+	/>
+
+	{#if showClearButton}
+		<button
+			type="button"
+			class="absolute top-1/2 right-3 -translate-y-1/2 transform text-muted-foreground transition-colors hover:text-foreground"
+			onclick={handleClear}
+			aria-label={value ? 'Clear search' : 'Close'}
+		>
+			<X class="h-4 w-4" />
+		</button>
+	{/if}
+</div>
diff --git a/tools/server/webui/src/lib/components/app/models/ModelsSelector.svelte b/tools/server/webui/src/lib/components/app/models/ModelsSelector.svelte
index c4331e92f1..ac0937696d 100644
--- a/tools/server/webui/src/lib/components/app/models/ModelsSelector.svelte
+++ b/tools/server/webui/src/lib/components/app/models/ModelsSelector.svelte
@@ -2,8 +2,8 @@
 	import { onMount, tick } from 'svelte';
 	import { ChevronDown, EyeOff, Loader2, MicOff, Package, Power } from '@lucide/svelte';
 	import * as Tooltip from '$lib/components/ui/tooltip';
+	import * as Popover from '$lib/components/ui/popover';
 	import { cn } from '$lib/components/ui/utils';
-	import { portalToBody } from '$lib/utils';
 	import {
 		modelsStore,
 		modelOptions,
@@ -17,12 +17,8 @@
 	import { usedModalities, conversationsStore } from '$lib/stores/conversations.svelte';
 	import { ServerModelStatus } from '$lib/enums';
 	import { isRouterMode } from '$lib/stores/server.svelte';
-	import { DialogModelInformation } from '$lib/components/app';
-	import {
-		MENU_MAX_WIDTH,
-		MENU_OFFSET,
-		VIEWPORT_GUTTER
-	} from '$lib/constants/floating-ui-constraints';
+	import { DialogModelInformation, SearchInput } from '$lib/components/app';
+	import type { ModelOption } from '$lib/types/models';
 
 	interface Props {
 		class?: string;
@@ -145,185 +141,126 @@
 		return options.some((option) => option.model === currentModel);
 	});
 
-	let isOpen = $state(false);
-	let showModelDialog = $state(false);
-	let container: HTMLDivElement | null = null;
-	let menuRef = $state<HTMLDivElement | null>(null);
-	let triggerButton = $state<HTMLButtonElement | null>(null);
-	let menuPosition = $state<{
-		top: number;
-		left: number;
-		width: number;
-		placement: 'top' | 'bottom';
-		maxHeight: number;
-	} | null>(null);
+	let searchTerm = $state('');
+	let searchInputRef = $state<HTMLInputElement | null>(null);
+	let highlightedIndex = $state<number>(-1);
 
-	onMount(async () => {
-		try {
-			await modelsStore.fetch();
-		} catch (error) {
-			console.error('Unable to load models:', error);
-		}
+	let filteredOptions: ModelOption[] = $derived(
+		(() => {
+			const term = searchTerm.trim().toLowerCase();
+			if (!term) return options;
+
+			return options.filter(
+				(option) =>
+					option.model.toLowerCase().includes(term) || option.name?.toLowerCase().includes(term)
+			);
+		})()
+	);
+
+	// Get indices of compatible options for keyboard navigation
+	let compatibleIndices = $derived(
+		filteredOptions
+			.map((option, index) => (isModelCompatible(option) ? index : -1))
+			.filter((i) => i !== -1)
+	);
+
+	// Reset highlighted index when search term changes
+	$effect(() => {
+		void searchTerm;
+		highlightedIndex = -1;
 	});
 
-	function toggleOpen() {
+	let isOpen = $state(false);
+	let showModelDialog = $state(false);
+
+	onMount(() => {
+		modelsStore.fetch().catch((error) => {
+			console.error('Unable to load models:', error);
+		});
+	});
+
+	function handleOpenChange(open: boolean) {
 		if (loading || updating) return;
 
-		if (isRouter) {
-			// Router mode: show dropdown
-			if (isOpen) {
-				closeMenu();
-			} else {
-				openMenu();
+		if (open) {
+			isOpen = true;
+			searchTerm = '';
+			highlightedIndex = -1;
+
+			// Focus search input after popover opens
+			tick().then(() => {
+				requestAnimationFrame(() => searchInputRef?.focus());
+			});
+
+			if (isRouter) {
+				modelsStore.fetchRouterModels().then(() => {
+					modelsStore.fetchModalitiesForLoadedModels();
+				});
 			}
 		} else {
-			// Single model mode: show dialog
-			showModelDialog = true;
+			isOpen = false;
+			searchTerm = '';
+			highlightedIndex = -1;
 		}
 	}
 
-	async function openMenu() {
+	function handleTriggerClick() {
 		if (loading || updating) return;
 
-		isOpen = true;
-		await tick();
-		updateMenuPosition();
-		requestAnimationFrame(() => updateMenuPosition());
-
-		if (isRouter) {
-			modelsStore.fetchRouterModels().then(() => {
-				modelsStore.fetchModalitiesForLoadedModels();
-			});
+		if (!isRouter) {
+			// Single model mode: show dialog instead of popover
+			showModelDialog = true;
 		}
+		// For router mode, the Popover handles open/close
 	}
 
 	export function open() {
 		if (isRouter) {
-			openMenu();
+			handleOpenChange(true);
 		} else {
 			showModelDialog = true;
 		}
 	}
 
 	function closeMenu() {
-		if (!isOpen) return;
-
-		isOpen = false;
-		menuPosition = null;
+		handleOpenChange(false);
 	}
 
-	function handlePointerDown(event: PointerEvent) {
-		if (!container) return;
+	function handleSearchKeyDown(event: KeyboardEvent) {
+		if (event.isComposing) return;
 
-		const target = event.target as Node | null;
+		if (event.key === 'ArrowDown') {
+			event.preventDefault();
+			if (compatibleIndices.length === 0) return;
 
-		if (target && !container.contains(target) && !(menuRef && menuRef.contains(target))) {
-			closeMenu();
-		}
-	}
-
-	function handleKeydown(event: KeyboardEvent) {
-		if (event.key === 'Escape') {
-			closeMenu();
-		}
-	}
-
-	function handleResize() {
-		if (isOpen) {
-			updateMenuPosition();
-		}
-	}
-
-	function updateMenuPosition() {
-		if (!isOpen || !triggerButton || !menuRef) return;
-
-		const triggerRect = triggerButton.getBoundingClientRect();
-		const viewportWidth = window.innerWidth;
-		const viewportHeight = window.innerHeight;
-
-		if (viewportWidth === 0 || viewportHeight === 0) return;
-
-		const scrollWidth = menuRef.scrollWidth;
-		const scrollHeight = menuRef.scrollHeight;
-
-		const availableWidth = Math.max(0, viewportWidth - VIEWPORT_GUTTER * 2);
-		const constrainedMaxWidth = Math.min(MENU_MAX_WIDTH, availableWidth || MENU_MAX_WIDTH);
-		const safeMaxWidth =
-			constrainedMaxWidth > 0 ? constrainedMaxWidth : Math.min(MENU_MAX_WIDTH, viewportWidth);
-		const desiredMinWidth = Math.min(160, safeMaxWidth || 160);
-
-		let width = Math.min(
-			Math.max(triggerRect.width, scrollWidth, desiredMinWidth),
-			safeMaxWidth || 320
-		);
-
-		const availableBelow = Math.max(
-			0,
-			viewportHeight - VIEWPORT_GUTTER - triggerRect.bottom - MENU_OFFSET
-		);
-		const availableAbove = Math.max(0, triggerRect.top - VIEWPORT_GUTTER - MENU_OFFSET);
-		const viewportAllowance = Math.max(0, viewportHeight - VIEWPORT_GUTTER * 2);
-		const fallbackAllowance = Math.max(1, viewportAllowance > 0 ? viewportAllowance : scrollHeight);
-
-		function computePlacement(placement: 'top' | 'bottom') {
-			const available = placement === 'bottom' ? availableBelow : availableAbove;
-			const allowedHeight =
-				available > 0 ? Math.min(available, fallbackAllowance) : fallbackAllowance;
-			const maxHeight = Math.min(scrollHeight, allowedHeight);
-			const height = Math.max(0, maxHeight);
-
-			let top: number;
-			if (placement === 'bottom') {
-				const rawTop = triggerRect.bottom + MENU_OFFSET;
-				const minTop = VIEWPORT_GUTTER;
-				const maxTop = viewportHeight - VIEWPORT_GUTTER - height;
-				if (maxTop < minTop) {
-					top = minTop;
-				} else {
-					top = Math.min(Math.max(rawTop, minTop), maxTop);
-				}
+			const currentPos = compatibleIndices.indexOf(highlightedIndex);
+			if (currentPos === -1 || currentPos === compatibleIndices.length - 1) {
+				highlightedIndex = compatibleIndices[0];
 			} else {
-				const rawTop = triggerRect.top - MENU_OFFSET - height;
-				const minTop = VIEWPORT_GUTTER;
-				const maxTop = viewportHeight - VIEWPORT_GUTTER - height;
-				if (maxTop < minTop) {
-					top = minTop;
-				} else {
-					top = Math.max(Math.min(rawTop, maxTop), minTop);
+				highlightedIndex = compatibleIndices[currentPos + 1];
+			}
+		} else if (event.key === 'ArrowUp') {
+			event.preventDefault();
+			if (compatibleIndices.length === 0) return;
+
+			const currentPos = compatibleIndices.indexOf(highlightedIndex);
+			if (currentPos === -1 || currentPos === 0) {
+				highlightedIndex = compatibleIndices[compatibleIndices.length - 1];
+			} else {
+				highlightedIndex = compatibleIndices[currentPos - 1];
+			}
+		} else if (event.key === 'Enter') {
+			event.preventDefault();
+			if (highlightedIndex >= 0 && highlightedIndex < filteredOptions.length) {
+				const option = filteredOptions[highlightedIndex];
+				if (isModelCompatible(option)) {
+					handleSelect(option.id);
 				}
-			}
-
-			return { placement, top, height, maxHeight };
-		}
-
-		const belowMetrics = computePlacement('bottom');
-		const aboveMetrics = computePlacement('top');
-
-		let metrics = belowMetrics;
-		if (scrollHeight > belowMetrics.maxHeight && aboveMetrics.maxHeight > belowMetrics.maxHeight) {
-			metrics = aboveMetrics;
-		}
-
-		let left = triggerRect.right - width;
-		const maxLeft = viewportWidth - VIEWPORT_GUTTER - width;
-		if (maxLeft < VIEWPORT_GUTTER) {
-			left = VIEWPORT_GUTTER;
-		} else {
-			if (left > maxLeft) {
-				left = maxLeft;
-			}
-			if (left < VIEWPORT_GUTTER) {
-				left = VIEWPORT_GUTTER;
+			} else if (compatibleIndices.length > 0) {
+				// No selection - highlight first compatible option
+				highlightedIndex = compatibleIndices[0];
 			}
 		}
-
-		menuPosition = {
-			top: Math.round(metrics.top),
-			left: Math.round(left),
-			width: Math.round(width),
-			placement: metrics.placement,
-			maxHeight: Math.round(metrics.maxHeight)
-		};
 	}
 
 	async function handleSelect(modelId: string) {
@@ -356,6 +293,14 @@
 
 		if (shouldCloseMenu) {
 			closeMenu();
+
+			// Focus the chat textarea after model selection
+			requestAnimationFrame(() => {
+				const textarea = document.querySelector<HTMLTextAreaElement>(
+					'[data-slot="chat-form"] textarea'
+				);
+				textarea?.focus();
+			});
 		}
 	}
 
@@ -404,10 +349,7 @@
 	}
 </script>
 
-<svelte:window onresize={handleResize} />
-<svelte:document onpointerdown={handlePointerDown} onkeydown={handleKeydown} />
-
-<div class={cn('relative inline-flex flex-col items-end gap-1', className)} bind:this={container}>
+<div class={cn('relative inline-flex flex-col items-end gap-1', className)}>
 	{#if loading && options.length === 0 && isRouter}
 		<div class="flex items-center gap-2 text-xs text-muted-foreground">
 			<Loader2 class="h-3.5 w-3.5 animate-spin" />
@@ -418,9 +360,8 @@
 	{:else}
 		{@const selectedOption = getDisplayOption()}
 
-		<div class="relative">
-			<button
-				type="button"
+		<Popover.Root bind:open={isOpen} onOpenChange={handleOpenChange}>
+			<Popover.Trigger
 				class={cn(
 					`inline-flex cursor-pointer items-center gap-1.5 rounded-sm bg-muted-foreground/10 px-1.5 py-1 text-xs transition hover:text-foreground focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60`,
 					!isCurrentModelInCache()
@@ -430,15 +371,11 @@
 							: isHighlightedCurrentModelActive
 								? 'text-foreground'
 								: 'text-muted-foreground',
-					isOpen ? 'text-foreground' : '',
-					className
+					isOpen ? 'text-foreground' : ''
 				)}
 				style="max-width: min(calc(100cqw - 6.5rem), 32rem)"
-				aria-haspopup={isRouter ? 'listbox' : undefined}
-				aria-expanded={isRouter ? isOpen : undefined}
-				onclick={toggleOpen}
-				bind:this={triggerButton}
-				disabled={disabled || updating}
+				onclick={handleTriggerClick}
+				disabled={disabled || updating || !isRouter}
 			>
 				<Package class="h-3.5 w-3.5" />
 
@@ -451,33 +388,35 @@
 				{:else if isRouter}
 					<ChevronDown class="h-3 w-3.5" />
 				{/if}
-			</button>
+			</Popover.Trigger>
 
-			{#if isOpen && isRouter}
-				<div
-					bind:this={menuRef}
-					use:portalToBody
-					class={cn(
-						'fixed z-[1000] overflow-hidden rounded-md border bg-popover shadow-lg transition-opacity',
-						menuPosition ? 'opacity-100' : 'pointer-events-none opacity-0'
-					)}
-					role="listbox"
-					style:top={menuPosition ? `${menuPosition.top}px` : undefined}
-					style:left={menuPosition ? `${menuPosition.left}px` : undefined}
-					style:width={menuPosition ? `${menuPosition.width}px` : undefined}
-					data-placement={menuPosition?.placement ?? 'bottom'}
-				>
+			<Popover.Content
+				class="group/popover-content w-96 max-w-[calc(100vw-2rem)] p-0"
+				align="end"
+				sideOffset={8}
+				collisionPadding={16}
+			>
+				<div class="flex max-h-[50dvh] flex-col overflow-hidden">
 					<div
-						class="overflow-y-auto py-1"
-						style:max-height={menuPosition && menuPosition.maxHeight > 0
-							? `${menuPosition.maxHeight}px`
-							: undefined}
+						class="order-1 shrink-0 border-b p-4 group-data-[side=top]/popover-content:order-2 group-data-[side=top]/popover-content:border-t group-data-[side=top]/popover-content:border-b-0"
+					>
+						<SearchInput
+							id="model-search"
+							placeholder="Search models..."
+							bind:value={searchTerm}
+							bind:ref={searchInputRef}
+							onClose={closeMenu}
+							onKeyDown={handleSearchKeyDown}
+						/>
+					</div>
+					<div
+						class="models-list order-2 min-h-0 flex-1 overflow-y-auto group-data-[side=top]/popover-content:order-1"
 					>
 						{#if !isCurrentModelInCache() && currentModel}
 							<!-- Show unavailable model as first option (disabled) -->
 							<button
 								type="button"
-								class="flex w-full cursor-not-allowed items-center bg-red-400/10 px-3 py-2 text-left text-sm text-red-400"
+								class="flex w-full cursor-not-allowed items-center bg-red-400/10 px-4 py-2 text-left text-sm text-red-400"
 								role="option"
 								aria-selected="true"
 								aria-disabled="true"
@@ -488,20 +427,25 @@
 							</button>
 							<div class="my-1 h-px bg-border"></div>
 						{/if}
-						{#each options as option (option.id)}
+						{#if filteredOptions.length === 0}
+							<p class="px-4 py-3 text-sm text-muted-foreground">No models found.</p>
+						{/if}
+						{#each filteredOptions as option, index (option.id)}
 							{@const status = getModelStatus(option.model)}
 							{@const isLoaded = status === ServerModelStatus.LOADED}
 							{@const isLoading = status === ServerModelStatus.LOADING}
 							{@const isSelected = currentModel === option.model || activeId === option.id}
 							{@const isCompatible = isModelCompatible(option)}
+							{@const isHighlighted = index === highlightedIndex}
 							{@const missingModalities = getMissingModalities(option)}
+
 							<div
 								class={cn(
-									'group flex w-full items-center gap-2 px-3 py-2 text-left text-sm transition focus:outline-none',
+									'group flex w-full items-center gap-2 px-4 py-2 text-left text-sm transition focus:outline-none',
 									isCompatible
 										? 'cursor-pointer hover:bg-muted focus:bg-muted'
 										: 'cursor-not-allowed opacity-50',
-									isSelected
+									isSelected || isHighlighted
 										? 'bg-accent text-accent-foreground'
 										: isCompatible
 											? 'hover:bg-accent hover:text-accent-foreground'
@@ -509,10 +453,11 @@
 									isLoaded ? 'text-popover-foreground' : 'text-muted-foreground'
 								)}
 								role="option"
-								aria-selected={isSelected}
+								aria-selected={isSelected || isHighlighted}
 								aria-disabled={!isCompatible}
 								tabindex={isCompatible ? 0 : -1}
 								onclick={() => isCompatible && handleSelect(option.id)}
+								onmouseenter={() => (highlightedIndex = index)}
 								onkeydown={(e) => {
 									if (isCompatible && (e.key === 'Enter' || e.key === ' ')) {
 										e.preventDefault();
@@ -586,8 +531,8 @@
 						{/each}
 					</div>
 				</div>
-			{/if}
-		</div>
+			</Popover.Content>
+		</Popover.Root>
 	{/if}
 </div>
 
diff --git a/tools/server/webui/src/lib/components/ui/popover/index.ts b/tools/server/webui/src/lib/components/ui/popover/index.ts
new file mode 100644
index 0000000000..c5937fb3a0
--- /dev/null
+++ b/tools/server/webui/src/lib/components/ui/popover/index.ts
@@ -0,0 +1,19 @@
+import Root from './popover.svelte';
+import Close from './popover-close.svelte';
+import Content from './popover-content.svelte';
+import Trigger from './popover-trigger.svelte';
+import Portal from './popover-portal.svelte';
+
+export {
+	Root,
+	Content,
+	Trigger,
+	Close,
+	Portal,
+	//
+	Root as Popover,
+	Content as PopoverContent,
+	Trigger as PopoverTrigger,
+	Close as PopoverClose,
+	Portal as PopoverPortal
+};
diff --git a/tools/server/webui/src/lib/components/ui/popover/popover-close.svelte b/tools/server/webui/src/lib/components/ui/popover/popover-close.svelte
new file mode 100644
index 0000000000..dc4dec4b33
--- /dev/null
+++ b/tools/server/webui/src/lib/components/ui/popover/popover-close.svelte
@@ -0,0 +1,7 @@
+<script lang="ts">
+	import { Popover as PopoverPrimitive } from 'bits-ui';
+
+	let { ref = $bindable(null), ...restProps }: PopoverPrimitive.CloseProps = $props();
+</script>
+
+<PopoverPrimitive.Close bind:ref data-slot="popover-close" {...restProps} />
diff --git a/tools/server/webui/src/lib/components/ui/popover/popover-content.svelte b/tools/server/webui/src/lib/components/ui/popover/popover-content.svelte
new file mode 100644
index 0000000000..2d3513d347
--- /dev/null
+++ b/tools/server/webui/src/lib/components/ui/popover/popover-content.svelte
@@ -0,0 +1,37 @@
+<script lang="ts">
+	import { Popover as PopoverPrimitive } from 'bits-ui';
+	import PopoverPortal from './popover-portal.svelte';
+	import { cn, type WithoutChildrenOrChild } from '$lib/components/ui/utils.js';
+	import type { ComponentProps } from 'svelte';
+
+	let {
+		ref = $bindable(null),
+		class: className,
+		sideOffset = 4,
+		side,
+		align = 'center',
+		collisionPadding = 8,
+		avoidCollisions = true,
+		portalProps,
+		...restProps
+	}: PopoverPrimitive.ContentProps & {
+		portalProps?: WithoutChildrenOrChild<ComponentProps<typeof PopoverPortal>>;
+	} = $props();
+</script>
+
+<PopoverPortal {...portalProps}>
+	<PopoverPrimitive.Content
+		bind:ref
+		data-slot="popover-content"
+		{sideOffset}
+		{side}
+		{align}
+		{collisionPadding}
+		{avoidCollisions}
+		class={cn(
+			'z-50 w-72 origin-(--bits-popover-content-transform-origin) rounded-md border bg-popover p-4 text-popover-foreground shadow-md outline-hidden data-[side=bottom]:slide-in-from-top-2 data-[side=left]:slide-in-from-end-2 data-[side=right]:slide-in-from-start-2 data-[side=top]:slide-in-from-bottom-2 data-[state=closed]:animate-out data-[state=closed]:fade-out-0 data-[state=closed]:zoom-out-95 data-[state=open]:animate-in data-[state=open]:fade-in-0 data-[state=open]:zoom-in-95',
+			className
+		)}
+		{...restProps}
+	/>
+</PopoverPortal>
diff --git a/tools/server/webui/src/lib/components/ui/popover/popover-portal.svelte b/tools/server/webui/src/lib/components/ui/popover/popover-portal.svelte
new file mode 100644
index 0000000000..25efb877b7
--- /dev/null
+++ b/tools/server/webui/src/lib/components/ui/popover/popover-portal.svelte
@@ -0,0 +1,7 @@
+<script lang="ts">
+	import { Popover as PopoverPrimitive } from 'bits-ui';
+
+	let { ...restProps }: PopoverPrimitive.PortalProps = $props();
+</script>
+
+<PopoverPrimitive.Portal {...restProps} />
diff --git a/tools/server/webui/src/lib/components/ui/popover/popover-trigger.svelte b/tools/server/webui/src/lib/components/ui/popover/popover-trigger.svelte
new file mode 100644
index 0000000000..5ef3d0e932
--- /dev/null
+++ b/tools/server/webui/src/lib/components/ui/popover/popover-trigger.svelte
@@ -0,0 +1,17 @@
+<script lang="ts">
+	import { cn } from '$lib/components/ui/utils.js';
+	import { Popover as PopoverPrimitive } from 'bits-ui';
+
+	let {
+		ref = $bindable(null),
+		class: className,
+		...restProps
+	}: PopoverPrimitive.TriggerProps = $props();
+</script>
+
+<PopoverPrimitive.Trigger
+	bind:ref
+	data-slot="popover-trigger"
+	class={cn('', className)}
+	{...restProps}
+/>
diff --git a/tools/server/webui/src/lib/components/ui/popover/popover.svelte b/tools/server/webui/src/lib/components/ui/popover/popover.svelte
new file mode 100644
index 0000000000..f39b867a69
--- /dev/null
+++ b/tools/server/webui/src/lib/components/ui/popover/popover.svelte
@@ -0,0 +1,7 @@
+<script lang="ts">
+	import { Popover as PopoverPrimitive } from 'bits-ui';
+
+	let { open = $bindable(false), ...restProps }: PopoverPrimitive.RootProps = $props();
+</script>
+
+<PopoverPrimitive.Root bind:open {...restProps} />
diff --git a/tools/server/webui/src/lib/constants/floating-ui-constraints.ts b/tools/server/webui/src/lib/constants/floating-ui-constraints.ts
index c95d3f1841..003fc77acb 100644
--- a/tools/server/webui/src/lib/constants/floating-ui-constraints.ts
+++ b/tools/server/webui/src/lib/constants/floating-ui-constraints.ts
@@ -1,3 +1,2 @@
 export const VIEWPORT_GUTTER = 8;
 export const MENU_OFFSET = 6;
-export const MENU_MAX_WIDTH = 320;
diff --git a/tools/server/webui/src/lib/stores/models.svelte.ts b/tools/server/webui/src/lib/stores/models.svelte.ts
index 29416c2fe5..34b26403e4 100644
--- a/tools/server/webui/src/lib/stores/models.svelte.ts
+++ b/tools/server/webui/src/lib/stores/models.svelte.ts
@@ -295,14 +295,21 @@ class ModelsStore {
 	 * Fetch props for a specific model from /props endpoint
 	 * Uses caching to avoid redundant requests
 	 *
+	 * In ROUTER mode, this will only fetch props if the model is loaded,
+	 * since unloaded models return 400 from /props endpoint.
+	 *
 	 * @param modelId - Model identifier to fetch props for
-	 * @returns Props data or null if fetch failed
+	 * @returns Props data or null if fetch failed or model not loaded
 	 */
 	async fetchModelProps(modelId: string): Promise<ApiLlamaCppServerProps | null> {
 		// Return cached props if available
 		const cached = this.modelPropsCache.get(modelId);
 		if (cached) return cached;
 
+		if (serverStore.isRouterMode && !this.isModelLoaded(modelId)) {
+			return null;
+		}
+
 		// Avoid duplicate fetches
 		if (this.modelPropsFetching.has(modelId)) return null;
 

From c33a58bced912711aad08e8b2feb6dc502ac81f6 Mon Sep 17 00:00:00 2001
From: yulo <77381088+zhang-hui-yulo@users.noreply.github.com>
Date: Fri, 12 Dec 2025 18:34:33 +0800
Subject: [PATCH 061/135] HIP: enable mmf for RDNA3 (#17879)

* enable mmf for RDNA3

* disable mmf for some shape

* move some mmvf to mmf

* more mmfv to mmf

* 3 is good in mmvf

---------

Co-authored-by: zhang hui <you@example.com>
---
 ggml/src/ggml-cuda/common.cuh | 25 +++++++------
 ggml/src/ggml-cuda/mma.cuh    | 67 +++++++++++++++++++++++++++++++----
 ggml/src/ggml-cuda/mmf.cu     |  8 +++--
 ggml/src/ggml-cuda/mmvf.cu    |  5 ++-
 4 files changed, 84 insertions(+), 21 deletions(-)

diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index c4529f5d94..9fcb2f9fd2 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -67,19 +67,22 @@
 #define GGML_CUDA_CC_RDNA1      (GGML_CUDA_CC_OFFSET_AMD + 0x1010) // RX 5000
 #define GGML_CUDA_CC_RDNA2      (GGML_CUDA_CC_OFFSET_AMD + 0x1030) // RX 6000, minimum for dp4a
 #define GGML_CUDA_CC_RDNA3      (GGML_CUDA_CC_OFFSET_AMD + 0x1100) // RX 7000, minimum for WMMA
+#define GGML_CUDA_CC_RDNA3_5    (GGML_CUDA_CC_OFFSET_AMD + 0x1150) // AI 370, AI Max 395 laptops.
 #define GGML_CUDA_CC_RDNA4      (GGML_CUDA_CC_OFFSET_AMD + 0x1200) // RX 9000
 
-#define GGML_CUDA_CC_IS_AMD(cc)   (cc >= GGML_CUDA_CC_OFFSET_AMD)
-#define GGML_CUDA_CC_IS_RDNA(cc)  (cc >= GGML_CUDA_CC_RDNA1)
-#define GGML_CUDA_CC_IS_RDNA1(cc) (cc >= GGML_CUDA_CC_RDNA1 && cc < GGML_CUDA_CC_RDNA2)
-#define GGML_CUDA_CC_IS_RDNA2(cc) (cc >= GGML_CUDA_CC_RDNA2 && cc < GGML_CUDA_CC_RDNA3)
-#define GGML_CUDA_CC_IS_RDNA3(cc) (cc >= GGML_CUDA_CC_RDNA3 && cc < GGML_CUDA_CC_RDNA4)
-#define GGML_CUDA_CC_IS_RDNA4(cc) (cc >= GGML_CUDA_CC_RDNA4)
-#define GGML_CUDA_CC_IS_GCN(cc)   (cc > GGML_CUDA_CC_OFFSET_AMD && cc < GGML_CUDA_CC_CDNA1)
-#define GGML_CUDA_CC_IS_CDNA(cc)  (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_RDNA1)
-#define GGML_CUDA_CC_IS_CDNA1(cc) (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_CDNA2)
-#define GGML_CUDA_CC_IS_CDNA2(cc) (cc >= GGML_CUDA_CC_CDNA2 && cc < GGML_CUDA_CC_CDNA3)
-#define GGML_CUDA_CC_IS_CDNA3(cc) (cc >= GGML_CUDA_CC_CDNA3 && cc < GGML_CUDA_CC_RDNA1)
+#define GGML_CUDA_CC_IS_AMD(cc)     (cc >= GGML_CUDA_CC_OFFSET_AMD)
+#define GGML_CUDA_CC_IS_RDNA(cc)    (cc >= GGML_CUDA_CC_RDNA1)
+#define GGML_CUDA_CC_IS_RDNA1(cc)   (cc >= GGML_CUDA_CC_RDNA1 && cc < GGML_CUDA_CC_RDNA2)
+#define GGML_CUDA_CC_IS_RDNA2(cc)   (cc >= GGML_CUDA_CC_RDNA2 && cc < GGML_CUDA_CC_RDNA3)
+#define GGML_CUDA_CC_IS_RDNA3_0(cc) (cc >= GGML_CUDA_CC_RDNA3 && cc < GGML_CUDA_CC_RDNA3_5)
+#define GGML_CUDA_CC_IS_RDNA3_5(cc) (cc >= GGML_CUDA_CC_RDNA3_5 && cc < GGML_CUDA_CC_RDNA4)
+#define GGML_CUDA_CC_IS_RDNA3(cc)   (GGML_CUDA_CC_IS_RDNA3_0(cc) || GGML_CUDA_CC_IS_RDNA3_5(cc))
+#define GGML_CUDA_CC_IS_RDNA4(cc)   (cc >= GGML_CUDA_CC_RDNA4)
+#define GGML_CUDA_CC_IS_GCN(cc)     (cc > GGML_CUDA_CC_OFFSET_AMD && cc < GGML_CUDA_CC_CDNA1)
+#define GGML_CUDA_CC_IS_CDNA(cc)    (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_RDNA1)
+#define GGML_CUDA_CC_IS_CDNA1(cc)   (cc >= GGML_CUDA_CC_CDNA1 && cc < GGML_CUDA_CC_CDNA2)
+#define GGML_CUDA_CC_IS_CDNA2(cc)   (cc >= GGML_CUDA_CC_CDNA2 && cc < GGML_CUDA_CC_CDNA3)
+#define GGML_CUDA_CC_IS_CDNA3(cc)   (cc >= GGML_CUDA_CC_CDNA3 && cc < GGML_CUDA_CC_RDNA1)
 
 // Moore Threads
 #define MUSART_HMASK 40300 // MUSA rc4.3, min. ver. for half2 -> uint mask comparisons
diff --git a/ggml/src/ggml-cuda/mma.cuh b/ggml/src/ggml-cuda/mma.cuh
index 0b13293da9..dcfa40f4d5 100644
--- a/ggml/src/ggml-cuda/mma.cuh
+++ b/ggml/src/ggml-cuda/mma.cuh
@@ -189,6 +189,9 @@ namespace ggml_cuda_mma {
                 return 8 * (threadIdx.x / 16) + l;
 #elif defined(RDNA3)
                 return 2 * l + (threadIdx.x / 16);
+#else
+                NO_DEVICE_CODE;
+                return -1;
 #endif // defined(RDNA4)
             } else {
                 NO_DEVICE_CODE;
@@ -290,8 +293,12 @@ namespace ggml_cuda_mma {
             }
         }
 #elif defined(AMD_WMMA_AVAILABLE)
-
+#if defined(RDNA3)
+        // RDNA3 has duplicated data as input.
+        static constexpr int ne = I * J / 32 * 2;
+#else
         static constexpr int ne = I * J / 32;
+#endif // defined(RDNA3)
         half2 x[ne] = {{0.0f, 0.0f}};
 
         static constexpr __device__ bool supported() {
@@ -310,7 +317,14 @@ namespace ggml_cuda_mma {
 
         static __device__ __forceinline__ int get_j(const int l) {
             if constexpr (I == 16 && J == 8) {
+#if defined(RDNA4)
                 return 4 * (threadIdx.x / 16) + l;
+#elif defined(RDNA3)
+                return l;
+#else
+                NO_DEVICE_CODE;
+                return -1;
+#endif // defined(RDNA4)
             } else {
                 NO_DEVICE_CODE;
                 return -1;
@@ -366,11 +380,16 @@ namespace ggml_cuda_mma {
         static constexpr int         I  = I_;
         static constexpr int         J  = J_;
         static constexpr data_layout dl = DATA_LAYOUT_I_MAJOR;
-        static constexpr int         ne = I * J / WARP_SIZE;
-
-        nv_bfloat162 x[ne] = {{0.0f, 0.0f}};
 
 #if defined(AMD_WMMA_AVAILABLE)
+#if defined(RDNA3)
+        // RDNA3 has duplicated data as input.
+        static constexpr int ne = I * J / 32 * 2;
+#else
+        static constexpr int ne = I * J / 32;
+#endif // defined(RDNA3)
+        nv_bfloat162 x[ne] = {{0.0f, 0.0f}};
+
         static constexpr __device__ bool supported() {
             if (I == 16 && J == 8) return true;
             return false;
@@ -387,13 +406,23 @@ namespace ggml_cuda_mma {
 
         static __device__ __forceinline__ int get_j(const int l) {
             if constexpr (I == 16 && J == 8) {
+#if defined(RDNA4)
                 return 4 * (threadIdx.x / 16) + l;
+#elif defined(RDNA3)
+                return l;
+#else
+                NO_DEVICE_CODE;
+                return -1;
+#endif // defined(RDNA4)
             } else {
                 NO_DEVICE_CODE;
                 return -1;
             }
         }
 #else
+        static constexpr int ne = I * J / WARP_SIZE;
+        nv_bfloat162 x[ne] = {{0.0f, 0.0f}};
+
         static constexpr __device__ bool supported() {
             if (I ==  8 && J ==  8) return true;
             if (I == 16 && J ==  4) return true;
@@ -546,8 +575,14 @@ namespace ggml_cuda_mma {
         }
 #elif defined(AMD_WMMA_AVAILABLE)
         if constexpr (std::is_same_v<T, half2> || std::is_same_v<T, nv_bfloat162>) {
-            ggml_cuda_memcpy_1<sizeof(t.x)>(t.x, xs0 + t.get_i(0) * stride + t.get_j(0));
-
+#if defined(RDNA4)
+                ggml_cuda_memcpy_1<sizeof(t.x)>(t.x, xs0 + t.get_i(0) * stride + t.get_j(0));
+#elif defined(RDNA3)
+                ggml_cuda_memcpy_1<sizeof(t.x)/2>(t.x, xs0 + t.get_i(0) * stride + t.get_j(0));
+                ggml_cuda_memcpy_1<sizeof(t.x)/2>(t.x + t.ne/2, xs0 + t.get_i(0) * stride + t.get_j(t.ne/2));
+#else
+                NO_DEVICE_CODE;
+#endif // defined(RDNA4)
         } else if constexpr (std::is_same_v<T, int>) {
             if constexpr (I == 16 && J == 4) {
                 int64_t * xi = (int64_t *) t.x;
@@ -888,6 +923,16 @@ namespace ggml_cuda_mma {
         const halfx8_t& a_frag = reinterpret_cast<const halfx8_t&>(A.x[0]);
         const halfx8_t& b_frag = reinterpret_cast<const halfx8_t&>(B.x[0]);
         acc_frag = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32_gfx12(a_frag, b_frag, acc_frag);
+#elif defined(RDNA3)
+        using halfx16_t = __attribute__((ext_vector_type(16))) _Float16;
+        using floatx8_t = __attribute__((ext_vector_type(8))) float;
+        floatx8_t& acc_frag = reinterpret_cast<floatx8_t&>(D.x[0]);
+        const halfx16_t& a_frag = reinterpret_cast<const halfx16_t&>(A.x[0]);
+        const halfx16_t& b_frag = reinterpret_cast<const halfx16_t&>(B.x[0]);
+        acc_frag = __builtin_amdgcn_wmma_f32_16x16x16_f16_w32(a_frag, b_frag, acc_frag);
+#else
+        GGML_UNUSED_VARS(D, A, B);
+        NO_DEVICE_CODE;
 #endif // RDNA4
 #else
         GGML_UNUSED_VARS(D, A, B);
@@ -905,6 +950,16 @@ namespace ggml_cuda_mma {
         const bf16x8_t& a_frag = reinterpret_cast<const bf16x8_t&>(A.x[0]);
         const bf16x8_t& b_frag = reinterpret_cast<const bf16x8_t&>(B.x[0]);
         acc_frag = __builtin_amdgcn_wmma_f32_16x16x16_bf16_w32_gfx12(a_frag, b_frag, acc_frag);
+#elif defined(RDNA3)
+        using bf16x16_t = __attribute__((ext_vector_type(16))) __bf16;
+        using floatx8_t = __attribute__((ext_vector_type(8))) float;
+        floatx8_t& acc_frag = reinterpret_cast<floatx8_t&>(D.x[0]);
+        const bf16x16_t& a_frag = reinterpret_cast<const bf16x16_t&>(A.x[0]);
+        const bf16x16_t& b_frag = reinterpret_cast<const bf16x16_t&>(B.x[0]);
+        acc_frag = __builtin_amdgcn_wmma_f32_16x16x16_bf16_w32(a_frag, b_frag, acc_frag);
+#else
+        GGML_UNUSED_VARS(D, A, B);
+        NO_DEVICE_CODE;
 #endif // RDNA4
 #else
         GGML_UNUSED_VARS(D, A, B);
diff --git a/ggml/src/ggml-cuda/mmf.cu b/ggml/src/ggml-cuda/mmf.cu
index 7cf33f0ddf..6643f243b1 100644
--- a/ggml/src/ggml-cuda/mmf.cu
+++ b/ggml/src/ggml-cuda/mmf.cu
@@ -151,7 +151,9 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const
             return false;
         }
     } else {
-        if (src1_ncols > 16) {
+        if (GGML_CUDA_CC_IS_RDNA3_0(cc) && src1_ncols > 8) {
+            return false;
+        } else if (src1_ncols > 16) {
             return false;
         }
     }
@@ -160,9 +162,9 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const
         case GGML_TYPE_F32:
             return ampere_mma_available(cc);
         case GGML_TYPE_F16:
-            return volta_mma_available(cc) || turing_mma_available(cc) || (amd_wmma_available(cc) && GGML_CUDA_CC_IS_RDNA4(cc));
+            return volta_mma_available(cc) || turing_mma_available(cc) || amd_wmma_available(cc);
         case GGML_TYPE_BF16:
-            return ampere_mma_available(cc) || (amd_wmma_available(cc) && GGML_CUDA_CC_IS_RDNA4(cc));
+            return ampere_mma_available(cc) || amd_wmma_available(cc);
         default:
             return false;
     }
diff --git a/ggml/src/ggml-cuda/mmvf.cu b/ggml/src/ggml-cuda/mmvf.cu
index 6238ce7ebd..32948e4d7a 100644
--- a/ggml/src/ggml-cuda/mmvf.cu
+++ b/ggml/src/ggml-cuda/mmvf.cu
@@ -765,7 +765,10 @@ bool ggml_cuda_should_use_mmvf(enum ggml_type type, int cc, const int64_t * src0
                 return ne11 <= 8;
             } else if (GGML_CUDA_CC_IS_AMD(cc)) {
                 if (fp16_mma_hardware_available(cc)) {
-                    if (GGML_CUDA_CC_IS_RDNA3(cc) || GGML_CUDA_CC_IS_RDNA4(cc)) {
+                    if (GGML_CUDA_CC_IS_RDNA3(cc)) {
+                        return ne11 <= 3;
+                    }
+                    if (GGML_CUDA_CC_IS_RDNA4(cc)) {
                         return ne11 <= 5;
                     }
                     return ne11 <= 2;

From 2eaa2c65cbc2e532e2353e115bf5151a82704534 Mon Sep 17 00:00:00 2001
From: Gustavo Rocha Dias <91472747+gustrd@users.noreply.github.com>
Date: Fri, 12 Dec 2025 08:02:28 -0300
Subject: [PATCH 062/135] cmake: link ws2_32 for MinGW/w64devkit builds in
 cpp-httplib (#17949)

---
 vendor/cpp-httplib/CMakeLists.txt | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/vendor/cpp-httplib/CMakeLists.txt b/vendor/cpp-httplib/CMakeLists.txt
index 369502d7ae..e90e8e2d1b 100644
--- a/vendor/cpp-httplib/CMakeLists.txt
+++ b/vendor/cpp-httplib/CMakeLists.txt
@@ -9,6 +9,10 @@ if (NOT MSVC)
 endif()
 
 target_link_libraries  (${TARGET} PRIVATE Threads::Threads)
+
+if (WIN32 AND NOT MSVC)
+    target_link_libraries(${TARGET} PUBLIC ws2_32)
+endif()
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
 
 target_compile_definitions(${TARGET} PRIVATE

From b8ee22cfde0eac9e20ce7963c02138a74c9471f7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= <angt@huggingface.co>
Date: Fri, 12 Dec 2025 12:44:35 +0100
Subject: [PATCH 063/135] common : add minimalist multi-thread progress bar
 (#17602)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---
 common/download.cpp | 94 +++++++++++++++++++++++++++++++++------------
 1 file changed, 69 insertions(+), 25 deletions(-)

diff --git a/common/download.cpp b/common/download.cpp
index ab68c53b43..ef87472560 100644
--- a/common/download.cpp
+++ b/common/download.cpp
@@ -12,6 +12,8 @@
 #include <filesystem>
 #include <fstream>
 #include <future>
+#include <map>
+#include <mutex>
 #include <regex>
 #include <string>
 #include <thread>
@@ -472,36 +474,79 @@ std::pair<long, std::vector<char>> common_remote_get_content(const std::string &
 
 #elif defined(LLAMA_USE_HTTPLIB)
 
-static bool is_output_a_tty() {
+class ProgressBar {
+    static inline std::mutex mutex;
+    static inline std::map<const ProgressBar *, int> lines;
+    static inline int max_line = 0;
+
+    static void cleanup(const ProgressBar * line) {
+        lines.erase(line);
+        if (lines.empty()) {
+            max_line = 0;
+        }
+    }
+
+    static bool is_output_a_tty() {
 #if defined(_WIN32)
-    return _isatty(_fileno(stdout));
+        return _isatty(_fileno(stdout));
 #else
-    return isatty(1);
+        return isatty(1);
 #endif
-}
-
-static void print_progress(size_t current, size_t total) {
-    if (!is_output_a_tty()) {
-        return;
     }
 
-    if (!total) {
-        return;
+public:
+    ProgressBar() = default;
+
+    ~ProgressBar() {
+        std::lock_guard<std::mutex> lock(mutex);
+        cleanup(this);
     }
 
-    size_t width = 50;
-    size_t pct = (100 * current) / total;
-    size_t pos = (width * current) / total;
+    void update(size_t current, size_t total) {
+        if (!is_output_a_tty()) {
+            return;
+        }
 
-    std::cout << "["
-              << std::string(pos, '=')
-              << (pos < width ? ">" : "")
-              << std::string(width - pos, ' ')
-              << "] " << std::setw(3) << pct << "%  ("
-              << current / (1024 * 1024) << " MB / "
-              << total / (1024 * 1024) << " MB)\r";
-    std::cout.flush();
-}
+        if (!total) {
+            return;
+        }
+
+        std::lock_guard<std::mutex> lock(mutex);
+
+        if (lines.find(this) == lines.end()) {
+            lines[this] = max_line++;
+            std::cout << "\n";
+        }
+        int lines_up = max_line - lines[this];
+
+        size_t width = 50;
+        size_t pct = (100 * current) / total;
+        size_t pos = (width * current) / total;
+
+        std::cout << "\033[s";
+
+        if (lines_up > 0) {
+            std::cout << "\033[" << lines_up << "A";
+        }
+        std::cout << "\033[2K\r["
+            << std::string(pos, '=')
+            << (pos < width ? ">" : "")
+            << std::string(width - pos, ' ')
+            << "] " << std::setw(3) << pct << "%  ("
+            << current / (1024 * 1024) << " MB / "
+            << total / (1024 * 1024) << " MB) "
+            << "\033[u";
+
+        std::cout.flush();
+
+        if (current == total) {
+             cleanup(this);
+        }
+    }
+
+    ProgressBar(const ProgressBar &) = delete;
+    ProgressBar & operator=(const ProgressBar &) = delete;
+};
 
 static bool common_pull_file(httplib::Client & cli,
                              const std::string & resolve_path,
@@ -523,6 +568,7 @@ static bool common_pull_file(httplib::Client & cli,
     const char * func = __func__; // avoid __func__ inside a lambda
     size_t downloaded = existing_size;
     size_t progress_step = 0;
+    ProgressBar bar;
 
     auto res = cli.Get(resolve_path, headers,
         [&](const httplib::Response &response) {
@@ -554,7 +600,7 @@ static bool common_pull_file(httplib::Client & cli,
             progress_step += len;
 
             if (progress_step >= total_size / 1000 || downloaded == total_size) {
-                print_progress(downloaded, total_size);
+                bar.update(downloaded, total_size);
                 progress_step = 0;
             }
             return true;
@@ -562,8 +608,6 @@ static bool common_pull_file(httplib::Client & cli,
         nullptr
     );
 
-    std::cout << "\n";
-
     if (!res) {
         LOG_ERR("%s: error during download. Status: %d\n", __func__, res ? res->status : -1);
         return false;

From dada4c846df5e2bea1800e70b56ceacac7cfb54b Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Fri, 12 Dec 2025 13:25:16 +0100
Subject: [PATCH 064/135] model-conversion : remove max diff check in
 compare-logits [no ci] (#17954)

This commit removes the maximum difference check from the
compare-logits.py which would stop early if the difference between
the logits exceeded a threshold.

The motivation for removing this is that it can be useful to be able to
get the complete log for debugging/reporting purposes.
---
 examples/model-conversion/scripts/causal/compare-logits.py | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/examples/model-conversion/scripts/causal/compare-logits.py b/examples/model-conversion/scripts/causal/compare-logits.py
index afa0d5b263..2744789099 100755
--- a/examples/model-conversion/scripts/causal/compare-logits.py
+++ b/examples/model-conversion/scripts/causal/compare-logits.py
@@ -32,10 +32,6 @@ def quick_logits_check(pytorch_file, llamacpp_file):
     print(f"Top 10 llama.cpp logits: {llamacpp_logits[llamacpp_top10]}")
     print(f"Max absolute difference: {max_diff:.4f}")
 
-    if max_diff > 1.0:
-        print(f"❌ NOK: Large differences detected - max diff: {max_diff:.4f}")
-        return False
-
     return True
 
 def main():

From 54a0fee4b73e9023c6faef9b2cfe4963edb6776c Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Fri, 12 Dec 2025 14:06:06 +0100
Subject: [PATCH 065/135] arg: add -mm and -mmu as short form of --mmproj and
 --mmproj-url (#17958)

* arg: add -mm and -mmu as short form of --mmproj and --mmproj-url

* correct order

* update docs
---
 common/arg.cpp         | 4 ++--
 tools/server/README.md | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index a31dcbc689..5528eeb169 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -1856,7 +1856,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONT_BATCHING"));
     add_opt(common_arg(
-        {"--mmproj"}, "FILE",
+        {"-mm", "--mmproj"}, "FILE",
         "path to a multimodal projector file. see tools/mtmd/README.md\n"
         "note: if -hf is used, this argument can be omitted",
         [](common_params & params, const std::string & value) {
@@ -1864,7 +1864,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ"));
     add_opt(common_arg(
-        {"--mmproj-url"}, "URL",
+        {"-mmu", "--mmproj-url"}, "URL",
         "URL to a multimodal projector file. see tools/mtmd/README.md",
         [](common_params & params, const std::string & value) {
             params.mmproj.url = value;
diff --git a/tools/server/README.md b/tools/server/README.md
index d6b9b87dcf..91cafa9425 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -166,8 +166,8 @@ For the ful list of features, please refer to [server's changelog](https://githu
 | `--pooling {none,mean,cls,last,rank}` | pooling type for embeddings, use model default if unspecified<br/>(env: LLAMA_ARG_POOLING) |
 | `-cb, --cont-batching` | enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
 | `-nocb, --no-cont-batching` | disable continuous batching<br/>(env: LLAMA_ARG_NO_CONT_BATCHING) |
-| `--mmproj FILE` | path to a multimodal projector file. see tools/mtmd/README.md<br/>note: if -hf is used, this argument can be omitted<br/>(env: LLAMA_ARG_MMPROJ) |
-| `--mmproj-url URL` | URL to a multimodal projector file. see tools/mtmd/README.md<br/>(env: LLAMA_ARG_MMPROJ_URL) |
+| `-mm, --mmproj FILE` | path to a multimodal projector file. see tools/mtmd/README.md<br/>note: if -hf is used, this argument can be omitted<br/>(env: LLAMA_ARG_MMPROJ) |
+| `-mmu, --mmproj-url URL` | URL to a multimodal projector file. see tools/mtmd/README.md<br/>(env: LLAMA_ARG_MMPROJ_URL) |
 | `--no-mmproj` | explicitly disable multimodal projector, useful when using -hf<br/>(env: LLAMA_ARG_NO_MMPROJ) |
 | `--no-mmproj-offload` | do not offload multimodal projector to GPU<br/>(env: LLAMA_ARG_NO_MMPROJ_OFFLOAD) |
 | `--image-min-tokens N` | minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)<br/>(env: LLAMA_ARG_IMAGE_MIN_TOKENS) |

From 12280ae9058600b0b14335fe59848e601e10eebc Mon Sep 17 00:00:00 2001
From: Aleksander Grygier <aleksander.grygier@gmail.com>
Date: Fri, 12 Dec 2025 15:13:36 +0100
Subject: [PATCH 066/135] webui: Fix parsing non-LaTeX occurrencies of `\(` or
 `\)` (#17810)

* fix: Improve latex protection logic to prevent turning non-latex `\(` into `$`

* chore: update webui build output
---
 tools/server/public/index.html.gz             | Bin 1445117 -> 1445111 bytes
 .../src/lib/utils/latex-protection.test.ts    |  21 ++++++++++++++++
 .../webui/src/lib/utils/latex-protection.ts   |  23 ++++++++++--------
 3 files changed, 34 insertions(+), 10 deletions(-)

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index 4cff76429e17e3278492ced5821f896eaf94decb..3fd631b77a85820f6fcec9cc3f5e9f60d5ef9fb3 100644
GIT binary patch
delta 1081897
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z)^xUo>WhCABT9+{LB5f~D>hkCfZh_n_qiF=`2Yv|r}toH0*%Hvz6JJCn=&XceLxaA
zu&jovP0t{nI#?TJBAwQ|K-e*`O{dk-snF{o&x9Xi#T>n?K8{C}faHXCkM=+-eeJt<
zHsKpQ*Ph2oNXE9hp4W*KORXBsv4;AJHE^1O!{vW!xDs8OV}-434Lts?Oc#*yOA+2o
zagZferAF<<NJSUk(lXMH|0Ho0Nis5&7){Ik(~0eQu|g8Jw%Hpj?2#aB3bSZ8!k{s(
zW-o|8Hsw+ZM3cmW+<*#jx}U0A0YM;+X+ub+y}uR?7k~>Y|9=2FH8S9;7;dqWu?r@N
zBjbMx!6Ts|mUs?lurXqA5V7acEsToEZ7su?J%vD&Z3<i(p5@3NKeX}9<7QdicVUm>
z=bzX_K}Ya@??HQ4y*;pMNp@bqW)M<lR(y?(B`7FNya2|$T^#<ss&3Q-UMoU{Q53zS
zpT(jE8n9X4mCa&0-B-8E9W^S=T>#8QW)3QDr+ZuaJ)7W&T<)4VKFlVo@&OmK$+B8K
U&ZF6cAU^ZxKSrj{WbU#+0CTg4tpET3

diff --git a/tools/server/webui/src/lib/utils/latex-protection.test.ts b/tools/server/webui/src/lib/utils/latex-protection.test.ts
index 2354f8fa0e..40fe1b0db2 100644
--- a/tools/server/webui/src/lib/utils/latex-protection.test.ts
+++ b/tools/server/webui/src/lib/utils/latex-protection.test.ts
@@ -303,6 +303,27 @@ $$\n\\pi_n(\\mathbb{S}^3) = \\begin{cases}
 		expect(output).toBe(input); // Code blocks prevent misinterpretation
 	});
 
+	test('preserves backslash parentheses in code blocks (GitHub issue)', () => {
+		const input = '```python\nfoo = "\\(bar\\)"\n```';
+		const output = preprocessLaTeX(input);
+
+		expect(output).toBe(input); // Code blocks should not have LaTeX conversion applied
+	});
+
+	test('preserves backslash brackets in code blocks', () => {
+		const input = '```python\nfoo = "\\[bar\\]"\n```';
+		const output = preprocessLaTeX(input);
+
+		expect(output).toBe(input); // Code blocks should not have LaTeX conversion applied
+	});
+
+	test('preserves backslash parentheses in inline code', () => {
+		const input = 'Use `foo = "\\(bar\\)"` in your code.';
+		const output = preprocessLaTeX(input);
+
+		expect(output).toBe(input);
+	});
+
 	test('escape backslash in mchem ce', () => {
 		const input = 'mchem ce:\n$\\ce{2H2(g) + O2(g) -> 2H2O(l)}$';
 		const output = preprocessLaTeX(input);
diff --git a/tools/server/webui/src/lib/utils/latex-protection.ts b/tools/server/webui/src/lib/utils/latex-protection.ts
index 7f5cf2cddf..cafa2d4761 100644
--- a/tools/server/webui/src/lib/utils/latex-protection.ts
+++ b/tools/server/webui/src/lib/utils/latex-protection.ts
@@ -226,19 +226,16 @@ export function preprocessLaTeX(content: string): string {
 		return expr;
 	});
 
-	// Step 5: Restore code blocks
-	content = content.replace(/<<CODE_BLOCK_(\d+)>>/g, (_, index) => {
-		return codeBlocks[parseInt(index)];
-	});
-
-	// Step 6: Apply additional escaping functions (brackets and mhchem)
+	// Step 5: Apply additional escaping functions (brackets and mhchem)
+	// This must happen BEFORE restoring code blocks to avoid affecting code content
 	content = escapeBrackets(content);
 
 	if (doEscapeMhchem && (content.includes('\\ce{') || content.includes('\\pu{'))) {
 		content = escapeMhchem(content);
 	}
 
-	// Final pass: Convert \(...\) → $...$, \[...\] → $$...$$
+	// Step 6: Convert remaining \(...\) → $...$, \[...\] → $$...$$
+	// This must happen BEFORE restoring code blocks to avoid affecting code content
 	content = content
 		// Using the look‑behind pattern `(?<!\\)` we skip matches
 		// that are preceded by a backslash, e.g.
@@ -248,12 +245,18 @@ export function preprocessLaTeX(content: string): string {
 			// Using the look‑behind pattern `(?<!\\)` we skip matches
 			// that are preceded by a backslash, e.g. `\\[4pt]`.
 			/(?<!\\)\\\[([\s\S]*?)\\\]/g, // display, see also PR #16599
-			(_, prefix: string, content: string) => {
-				return `${prefix}$$${content}$$`;
+			(_, content: string) => {
+				return `$$${content}$$`;
 			}
 		);
 
-	// Step 7: Restore blockquote markers
+	// Step 7: Restore code blocks
+	// This happens AFTER all LaTeX conversions to preserve code content
+	content = content.replace(/<<CODE_BLOCK_(\d+)>>/g, (_, index) => {
+		return codeBlocks[parseInt(index)];
+	});
+
+	// Step 8: Restore blockquote markers
 	if (blockquoteMarkers.size > 0) {
 		const finalLines = content.split('\n');
 		const restoredLines = finalLines.map((line, index) => {

From 17158965ac95b8bea2cbeff559f4cdba5d44c36f Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Fri, 12 Dec 2025 15:16:06 +0100
Subject: [PATCH 067/135] mtmd: explicitly forbidden inclusion of private
 header and libcommon (#17946)

---
 tools/mtmd/CMakeLists.txt  | 9 +++++++++
 tools/mtmd/clip-impl.h     | 2 ++
 tools/mtmd/clip.h          | 2 ++
 tools/mtmd/mtmd-audio.h    | 2 ++
 tools/mtmd/mtmd-helper.cpp | 4 ++++
 5 files changed, 19 insertions(+)

diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt
index 13ab7c78f4..a104ea1c0e 100644
--- a/tools/mtmd/CMakeLists.txt
+++ b/tools/mtmd/CMakeLists.txt
@@ -53,6 +53,15 @@ if (TARGET BUILD_INFO)
     add_dependencies(mtmd-helper BUILD_INFO)
 endif()
 
+# if mtmd is linked against common, we throw an error
+if (TARGET mtmd)
+    get_target_property(libs mtmd LINK_LIBRARIES)
+    if (libs AND "common" IN_LIST libs)
+        message(FATAL_ERROR "mtmd is designed to be a public library.\n"
+                            "It must not link against common")
+    endif()
+endif()
+
 add_executable(llama-llava-cli    deprecation-warning.cpp)
 add_executable(llama-gemma3-cli   deprecation-warning.cpp)
 add_executable(llama-minicpmv-cli deprecation-warning.cpp)
diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h
index cd47865bf4..e5563eabfc 100644
--- a/tools/mtmd/clip-impl.h
+++ b/tools/mtmd/clip-impl.h
@@ -13,6 +13,8 @@
 
 // Internal header for clip.cpp
 
+#define MTMD_INTERNAL_HEADER
+
 #define KEY_FTYPE               "general.file_type"
 #define KEY_NAME                "general.name"
 #define KEY_DESCRIPTION         "general.description"
diff --git a/tools/mtmd/clip.h b/tools/mtmd/clip.h
index e8aeb2066c..5aae27a700 100644
--- a/tools/mtmd/clip.h
+++ b/tools/mtmd/clip.h
@@ -7,6 +7,8 @@
 
 // !!! Internal header, to be used by mtmd only !!!
 
+#define MTMD_INTERNAL_HEADER
+
 struct clip_ctx;
 
 struct clip_image_size {
diff --git a/tools/mtmd/mtmd-audio.h b/tools/mtmd/mtmd-audio.h
index b7b940affb..0e552347a0 100644
--- a/tools/mtmd/mtmd-audio.h
+++ b/tools/mtmd/mtmd-audio.h
@@ -6,6 +6,8 @@
 #include <vector>
 #include <string>
 
+#define MTMD_INTERNAL_HEADER
+
 #define WHISPER_ASSERT GGML_ASSERT
 
 #define WHISPER_SAMPLE_RATE 16000
diff --git a/tools/mtmd/mtmd-helper.cpp b/tools/mtmd/mtmd-helper.cpp
index f0891bba30..902a4b456d 100644
--- a/tools/mtmd/mtmd-helper.cpp
+++ b/tools/mtmd/mtmd-helper.cpp
@@ -32,6 +32,10 @@
 #define STB_IMAGE_IMPLEMENTATION
 #include "stb/stb_image.h"
 
+#ifdef MTMD_INTERNAL_HEADER
+#error "mtmd-helper is a public library outside of mtmd. it must not include internal headers"
+#endif
+
 //
 // internal logging functions
 //

From 51604435e8efffb4f2e1991ddd2bbc01774dd0fc Mon Sep 17 00:00:00 2001
From: ixgbe <1113177880@qq.com>
Date: Fri, 12 Dec 2025 22:26:03 +0800
Subject: [PATCH 068/135] ggml-cpu : fix RISC-V Q4_0 repack select and RVV
 feature reporting (#17951)

* ggml-cpu:fix RISC-V Q4_0 repack select and RVV feature reporting

Signed-off-by: Wang Yang <yangwang@iscas.ac.cn>

* using the name VLEN instead of CNT

* Update ggml/include/ggml-cpu.h

---------

Signed-off-by: Wang Yang <yangwang@iscas.ac.cn>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 ggml/include/ggml-cpu.h        |  1 +
 ggml/src/ggml-cpu/ggml-cpu.c   | 26 ++++++++++++++++++++++++++
 ggml/src/ggml-cpu/ggml-cpu.cpp |  4 ++++
 ggml/src/ggml-cpu/repack.cpp   |  3 ++-
 4 files changed, 33 insertions(+), 1 deletion(-)

diff --git a/ggml/include/ggml-cpu.h b/ggml/include/ggml-cpu.h
index 9edd485136..4f3b99c8d0 100644
--- a/ggml/include/ggml-cpu.h
+++ b/ggml/include/ggml-cpu.h
@@ -99,6 +99,7 @@ extern "C" {
     GGML_BACKEND_API int ggml_cpu_has_sme        (void);
     // other
     GGML_BACKEND_API int ggml_cpu_has_riscv_v    (void);
+    GGML_BACKEND_API int ggml_cpu_get_rvv_vlen   (void);  // risc-v vector length in bytes
     GGML_BACKEND_API int ggml_cpu_has_vsx        (void);
     GGML_BACKEND_API int ggml_cpu_has_vxe        (void);
     GGML_BACKEND_API int ggml_cpu_has_wasm_simd  (void);
diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index c47511adcb..a59b518938 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -81,6 +81,11 @@ struct ggml_arm_arch_features_type {
 } ggml_arm_arch_features = { 0 };
 #endif
 
+#if defined(__riscv)
+struct ggml_riscv_arch_features_type {
+    int rvv_vlen;
+} ggml_riscv_arch_features = { 0 };
+#endif
 
 #if defined(_WIN32)
 
@@ -703,6 +708,15 @@ static void ggml_init_arm_arch_features(void) {}
 #endif
 #endif // __ARM_ARCH
 
+#if defined(__riscv) && defined(__riscv_v_intrinsic)
+#include <riscv_vector.h>
+static void ggml_init_riscv_arch_features(void) {
+    ggml_riscv_arch_features.rvv_vlen = __riscv_vlenb();
+}
+#else
+static void ggml_init_riscv_arch_features(void) {}
+#endif
+
 struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value) {
     GGML_ASSERT(!ggml_get_no_alloc(ctx));
 
@@ -3459,6 +3473,14 @@ int ggml_cpu_has_riscv_v(void) {
 #endif
 }
 
+int ggml_cpu_get_rvv_vlen(void) {
+#if defined(__riscv) && defined(__riscv_v_intrinsic)
+    return ggml_riscv_arch_features.rvv_vlen;
+#else
+    return 0;
+#endif
+}
+
 int ggml_cpu_has_f16c(void) {
 #if defined(__F16C__)
     return 1;
@@ -3625,6 +3647,10 @@ void ggml_cpu_init(void) {
         ggml_init_arm_arch_features();
 #endif
 
+#if defined(__riscv)
+        ggml_init_riscv_arch_features();
+#endif
+
         is_first_call = false;
     }
 
diff --git a/ggml/src/ggml-cpu/ggml-cpu.cpp b/ggml/src/ggml-cpu/ggml-cpu.cpp
index 3191faaa4c..f4713a4218 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.cpp
+++ b/ggml/src/ggml-cpu/ggml-cpu.cpp
@@ -583,6 +583,10 @@ static ggml_backend_feature * ggml_backend_cpu_get_features(ggml_backend_reg_t r
         if (ggml_cpu_has_riscv_v()) {
             features.push_back({ "RISCV_V", "1" });
         }
+        if (ggml_cpu_get_rvv_vlen() > 0) {
+            static std::string rvv_vlen = std::to_string(ggml_cpu_get_rvv_vlen());
+            features.push_back({ "RVV_VLEN", rvv_vlen.c_str() });
+        }
         if (ggml_cpu_has_vsx()) {
             features.push_back({ "VSX", "1" });
         }
diff --git a/ggml/src/ggml-cpu/repack.cpp b/ggml/src/ggml-cpu/repack.cpp
index 9f0d449bd6..b70ea7d78b 100644
--- a/ggml/src/ggml-cpu/repack.cpp
+++ b/ggml/src/ggml-cpu/repack.cpp
@@ -2169,7 +2169,8 @@ static const ggml::cpu::tensor_traits * ggml_repack_get_optimal_repack_type(cons
     static const ggml::cpu::repack::tensor_traits<block_iq4_nl, 8, 8, GGML_TYPE_Q8_0> iq4_nl_8x8_q8_0;
 
     if (cur->type == GGML_TYPE_Q4_0) {
-        if (ggml_cpu_has_avx2() || (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0)) {
+        if (ggml_cpu_has_avx2() || (ggml_cpu_has_sve() && ggml_cpu_has_matmul_int8() && ggml_cpu_get_sve_cnt() == QK8_0)
+            || (ggml_cpu_has_riscv_v() && (ggml_cpu_get_rvv_vlen() >= QK4_0))) {
             if (cur->ne[1] % 8 == 0) {
                 return &q4_0_8x8_q8_0;
             }

From dcb7d17758a95bada4383869c8ce66ca60ac3efc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Fri, 12 Dec 2025 15:49:27 +0100
Subject: [PATCH 069/135] cann : fix ops broken by circular padding guard
 (#17825)

---
 ggml/src/ggml-cann/ggml-cann.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp
index 81288464c7..da624c587c 100644
--- a/ggml/src/ggml-cann/ggml-cann.cpp
+++ b/ggml/src/ggml-cann/ggml-cann.cpp
@@ -2548,6 +2548,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
         case GGML_OP_ARGSORT:
         case GGML_OP_ACC:
         case GGML_OP_GROUP_NORM:
+            return true;
         case GGML_OP_PAD:
             // TODO: add circular padding support for cann, see https://github.com/ggml-org/llama.cpp/pull/16985
             return ggml_get_op_params_i32(op, 8) == 0;

From 7bed317f5351eba037c2e0aa3dce617e277be1c4 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 12 Dec 2025 17:12:40 +0200
Subject: [PATCH 070/135] models : fix the attn_factor for mistral3 graphs +
 improve consistency (#17945)

* models : fix the attn_factor for mistral3 graphs

* cont : rework attn_factor correction logic

* cont : make deepseek2 consistent

* cont : add TODO

* cont : special-case DSv2

* cont : revert Mistral 3 Large changes

* cont : fix DS2 to use the original attn_factor

* cont : minor comments
---
 convert_hf_to_gguf.py    |  8 ++++++
 src/llama-graph.cpp      |  2 +-
 src/llama-hparams.cpp    | 12 +++++++++
 src/llama-hparams.h      |  9 ++++++-
 src/llama-kv-cache.cpp   | 13 +++-------
 src/llama-model.cpp      | 53 +++++++++++++++++++++++++++-------------
 src/models/deepseek2.cpp | 14 +++++++----
 7 files changed, 78 insertions(+), 33 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 867bc90531..151608d56b 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -7286,6 +7286,10 @@ class DeepseekV2Model(TextModel):
             self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
             self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
             self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
+
+            # [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
+            # note: for legacy reasons, this is not consistent with the other usages of self.gguf_writer.add_rope_scaling_yarn_log_mul
+            # ref https://github.com/ggml-org/llama.cpp/pull/17945
             self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * rope_scaling["mscale_all_dim"])
 
     _experts: list[dict[str, Tensor]] | None = None
@@ -10041,6 +10045,10 @@ class MistralMoeModel(DeepseekV2Model):
         MistralModel.set_mistral_config(self.gguf_writer, self.hparams)
         yarn_params = self.hparams["yarn"]
         self.gguf_writer.add_attn_temperature_length(yarn_params["original_max_position_embeddings"])
+
+        # [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
+        # note: for legacy reasons, this is not consistent with the other usages of self.gguf_writer.add_rope_scaling_yarn_log_mul
+        # ref https://github.com/ggml-org/llama.cpp/pull/17945
         self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1) # mscale_all_dim * 0.1
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 6cf9a883a6..a1a32494b7 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -574,7 +574,7 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
     freq_base        (cparams.rope_freq_base),
     freq_scale       (cparams.rope_freq_scale),
     ext_factor       (cparams.yarn_ext_factor),
-    attn_factor      (cparams.yarn_attn_factor),
+    attn_factor      (llama_hparams::yarn_attn_factor_adjust(cparams.yarn_attn_factor, cparams.rope_freq_scale, cparams.yarn_ext_factor)),
     beta_fast        (cparams.yarn_beta_fast),
     beta_slow        (cparams.yarn_beta_slow),
     norm_eps         (hparams.f_norm_eps),
diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp
index 8cdbaf69fc..277d0bcfd3 100644
--- a/src/llama-hparams.cpp
+++ b/src/llama-hparams.cpp
@@ -1,7 +1,9 @@
 #include "llama-hparams.h"
 
 #include "ggml.h"
+
 #include <cassert>
+#include <cmath>
 
 void llama_hparams::set_swa_pattern(uint32_t n_pattern, bool dense_first) {
     if (dense_first) {
@@ -229,3 +231,13 @@ bool llama_hparams::is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama
 
     return false;
 }
+
+float llama_hparams::yarn_attn_factor_adjust(float attn_factor, float freq_scale, float ext_factor) {
+    GGML_ASSERT(ext_factor >= 0.0f);
+
+    if (ext_factor != 0.0f) {
+        attn_factor *= 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale));
+    }
+
+    return attn_factor;
+}
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index 6eff334a5f..c9960e9169 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -107,6 +107,7 @@ struct llama_hparams {
     float    rope_freq_base_train_swa;
     float    rope_freq_scale_train;
     float    rope_freq_scale_train_swa;
+
     uint32_t n_ctx_orig_yarn;
     float    rope_yarn_log_mul = 0.0f;
 
@@ -267,7 +268,13 @@ struct llama_hparams {
     // TODO: think of a better place for this function
     // TODO: pack the SWA params in a struct?
     static bool is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama_pos p0, llama_pos p1);
+
+    // when YARN is applied with yarn_ext_factor != 0.0f, we need to cancel this factor:
+    // https://github.com/ggml-org/llama.cpp/blob/a81a569577cc38b32558958b048228150be63eae/ggml/src/ggml-cpu/ops.cpp#L5541-L5544
+    //
+    // ref: https://github.com/ggml-org/llama.cpp/discussions/7416
+    //      https://github.com/ggml-org/llama.cpp/pull/17945
+    static float yarn_attn_factor_adjust(float attn_factor, float freq_scale, float ext_factor);
 };
 
 static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
-
diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index 3e02bd6297..8f94c8820c 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -1369,9 +1369,10 @@ ggml_tensor * llama_kv_cache::build_rope_shift(
                       float   freq_scale) const {
     const auto & n_ctx_orig = cparams.n_ctx_orig_yarn;
 
-    const auto & yarn_ext_factor = cparams.yarn_ext_factor;
-    const auto & yarn_beta_fast  = cparams.yarn_beta_fast;
-    const auto & yarn_beta_slow  = cparams.yarn_beta_slow;
+    const auto & yarn_ext_factor  = cparams.yarn_ext_factor;
+    const auto & yarn_beta_fast   = cparams.yarn_beta_fast;
+    const auto & yarn_beta_slow   = cparams.yarn_beta_slow;
+    const auto & yarn_attn_factor = llama_hparams::yarn_attn_factor_adjust(cparams.yarn_attn_factor, cparams.rope_freq_scale, cparams.yarn_ext_factor);
 
     const auto & n_rot     = hparams.n_rot;
     const auto & rope_type = hparams.rope_type == LLAMA_ROPE_TYPE_MROPE || hparams.rope_type == LLAMA_ROPE_TYPE_IMROPE
@@ -1382,12 +1383,6 @@ ggml_tensor * llama_kv_cache::build_rope_shift(
                                 ? LLAMA_ROPE_TYPE_NEOX
                                 : hparams.rope_type;
 
-    // See llm_build_deepseek2() for why attn_factor has to be scaled for YaRN RoPE to work correctly.
-    // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation.
-    const float yarn_attn_factor = model.arch == LLM_ARCH_DEEPSEEK2
-                                    ? 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale))
-                                    : cparams.yarn_attn_factor;
-
     ggml_tensor * tmp;
 
     if (ggml_is_quantized(cur->type)) {
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index fc337b045e..e4808b1e1e 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1635,7 +1635,12 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     // that have no expert_gating_func model parameter set
                     hparams.expert_gating_func = LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX;
                 }
-                ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, false);
+
+                if (ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, 0.0f)) {
+                    // [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
+                    // cancel the factor from the convert script
+                    hparams.rope_yarn_log_mul /= 0.1f;
+                }
 
                 // (optional) temperature tuning - used by mistral-large
                 ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE,  hparams.f_attn_temp_scale,       false);
@@ -2267,9 +2272,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                 ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false);
 
-                ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST,   hparams.yarn_beta_fast, false);
-                ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW,   hparams.yarn_beta_slow, false);
-                ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL,     hparams.rope_yarn_log_mul, false);
+                ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_FAST, hparams.yarn_beta_fast,    false);
+                ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow,    false);
+                ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL,   hparams.rope_yarn_log_mul, 0.0f);
 
                 // TODO: maybe add n_attn_temp_floor_scale as a separate KV?
                 if (hparams.f_attn_temp_scale != 0.0f) {
@@ -2279,18 +2284,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     }
                 }
 
-                // TODO: this seems to be correct with the case of mscale == mscale_all_dims == 1.0f
-                //       but may need further verification with other values
-                if (hparams.rope_yarn_log_mul != 0.0f) {
-                    float factor = 1.0f / hparams.rope_freq_scale_train;
-                    float mscale = 1.0f;
-                    float mscale_all_dims = hparams.rope_yarn_log_mul;
-                    static auto get_mscale = [](float scale, float mscale) {
-                        return scale <= 1.0f ? 1.0f : (0.1f * mscale * logf(scale) + 1.0f);
-                    };
-                    hparams.yarn_attn_factor = get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dims);
-                }
-
                 switch (hparams.n_layer) {
                     case 26: type = LLM_TYPE_3B; break;
                     case 34: type = LLM_TYPE_8B; break;
@@ -2301,6 +2294,32 @@ void llama_model::load_hparams(llama_model_loader & ml) {
         default: throw std::runtime_error("unsupported model architecture");
     }
 
+    // ref: https://github.com/huggingface/transformers/blob/6d00f6b0a5679c36510f203e4226e36f517c3032/src/transformers/modeling_rope_utils.py#L336-L348
+    if (hparams.rope_yarn_log_mul != 0.0f) {
+        const float factor = 1.0f / hparams.rope_freq_scale_train;
+
+        // note: here we assume `mscale == 1.0f`
+        // TODO: start reading the actual value of mscale and handle the case where it is not 1.0f
+              float mscale          = 1.0f;
+        const float mscale_all_dims = hparams.rope_yarn_log_mul;
+
+        // [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
+        // special-case DEEPSEEK v2:
+        // https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat/blob/main/config.json#L42-L43
+        if (arch == LLM_ARCH_DEEPSEEK2 && mscale_all_dims != 1.0f) {
+            mscale = mscale_all_dims;
+        }
+
+        static auto get_mscale = [](float scale, float mscale) {
+            return scale <= 1.0f ? 1.0f : (0.1f * mscale * logf(scale) + 1.0f);
+        };
+
+        hparams.yarn_attn_factor = get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dims);
+
+        LLAMA_LOG_WARN("%s: setting new yarn_attn_factor = %.4f (mscale == %.1f, mscale_all_dim = %.1f)\n",
+                __func__, hparams.yarn_attn_factor, mscale, mscale_all_dims);
+    }
+
     pimpl->n_bytes = ml.n_bytes;
 
     pimpl->desc_str = arch_name() + " " + type_name() + " " + ml.ftype_name();
@@ -6806,6 +6825,7 @@ void llama_model::print_info() const {
         LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
         LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
         LLAMA_LOG_INFO("%s: n_ctx_orig_yarn  = %u\n",     __func__, hparams.n_ctx_orig_yarn);
+        LLAMA_LOG_INFO("%s: rope_yarn_log_mul= %.4f\n",   __func__, hparams.rope_yarn_log_mul);
         LLAMA_LOG_INFO("%s: rope_finetuned   = %s\n",     __func__, hparams.rope_finetuned ? "yes" : "unknown");
         // MRoPE (Multi-axis Rotary Position Embedding) sections
         if (const auto & s = hparams.rope_sections; s[0] || s[1] || s[2] || s[3]) {
@@ -6869,7 +6889,6 @@ void llama_model::print_info() const {
         LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n",   __func__, hparams.expert_weights_scale);
         LLAMA_LOG_INFO("%s: expert_weights_norm  = %d\n",     __func__, hparams.expert_weights_norm);
         LLAMA_LOG_INFO("%s: expert_gating_func   = %s\n",     __func__, llama_expert_gating_func_name((llama_expert_gating_func_type) hparams.expert_gating_func));
-        LLAMA_LOG_INFO("%s: rope_yarn_log_mul    = %.4f\n",   __func__, hparams.rope_yarn_log_mul);
     }
 
     if (arch == LLM_ARCH_QWEN2MOE) {
diff --git a/src/models/deepseek2.cpp b/src/models/deepseek2.cpp
index dbaa8297be..49382874ba 100644
--- a/src/models/deepseek2.cpp
+++ b/src/models/deepseek2.cpp
@@ -1,7 +1,5 @@
 #include "models.h"
 
-
-
 llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_graph_params & params) :
     llm_graph_context(params) {
     // lite variants include DeepSeek-V2-Lite, GigaChat3-10B-A1.8B
@@ -20,9 +18,15 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
 
     // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
     // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation.
-    const float mscale      = attn_factor * (1.0f + hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));
-    const float kq_scale    = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k));
-    const float attn_factor = 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale));
+    // And also: https://github.com/ggml-org/llama.cpp/pull/17945 [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
+
+    // first cancel the adjustment from llama_hparams::yarn_attn_factor_adjust to get the original attn_factor
+    GGML_ASSERT(ext_factor >= 0.0f);
+    const float attn_factor_org = attn_factor * (1.0f + 0.1f * logf(1.0f / freq_scale));
+
+    // use the original attn_factor to pre-scale the kq_scale
+    const float mscale   = attn_factor_org * (1.0f + 0.1f * hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));
+    const float kq_scale = 1.0f * mscale * mscale / sqrtf(float(n_embd_head_k));
 
     ggml_tensor * cur;
     ggml_tensor * inpL;

From 482211438dd671224a7f176b7480b4ded424212c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Fri, 12 Dec 2025 17:43:58 +0100
Subject: [PATCH 071/135] CUDA: fix overflow in MMA kernel without stream-k
 (#17939)

---
 ggml/src/ggml-cuda/fattn-common.cuh  | 6 +++---
 ggml/src/ggml-cuda/fattn-mma-f16.cuh | 6 +++---
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh
index 2750117aa9..8dc82a9d3b 100644
--- a/ggml/src/ggml-cuda/fattn-common.cuh
+++ b/ggml/src/ggml-cuda/fattn-common.cuh
@@ -642,8 +642,8 @@ static __global__ void flash_attn_stream_k_fixup(
     const int iter_k = (ne11 + (nbatch_fa - 1)) / nbatch_fa;
     const int iter_j = (ne01 + (ncols1    - 1)) / ncols1;
 
-    const int kbc0      = (bidx0 + 0)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
-    const int kbc0_stop = (bidx0 + 1)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
+    const int kbc0      = int64_t(bidx0 + 0)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
+    const int kbc0_stop = int64_t(bidx0 + 1)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
 
     const bool did_not_have_any_data   = kbc0 == kbc0_stop;
     const bool wrote_beginning_of_tile = kbc0 % iter_k == 0;
@@ -679,7 +679,7 @@ static __global__ void flash_attn_stream_k_fixup(
     int bidx = bidx0 - 1;
     int kbc_stop = kbc0;
     while(true) {
-        const int kbc = bidx*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
+        const int kbc = int64_t(bidx)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
         if (kbc == kbc_stop) { // Did not have any data.
             bidx--;
             kbc_stop = kbc;
diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
index d51537f7d0..7bd1044c19 100644
--- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh
+++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh
@@ -1380,8 +1380,8 @@ static __global__ void flash_attn_ext_f16(
     const int iter_j = (ne01.z + (ncols1    - 1)) / ncols1;
 
     // kbc == k block continuous, current index in continuous ijk space.
-    int       kbc      = (blockIdx.x + 0)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
-    const int kbc_stop = (blockIdx.x + 1)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
+    int       kbc      = int64_t(blockIdx.x + 0)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
+    const int kbc_stop = int64_t(blockIdx.x + 1)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x;
 
     // If the seams of 2 CUDA blocks fall within an output tile their results need to be combined.
     // For this we need to track both the block that starts the tile (needs_fixup) and the block that finishes the tile (is_fixup).
@@ -1401,7 +1401,7 @@ static __global__ void flash_attn_ext_f16(
         const float2 * Q_f2   = (const float2 *) (Q + nb03*sequence + nb02* head0);
         const half2  * K_h2   = (const half2  *) (K + nb13*sequence + nb12*(head0 / gqa_ratio));
         const half   * mask_h = ncols2 == 1 && !mask ? nullptr :
-            (const half  *) (mask + nb33*(sequence % ne33));
+            (const half *) (mask + nb33*(sequence % ne33));
         float2       * dstk   = ((float2 *) dst) + (sequence*ne01.z*ne02 + head0) * (DV/2);
 
         const half2 * V_h2 = mla ? K_h2 + (DKQ/2 - DV/2) : (const half2 *) (V + nb23*sequence + nb22*(head0 / gqa_ratio));

From b7f5f46e03edbe73abb0784e27faa20efb8a42d5 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Fri, 12 Dec 2025 19:39:23 +0100
Subject: [PATCH 072/135] docker : include legacy llama-completion binary
 (#17964)

---
 .devops/cann.Dockerfile   | 2 +-
 .devops/cpu.Dockerfile    | 2 +-
 .devops/cuda.Dockerfile   | 2 +-
 .devops/intel.Dockerfile  | 2 +-
 .devops/musa.Dockerfile   | 2 +-
 .devops/rocm.Dockerfile   | 2 +-
 .devops/s390x.Dockerfile  | 2 +-
 .devops/tools.sh          | 8 ++++++--
 .devops/vulkan.Dockerfile | 2 +-
 9 files changed, 14 insertions(+), 10 deletions(-)

diff --git a/.devops/cann.Dockerfile b/.devops/cann.Dockerfile
index cd8f87b2ea..9d27e00fb1 100644
--- a/.devops/cann.Dockerfile
+++ b/.devops/cann.Dockerfile
@@ -111,7 +111,7 @@ ENTRYPOINT ["/app/tools.sh"]
 # ==============================================================================
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app
+COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
 
 ENTRYPOINT [ "/app/llama-cli" ]
 
diff --git a/.devops/cpu.Dockerfile b/.devops/cpu.Dockerfile
index 6e16ecda44..b9e84ab986 100644
--- a/.devops/cpu.Dockerfile
+++ b/.devops/cpu.Dockerfile
@@ -68,7 +68,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app
+COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
diff --git a/.devops/cuda.Dockerfile b/.devops/cuda.Dockerfile
index 54f793d0a3..fed5863157 100644
--- a/.devops/cuda.Dockerfile
+++ b/.devops/cuda.Dockerfile
@@ -74,7 +74,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app
+COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
diff --git a/.devops/intel.Dockerfile b/.devops/intel.Dockerfile
index d1a8fbed4c..adebf08229 100644
--- a/.devops/intel.Dockerfile
+++ b/.devops/intel.Dockerfile
@@ -73,7 +73,7 @@ ENTRYPOINT ["/app/tools.sh"]
 FROM base AS light
 
 COPY --from=build /app/lib/ /app
-COPY --from=build /app/full/llama-cli /app
+COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
diff --git a/.devops/musa.Dockerfile b/.devops/musa.Dockerfile
index faa3500e61..34d6ad9f40 100644
--- a/.devops/musa.Dockerfile
+++ b/.devops/musa.Dockerfile
@@ -81,7 +81,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app
+COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
diff --git a/.devops/rocm.Dockerfile b/.devops/rocm.Dockerfile
index d6bf28b105..53c3ed8d88 100644
--- a/.devops/rocm.Dockerfile
+++ b/.devops/rocm.Dockerfile
@@ -94,7 +94,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app
+COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 
diff --git a/.devops/s390x.Dockerfile b/.devops/s390x.Dockerfile
index b7c9457680..1e66f061d5 100644
--- a/.devops/s390x.Dockerfile
+++ b/.devops/s390x.Dockerfile
@@ -105,7 +105,7 @@ WORKDIR /llama.cpp/bin
 
 # Copy llama.cpp binaries and libraries
 COPY --from=collector /llama.cpp/bin/*.so /llama.cpp/bin
-COPY --from=collector /llama.cpp/bin/llama-cli /llama.cpp/bin
+COPY --from=collector /llama.cpp/bin/llama-cli /llama.cpp/bin/llama-completion /llama.cpp/bin
 
 ENTRYPOINT [ "/llama.cpp/bin/llama-cli" ]
 
diff --git a/.devops/tools.sh b/.devops/tools.sh
index 8a3a693400..cc5ee17dfd 100755
--- a/.devops/tools.sh
+++ b/.devops/tools.sh
@@ -13,6 +13,8 @@ elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
     exec ./llama-quantize "$@"
 elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
     exec ./llama-cli "$@"
+elif [[ "$arg1" == '--run-legacy' || "$arg1" == '-l' ]]; then
+    exec ./llama-completion "$@"
 elif [[ "$arg1" == '--bench' || "$arg1" == '-b' ]]; then
     exec ./llama-bench "$@"
 elif [[ "$arg1" == '--perplexity' || "$arg1" == '-p' ]]; then
@@ -32,8 +34,10 @@ elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then
 else
     echo "Unknown command: $arg1"
     echo "Available commands: "
-    echo "  --run (-r): Run a model previously converted into ggml"
-    echo "              ex: -m /models/7B/ggml-model-q4_0.bin -p \"Building a website can be done in 10 simple steps:\" -n 512"
+    echo "  --run (-r): Run a model (chat) previously converted into ggml"
+    echo "              ex: -m /models/7B/ggml-model-q4_0.bin"
+    echo "  --run-legacy (-l): Run a model (legacy completion) previously converted into ggml"
+    echo "              ex: -m /models/7B/ggml-model-q4_0.bin -no-cnv -p \"Building a website can be done in 10 simple steps:\" -n 512"
     echo "  --bench (-b): Benchmark the performance of the inference for various parameters."
     echo "              ex: -m model.gguf"
     echo "  --perplexity (-p): Measure the perplexity of a model over a given text."
diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile
index fd7195c5be..b37b4f277d 100644
--- a/.devops/vulkan.Dockerfile
+++ b/.devops/vulkan.Dockerfile
@@ -68,7 +68,7 @@ ENTRYPOINT ["/app/tools.sh"]
 ### Light, CLI only
 FROM base AS light
 
-COPY --from=build /app/full/llama-cli /app
+COPY --from=build /app/full/llama-cli /app/full/llama-completion /app
 
 WORKDIR /app
 

From a8c7f33d792c7ba8ecb889752f80945373e2ddfc Mon Sep 17 00:00:00 2001
From: jiahao su <damow890@gmail.com>
Date: Sat, 13 Dec 2025 03:43:00 +0800
Subject: [PATCH 073/135] ci : change the cann version and the container pull
 method (#17953)

fix error format

Update build.yml

Remove unnecessary zip files

fix

update
---
 .devops/cann.Dockerfile       |  2 +-
 .github/workflows/build.yml   | 49 +++++++++++++++++-----
 .github/workflows/release.yml | 79 +++++++++++++++++++++++++++++++++++
 3 files changed, 119 insertions(+), 11 deletions(-)

diff --git a/.devops/cann.Dockerfile b/.devops/cann.Dockerfile
index 9d27e00fb1..83182c9700 100644
--- a/.devops/cann.Dockerfile
+++ b/.devops/cann.Dockerfile
@@ -4,7 +4,7 @@
 
 # Define the CANN base image for easier version updates later
 ARG CHIP_TYPE=910b
-ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.3.rc1.alpha001-${CHIP_TYPE}-openeuler22.03-py3.11
+ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.3.rc2-${CHIP_TYPE}-openeuler24.03-py3.11
 
 # ==============================================================================
 # BUILD STAGE
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 383427f36f..9fe1401df4 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -1400,25 +1400,54 @@ jobs:
         chip_type: ['910b', '310p']
         build: ['Release']
     runs-on: ${{ matrix.arch == 'aarch64' && 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
-    container: ascendai/cann:${{ matrix.chip_type == '910b' &&  '8.3.rc1.alpha001-910b-openeuler22.03-py3.11' || '8.2.rc1-310p-openeuler22.03-py3.11' }}
     steps:
       - name: Checkout
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
 
-      - name: Dependencies
+      - name: Free up disk space
+        uses: ggml-org/free-disk-space@v1.3.1
+        with:
+          tool-cache: true
+
+      - name: Set container image
+        id: cann-image
         run: |
-          yum update -y
-          yum install -y git gcc gcc-c++ make cmake libcurl-devel
+          image="ascendai/cann:${{ matrix.chip_type == '910b' &&  '8.3.rc2-910b-openeuler24.03-py3.11' || '8.3.rc2-310p-openeuler24.03-py3.11' }}"
+          echo "image=${image}" >> "${GITHUB_OUTPUT}"
+
+      - name: Pull container image
+        run: docker pull "${{ steps.cann-image.outputs.image }}"
 
       - name: Build
+        env:
+          BUILD_TYPE: ${{ matrix.build }}
+          SOC_TYPE: ascend${{ matrix.chip_type }}
         run: |
-          export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH}
+          HOST_UID=$(id -u)
+          HOST_GID=$(id -g)
 
-          cmake -S . -B build \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build }} \
-              -DGGML_CANN=on \
-              -DSOC_TYPE=ascend${{ matrix.chip_type }}
-          cmake --build build -j $(nproc)
+          docker run --rm \
+            -v "${PWD}:/workspace" \
+            -w /workspace \
+            -e SOC_TYPE=${SOC_TYPE} \
+            -e BUILD_TYPE=${BUILD_TYPE} \
+            "${{ steps.cann-image.outputs.image }}" \
+            bash -lc '
+              set -e
+              yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake libcurl-devel
+              yum clean all && rm -rf /var/cache/yum
+              git config --global --add safe.directory "/workspace"
+              export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH}
+              cmake -S . -B build \
+                  -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
+                  -DGGML_CANN=on \
+                  -DSOC_TYPE=${SOC_TYPE}
+              cmake --build build -j $(nproc)
+
+              chown -R '"${HOST_UID}"':'"${HOST_GID}"' /workspace/build
+            '
 
 # TODO: simplify the following workflows using a matrix
 # TODO: run lighter CI on PRs and the full CI only on master (if needed)
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
index 77aec20c11..446cae9f84 100644
--- a/.github/workflows/release.yml
+++ b/.github/workflows/release.yml
@@ -731,6 +731,78 @@ jobs:
           path: llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz
           name: llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz
 
+
+  openEuler-cann:
+    strategy:
+      matrix:
+        arch: [x86, aarch64]
+        chip_type: ['910b', '310p']
+        build: ['Release']
+    runs-on: ${{ matrix.arch == 'aarch64' && 'ubuntu-24.04-arm' || 'ubuntu-24.04' }}
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+
+      - name: Free up disk space
+        uses: ggml-org/free-disk-space@v1.3.1
+        with:
+          tool-cache: true
+
+      - name: Set container image
+        id: cann-image
+        run: |
+          image="ascendai/cann:${{ matrix.chip_type == '910b' &&  '8.3.rc2-910b-openeuler24.03-py3.11' || '8.3.rc2-310p-openeuler24.03-py3.11' }}"
+          echo "image=${image}" >> "${GITHUB_OUTPUT}"
+
+      - name: Pull container image
+        run: docker pull "${{ steps.cann-image.outputs.image }}"
+
+      - name: Build
+        env:
+          BUILD_TYPE: ${{ matrix.build }}
+          SOC_TYPE: ascend${{ matrix.chip_type }}
+        run: |
+          HOST_UID=$(id -u)
+          HOST_GID=$(id -g)
+
+          docker run --rm \
+            -v "${PWD}:/workspace" \
+            -w /workspace \
+            -e SOC_TYPE=${SOC_TYPE} \
+            -e BUILD_TYPE=${BUILD_TYPE} \
+            "${{ steps.cann-image.outputs.image }}" \
+            bash -lc '
+              set -e
+              yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake libcurl-devel
+              yum clean all && rm -rf /var/cache/yum
+              git config --global --add safe.directory "/workspace"
+              export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH}
+              cmake -S . -B build \
+                  -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \
+                  -DGGML_CANN=on \
+                  -DSOC_TYPE=${SOC_TYPE}
+              cmake --build build -j $(nproc)
+
+              chown -R '"${HOST_UID}"':'"${HOST_GID}"' /workspace/build
+            '
+
+      - name: Determine tag name
+        id: tag
+        uses: ./.github/actions/get-tag-name
+
+      - name: Pack artifacts
+        run: |
+          cp LICENSE ./build/bin/
+          tar -czvf llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin .
+
+      - name: Upload artifacts (tar)
+        uses: actions/upload-artifact@v4
+        with:
+          path: llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz
+          name: llama-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz
+
   release:
     if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
 
@@ -752,6 +824,7 @@ jobs:
       - macOS-arm64
       - macOS-x64
       - ios-xcode-build
+      - openEuler-cann
 
     steps:
       - name: Clone
@@ -844,6 +917,12 @@ jobs:
             - [Windows x64 (SYCL)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip)
             - [Windows x64 (HIP)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-hip-radeon-x64.zip)
 
+            **openEuler:**
+            - [openEuler x86 (310p)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-310p-openEuler-x86.tar.gz)
+            - [openEuler x86 (910b)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-910b-openEuler-x86.tar.gz)
+            - [openEuler aarch64 (310p)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-310p-openEuler-aarch64.tar.gz)
+            - [openEuler aarch64 (910b)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-910b-openEuler-aarch64.tar.gz)
+
       - name: Upload release
         id: upload_release
         uses: actions/github-script@v3

From e39a2ce66d0a61915f22097e5453e291618b3518 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Fri, 12 Dec 2025 21:14:48 +0100
Subject: [PATCH 074/135] clip: move model cgraphs into their own files
 (#17965)

* clip: move model cgraphs into their own files

* more explicit enums

* fix linux build

* fix naming

* missing headers

* nits: add comments for contributors
---
 tools/mtmd/CMakeLists.txt         |   18 +-
 tools/mtmd/clip-graph.h           |  115 ++
 tools/mtmd/clip-impl.h            |    6 +
 tools/mtmd/clip-model.h           |  279 +++
 tools/mtmd/clip.cpp               | 2725 ++++++-----------------------
 tools/mtmd/models/cogvlm.cpp      |   98 ++
 tools/mtmd/models/internvl.cpp    |   69 +
 tools/mtmd/models/kimivl.cpp      |   63 +
 tools/mtmd/models/llama4.cpp      |   96 +
 tools/mtmd/models/llava.cpp       |  374 ++++
 tools/mtmd/models/minicpmv.cpp    |  114 ++
 tools/mtmd/models/models.h        |   58 +
 tools/mtmd/models/pixtral.cpp     |   86 +
 tools/mtmd/models/qwen2vl.cpp     |  183 ++
 tools/mtmd/models/qwen3vl.cpp     |  191 ++
 tools/mtmd/models/siglip.cpp      |   81 +
 tools/mtmd/models/whisper-enc.cpp |  107 ++
 tools/mtmd/mtmd.h                 |    5 +
 18 files changed, 2433 insertions(+), 2235 deletions(-)
 create mode 100644 tools/mtmd/clip-graph.h
 create mode 100644 tools/mtmd/clip-model.h
 create mode 100644 tools/mtmd/models/cogvlm.cpp
 create mode 100644 tools/mtmd/models/internvl.cpp
 create mode 100644 tools/mtmd/models/kimivl.cpp
 create mode 100644 tools/mtmd/models/llama4.cpp
 create mode 100644 tools/mtmd/models/llava.cpp
 create mode 100644 tools/mtmd/models/minicpmv.cpp
 create mode 100644 tools/mtmd/models/models.h
 create mode 100644 tools/mtmd/models/pixtral.cpp
 create mode 100644 tools/mtmd/models/qwen2vl.cpp
 create mode 100644 tools/mtmd/models/qwen3vl.cpp
 create mode 100644 tools/mtmd/models/siglip.cpp
 create mode 100644 tools/mtmd/models/whisper-enc.cpp

diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt
index a104ea1c0e..3ee42036fd 100644
--- a/tools/mtmd/CMakeLists.txt
+++ b/tools/mtmd/CMakeLists.txt
@@ -6,11 +6,25 @@ add_library(mtmd
             mtmd.cpp
             mtmd-audio.cpp
             mtmd.h
+            mtmd-helper.cpp
+            mtmd-helper.h
             clip.cpp
             clip.h
             clip-impl.h
-            mtmd-helper.cpp
-            mtmd-helper.h
+            clip-model.h
+            clip-graph.h
+            models/models.h
+            models/cogvlm.cpp
+            models/internvl.cpp
+            models/kimivl.cpp
+            models/llama4.cpp
+            models/llava.cpp
+            models/minicpmv.cpp
+            models/pixtral.cpp
+            models/qwen2vl.cpp
+            models/qwen3vl.cpp
+            models/siglip.cpp
+            models/whisper-enc.cpp
             )
 
 set_target_properties(mtmd PROPERTIES
diff --git a/tools/mtmd/clip-graph.h b/tools/mtmd/clip-graph.h
new file mode 100644
index 0000000000..6d303b4e48
--- /dev/null
+++ b/tools/mtmd/clip-graph.h
@@ -0,0 +1,115 @@
+#pragma once
+
+#include "ggml.h"
+#include "ggml-cpp.h"
+#include "clip.h"
+#include "clip-impl.h"
+#include "clip-model.h"
+
+#include <vector>
+#include <functional>
+
+struct clip_graph {
+    const clip_model & model;
+    const clip_hparams & hparams;
+    projector_type proj_type;
+
+    // we only support single image per batch
+    const clip_image_f32 & img;
+
+    const int patch_size;
+    const int n_patches_x;
+    const int n_patches_y;
+    const int n_patches;
+    const int n_embd;
+    const int n_head;
+    const int d_head;
+    const int n_layer;
+    const int n_mmproj_embd;
+    const float eps;
+    const float kq_scale;
+    const clip_flash_attn_type flash_attn_type;
+
+    // for debugging
+    const bool debug_graph;
+    std::vector<ggml_tensor *> & debug_print_tensors;
+
+    ggml_context_ptr ctx0_ptr;
+    ggml_context * ctx0;
+    ggml_cgraph * gf;
+
+    clip_graph(clip_ctx * ctx, const clip_image_f32 & img);
+
+    virtual ~clip_graph() = default;
+    virtual ggml_cgraph * build() = 0;
+
+    //
+    // utility functions
+    //
+    void cb(ggml_tensor * cur0, const char * name, int il) const;
+
+    // siglip2 naflex
+    ggml_tensor * resize_position_embeddings();
+
+    // build vision transformer (ViT) cgraph
+    // this function should cover most of the models
+    // if your model has specific features, you should probably duplicate this function
+    ggml_tensor * build_vit(
+                ggml_tensor * inp,
+                int64_t n_pos,
+                norm_type norm_t,
+                ffn_op_type ffn_t,
+                ggml_tensor * learned_pos_embd,
+                std::function<ggml_tensor *(ggml_tensor *, const clip_layer &)> add_pos);
+
+    // build the input after conv2d (inp_raw --> patches)
+    // returns tensor with shape [n_embd, n_patches]
+    ggml_tensor * build_inp();
+
+    ggml_tensor * build_inp_raw(int channels = 3);
+
+    ggml_tensor * build_norm(
+            ggml_tensor * cur,
+            ggml_tensor * mw,
+            ggml_tensor * mb,
+            norm_type type,
+            float norm_eps,
+            int il) const;
+
+    ggml_tensor * build_ffn(
+            ggml_tensor * cur,
+            ggml_tensor * up,
+            ggml_tensor * up_b,
+            ggml_tensor * gate,
+            ggml_tensor * gate_b,
+            ggml_tensor * down,
+            ggml_tensor * down_b,
+            ffn_op_type type_op,
+            int il) const;
+
+    ggml_tensor * build_attn(
+            ggml_tensor * wo,
+            ggml_tensor * wo_b,
+            ggml_tensor * q_cur,
+            ggml_tensor * k_cur,
+            ggml_tensor * v_cur,
+            ggml_tensor * kq_mask,
+            float kq_scale,
+            int il) const;
+
+    // implementation of the 2D RoPE without adding a new op in ggml
+    // this is not efficient (use double the memory), but works on all backends
+    // TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065
+    ggml_tensor * build_rope_2d(
+        ggml_context * ctx0,
+        ggml_tensor * cur,
+        ggml_tensor * pos_a, // first half
+        ggml_tensor * pos_b, // second half
+        const float freq_base,
+        const bool interleave_freq
+    );
+
+    // aka pixel_shuffle / pixel_unshuffle / patch_merger (Kimi-VL)
+    // support dynamic resolution
+    ggml_tensor * build_patch_merge_permute(ggml_tensor * cur, int scale_factor);
+};
diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h
index e5563eabfc..1726823ec6 100644
--- a/tools/mtmd/clip-impl.h
+++ b/tools/mtmd/clip-impl.h
@@ -1,3 +1,5 @@
+#pragma once
+
 #include "ggml.h"
 #include "gguf.h"
 #include "clip.h"
@@ -134,6 +136,10 @@
 // align x to upper multiple of n
 #define CLIP_ALIGN(x, n) ((((x) + (n) - 1) / (n)) * (n))
 
+// forward declaration
+// TODO: improve this later
+struct clip_ctx;
+
 enum projector_type {
     PROJECTOR_TYPE_MLP,
     PROJECTOR_TYPE_MLP_NORM,
diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h
new file mode 100644
index 0000000000..51bcce1ebb
--- /dev/null
+++ b/tools/mtmd/clip-model.h
@@ -0,0 +1,279 @@
+#pragma once
+
+#include "ggml.h"
+#include "clip.h"
+#include "clip-impl.h"
+
+#include <vector>
+#include <unordered_set>
+#include <cstdint>
+#include <cmath>
+
+enum ffn_op_type {
+    FFN_GELU,
+    FFN_GELU_ERF,
+    FFN_SILU,
+    FFN_GELU_QUICK,
+};
+
+enum norm_type {
+    NORM_TYPE_NORMAL,
+    NORM_TYPE_RMS,
+};
+
+enum patch_merge_type {
+    PATCH_MERGE_FLAT,
+    PATCH_MERGE_SPATIAL_UNPAD,
+};
+
+struct clip_hparams {
+    int32_t image_size = 0;
+    int32_t patch_size = 0;
+    int32_t n_embd = 0;
+    int32_t n_ff = 0;
+    int32_t projection_dim = 0;
+    int32_t n_head = 0;
+    int32_t n_layer = 0;
+    // idefics3
+    int32_t image_longest_edge = 0;
+    int32_t image_min_pixels = -1;
+    int32_t image_max_pixels = -1;
+    int32_t n_merge = 0; // number of patch merges **per-side**
+
+    float image_mean[3];
+    float image_std[3];
+
+    // for models using dynamic image size, we need to have a smaller image size to warmup
+    // otherwise, user will get OOM everytime they load the model
+    int32_t warmup_image_size = 0;
+    int32_t warmup_audio_size = 3000;
+
+    ffn_op_type ffn_op = FFN_GELU;
+
+    patch_merge_type mm_patch_merge_type = PATCH_MERGE_FLAT;
+
+    float eps = 1e-6;
+    float rope_theta = 0.0;
+
+    std::vector<clip_image_size> image_res_candidates; // for llava-uhd style models
+    int32_t image_crop_resolution;
+    std::unordered_set<int32_t> vision_feature_layer;
+    int32_t attn_window_size = 0;
+    int32_t n_wa_pattern = 0;
+
+    // audio
+    int32_t n_mel_bins = 0; // whisper preprocessor
+    int32_t proj_stack_factor = 0; // ultravox
+
+    // legacy
+    bool has_llava_projector = false;
+    int minicpmv_version = 0;
+    int32_t minicpmv_query_num = 0;         // MiniCPM-V query number
+
+    // custom value provided by user, can be undefined if not set
+    int32_t custom_image_min_tokens = -1;
+    int32_t custom_image_max_tokens = -1;
+
+    void set_limit_image_tokens(int n_tokens_min, int n_tokens_max) {
+        const int cur_merge = n_merge == 0 ? 1 : n_merge;
+        const int patch_area = patch_size * patch_size * cur_merge * cur_merge;
+        image_min_pixels = (custom_image_min_tokens > 0 ? custom_image_min_tokens : n_tokens_min) * patch_area;
+        image_max_pixels = (custom_image_max_tokens > 0 ? custom_image_max_tokens : n_tokens_max) * patch_area;
+        warmup_image_size = static_cast<int>(std::sqrt(image_max_pixels));
+    }
+
+    void set_warmup_n_tokens(int n_tokens) {
+        int n_tok_per_side = static_cast<int>(std::sqrt(n_tokens));
+        GGML_ASSERT(n_tok_per_side * n_tok_per_side == n_tokens && "n_tokens must be n*n");
+        const int cur_merge = n_merge == 0 ? 1 : n_merge;
+        warmup_image_size = n_tok_per_side * patch_size * cur_merge;
+        // TODO: support warmup size for custom token numbers
+    }
+};
+
+struct clip_layer {
+    // attention
+    ggml_tensor * k_w = nullptr;
+    ggml_tensor * k_b = nullptr;
+    ggml_tensor * q_w = nullptr;
+    ggml_tensor * q_b = nullptr;
+    ggml_tensor * v_w = nullptr;
+    ggml_tensor * v_b = nullptr;
+    ggml_tensor * qkv_w = nullptr;
+    ggml_tensor * qkv_b = nullptr;
+
+    ggml_tensor * o_w = nullptr;
+    ggml_tensor * o_b = nullptr;
+
+    ggml_tensor * k_norm = nullptr;
+    ggml_tensor * q_norm = nullptr;
+
+    // layernorm 1
+    ggml_tensor * ln_1_w = nullptr;
+    ggml_tensor * ln_1_b = nullptr;
+
+    ggml_tensor * ff_up_w = nullptr;
+    ggml_tensor * ff_up_b = nullptr;
+    ggml_tensor * ff_gate_w = nullptr;
+    ggml_tensor * ff_gate_b = nullptr;
+    ggml_tensor * ff_down_w = nullptr;
+    ggml_tensor * ff_down_b = nullptr;
+
+    // layernorm 2
+    ggml_tensor * ln_2_w = nullptr;
+    ggml_tensor * ln_2_b = nullptr;
+
+    // layer scale (no bias)
+    ggml_tensor * ls_1_w = nullptr;
+    ggml_tensor * ls_2_w = nullptr;
+
+    // qwen3vl deepstack merger
+    ggml_tensor * deepstack_norm_w = nullptr;
+    ggml_tensor * deepstack_norm_b = nullptr;
+    ggml_tensor * deepstack_fc1_w = nullptr;
+    ggml_tensor * deepstack_fc1_b = nullptr;
+    ggml_tensor * deepstack_fc2_w = nullptr;
+    ggml_tensor * deepstack_fc2_b = nullptr;
+
+    bool has_deepstack() const {
+        return deepstack_fc1_w != nullptr;
+    }
+};
+
+struct clip_model {
+    clip_modality modality = CLIP_MODALITY_VISION;
+    projector_type proj_type = PROJECTOR_TYPE_MLP;
+    clip_hparams hparams;
+
+    // embeddings
+    ggml_tensor * class_embedding = nullptr;
+    ggml_tensor * patch_embeddings_0 = nullptr;
+    ggml_tensor * patch_embeddings_1 = nullptr;  // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL)
+    ggml_tensor * patch_bias = nullptr;
+    ggml_tensor * position_embeddings = nullptr;
+
+    ggml_tensor * pre_ln_w = nullptr;
+    ggml_tensor * pre_ln_b = nullptr;
+
+    std::vector<clip_layer> layers;
+
+    int32_t n_deepstack_layers = 0; // used by Qwen3-VL, calculated from clip_layer
+
+    ggml_tensor * post_ln_w;
+    ggml_tensor * post_ln_b;
+
+    ggml_tensor * projection; // TODO: rename it to fc (fully connected layer)
+    ggml_tensor * mm_fc_w;
+    ggml_tensor * mm_fc_b;
+
+    // LLaVA projection
+    ggml_tensor * mm_input_norm_w = nullptr;
+    ggml_tensor * mm_input_norm_b = nullptr;
+    ggml_tensor * mm_0_w = nullptr;
+    ggml_tensor * mm_0_b = nullptr;
+    ggml_tensor * mm_2_w = nullptr;
+    ggml_tensor * mm_2_b = nullptr;
+
+    ggml_tensor * image_newline = nullptr;
+
+    // Yi type models with mlp+normalization projection
+    ggml_tensor * mm_1_w = nullptr; // Yi type models have 0, 1, 3, 4
+    ggml_tensor * mm_1_b = nullptr;
+    ggml_tensor * mm_3_w = nullptr;
+    ggml_tensor * mm_3_b = nullptr;
+    ggml_tensor * mm_4_w = nullptr;
+    ggml_tensor * mm_4_b = nullptr;
+
+    // GLMV-Edge projection
+    ggml_tensor * mm_model_adapter_conv_w = nullptr;
+    ggml_tensor * mm_model_adapter_conv_b = nullptr;
+
+    // MobileVLM projection
+    ggml_tensor * mm_model_mlp_1_w = nullptr;
+    ggml_tensor * mm_model_mlp_1_b = nullptr;
+    ggml_tensor * mm_model_mlp_3_w = nullptr;
+    ggml_tensor * mm_model_mlp_3_b = nullptr;
+    ggml_tensor * mm_model_block_1_block_0_0_w = nullptr;
+    ggml_tensor * mm_model_block_1_block_0_1_w = nullptr;
+    ggml_tensor * mm_model_block_1_block_0_1_b = nullptr;
+    ggml_tensor * mm_model_block_1_block_1_fc1_w = nullptr;
+    ggml_tensor * mm_model_block_1_block_1_fc1_b = nullptr;
+    ggml_tensor * mm_model_block_1_block_1_fc2_w = nullptr;
+    ggml_tensor * mm_model_block_1_block_1_fc2_b = nullptr;
+    ggml_tensor * mm_model_block_1_block_2_0_w = nullptr;
+    ggml_tensor * mm_model_block_1_block_2_1_w = nullptr;
+    ggml_tensor * mm_model_block_1_block_2_1_b = nullptr;
+    ggml_tensor * mm_model_block_2_block_0_0_w = nullptr;
+    ggml_tensor * mm_model_block_2_block_0_1_w = nullptr;
+    ggml_tensor * mm_model_block_2_block_0_1_b = nullptr;
+    ggml_tensor * mm_model_block_2_block_1_fc1_w = nullptr;
+    ggml_tensor * mm_model_block_2_block_1_fc1_b = nullptr;
+    ggml_tensor * mm_model_block_2_block_1_fc2_w = nullptr;
+    ggml_tensor * mm_model_block_2_block_1_fc2_b = nullptr;
+    ggml_tensor * mm_model_block_2_block_2_0_w = nullptr;
+    ggml_tensor * mm_model_block_2_block_2_1_w = nullptr;
+    ggml_tensor * mm_model_block_2_block_2_1_b = nullptr;
+
+    // MobileVLM_V2 projection
+    ggml_tensor * mm_model_mlp_0_w = nullptr;
+    ggml_tensor * mm_model_mlp_0_b = nullptr;
+    ggml_tensor * mm_model_mlp_2_w = nullptr;
+    ggml_tensor * mm_model_mlp_2_b = nullptr;
+    ggml_tensor * mm_model_peg_0_w = nullptr;
+    ggml_tensor * mm_model_peg_0_b = nullptr;
+
+    // MINICPMV projection
+    ggml_tensor * mm_model_pos_embed_k = nullptr;
+    ggml_tensor * mm_model_query = nullptr;
+    ggml_tensor * mm_model_proj = nullptr;
+    ggml_tensor * mm_model_kv_proj = nullptr;
+    ggml_tensor * mm_model_attn_q_w = nullptr;
+    ggml_tensor * mm_model_attn_q_b = nullptr;
+    ggml_tensor * mm_model_attn_k_w = nullptr;
+    ggml_tensor * mm_model_attn_k_b = nullptr;
+    ggml_tensor * mm_model_attn_v_w = nullptr;
+    ggml_tensor * mm_model_attn_v_b = nullptr;
+    ggml_tensor * mm_model_attn_o_w = nullptr;
+    ggml_tensor * mm_model_attn_o_b = nullptr;
+    ggml_tensor * mm_model_ln_q_w = nullptr;
+    ggml_tensor * mm_model_ln_q_b = nullptr;
+    ggml_tensor * mm_model_ln_kv_w = nullptr;
+    ggml_tensor * mm_model_ln_kv_b = nullptr;
+    ggml_tensor * mm_model_ln_post_w = nullptr;
+    ggml_tensor * mm_model_ln_post_b = nullptr;
+
+    // gemma3
+    ggml_tensor * mm_input_proj_w = nullptr;
+    ggml_tensor * mm_soft_emb_norm_w = nullptr;
+
+    // pixtral
+    ggml_tensor * token_embd_img_break = nullptr;
+    ggml_tensor * mm_patch_merger_w = nullptr;
+
+    // ultravox / whisper encoder
+    ggml_tensor * conv1d_1_w = nullptr;
+    ggml_tensor * conv1d_1_b = nullptr;
+    ggml_tensor * conv1d_2_w = nullptr;
+    ggml_tensor * conv1d_2_b = nullptr;
+    ggml_tensor * mm_norm_pre_w = nullptr;
+    ggml_tensor * mm_norm_mid_w = nullptr;
+
+    // cogvlm
+    ggml_tensor * mm_post_fc_norm_w = nullptr;
+    ggml_tensor * mm_post_fc_norm_b = nullptr;
+    ggml_tensor * mm_h_to_4h_w = nullptr;
+    ggml_tensor * mm_gate_w = nullptr;
+    ggml_tensor * mm_4h_to_h_w = nullptr;
+    ggml_tensor * mm_boi = nullptr;
+    ggml_tensor * mm_eoi = nullptr;
+
+    bool audio_has_avgpool() const {
+        return proj_type == PROJECTOR_TYPE_QWEN2A
+            || proj_type == PROJECTOR_TYPE_VOXTRAL;
+    }
+
+    bool audio_has_stack_frames() const {
+        return proj_type == PROJECTOR_TYPE_ULTRAVOX
+            || proj_type == PROJECTOR_TYPE_VOXTRAL;
+    }
+};
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index 7360e8e09d..bb922e30b4 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -1,9 +1,9 @@
-// NOTE: This is modified from clip.cpp only for LLaVA,
-// so there might be still unnecessary artifacts hanging around
-// I'll gradually clean and extend it
-// Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch
 #include "clip.h"
 #include "clip-impl.h"
+#include "clip-model.h"
+#include "clip-graph.h"
+#include "models/models.h"
+
 #include "ggml.h"
 #include "ggml-cpp.h"
 #include "ggml-alloc.h"
@@ -26,18 +26,6 @@
 
 struct clip_logger_state g_logger_state = {clip_log_callback_default, NULL};
 
-enum ffn_op_type {
-    FFN_GELU,
-    FFN_GELU_ERF,
-    FFN_SILU,
-    FFN_GELU_QUICK,
-};
-
-enum norm_type {
-    NORM_TYPE_NORMAL,
-    NORM_TYPE_RMS,
-};
-
 //#define CLIP_DEBUG_FUNCTIONS
 
 #ifdef CLIP_DEBUG_FUNCTIONS
@@ -149,267 +137,6 @@ static void clip_image_convert_f32_to_u8(const clip_image_f32& src, clip_image_u
 #endif
 
 
-//
-// clip layers
-//
-
-enum patch_merge_type {
-    PATCH_MERGE_FLAT,
-    PATCH_MERGE_SPATIAL_UNPAD,
-};
-
-struct clip_hparams {
-    int32_t image_size = 0;
-    int32_t patch_size = 0;
-    int32_t n_embd = 0;
-    int32_t n_ff = 0;
-    int32_t projection_dim = 0;
-    int32_t n_head = 0;
-    int32_t n_layer = 0;
-    // idefics3
-    int32_t image_longest_edge = 0;
-    int32_t image_min_pixels = -1;
-    int32_t image_max_pixels = -1;
-    int32_t n_merge = 0; // number of patch merges **per-side**
-
-    float image_mean[3];
-    float image_std[3];
-
-    // for models using dynamic image size, we need to have a smaller image size to warmup
-    // otherwise, user will get OOM everytime they load the model
-    int32_t warmup_image_size = 0;
-    int32_t warmup_audio_size = 3000;
-
-    ffn_op_type ffn_op = FFN_GELU;
-
-    patch_merge_type mm_patch_merge_type = PATCH_MERGE_FLAT;
-
-    float eps = 1e-6;
-    float rope_theta = 0.0;
-
-    std::vector<clip_image_size> image_res_candidates; // for llava-uhd style models
-    int32_t image_crop_resolution;
-    std::unordered_set<int32_t> vision_feature_layer;
-    int32_t attn_window_size = 0;
-    int32_t n_wa_pattern = 0;
-
-    // audio
-    int32_t n_mel_bins = 0; // whisper preprocessor
-    int32_t proj_stack_factor = 0; // ultravox
-
-    // legacy
-    bool has_llava_projector = false;
-    int minicpmv_version = 0;
-    int32_t minicpmv_query_num = 0;         // MiniCPM-V query number
-
-    // custom value provided by user, can be undefined if not set
-    int32_t custom_image_min_tokens = -1;
-    int32_t custom_image_max_tokens = -1;
-
-    void set_limit_image_tokens(int n_tokens_min, int n_tokens_max) {
-        const int cur_merge = n_merge == 0 ? 1 : n_merge;
-        const int patch_area = patch_size * patch_size * cur_merge * cur_merge;
-        image_min_pixels = (custom_image_min_tokens > 0 ? custom_image_min_tokens : n_tokens_min) * patch_area;
-        image_max_pixels = (custom_image_max_tokens > 0 ? custom_image_max_tokens : n_tokens_max) * patch_area;
-        warmup_image_size = static_cast<int>(std::sqrt(image_max_pixels));
-    }
-
-    void set_warmup_n_tokens(int n_tokens) {
-        int n_tok_per_side = static_cast<int>(std::sqrt(n_tokens));
-        GGML_ASSERT(n_tok_per_side * n_tok_per_side == n_tokens && "n_tokens must be n*n");
-        const int cur_merge = n_merge == 0 ? 1 : n_merge;
-        warmup_image_size = n_tok_per_side * patch_size * cur_merge;
-        // TODO: support warmup size for custom token numbers
-    }
-};
-
-struct clip_layer {
-    // attention
-    ggml_tensor * k_w = nullptr;
-    ggml_tensor * k_b = nullptr;
-    ggml_tensor * q_w = nullptr;
-    ggml_tensor * q_b = nullptr;
-    ggml_tensor * v_w = nullptr;
-    ggml_tensor * v_b = nullptr;
-    ggml_tensor * qkv_w = nullptr;
-    ggml_tensor * qkv_b = nullptr;
-
-    ggml_tensor * o_w = nullptr;
-    ggml_tensor * o_b = nullptr;
-
-    ggml_tensor * k_norm = nullptr;
-    ggml_tensor * q_norm = nullptr;
-
-    // layernorm 1
-    ggml_tensor * ln_1_w = nullptr;
-    ggml_tensor * ln_1_b = nullptr;
-
-    ggml_tensor * ff_up_w = nullptr;
-    ggml_tensor * ff_up_b = nullptr;
-    ggml_tensor * ff_gate_w = nullptr;
-    ggml_tensor * ff_gate_b = nullptr;
-    ggml_tensor * ff_down_w = nullptr;
-    ggml_tensor * ff_down_b = nullptr;
-
-    // layernorm 2
-    ggml_tensor * ln_2_w = nullptr;
-    ggml_tensor * ln_2_b = nullptr;
-
-    // layer scale (no bias)
-    ggml_tensor * ls_1_w = nullptr;
-    ggml_tensor * ls_2_w = nullptr;
-
-    // qwen3vl deepstack merger
-    ggml_tensor * deepstack_norm_w = nullptr;
-    ggml_tensor * deepstack_norm_b = nullptr;
-    ggml_tensor * deepstack_fc1_w = nullptr;
-    ggml_tensor * deepstack_fc1_b = nullptr;
-    ggml_tensor * deepstack_fc2_w = nullptr;
-    ggml_tensor * deepstack_fc2_b = nullptr;
-
-    bool has_deepstack() const {
-        return deepstack_fc1_w != nullptr;
-    }
-};
-
-struct clip_model {
-    clip_modality modality = CLIP_MODALITY_VISION;
-    projector_type proj_type = PROJECTOR_TYPE_MLP;
-    clip_hparams hparams;
-
-    // embeddings
-    ggml_tensor * class_embedding = nullptr;
-    ggml_tensor * patch_embeddings_0 = nullptr;
-    ggml_tensor * patch_embeddings_1 = nullptr;  // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL)
-    ggml_tensor * patch_bias = nullptr;
-    ggml_tensor * position_embeddings = nullptr;
-
-    ggml_tensor * pre_ln_w = nullptr;
-    ggml_tensor * pre_ln_b = nullptr;
-
-    std::vector<clip_layer> layers;
-
-    int32_t n_deepstack_layers = 0; // used by Qwen3-VL, calculated from clip_layer
-
-    ggml_tensor * post_ln_w;
-    ggml_tensor * post_ln_b;
-
-    ggml_tensor * projection; // TODO: rename it to fc (fully connected layer)
-    ggml_tensor * mm_fc_w;
-    ggml_tensor * mm_fc_b;
-
-    // LLaVA projection
-    ggml_tensor * mm_input_norm_w = nullptr;
-    ggml_tensor * mm_input_norm_b = nullptr;
-    ggml_tensor * mm_0_w = nullptr;
-    ggml_tensor * mm_0_b = nullptr;
-    ggml_tensor * mm_2_w = nullptr;
-    ggml_tensor * mm_2_b = nullptr;
-
-    ggml_tensor * image_newline = nullptr;
-
-    // Yi type models with mlp+normalization projection
-    ggml_tensor * mm_1_w = nullptr; // Yi type models have 0, 1, 3, 4
-    ggml_tensor * mm_1_b = nullptr;
-    ggml_tensor * mm_3_w = nullptr;
-    ggml_tensor * mm_3_b = nullptr;
-    ggml_tensor * mm_4_w = nullptr;
-    ggml_tensor * mm_4_b = nullptr;
-
-    // GLMV-Edge projection
-    ggml_tensor * mm_model_adapter_conv_w = nullptr;
-    ggml_tensor * mm_model_adapter_conv_b = nullptr;
-
-    // MobileVLM projection
-    ggml_tensor * mm_model_mlp_1_w = nullptr;
-    ggml_tensor * mm_model_mlp_1_b = nullptr;
-    ggml_tensor * mm_model_mlp_3_w = nullptr;
-    ggml_tensor * mm_model_mlp_3_b = nullptr;
-    ggml_tensor * mm_model_block_1_block_0_0_w = nullptr;
-    ggml_tensor * mm_model_block_1_block_0_1_w = nullptr;
-    ggml_tensor * mm_model_block_1_block_0_1_b = nullptr;
-    ggml_tensor * mm_model_block_1_block_1_fc1_w = nullptr;
-    ggml_tensor * mm_model_block_1_block_1_fc1_b = nullptr;
-    ggml_tensor * mm_model_block_1_block_1_fc2_w = nullptr;
-    ggml_tensor * mm_model_block_1_block_1_fc2_b = nullptr;
-    ggml_tensor * mm_model_block_1_block_2_0_w = nullptr;
-    ggml_tensor * mm_model_block_1_block_2_1_w = nullptr;
-    ggml_tensor * mm_model_block_1_block_2_1_b = nullptr;
-    ggml_tensor * mm_model_block_2_block_0_0_w = nullptr;
-    ggml_tensor * mm_model_block_2_block_0_1_w = nullptr;
-    ggml_tensor * mm_model_block_2_block_0_1_b = nullptr;
-    ggml_tensor * mm_model_block_2_block_1_fc1_w = nullptr;
-    ggml_tensor * mm_model_block_2_block_1_fc1_b = nullptr;
-    ggml_tensor * mm_model_block_2_block_1_fc2_w = nullptr;
-    ggml_tensor * mm_model_block_2_block_1_fc2_b = nullptr;
-    ggml_tensor * mm_model_block_2_block_2_0_w = nullptr;
-    ggml_tensor * mm_model_block_2_block_2_1_w = nullptr;
-    ggml_tensor * mm_model_block_2_block_2_1_b = nullptr;
-
-    // MobileVLM_V2 projection
-    ggml_tensor * mm_model_mlp_0_w = nullptr;
-    ggml_tensor * mm_model_mlp_0_b = nullptr;
-    ggml_tensor * mm_model_mlp_2_w = nullptr;
-    ggml_tensor * mm_model_mlp_2_b = nullptr;
-    ggml_tensor * mm_model_peg_0_w = nullptr;
-    ggml_tensor * mm_model_peg_0_b = nullptr;
-
-    // MINICPMV projection
-    ggml_tensor * mm_model_pos_embed_k = nullptr;
-    ggml_tensor * mm_model_query = nullptr;
-    ggml_tensor * mm_model_proj = nullptr;
-    ggml_tensor * mm_model_kv_proj = nullptr;
-    ggml_tensor * mm_model_attn_q_w = nullptr;
-    ggml_tensor * mm_model_attn_q_b = nullptr;
-    ggml_tensor * mm_model_attn_k_w = nullptr;
-    ggml_tensor * mm_model_attn_k_b = nullptr;
-    ggml_tensor * mm_model_attn_v_w = nullptr;
-    ggml_tensor * mm_model_attn_v_b = nullptr;
-    ggml_tensor * mm_model_attn_o_w = nullptr;
-    ggml_tensor * mm_model_attn_o_b = nullptr;
-    ggml_tensor * mm_model_ln_q_w = nullptr;
-    ggml_tensor * mm_model_ln_q_b = nullptr;
-    ggml_tensor * mm_model_ln_kv_w = nullptr;
-    ggml_tensor * mm_model_ln_kv_b = nullptr;
-    ggml_tensor * mm_model_ln_post_w = nullptr;
-    ggml_tensor * mm_model_ln_post_b = nullptr;
-
-    // gemma3
-    ggml_tensor * mm_input_proj_w = nullptr;
-    ggml_tensor * mm_soft_emb_norm_w = nullptr;
-
-    // pixtral
-    ggml_tensor * token_embd_img_break = nullptr;
-    ggml_tensor * mm_patch_merger_w = nullptr;
-
-    // ultravox / whisper encoder
-    ggml_tensor * conv1d_1_w = nullptr;
-    ggml_tensor * conv1d_1_b = nullptr;
-    ggml_tensor * conv1d_2_w = nullptr;
-    ggml_tensor * conv1d_2_b = nullptr;
-    ggml_tensor * mm_norm_pre_w = nullptr;
-    ggml_tensor * mm_norm_mid_w = nullptr;
-
-    // cogvlm
-    ggml_tensor * mm_post_fc_norm_w = nullptr;
-    ggml_tensor * mm_post_fc_norm_b = nullptr;
-    ggml_tensor * mm_h_to_4h_w = nullptr;
-    ggml_tensor * mm_gate_w = nullptr;
-    ggml_tensor * mm_4h_to_h_w = nullptr;
-    ggml_tensor * mm_boi = nullptr;
-    ggml_tensor * mm_eoi = nullptr;
-
-    bool audio_has_avgpool() const {
-        return proj_type == PROJECTOR_TYPE_QWEN2A
-            || proj_type == PROJECTOR_TYPE_VOXTRAL;
-    }
-
-    bool audio_has_stack_frames() const {
-        return proj_type == PROJECTOR_TYPE_ULTRAVOX
-            || proj_type == PROJECTOR_TYPE_VOXTRAL;
-    }
-};
-
 struct clip_ctx {
     clip_model model;
 
@@ -492,2081 +219,613 @@ struct clip_ctx {
     }
 };
 
-struct clip_graph {
-    clip_ctx * ctx;
-    const clip_model & model;
-    const clip_hparams & hparams;
+//
+// clip_graph
+//
 
-    // we only support single image per batch
-    const clip_image_f32 & img;
+clip_graph::clip_graph(clip_ctx * ctx, const clip_image_f32 & img) :
+        model(ctx->model),
+        hparams(model.hparams),
+        proj_type(ctx->proj_type()),
+        img(img),
+        patch_size(hparams.patch_size),
+        n_patches_x(img.nx / patch_size),
+        n_patches_y(img.ny / patch_size),
+        n_patches(n_patches_x * n_patches_y),
+        n_embd(hparams.n_embd),
+        n_head(hparams.n_head),
+        d_head(n_embd / n_head),
+        n_layer(hparams.n_layer),
+        n_mmproj_embd(clip_n_mmproj_embd(ctx)),
+        eps(hparams.eps),
+        kq_scale(1.0f / sqrtf((float)d_head)),
+        flash_attn_type(ctx->flash_attn_type),
+        debug_graph(ctx->debug_graph),
+        debug_print_tensors(ctx->debug_print_tensors) {
+    struct ggml_init_params params = {
+        /*.mem_size   =*/ ctx->buf_compute_meta.size(),
+        /*.mem_buffer =*/ ctx->buf_compute_meta.data(),
+        /*.no_alloc   =*/ true,
+    };
+    ctx0_ptr.reset(ggml_init(params));
+    ctx0 = ctx0_ptr.get();
+    gf = ggml_new_graph_custom(ctx0, ctx->max_nodes, false);
+}
 
-    const int patch_size;
-    const int n_patches_x;
-    const int n_patches_y;
-    const int n_patches;
-    const int n_embd;
-    const int n_head;
-    const int d_head;
-    const int n_layer;
-    const float eps;
-    const float kq_scale;
-
-    ggml_context_ptr ctx0_ptr;
-    ggml_context * ctx0;
-    ggml_cgraph * gf;
-
-    clip_graph(clip_ctx * ctx, const clip_image_f32 & img) :
-            ctx(ctx),
-            model(ctx->model),
-            hparams(model.hparams),
-            img(img),
-            patch_size(hparams.patch_size),
-            n_patches_x(img.nx / patch_size),
-            n_patches_y(img.ny / patch_size),
-            n_patches(n_patches_x * n_patches_y),
-            n_embd(hparams.n_embd),
-            n_head(hparams.n_head),
-            d_head(n_embd / n_head),
-            n_layer(hparams.n_layer),
-            eps(hparams.eps),
-            kq_scale(1.0f / sqrtf((float)d_head)) {
-        struct ggml_init_params params = {
-            /*.mem_size   =*/ ctx->buf_compute_meta.size(),
-            /*.mem_buffer =*/ ctx->buf_compute_meta.data(),
-            /*.no_alloc   =*/ true,
-        };
-        ctx0_ptr.reset(ggml_init(params));
-        ctx0 = ctx0_ptr.get();
-        gf = ggml_new_graph_custom(ctx0, ctx->max_nodes, false);
-    }
-
-    ggml_cgraph * build_siglip() {
-        ggml_tensor * inp = build_inp();
-
-        ggml_tensor * learned_pos_embd = model.position_embeddings;
-        if (ctx->proj_type() == PROJECTOR_TYPE_LFM2) {
-            learned_pos_embd = resize_position_embeddings();
-        }
-
-        ggml_tensor * cur = build_vit(
-                                inp, n_patches,
-                                NORM_TYPE_NORMAL,
-                                hparams.ffn_op,
-                                learned_pos_embd,
-                                nullptr);
-
-        if (ctx->proj_type() == PROJECTOR_TYPE_GEMMA3) {
-            const int batch_size = 1;
-            GGML_ASSERT(n_patches_x == n_patches_y);
-            const int patches_per_image = n_patches_x;
-            const int kernel_size = hparams.n_merge;
-
-            cur = ggml_transpose(ctx0, cur);
-            cur = ggml_cont_4d(ctx0, cur, patches_per_image, patches_per_image, n_embd, batch_size);
-
-            // doing a pool2d to reduce the number of output tokens
-            cur = ggml_pool_2d(ctx0, cur, GGML_OP_POOL_AVG, kernel_size, kernel_size, kernel_size, kernel_size, 0, 0);
-            cur = ggml_reshape_3d(ctx0, cur, cur->ne[0] * cur->ne[0], n_embd, batch_size);
-            cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
-
-            // apply norm before projection
-            cur = ggml_rms_norm(ctx0, cur, eps);
-            cur = ggml_mul(ctx0, cur, model.mm_soft_emb_norm_w);
-
-            // apply projection
-            cur = ggml_mul_mat(ctx0,
-                ggml_cont(ctx0, ggml_transpose(ctx0, model.mm_input_proj_w)),
-                cur);
-
-        } else if (ctx->proj_type() == PROJECTOR_TYPE_IDEFICS3) {
-            // pixel_shuffle
-            // https://github.com/huggingface/transformers/blob/0a950e0bbe1ed58d5401a6b547af19f15f0c195e/src/transformers/models/idefics3/modeling_idefics3.py#L578
-            const int scale_factor = model.hparams.n_merge;
-            cur = build_patch_merge_permute(cur, scale_factor);
-            cur = ggml_mul_mat(ctx0, model.projection, cur);
-
-        } else if (ctx->proj_type() == PROJECTOR_TYPE_LFM2) {
-            // pixel unshuffle block
-            const int scale_factor = model.hparams.n_merge;
-            cur = build_patch_merge_permute(cur, scale_factor);
-
-            // projection
-            cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm
-            cur = ggml_mul(ctx0, cur, model.mm_input_norm_w);
-            cur = ggml_add(ctx0, cur, model.mm_input_norm_b);
-
-            cur = build_ffn(cur,
-                model.mm_1_w, model.mm_1_b,
-                nullptr, nullptr,
-                model.mm_2_w, model.mm_2_b,
-                FFN_GELU,
-                -1);
-
-        } else if (ctx->proj_type() == PROJECTOR_TYPE_JANUS_PRO) {
-            cur = build_ffn(cur,
-                model.mm_0_w, model.mm_0_b,
-                nullptr, nullptr,
-                model.mm_1_w, model.mm_1_b,
-                hparams.ffn_op,
-                -1);
-
-        } else {
-            GGML_ABORT("SigLIP: Unsupported projector type");
-        }
-
-        // build the graph
+void clip_graph::cb(ggml_tensor * cur0, const char * name, int il) const {
+    if (debug_graph) {
+        ggml_tensor * cur = ggml_cpy(ctx0, cur0, ggml_dup_tensor(ctx0, cur0));
+        std::string cur_name = il >= 0 ? std::string(name) + "_" + std::to_string(il) : name;
+        ggml_set_name(cur, cur_name.c_str());
+        ggml_set_output(cur);
         ggml_build_forward_expand(gf, cur);
+        debug_print_tensors.push_back(cur);
+    }
+}
 
-        return gf;
+// siglip2 naflex
+ggml_tensor * clip_graph::resize_position_embeddings() {
+    ggml_tensor * pos_embd = model.position_embeddings;
+    const int height       = img.ny / patch_size;
+    const int width        = img.nx / patch_size;
+    const uint32_t mode    = GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS;
+    const int n_per_side   = (int)std::sqrt(pos_embd->ne[1]);
+
+    GGML_ASSERT(pos_embd);
+
+    if (height == n_per_side && width == n_per_side) {
+        return pos_embd;
     }
 
-    ggml_cgraph * build_pixtral() {
-        const int n_merge = hparams.n_merge;
+    pos_embd = ggml_reshape_3d(ctx0, pos_embd, n_embd, n_per_side, n_per_side);  // -> (n_embd, n_per_side, n_per_side)
+    pos_embd = ggml_permute(ctx0, pos_embd, 2, 0, 1, 3);                         // -> (n_per_side, n_per_side, n_embd)
+    pos_embd = ggml_interpolate(ctx0, pos_embd, width, height, n_embd, 1, mode); // -> (width, height, n_embd)
+    pos_embd = ggml_permute(ctx0, pos_embd, 1, 2, 0, 3);                         // -> (n_embd, width, height)
+    pos_embd = ggml_cont_2d(ctx0, pos_embd, n_embd, width * height);             // -> (n_embd, width * height)
 
-        // 2D input positions
-        ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
-        ggml_set_name(pos_h, "pos_h");
-        ggml_set_input(pos_h);
+    return pos_embd;
+}
 
-        ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
-        ggml_set_name(pos_w, "pos_w");
-        ggml_set_input(pos_w);
-
-        auto add_pos = [&](ggml_tensor * cur, const clip_layer &) {
-            return build_rope_2d(ctx0, cur, pos_h, pos_w, hparams.rope_theta, true);
-        };
-
-        ggml_tensor * inp = build_inp();
-        ggml_tensor * cur = build_vit(
-                                inp, n_patches,
-                                NORM_TYPE_RMS,
-                                hparams.ffn_op,
-                                nullptr, // no learned pos embd
-                                add_pos);
-
-        // mistral small 3.1 patch merger
-        // ref: https://github.com/huggingface/transformers/blob/7a3e208892c06a5e278144eaf38c8599a42f53e7/src/transformers/models/mistral3/modeling_mistral3.py#L67
-        if (model.mm_patch_merger_w) {
-            GGML_ASSERT(hparams.n_merge > 0);
-
-            cur = ggml_mul(ctx0, ggml_rms_norm(ctx0, cur, eps), model.mm_input_norm_w);
-
-            // reshape image tokens to 2D grid
-            cur = ggml_reshape_3d(ctx0, cur, n_embd, n_patches_x, n_patches_y);
-            cur = ggml_permute(ctx0, cur, 2, 0, 1, 3); // [x, y, n_embd]
-            cur = ggml_cont(ctx0, cur);
-
-            // torch.nn.functional.unfold is just an im2col under the hood
-            // we just need a dummy kernel to make it work
-            ggml_tensor * kernel = ggml_view_3d(ctx0, cur, n_merge, n_merge, cur->ne[2], 0, 0, 0);
-            cur = ggml_im2col(ctx0, kernel, cur, n_merge, n_merge, 0, 0, 1, 1, true, inp->type);
-
-            // project to n_embd
-            cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]);
-            cur = ggml_mul_mat(ctx0, model.mm_patch_merger_w, cur);
-        }
-
-        // LlavaMultiModalProjector (always using GELU activation)
-        {
-            cur = build_ffn(cur,
-                model.mm_1_w, model.mm_1_b,
-                nullptr, nullptr,
-                model.mm_2_w, model.mm_2_b,
-                FFN_GELU,
-                -1);
-        }
-
-        // arrangement of the [IMG_BREAK] token
-        if (model.token_embd_img_break) {
-            // not efficient, but works
-            // the trick is to view the embeddings as a 3D tensor with shape [n_embd, n_patches_per_row, n_rows]
-            // and then concatenate the [IMG_BREAK] token to the end of each row, aka n_patches_per_row dimension
-            // after the concatenation, we have a tensor with shape [n_embd, n_patches_per_row + 1, n_rows]
-
-            const int p_y             = n_merge > 0 ? n_patches_y / n_merge : n_patches_y;
-            const int p_x             = n_merge > 0 ? n_patches_x / n_merge : n_patches_x;
-            const int p_total         = p_x * p_y;
-            const int n_embd_text     = cur->ne[0];
-            const int n_tokens_output = p_total + p_y - 1; // one [IMG_BREAK] per row, except the last row
-
-            ggml_tensor * tmp = ggml_reshape_3d(ctx0, cur, n_embd_text, p_x, p_y);
-            ggml_tensor * tok = ggml_new_tensor_3d(ctx0, tmp->type, n_embd_text, 1, p_y);
-            tok = ggml_scale(ctx0, tok, 0.0); // clear the tensor
-            tok = ggml_add(ctx0, tok, model.token_embd_img_break);
-            tmp = ggml_concat(ctx0, tmp, tok, 1);
-            cur = ggml_view_2d(ctx0, tmp,
-                n_embd_text, n_tokens_output,
-                ggml_row_size(tmp->type, n_embd_text), 0);
-        }
-
-        // build the graph
-        ggml_build_forward_expand(gf, cur);
-
-        return gf;
-    }
-
-    // Qwen2VL and Qwen2.5VL use M-RoPE
-    ggml_cgraph * build_qwen2vl() {
-        GGML_ASSERT(model.patch_bias == nullptr);
-        GGML_ASSERT(model.class_embedding == nullptr);
-
-        const int batch_size       = 1;
-        const bool use_window_attn = hparams.n_wa_pattern > 0;
-        const int n_wa_pattern     = hparams.n_wa_pattern;
-        const int n_pos            = n_patches;
-        const int num_position_ids = n_pos * 4; // m-rope requires 4 dim per position
-
-        norm_type norm_t = ctx->proj_type() == PROJECTOR_TYPE_QWEN25VL
-            ? NORM_TYPE_RMS // qwen 2.5 vl
-            : NORM_TYPE_NORMAL; // qwen 2 vl
-
-        int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
-
-        ggml_tensor * inp_raw = build_inp_raw();
-        ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
-
-        GGML_ASSERT(img.nx % (patch_size * 2) == 0);
-        GGML_ASSERT(img.ny % (patch_size * 2) == 0);
-
-        // second conv dimension
-        {
-            auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
-            inp = ggml_add(ctx0, inp, inp_1);
-
-            inp = ggml_permute(ctx0, inp, 1, 2, 0, 3);  // [w, h, c, b] -> [c, w, h, b]
-            inp = ggml_cont_4d(
-                ctx0, inp,
-                n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
-            inp = ggml_reshape_4d(
-                ctx0, inp,
-                n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
-            inp = ggml_permute(ctx0, inp, 0, 2, 1, 3);
-            inp = ggml_cont_3d(
-                ctx0, inp,
-                n_embd, n_patches_x * n_patches_y, batch_size);
-        }
-
-        ggml_tensor * inpL           = inp;
-        ggml_tensor * window_mask    = nullptr;
-        ggml_tensor * window_idx     = nullptr;
-        ggml_tensor * inv_window_idx = nullptr;
-
-        ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids);
-        ggml_set_name(positions, "positions");
-        ggml_set_input(positions);
-
-        // pre-layernorm
-        if (model.pre_ln_w) {
-            inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1);
-        }
-
-        if (use_window_attn) {
-            // handle window attention inputs
-            inv_window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4);
-            ggml_set_name(inv_window_idx, "inv_window_idx");
-            ggml_set_input(inv_window_idx);
-            // mask for window attention
-            window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos);
-            ggml_set_name(window_mask, "window_mask");
-            ggml_set_input(window_mask);
-
-            // if flash attn is used, we need to pad the mask and cast to f16
-            if (ctx->flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) {
-                window_mask = ggml_cast(ctx0, window_mask, GGML_TYPE_F16);
-            }
-
-            // inpL shape: [n_embd, n_patches_x * n_patches_y, batch_size]
-            GGML_ASSERT(batch_size == 1);
-            inpL = ggml_reshape_2d(ctx0, inpL, n_embd * 4, n_patches_x * n_patches_y * batch_size / 4);
-            inpL = ggml_get_rows(ctx0, inpL, inv_window_idx);
-            inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_patches_x * n_patches_y, batch_size);
-        }
-
-        // loop over layers
-        for (int il = 0; il < n_layer; il++) {
-            const auto & layer = model.layers[il];
-            const bool full_attn = use_window_attn ? (il + 1) % n_wa_pattern == 0 : true;
-
-            ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states
-
-            // layernorm1
-            cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il);
-            cb(cur, "ln1", il);
-
-            // self-attention
-            {
-                ggml_tensor * Qcur = ggml_add(ctx0,
-                    ggml_mul_mat(ctx0, layer.q_w, cur), layer.q_b);
-                ggml_tensor * Kcur = ggml_add(ctx0,
-                    ggml_mul_mat(ctx0, layer.k_w, cur), layer.k_b);
-                ggml_tensor * Vcur = ggml_add(ctx0,
-                    ggml_mul_mat(ctx0, layer.v_w, cur), layer.v_b);
-
-                Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_patches);
-                Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_patches);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_patches);
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                // apply M-RoPE
-                Qcur = ggml_rope_multi(
-                    ctx0, Qcur, positions, nullptr,
-                    d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
-                Kcur = ggml_rope_multi(
-                    ctx0, Kcur, positions, nullptr,
-                    d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
-
-                cb(Qcur, "Qcur_rope", il);
-                cb(Kcur, "Kcur_rope", il);
-
-                ggml_tensor * attn_mask = full_attn ? nullptr : window_mask;
-
-                cur = build_attn(layer.o_w, layer.o_b,
-                    Qcur, Kcur, Vcur, attn_mask, kq_scale, il);
-                cb(cur, "attn_out", il);
-            }
-
-            // re-add the layer input, e.g., residual
-            cur = ggml_add(ctx0, cur, inpL);
-
-            inpL = cur; // inpL = residual, cur = hidden_states
-
-            cb(cur, "ffn_inp", il);
-
-            // layernorm2
-            cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il);
-            cb(cur, "ffn_inp_normed", il);
-
-            // ffn
-            cur = build_ffn(cur,
-                layer.ff_up_w, layer.ff_up_b,
-                layer.ff_gate_w, layer.ff_gate_b,
-                layer.ff_down_w, layer.ff_down_b,
-                hparams.ffn_op, il);
-
-            cb(cur, "ffn_out", il);
-
-            // residual 2
-            cur = ggml_add(ctx0, inpL, cur);
-            cb(cur, "layer_out", il);
-
-            inpL = cur;
-        }
-
-        // post-layernorm
-        if (model.post_ln_w) {
-            inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, n_layer);
-        }
-
-        // multimodal projection
-        ggml_tensor * embeddings = inpL;
-        embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size);
-        embeddings = build_ffn(embeddings,
-                            model.mm_0_w, model.mm_0_b,
-                            nullptr, nullptr,
-                            model.mm_1_w, model.mm_1_b,
-                            FFN_GELU,
-                            -1);
-
-        if (use_window_attn) {
-            window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4);
-            ggml_set_name(window_idx, "window_idx");
-            ggml_set_input(window_idx);
-
-            // embeddings shape: [n_embd, n_patches_x * n_patches_y, batch_size]
-            GGML_ASSERT(batch_size == 1);
-            embeddings = ggml_reshape_2d(ctx0, embeddings, hparams.projection_dim, n_patches_x * n_patches_y / 4);
-            embeddings = ggml_get_rows(ctx0, embeddings, window_idx);
-            embeddings = ggml_reshape_3d(ctx0, embeddings, hparams.projection_dim, n_patches_x * n_patches_y / 4, batch_size);
-        }
-
-        // build the graph
-        ggml_build_forward_expand(gf, embeddings);
-
-        return gf;
-    }
-
-    // Qwen3VL
-    ggml_cgraph * build_qwen3vl() {
-        GGML_ASSERT(model.patch_bias != nullptr);
-        GGML_ASSERT(model.position_embeddings != nullptr);
-        GGML_ASSERT(model.class_embedding == nullptr);
-
-        const int batch_size       = 1;
-        const int n_pos            = n_patches;
-        const int num_position_ids = n_pos * 4; // m-rope requires 4 dim per position
-
-        norm_type norm_t = NORM_TYPE_NORMAL;
-
-        int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
-
-        ggml_tensor * inp_raw = build_inp_raw();
-        ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
-
-        GGML_ASSERT(img.nx % (patch_size * 2) == 0);
-        GGML_ASSERT(img.ny % (patch_size * 2) == 0);
-
-        // second conv dimension
-        {
-            auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
-            inp = ggml_add(ctx0, inp, inp_1);
-
-            inp = ggml_permute(ctx0, inp, 1, 2, 0, 3);  // [w, h, c, b] -> [c, w, h, b]
-            inp = ggml_cont_4d(
-                ctx0, inp,
-                n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
-            inp = ggml_reshape_4d(
-                ctx0, inp,
-                n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
-            inp = ggml_permute(ctx0, inp, 0, 2, 1, 3);
-            inp = ggml_cont_3d(
-                ctx0, inp,
-                n_embd, n_patches_x * n_patches_y, batch_size);
-        }
-
-        // add patch bias
-        if (model.patch_bias != nullptr) {
-            inp = ggml_add(ctx0, inp, model.patch_bias);
-            cb(inp, "patch_bias", -1);
-        }
-
-        // calculate absolute position embedding and apply
-        ggml_tensor * learned_pos_embd = resize_position_embeddings();
-        learned_pos_embd = ggml_cont_4d(
-            ctx0, learned_pos_embd,
-            n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
-        learned_pos_embd = ggml_reshape_4d(
-            ctx0, learned_pos_embd,
-            n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
-        learned_pos_embd = ggml_permute(ctx0, learned_pos_embd, 0, 2, 1, 3);
-        learned_pos_embd = ggml_cont_3d(
-            ctx0, learned_pos_embd,
-            n_embd, n_patches_x * n_patches_y, batch_size);
+// build vision transformer (ViT) cgraph
+// this function should cover most of the models
+// if your model has specific features, you should probably duplicate this function
+ggml_tensor * clip_graph::build_vit(
+            ggml_tensor * inp,
+            int64_t n_pos,
+            norm_type norm_t,
+            ffn_op_type ffn_t,
+            ggml_tensor * learned_pos_embd,
+            std::function<ggml_tensor *(ggml_tensor *, const clip_layer &)> add_pos
+        ) {
+    if (learned_pos_embd) {
         inp = ggml_add(ctx0, inp, learned_pos_embd);
-        cb(inp, "inp_pos_emb", -1);
+        cb(inp, "pos_embed", -1);
+    }
 
-        ggml_tensor * inpL = inp;
+    ggml_tensor * inpL = inp;
 
-        ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids);
-        ggml_set_name(positions, "positions");
-        ggml_set_input(positions);
+    // pre-layernorm
+    if (model.pre_ln_w) {
+        inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1);
+        cb(inpL, "pre_ln", -1);
+    }
 
-        // pre-layernorm
-        if (model.pre_ln_w) {
-            inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1);
-        }
+    // loop over layers
+    for (int il = 0; il < n_layer; il++) {
+        auto & layer = model.layers[il];
+        ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states
 
-        // deepstack features (stack along the feature dimension), [n_embd * len(deepstack_layers), n_patches_x * n_patches_y, batch_size]
-        ggml_tensor * deepstack_features = nullptr;
-        const int merge_factor = hparams.n_merge > 0 ? hparams.n_merge * hparams.n_merge : 4; // default 2x2=4 for qwen3vl
+        // layernorm1
+        cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il);
+        cb(cur, "layer_inp_normed", il);
 
-        // loop over layers
-        for (int il = 0; il < n_layer; il++) {
-            auto & layer = model.layers[il];
-
-            ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states
-
-            // layernorm1
-            cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il);
-            cb(cur, "ln1", il);
-
-            // self-attention
-            {
+        // self-attention
+        {
+            ggml_tensor * Qcur = nullptr;
+            ggml_tensor * Kcur = nullptr;
+            ggml_tensor * Vcur = nullptr;
+            if (layer.qkv_w != nullptr) {
+                // fused qkv
                 cur = ggml_mul_mat(ctx0, layer.qkv_w, cur);
-                cur = ggml_add(ctx0, cur, layer.qkv_b);
-
-                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
-                        /* nb1    */ ggml_row_size(cur->type, d_head),
-                        /* nb2    */ cur->nb[1],
-                        /* offset */ 0);
-
-                ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
-                        /* nb1    */ ggml_row_size(cur->type, d_head),
-                        /* nb2    */ cur->nb[1],
-                        /* offset */ ggml_row_size(cur->type, n_embd));
-
-                ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
-                        /* nb1    */ ggml_row_size(cur->type, d_head),
-                        /* nb2    */ cur->nb[1],
-                        /* offset */ ggml_row_size(cur->type, 2 * n_embd));
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                // apply M-RoPE
-                Qcur = ggml_rope_multi(
-                    ctx0, Qcur, positions, nullptr,
-                    d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
-                Kcur = ggml_rope_multi(
-                    ctx0, Kcur, positions, nullptr,
-                    d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
-
-                cb(Qcur, "Qcur_rope", il);
-                cb(Kcur, "Kcur_rope", il);
-
-                cur = build_attn(layer.o_w, layer.o_b,
-                    Qcur, Kcur, Vcur, nullptr, kq_scale, il);
-                cb(cur, "attn_out", il);
-            }
-
-            // re-add the layer input, e.g., residual
-            cur = ggml_add(ctx0, cur, inpL);
-
-            inpL = cur; // inpL = residual, cur = hidden_states
-
-            cb(cur, "ffn_inp", il);
-
-            // layernorm2
-            cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il);
-            cb(cur, "ffn_inp_normed", il);
-
-            // ffn
-            cur = build_ffn(cur,
-                layer.ff_up_w, layer.ff_up_b,
-                layer.ff_gate_w, layer.ff_gate_b,
-                layer.ff_down_w, layer.ff_down_b,
-                hparams.ffn_op, il);
-
-            cb(cur, "ffn_out", il);
-
-            // residual 2
-            cur = ggml_add(ctx0, inpL, cur);
-            cb(cur, "layer_out", il);
-
-            if (layer.has_deepstack()) {
-                ggml_tensor * feat = ggml_reshape_3d(ctx0, cur, n_embd * merge_factor, n_pos / merge_factor, batch_size);
-                feat = build_norm(feat, layer.deepstack_norm_w, layer.deepstack_norm_b, norm_t, eps, il);
-                feat = build_ffn(feat,
-                    layer.deepstack_fc1_w, layer.deepstack_fc1_b,
-                    nullptr, nullptr,
-                    layer.deepstack_fc2_w, layer.deepstack_fc2_b,
-                    ffn_op_type::FFN_GELU, il);
-
-                if(!deepstack_features) {
-                    deepstack_features = feat;
-                } else {
-                    // concat along the feature dimension
-                    deepstack_features = ggml_concat(ctx0, deepstack_features, feat, 0);
+                if (layer.qkv_b != nullptr) {
+                    cur = ggml_add(ctx0, cur, layer.qkv_b);
                 }
-            }
 
-            inpL = cur;
-        }
+                Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
+                    /* nb1    */ ggml_row_size(cur->type, d_head),
+                    /* nb2    */ cur->nb[1],
+                    /* offset */ 0);
 
-        // post-layernorm
-        if (model.post_ln_w) {
-            inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, n_layer);
-        }
+                Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
+                    /* nb1    */ ggml_row_size(cur->type, d_head),
+                    /* nb2    */ cur->nb[1],
+                    /* offset */ ggml_row_size(cur->type, n_embd));
 
-        // multimodal projection
-        ggml_tensor * embeddings = inpL;
-        embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size);
+                Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
+                    /* nb1    */ ggml_row_size(cur->type, d_head),
+                    /* nb2    */ cur->nb[1],
+                    /* offset */ ggml_row_size(cur->type, 2 * n_embd));
 
-        embeddings = build_ffn(embeddings,
-            model.mm_0_w, model.mm_0_b,
-            nullptr, nullptr,
-            model.mm_1_w, model.mm_1_b,
-            ffn_op_type::FFN_GELU, -1);
+                // TODO: q/k norm requires row size == n_embd, while here it's d_head
+                // we can add support in the future if needed
+                GGML_ASSERT(layer.q_norm == nullptr && layer.k_norm == nullptr);
 
-        embeddings = ggml_concat(ctx0, embeddings, deepstack_features, 0); // concat along the feature dimension
-
-        // build the graph
-        ggml_build_forward_expand(gf, embeddings);
-
-        return gf;
-    }
-
-    ggml_cgraph * build_minicpmv() {
-        GGML_ASSERT(model.class_embedding == nullptr);
-        const int n_pos       = n_patches;
-        const int n_embd_proj = clip_n_mmproj_embd(ctx);
-
-        // position embeddings for the projector (not for ViT)
-        // see: https://huggingface.co/openbmb/MiniCPM-o-2_6/blob/main/resampler.py#L70
-        // base frequency omega
-        ggml_tensor * omega = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_embd_proj / 4);
-        ggml_set_name(omega, "omega");
-        ggml_set_input(omega);
-
-        // 2D input positions (using float for sinusoidal embeddings)
-        ggml_tensor * pos_h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_pos);
-        ggml_set_name(pos_h, "pos_h");
-        ggml_set_input(pos_h);
-        ggml_tensor * pos_w = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_pos);
-        ggml_set_name(pos_w, "pos_w");
-        ggml_set_input(pos_w);
-
-        // for selecting learned pos embd, used by ViT
-        struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos);
-        ggml_set_name(positions, "positions");
-        ggml_set_input(positions);
-
-        ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, model.position_embeddings, positions);
-
-        ggml_tensor * inp = build_inp();
-        ggml_tensor * embeddings = build_vit(
-                                inp, n_pos,
-                                NORM_TYPE_NORMAL,
-                                hparams.ffn_op,
-                                learned_pos_embd,
-                                nullptr);
-
-        // resampler projector (it is just another transformer)
-
-        ggml_tensor * q = model.mm_model_query;
-        ggml_tensor * v = ggml_mul_mat(ctx0, model.mm_model_kv_proj, embeddings);
-
-        // norm
-        q = build_norm(q, model.mm_model_ln_q_w,  model.mm_model_ln_q_b,  NORM_TYPE_NORMAL, eps, -1);
-        v = build_norm(v, model.mm_model_ln_kv_w, model.mm_model_ln_kv_b, NORM_TYPE_NORMAL, eps, -1);
-
-        // calculate sinusoidal pos embd
-        ggml_tensor * pos_embed = nullptr;
-        {
-            // outer product
-            ggml_tensor * omega_b = ggml_repeat_4d(ctx0, omega, omega->ne[0], n_pos, 1, 1); // n_pos rows
-            ggml_tensor * theta_x = ggml_mul(ctx0, omega_b, pos_w);
-            ggml_tensor * theta_y = ggml_mul(ctx0, omega_b, pos_h);
-            // sin and cos
-            ggml_tensor * pos_embd_x = ggml_concat(
-                ctx0,
-                ggml_sin(ctx0, theta_x),
-                ggml_cos(ctx0, theta_x),
-                0 // concat on first dim
-            );
-            ggml_tensor * pos_embd_y = ggml_concat(
-                ctx0,
-                ggml_sin(ctx0, theta_y),
-                ggml_cos(ctx0, theta_y),
-                0 // concat on first dim
-            );
-            pos_embed = ggml_concat(ctx0, pos_embd_x, pos_embd_y, 0);
-        }
-
-        // k = v + pos_embed
-        ggml_tensor * k = ggml_add(ctx0, v, pos_embed);
-
-        // attention
-        {
-            const int d_head = 128;
-            int n_head = n_embd_proj/d_head;
-            // Use actual config value if available, otherwise fall back to hardcoded values
-            int num_query = ctx->model.hparams.minicpmv_query_num;
-            ggml_tensor * Q = ggml_add(ctx0,
-                ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q),
-                model.mm_model_attn_q_b);
-            ggml_tensor * K = ggml_add(ctx0,
-                ggml_mul_mat(ctx0, model.mm_model_attn_k_w, k),
-                model.mm_model_attn_k_b);
-            ggml_tensor * V = ggml_add(ctx0,
-                ggml_mul_mat(ctx0, model.mm_model_attn_v_w, v),
-                model.mm_model_attn_v_b);
-
-            Q = ggml_reshape_3d(ctx0, Q, d_head, n_head, num_query);
-            K = ggml_reshape_3d(ctx0, K, d_head, n_head, n_pos);
-            V = ggml_reshape_3d(ctx0, V, d_head, n_head, n_pos);
-
-            cb(Q, "resampler_Q", -1);
-            cb(K, "resampler_K", -1);
-            cb(V, "resampler_V", -1);
-
-            float resampler_kq_scale = 1.0f/ sqrtf(float(d_head));
-            embeddings = build_attn(
-                model.mm_model_attn_o_w,
-                model.mm_model_attn_o_b,
-                Q, K, V, nullptr, resampler_kq_scale, -1);
-            cb(embeddings, "resampler_attn_out", -1);
-        }
-        // layernorm
-        embeddings = build_norm(embeddings, model.mm_model_ln_post_w, model.mm_model_ln_post_b, NORM_TYPE_NORMAL, eps, -1);
-
-        // projection
-        embeddings = ggml_mul_mat(ctx0, model.mm_model_proj, embeddings);
-
-        // build the graph
-        ggml_build_forward_expand(gf, embeddings);
-
-        return gf;
-    }
-
-    ggml_cgraph * build_internvl() {
-        GGML_ASSERT(model.class_embedding != nullptr);
-        GGML_ASSERT(model.position_embeddings != nullptr);
-
-        const int n_pos = n_patches + 1;
-        ggml_tensor * inp = build_inp();
-
-        // add CLS token
-        inp = ggml_concat(ctx0, inp, model.class_embedding, 1);
-
-        // The larger models use a different ViT, which uses RMS norm instead of layer norm
-        // ref: https://github.com/ggml-org/llama.cpp/pull/13443#issuecomment-2869786188
-        norm_type norm_t = (hparams.n_embd == 3200 && hparams.n_layer == 45)
-            ? NORM_TYPE_RMS // 6B ViT (Used by InternVL 2.5/3 - 26B, 38B, 78B)
-            : NORM_TYPE_NORMAL; // 300M ViT (Used by all smaller InternVL models)
-
-        ggml_tensor * cur = build_vit(
-                                inp, n_pos,
-                                norm_t,
-                                hparams.ffn_op,
-                                model.position_embeddings,
-                                nullptr);
-
-        // remove CLS token
-        cur = ggml_view_2d(ctx0, cur,
-            n_embd, n_patches,
-            ggml_row_size(cur->type, n_embd), 0);
-
-        // pixel shuffle
-        {
-            const int scale_factor = model.hparams.n_merge;
-            const int bsz    = 1; // batch size, always 1 for now since we don't support batching
-            const int height = n_patches_y;
-            const int width  = n_patches_x;
-            GGML_ASSERT(scale_factor > 0);
-            cur = ggml_reshape_4d(ctx0, cur, n_embd * scale_factor, height / scale_factor, width, bsz);
-            cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
-            cur = ggml_cont_4d(ctx0, cur,
-                n_embd * scale_factor * scale_factor,
-                height / scale_factor,
-                width / scale_factor,
-                bsz);
-            cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
-            // flatten to 2D
-            cur = ggml_cont_2d(ctx0, cur,
-                n_embd * scale_factor * scale_factor,
-                cur->ne[1] * cur->ne[2]);
-        }
-
-        // projector (always using GELU activation)
-        {
-            // projector LayerNorm uses pytorch's default eps = 1e-5
-            // ref: https://huggingface.co/OpenGVLab/InternVL3-8B-Instruct/blob/a34d3e4e129a5856abfd6aa6de79776484caa14e/modeling_internvl_chat.py#L79
-            cur = build_norm(cur, model.mm_0_w, model.mm_0_b, NORM_TYPE_NORMAL, 1e-5, -1);
-            cur = build_ffn(cur,
-                model.mm_1_w, model.mm_1_b,
-                nullptr, nullptr,
-                model.mm_3_w, model.mm_3_b,
-                FFN_GELU,
-                -1);
-        }
-
-        // build the graph
-        ggml_build_forward_expand(gf, cur);
-
-        return gf;
-    }
-
-    ggml_cgraph * build_llama4() {
-        GGML_ASSERT(model.class_embedding != nullptr);
-        GGML_ASSERT(model.position_embeddings != nullptr);
-
-        const int n_pos = n_patches + 1; // +1 for [CLS]
-
-        // 2D input positions
-        ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos);
-        ggml_set_name(pos_h, "pos_h");
-        ggml_set_input(pos_h);
-
-        ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos);
-        ggml_set_name(pos_w, "pos_w");
-        ggml_set_input(pos_w);
-
-        ggml_tensor * inp = build_inp_raw();
-
-        // Llama4UnfoldConvolution
-        {
-            ggml_tensor * kernel = ggml_reshape_4d(ctx0, model.patch_embeddings_0,
-                                                    patch_size, patch_size, 3, n_embd);
-            inp = ggml_im2col(ctx0, kernel, inp, patch_size, patch_size, 0, 0, 1, 1, true, inp->type);
-            inp = ggml_mul_mat(ctx0, model.patch_embeddings_0, inp);
-            inp = ggml_reshape_2d(ctx0, inp, n_embd, n_patches);
-            cb(inp, "patch_conv", -1);
-        }
-
-        // add CLS token
-        inp = ggml_concat(ctx0, inp, model.class_embedding, 1);
-
-        // build ViT with 2D position embeddings
-        auto add_pos = [&](ggml_tensor * cur, const clip_layer &) {
-            // first half is X axis and second half is Y axis
-            // ref: https://github.com/huggingface/transformers/blob/40a493c7ed4f19f08eadb0639cf26d49bfa5e180/src/transformers/models/llama4/modeling_llama4.py#L1312
-            // ref: https://github.com/Blaizzy/mlx-vlm/blob/a57156aa87b33cca6e5ee6cfc14dd4ef8f611be6/mlx_vlm/models/llama4/vision.py#L441
-            return build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false);
-        };
-        ggml_tensor * cur = build_vit(
-                                inp, n_pos,
-                                NORM_TYPE_NORMAL,
-                                hparams.ffn_op,
-                                model.position_embeddings,
-                                add_pos);
-
-        // remove CLS token
-        cur = ggml_view_2d(ctx0, cur,
-            n_embd, n_patches,
-            ggml_row_size(cur->type, n_embd), 0);
-
-        // pixel shuffle
-        // based on Llama4VisionPixelShuffleMLP
-        // https://github.com/huggingface/transformers/blob/2932f318a20d9e54cc7aea052e040164d85de7d6/src/transformers/models/llama4/modeling_llama4.py#L1151
-        {
-            const int scale_factor = model.hparams.n_merge;
-            const int bsz = 1; // batch size, always 1 for now since we don't support batching
-            GGML_ASSERT(scale_factor > 0);
-            GGML_ASSERT(n_patches_x == n_patches_y); // llama4 only supports square images
-            cur = ggml_reshape_4d(ctx0, cur,
-                n_embd * scale_factor,
-                n_patches_x / scale_factor,
-                n_patches_y,
-                bsz);
-            cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
-            cur = ggml_cont_4d(ctx0, cur,
-                n_embd * scale_factor * scale_factor,
-                n_patches_x / scale_factor,
-                n_patches_y / scale_factor,
-                bsz);
-            //cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
-            // flatten to 2D
-            cur = ggml_cont_2d(ctx0, cur,
-                n_embd * scale_factor * scale_factor,
-                n_patches / scale_factor / scale_factor);
-            cb(cur, "pixel_shuffle", -1);
-        }
-
-        // based on Llama4VisionMLP2 (always uses GELU activation, no bias)
-        {
-            cur = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, cur);
-            cur = ggml_gelu(ctx0, cur);
-            cur = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, cur);
-            cur = ggml_gelu(ctx0, cur);
-            cb(cur, "adapter_mlp", -1);
-        }
-
-        // Llama4MultiModalProjector
-        cur = ggml_mul_mat(ctx0, model.mm_model_proj, cur);
-        cb(cur, "projected", -1);
-
-        // build the graph
-        ggml_build_forward_expand(gf, cur);
-
-        return gf;
-    }
-
-    ggml_cgraph * build_kimivl() {
-        // 2D input positions
-        ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
-        ggml_set_name(pos_h, "pos_h");
-        ggml_set_input(pos_h);
-
-        ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
-        ggml_set_name(pos_w, "pos_w");
-        ggml_set_input(pos_w);
-
-        ggml_tensor * learned_pos_embd = resize_position_embeddings();
-
-        // build ViT with 2D position embeddings
-        auto add_pos = [&](ggml_tensor * cur, const clip_layer &) {
-            // first half is X axis and second half is Y axis
-            return build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false);
-        };
-
-        ggml_tensor * inp = build_inp();
-        ggml_tensor * cur = build_vit(
-                                inp, n_patches,
-                                NORM_TYPE_NORMAL,
-                                hparams.ffn_op,
-                                learned_pos_embd,
-                                add_pos);
-
-        cb(cur, "vit_out", -1);
-
-        {
-            // patch_merger
-            const int scale_factor = model.hparams.n_merge;
-            cur = build_patch_merge_permute(cur, scale_factor);
-
-            // projection norm
-            int proj_inp_dim = cur->ne[0];
-            cur = ggml_view_2d(ctx0, cur,
-                n_embd, cur->ne[1] * scale_factor * scale_factor,
-                ggml_row_size(cur->type, n_embd), 0);
-            cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm
-            cur = ggml_mul(ctx0, cur, model.mm_input_norm_w);
-            cur = ggml_add(ctx0, cur, model.mm_input_norm_b);
-            cur = ggml_view_2d(ctx0, cur,
-                proj_inp_dim, cur->ne[1] / scale_factor / scale_factor,
-                ggml_row_size(cur->type, proj_inp_dim), 0);
-            cb(cur, "proj_inp_normed", -1);
-
-            // projection mlp
-            cur = build_ffn(cur,
-                model.mm_1_w, model.mm_1_b,
-                nullptr, nullptr,
-                model.mm_2_w, model.mm_2_b,
-                FFN_GELU,
-                -1);
-            cb(cur, "proj_out", -1);
-        }
-
-        // build the graph
-        ggml_build_forward_expand(gf, cur);
-
-        return gf;
-    }
-
-    // this graph is used by llava, granite and glm
-    // due to having embedding_stack (used by granite), we cannot reuse build_vit
-    ggml_cgraph * build_llava() {
-        const int batch_size = 1;
-        const int n_pos = n_patches + (model.class_embedding ? 1 : 0);
-
-        GGML_ASSERT(n_patches_x == n_patches_y && "only square images supported");
-
-        // Calculate the deepest feature layer based on hparams and projector type
-        int max_feature_layer = n_layer;
-        {
-            // Get the index of the second to last layer; this is the default for models that have a llava projector
-            int il_last = hparams.n_layer - 1;
-            int deepest_feature_layer = -1;
-
-            if (ctx->proj_type() == PROJECTOR_TYPE_MINICPMV || ctx->proj_type() == PROJECTOR_TYPE_GLM_EDGE) {
-                il_last += 1;
-            }
-
-            // If we set explicit vision feature layers, only go up to the deepest one
-            // NOTE: only used by granite-vision models for now
-            for (const auto & feature_layer : hparams.vision_feature_layer) {
-                if (feature_layer > deepest_feature_layer) {
-                    deepest_feature_layer = feature_layer;
-                }
-            }
-            max_feature_layer = deepest_feature_layer < 0 ? il_last : deepest_feature_layer;
-        }
-
-        ggml_tensor * inp = build_inp();
-
-        // concat class_embeddings and patch_embeddings
-        if (model.class_embedding) {
-            inp = ggml_concat(ctx0, inp, model.class_embedding, 1);
-        }
-
-        ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos);
-        ggml_set_name(positions, "positions");
-        ggml_set_input(positions);
-
-        inp = ggml_add(ctx0, inp, ggml_get_rows(ctx0, model.position_embeddings, positions));
-
-        ggml_tensor * inpL = inp;
-
-        // pre-layernorm
-        if (model.pre_ln_w) {
-            inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, NORM_TYPE_NORMAL, eps, -1);
-            cb(inpL, "pre_ln", -1);
-        }
-
-        std::vector<ggml_tensor *> embedding_stack;
-        const auto & vision_feature_layer = hparams.vision_feature_layer;
-
-        // loop over layers
-        for (int il = 0; il < max_feature_layer; il++) {
-            auto & layer = model.layers[il];
-            ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states
-
-            // If this is an embedding feature layer, save the output.
-            // NOTE: 0 index here refers to the input to the encoder.
-            if (vision_feature_layer.find(il) != vision_feature_layer.end()) {
-                embedding_stack.push_back(cur);
-            }
-
-            // layernorm1
-            cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il);
-            cb(cur, "layer_inp_normed", il);
-
-            // self-attention
-            {
-                ggml_tensor * Qcur = ggml_mul_mat(ctx0, layer.q_w, cur);
+            } else {
+                // separate q, k, v
+                Qcur = ggml_mul_mat(ctx0, layer.q_w, cur);
                 if (layer.q_b) {
                     Qcur = ggml_add(ctx0, Qcur, layer.q_b);
                 }
 
-                ggml_tensor * Kcur = ggml_mul_mat(ctx0, layer.k_w, cur);
+                Kcur = ggml_mul_mat(ctx0, layer.k_w, cur);
                 if (layer.k_b) {
                     Kcur = ggml_add(ctx0, Kcur, layer.k_b);
                 }
 
-                ggml_tensor * Vcur = ggml_mul_mat(ctx0, layer.v_w, cur);
+                Vcur = ggml_mul_mat(ctx0, layer.v_w, cur);
                 if (layer.v_b) {
                     Vcur = ggml_add(ctx0, Vcur, layer.v_b);
                 }
 
+                if (layer.q_norm) {
+                    Qcur = build_norm(Qcur, layer.q_norm, NULL, norm_t, eps, il);
+                    cb(Qcur, "Qcur_norm", il);
+                }
+
+                if (layer.k_norm) {
+                    Kcur = build_norm(Kcur, layer.k_norm, NULL, norm_t, eps, il);
+                    cb(Kcur, "Kcur_norm", il);
+                }
+
                 Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos);
                 Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos);
                 Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos);
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(layer.o_w, layer.o_b,
-                    Qcur, Kcur, Vcur, nullptr, kq_scale, il);
-                cb(cur, "attn_out", il);
             }
 
-            // re-add the layer input, e.g., residual
-            cur = ggml_add(ctx0, cur, inpL);
-
-            inpL = cur; // inpL = residual, cur = hidden_states
-
-            cb(cur, "ffn_inp", il);
-
-            // layernorm2
-            cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il);
-            cb(cur, "ffn_inp_normed", il);
-
-            // ffn
-            cur = build_ffn(cur,
-                layer.ff_up_w, layer.ff_up_b,
-                layer.ff_gate_w, layer.ff_gate_b,
-                layer.ff_down_w, layer.ff_down_b,
-                hparams.ffn_op, il);
-
-            cb(cur, "ffn_out", il);
-
-            // residual 2
-            cur = ggml_add(ctx0, inpL, cur);
-            cb(cur, "layer_out", il);
-
-            inpL = cur;
-        }
-
-        // post-layernorm
-        if (model.post_ln_w) {
-            inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, NORM_TYPE_NORMAL, eps, -1);
-        }
-
-        ggml_tensor * embeddings = inpL;
-
-        // process vision feature layers (used by granite)
-        {
-            // final layer is a vision feature layer
-            if (vision_feature_layer.find(max_feature_layer) != vision_feature_layer.end()) {
-                embedding_stack.push_back(inpL);
-            }
-
-            // If feature layers are explicitly set, stack them (if we have multiple)
-            if (!embedding_stack.empty()) {
-                embeddings = embedding_stack[0];
-                for (size_t i = 1; i < embedding_stack.size(); i++) {
-                    embeddings = ggml_concat(ctx0, embeddings, embedding_stack[i], 0);
-                }
-            }
-        }
-
-        // llava projector (also used by granite)
-        if (ctx->model.hparams.has_llava_projector) {
-            embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]);
-
-            ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
-            ggml_set_name(patches, "patches");
-            ggml_set_input(patches);
-
-            // shape [1, 576, 1024]
-            // ne is whcn, ne = [1024, 576, 1, 1]
-            embeddings = ggml_get_rows(ctx0, embeddings, patches);
-
-            // print_tensor_info(embeddings, "embeddings");
-
-            // llava projector
-            if (ctx->proj_type() == PROJECTOR_TYPE_MLP) {
-                embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
-                embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
-
-                embeddings = ggml_gelu(ctx0, embeddings);
-                if (model.mm_2_w) {
-                    embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings);
-                    embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);
-                }
-            }
-            else if (ctx->proj_type() == PROJECTOR_TYPE_MLP_NORM) {
-                embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
-                embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
-                // ggml_tensor_printf(embeddings, "mm_0_w",0,true,false);
-                // First LayerNorm
-                embeddings = ggml_norm(ctx0, embeddings, eps);
-                embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_1_w),
-                                    model.mm_1_b);
-
-                // GELU activation
-                embeddings = ggml_gelu(ctx0, embeddings);
-
-                // Second linear layer
-                embeddings = ggml_mul_mat(ctx0, model.mm_3_w, embeddings);
-                embeddings = ggml_add(ctx0, embeddings, model.mm_3_b);
-
-                // Second LayerNorm
-                embeddings = ggml_norm(ctx0, embeddings, eps);
-                embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_4_w),
-                                    model.mm_4_b);
-            }
-            else if (ctx->proj_type() == PROJECTOR_TYPE_LDP) {
-                // MobileVLM projector
-                int n_patch = 24;
-                ggml_tensor * mlp_1 = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, embeddings);
-                mlp_1 = ggml_add(ctx0, mlp_1, model.mm_model_mlp_1_b);
-                mlp_1 = ggml_gelu(ctx0, mlp_1);
-                ggml_tensor * mlp_3 = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, mlp_1);
-                mlp_3 = ggml_add(ctx0, mlp_3, model.mm_model_mlp_3_b);
-                // mlp_3 shape = [1, 576, 2048], ne = [2048, 576, 1, 1]
-
-                // block 1
-                ggml_tensor * block_1 = nullptr;
-                {
-                    // transpose from [1, 576, 2048] --> [1, 2048, 576] --> [1, 2048, 24, 24]
-                    mlp_3 = ggml_permute(ctx0, mlp_3, 1, 0, 2, 3);
-                    mlp_3 = ggml_cont_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]);
-                    // stride = 1, padding = 1, bias is nullptr
-                    block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1);
-
-                    // layer norm
-                    // // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
-                    block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3));
-                    // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1]
-                    block_1 = ggml_norm(ctx0, block_1, eps);
-                    block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_0_1_w), model.mm_model_block_1_block_0_1_b);
-                    block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
-
-                    // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
-                    // hardswish
-                    ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1);
-
-                    block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0);
-                    // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
-                    // pointwise conv
-                    block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]);
-                    block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc1_w, block_1);
-                    block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc1_b);
-                    block_1 = ggml_relu(ctx0, block_1);
-                    block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc2_w, block_1);
-                    block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc2_b);
-                    block_1 = ggml_hardsigmoid(ctx0, block_1);
-                    // block_1_hw shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1], block_1 shape = [1, 2048], ne = [2048, 1, 1, 1]
-                    block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]);
-                    block_1 = ggml_mul(ctx0, block_1_hw, block_1);
-
-                    int w = block_1->ne[0], h = block_1->ne[1];
-                    block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]);
-                    block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3));
-
-                    // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1]
-                    block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_2_0_w, block_1);
-                    block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]);
-
-                    // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1]
-                    block_1 = ggml_norm(ctx0, block_1, eps);
-                    block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_2_1_w), model.mm_model_block_1_block_2_1_b);
-                    block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
-                    // block1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
-                    // residual
-                    block_1 = ggml_add(ctx0, mlp_3, block_1);
-                }
-
-                // block_2
-                {
-                    // stride = 2
-                    block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1);
-
-                    // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1]
-                    // layer norm
-                    block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3));
-                    // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1]
-                    block_1 = ggml_norm(ctx0, block_1, eps);
-                    block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_0_1_w), model.mm_model_block_2_block_0_1_b);
-                    block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
-                    // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1]
-                    // hardswish
-                    ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1);
-
-                    // not sure the parameters is right for globalAvgPooling
-                    block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0);
-                    // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
-                    // pointwise conv
-                    block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]);
-                    block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc1_w, block_1);
-                    block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc1_b);
-                    block_1 = ggml_relu(ctx0, block_1);
-                    block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc2_w, block_1);
-                    block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc2_b);
-                    block_1 = ggml_hardsigmoid(ctx0, block_1);
-
-                    // block_1_hw shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1], block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
-                    block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]);
-                    block_1 = ggml_mul(ctx0, block_1_hw, block_1);
-
-                    int w = block_1->ne[0], h = block_1->ne[1];
-                    block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]);
-                    block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3));
-                    // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1]
-                    block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_2_0_w, block_1);
-                    block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]);
-
-
-                    // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1]
-                    block_1 = ggml_norm(ctx0, block_1, eps);
-                    block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_2_1_w), model.mm_model_block_2_block_2_1_b);
-                    block_1 = ggml_reshape_3d(ctx0, block_1, block_1->ne[0], block_1->ne[1] * block_1->ne[2], block_1->ne[3]);
-                    // block_1 shape = [1, 144, 2048], ne = [2048, 144, 1]
-                }
-                embeddings = block_1;
-            }
-            else if (ctx->proj_type() == PROJECTOR_TYPE_LDPV2)
-            {
-                int n_patch = 24;
-                ggml_tensor * mlp_0 = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings);
-                mlp_0 = ggml_add(ctx0, mlp_0, model.mm_model_mlp_0_b);
-                mlp_0 = ggml_gelu(ctx0, mlp_0);
-                ggml_tensor * mlp_2 = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, mlp_0);
-                mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b);
-                // mlp_2 ne = [2048, 576, 1, 1]
-                // // AVG Pool Layer 2*2, strides = 2
-                mlp_2 = ggml_permute(ctx0, mlp_2, 1, 0, 2, 3);
-                // mlp_2 ne = [576, 2048, 1, 1]
-                mlp_2 = ggml_cont_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]);
-                // mlp_2 ne [24, 24, 2048, 1]
-                mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0);
-                // weight ne = [3, 3, 2048, 1]
-                ggml_tensor * peg_0 = ggml_conv_2d_dw(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1);
-                peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3));
-                peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b);
-                mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3));
-                peg_0 = ggml_add(ctx0, peg_0, mlp_2);
-                peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]);
-                embeddings = peg_0;
-            }
-            else {
-                GGML_ABORT("fatal error");
-            }
-        }
-
-        // glm projector
-        else if (ctx->proj_type() == PROJECTOR_TYPE_GLM_EDGE) {
-            size_t gridsz = (size_t)sqrt(embeddings->ne[1]);
-            embeddings = ggml_permute(ctx0,embeddings,1,0,2,3);
-            embeddings = ggml_cont_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]);
-            embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1);
-            embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size);
-            embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings, 1, 0, 2, 3));
-            embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b);
-            // GLU
-            {
-                embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings);
-                embeddings = ggml_norm(ctx0, embeddings, eps);
-                embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b);
-                embeddings = ggml_gelu_inplace(ctx0, embeddings);
-                ggml_tensor * x = embeddings;
-                embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings);
-                x = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w,x);
-                embeddings = ggml_swiglu_split(ctx0, embeddings, x);
-                embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings);
-            }
-            // arrangement of BOI/EOI token embeddings
-            // note: these embeddings are not present in text model, hence we cannot process them as text tokens
-            // see: https://huggingface.co/THUDM/glm-edge-v-2b/blob/main/siglip.py#L53
-            {
-                embeddings = ggml_concat(ctx0, model.mm_boi, embeddings, 1); // BOI
-                embeddings = ggml_concat(ctx0, embeddings, model.mm_eoi, 1); // EOI
-            }
-        }
-
-        else {
-            GGML_ABORT("llava: unknown projector type");
-        }
-
-        // build the graph
-        ggml_build_forward_expand(gf, embeddings);
-
-        return gf;
-    }
-    // whisper encoder with custom projector
-    ggml_cgraph * build_whisper_enc() {
-        const int n_frames = img.nx;
-        const int n_pos    = n_frames / 2;
-        GGML_ASSERT(model.position_embeddings->ne[1] >= n_pos);
-
-        ggml_tensor * inp = build_inp_raw(1);
-
-        // conv1d block
-        {
-            // convolution + gelu
-            ggml_tensor * cur = ggml_conv_1d_ph(ctx0, model.conv1d_1_w, inp, 1, 1);
-            cur = ggml_add(ctx0, cur, model.conv1d_1_b);
-
-            cur = ggml_gelu_erf(ctx0, cur);
-
-            cur = ggml_conv_1d_ph(ctx0, model.conv1d_2_w, cur, 2, 1);
-            cur = ggml_add(ctx0, cur, model.conv1d_2_b);
-
-            cur = ggml_gelu_erf(ctx0, cur);
-            // transpose
-            inp = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
-            cb(inp, "after_conv1d", -1);
-        }
-
-        // sanity check (only check one layer, but it should be the same for all)
-        GGML_ASSERT(model.layers[0].ln_1_w && model.layers[0].ln_1_b);
-        GGML_ASSERT(model.layers[0].ln_2_w && model.layers[0].ln_2_b);
-        GGML_ASSERT(model.layers[0].q_b);
-        GGML_ASSERT(model.layers[0].v_b);
-        GGML_ASSERT(!model.layers[0].k_b); // no bias for k
-        GGML_ASSERT(model.post_ln_w && model.post_ln_b);
-
-        ggml_tensor * pos_embd_selected = ggml_view_2d(
-            ctx0, model.position_embeddings,
-            model.position_embeddings->ne[0], n_pos,
-            model.position_embeddings->nb[1], 0
-        );
-        ggml_tensor * cur = build_vit(
-                                inp, n_pos,
-                                NORM_TYPE_NORMAL,
-                                hparams.ffn_op,
-                                pos_embd_selected,
-                                nullptr);
-
-        cb(cur, "after_transformer", -1);
-
-        if (model.audio_has_stack_frames()) {
-            // StackAudioFrames
-            // https://huggingface.co/fixie-ai/ultravox-v0_5-llama-3_2-1b/blob/main/ultravox_model.py
-            int64_t stride = n_embd * hparams.proj_stack_factor;
-            int64_t padded_len = GGML_PAD(ggml_nelements(cur), stride);
-            int64_t pad = padded_len - ggml_nelements(cur);
-            if (pad > 0) {
-                cur = ggml_view_1d(ctx0, cur, ggml_nelements(cur), 0);
-                cur = ggml_pad(ctx0, cur, pad, 0, 0, 0);
-            }
-            cur = ggml_view_2d(ctx0, cur, stride, padded_len / stride,
-                                ggml_row_size(cur->type, stride), 0);
-            cb(cur, "after_stacked", -1);
-        }
-
-        if (ctx->proj_type() == PROJECTOR_TYPE_ULTRAVOX) {
-            // UltravoxProjector
-            // pre-norm
-            cur = ggml_rms_norm(ctx0, cur, 1e-6);
-            cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w);
-
-            // ffn in
-            cur = ggml_mul_mat(ctx0, model.mm_1_w, cur);
-
-            // swiglu
-            // see SwiGLU in ultravox_model.py, the second half passed through is silu, not the first half
-            cur = ggml_swiglu_swapped(ctx0, cur);
-
-            // mid-norm
-            cur = ggml_rms_norm(ctx0, cur, 1e-6);
-            cur = ggml_mul(ctx0, cur, model.mm_norm_mid_w);
-
-            // ffn out
-            cur = ggml_mul_mat(ctx0, model.mm_2_w, cur);
-
-        } else if (ctx->proj_type() == PROJECTOR_TYPE_QWEN2A) {
-            // projector
-            cur = ggml_mul_mat(ctx0, model.mm_fc_w, cur);
-            cur = ggml_add(ctx0, cur, model.mm_fc_b);
-
-        } else if (ctx->proj_type() == PROJECTOR_TYPE_VOXTRAL) {
-            // projector
-            cur = build_ffn(cur,
-                model.mm_1_w, model.mm_1_b,
-                nullptr, nullptr,
-                model.mm_2_w, model.mm_2_b,
-                FFN_GELU_ERF,
-                -1);
-
-        } else {
-            GGML_ABORT("%s: unknown projector type", __func__);
-        }
-
-        cb(cur, "projected", -1);
-
-        ggml_build_forward_expand(gf, cur);
-
-        return gf;
-    }
-
-    // cogvlm vision encoder
-    ggml_cgraph * build_cogvlm() {
-        GGML_ASSERT(model.class_embedding != nullptr);
-        GGML_ASSERT(model.position_embeddings != nullptr);
-
-        const int n_pos = n_patches + 1; // +1 for [CLS]
-
-        // build input and concatenate class embedding
-        ggml_tensor * inp = build_inp();
-        inp = ggml_concat(ctx0, inp, model.class_embedding, 1);
-
-        inp = ggml_add(ctx0, inp, model.position_embeddings);
-        cb(inp, "inp_pos", -1);
-
-        ggml_tensor * inpL = inp;
-
-        for (int il = 0; il < n_layer; il++) {
-            auto & layer = model.layers[il];
-            ggml_tensor * cur = inpL;
-
-            cur = ggml_mul_mat(ctx0, layer.qkv_w, cur);
-
-            cur = ggml_add(ctx0, cur, layer.qkv_b);
-
-            ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float),
-                cur->nb[1], 0);
-            ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float),
-                cur->nb[1], n_embd * sizeof(float));
-            ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float),
-                cur->nb[1], 2 * n_embd * sizeof(float));
-
             cb(Qcur, "Qcur", il);
             cb(Kcur, "Kcur", il);
             cb(Vcur, "Vcur", il);
 
+            if (add_pos) {
+                Qcur = add_pos(Qcur, layer);
+                Kcur = add_pos(Kcur, layer);
+                cb(Qcur, "Qcur_pos", il);
+                cb(Kcur, "Kcur_pos", il);
+            }
+
             cur = build_attn(layer.o_w, layer.o_b,
                 Qcur, Kcur, Vcur, nullptr, kq_scale, il);
             cb(cur, "attn_out", il);
-
-            cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il);
-            cb(cur, "attn_post_norm", il);
-
-            cur = ggml_add(ctx0, cur, inpL);
-            inpL = cur;
-
-            cur = build_ffn(cur,
-                layer.ff_up_w, layer.ff_up_b,
-                layer.ff_gate_w, layer.ff_gate_b,
-                layer.ff_down_w, layer.ff_down_b,
-                hparams.ffn_op, il);
-
-            cb(cur, "ffn_out", il);
-
-            cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il);
-            cb(cur, "ffn_post_norm", il);
-
-            cur = ggml_add(ctx0, cur, inpL);
-            cb(cur, "layer_out", il);
-            inpL = cur;
-
         }
 
-        // remove CLS token (like build_llama4 does)
-        ggml_tensor * cur = ggml_view_2d(ctx0, inpL,
-            n_embd, n_patches,
-            ggml_row_size(inpL->type, n_embd), 0);
+        if (layer.ls_1_w) {
+            cur = ggml_mul(ctx0, cur, layer.ls_1_w);
+            cb(cur, "attn_out_scaled", il);
+        }
 
-        // Multiply with mm_model_proj
-        cur = ggml_mul_mat(ctx0, model.mm_model_proj, cur);
+        // re-add the layer input, e.g., residual
+        cur = ggml_add(ctx0, cur, inpL);
 
-        // Apply layernorm, weight, bias
-        cur = build_norm(cur, model.mm_post_fc_norm_w, model.mm_post_fc_norm_b, NORM_TYPE_NORMAL, 1e-5, -1);
+        inpL = cur; // inpL = residual, cur = hidden_states
 
-        // Apply GELU
-        cur = ggml_gelu_inplace(ctx0, cur);
+        cb(cur, "ffn_inp", il);
 
-        // Branch 1: multiply with mm_h_to_4h_w
-        ggml_tensor * h_to_4h = ggml_mul_mat(ctx0, model.mm_h_to_4h_w, cur);
+        // layernorm2
+        cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il);
+        cb(cur, "ffn_inp_normed", il);
 
-        // Branch 2: multiply with mm_gate_w
-        ggml_tensor * gate = ggml_mul_mat(ctx0, model.mm_gate_w, cur);
+        // ffn
+        cur = build_ffn(cur,
+            layer.ff_up_w, layer.ff_up_b,
+            layer.ff_gate_w, layer.ff_gate_b,
+            layer.ff_down_w, layer.ff_down_b,
+            ffn_t, il);
 
-        // Apply silu
-        gate = ggml_swiglu_split(ctx0, gate, h_to_4h);
+        cb(cur, "ffn_out", il);
 
-        // Apply mm_4h_to_h_w
-        cur = ggml_mul_mat(ctx0, model.mm_4h_to_h_w, gate);
+        if (layer.ls_2_w) {
+            cur = ggml_mul(ctx0, cur, layer.ls_2_w);
+            cb(cur, "ffn_out_scaled", il);
+        }
 
-        // Concatenate with boi and eoi
-        cur = ggml_concat(ctx0, model.mm_boi, cur, 1);
-        cur = ggml_concat(ctx0, cur, model.mm_eoi, 1);
+        // residual 2
+        cur = ggml_add(ctx0, inpL, cur);
+        cb(cur, "layer_out", il);
 
-        // build the graph
-        ggml_build_forward_expand(gf, cur);
-
-        return gf;
+        inpL = cur;
     }
 
-private:
-    //
-    // utility functions
-    //
-
-    void cb(ggml_tensor * cur0, const char * name, int il) const {
-        if (ctx->debug_graph) {
-            ggml_tensor * cur = ggml_cpy(ctx0, cur0, ggml_dup_tensor(ctx0, cur0));
-            std::string cur_name = il >= 0 ? std::string(name) + "_" + std::to_string(il) : name;
-            ggml_set_name(cur, cur_name.c_str());
-            ggml_set_output(cur);
-            ggml_build_forward_expand(gf, cur);
-            ctx->debug_print_tensors.push_back(cur);
-        }
+    if (model.audio_has_avgpool()) {
+        ggml_tensor * cur = inpL;
+        cur = ggml_transpose(ctx0, cur);
+        cur = ggml_cont(ctx0, cur);
+        cur = ggml_pool_1d(ctx0, cur, GGML_OP_POOL_AVG, 2, 2, 0);
+        cur = ggml_transpose(ctx0, cur);
+        cur = ggml_cont(ctx0, cur);
+        inpL = cur;
     }
 
-    // siglip2 naflex
-    ggml_tensor * resize_position_embeddings() {
-        ggml_tensor * pos_embd = model.position_embeddings;
-        const int height       = img.ny / patch_size;
-        const int width        = img.nx / patch_size;
-        const uint32_t mode    = GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS;
-        const int n_per_side   = (int)std::sqrt(pos_embd->ne[1]);
-
-        GGML_ASSERT(pos_embd);
-
-        if (height == n_per_side && width == n_per_side) {
-            return pos_embd;
-        }
-
-        pos_embd = ggml_reshape_3d(ctx0, pos_embd, n_embd, n_per_side, n_per_side);  // -> (n_embd, n_per_side, n_per_side)
-        pos_embd = ggml_permute(ctx0, pos_embd, 2, 0, 1, 3);                         // -> (n_per_side, n_per_side, n_embd)
-        pos_embd = ggml_interpolate(ctx0, pos_embd, width, height, n_embd, 1, mode); // -> (width, height, n_embd)
-        pos_embd = ggml_permute(ctx0, pos_embd, 1, 2, 0, 3);                         // -> (n_embd, width, height)
-        pos_embd = ggml_cont_2d(ctx0, pos_embd, n_embd, width * height);             // -> (n_embd, width * height)
-
-        return pos_embd;
+    // post-layernorm
+    if (model.post_ln_w) {
+        inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, -1);
     }
+    return inpL;
+}
 
-    // build vision transformer (ViT) cgraph
-    // this function should cover most of the models
-    // if your model has specific features, you should probably duplicate this function
-    ggml_tensor * build_vit(
-                ggml_tensor * inp,
-                int64_t n_pos,
-                norm_type norm_t,
-                ffn_op_type ffn_t,
-                ggml_tensor * learned_pos_embd,
-                std::function<ggml_tensor *(ggml_tensor *, const clip_layer &)> add_pos
-            ) {
-        if (learned_pos_embd) {
-            inp = ggml_add(ctx0, inp, learned_pos_embd);
-            cb(inp, "pos_embed", -1);
-        }
-
-        ggml_tensor * inpL = inp;
-
-        // pre-layernorm
-        if (model.pre_ln_w) {
-            inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1);
-            cb(inpL, "pre_ln", -1);
-        }
-
-        // loop over layers
-        for (int il = 0; il < n_layer; il++) {
-            auto & layer = model.layers[il];
-            ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states
-
-            // layernorm1
-            cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il);
-            cb(cur, "layer_inp_normed", il);
-
-            // self-attention
-            {
-                ggml_tensor * Qcur = nullptr;
-                ggml_tensor * Kcur = nullptr;
-                ggml_tensor * Vcur = nullptr;
-                if (layer.qkv_w != nullptr) {
-                    // fused qkv
-                    cur = ggml_mul_mat(ctx0, layer.qkv_w, cur);
-                    if (layer.qkv_b != nullptr) {
-                        cur = ggml_add(ctx0, cur, layer.qkv_b);
-                    }
-
-                    Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
-                        /* nb1    */ ggml_row_size(cur->type, d_head),
-                        /* nb2    */ cur->nb[1],
-                        /* offset */ 0);
-
-                    Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
-                        /* nb1    */ ggml_row_size(cur->type, d_head),
-                        /* nb2    */ cur->nb[1],
-                        /* offset */ ggml_row_size(cur->type, n_embd));
-
-                    Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
-                        /* nb1    */ ggml_row_size(cur->type, d_head),
-                        /* nb2    */ cur->nb[1],
-                        /* offset */ ggml_row_size(cur->type, 2 * n_embd));
-
-                    // TODO: q/k norm requires row size == n_embd, while here it's d_head
-                    // we can add support in the future if needed
-                    GGML_ASSERT(layer.q_norm == nullptr && layer.k_norm == nullptr);
-
-                } else {
-                    // separate q, k, v
-                    Qcur = ggml_mul_mat(ctx0, layer.q_w, cur);
-                    if (layer.q_b) {
-                        Qcur = ggml_add(ctx0, Qcur, layer.q_b);
-                    }
-
-                    Kcur = ggml_mul_mat(ctx0, layer.k_w, cur);
-                    if (layer.k_b) {
-                        Kcur = ggml_add(ctx0, Kcur, layer.k_b);
-                    }
-
-                    Vcur = ggml_mul_mat(ctx0, layer.v_w, cur);
-                    if (layer.v_b) {
-                        Vcur = ggml_add(ctx0, Vcur, layer.v_b);
-                    }
-
-                    if (layer.q_norm) {
-                        Qcur = build_norm(Qcur, layer.q_norm, NULL, norm_t, eps, il);
-                        cb(Qcur, "Qcur_norm", il);
-                    }
-
-                    if (layer.k_norm) {
-                        Kcur = build_norm(Kcur, layer.k_norm, NULL, norm_t, eps, il);
-                        cb(Kcur, "Kcur_norm", il);
-                    }
-
-                    Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos);
-                    Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos);
-                    Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos);
-                }
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                if (add_pos) {
-                    Qcur = add_pos(Qcur, layer);
-                    Kcur = add_pos(Kcur, layer);
-                    cb(Qcur, "Qcur_pos", il);
-                    cb(Kcur, "Kcur_pos", il);
-                }
-
-                cur = build_attn(layer.o_w, layer.o_b,
-                    Qcur, Kcur, Vcur, nullptr, kq_scale, il);
-                cb(cur, "attn_out", il);
-            }
-
-            if (layer.ls_1_w) {
-                cur = ggml_mul(ctx0, cur, layer.ls_1_w);
-                cb(cur, "attn_out_scaled", il);
-            }
-
-            // re-add the layer input, e.g., residual
-            cur = ggml_add(ctx0, cur, inpL);
-
-            inpL = cur; // inpL = residual, cur = hidden_states
-
-            cb(cur, "ffn_inp", il);
-
-            // layernorm2
-            cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il);
-            cb(cur, "ffn_inp_normed", il);
-
-            // ffn
-            cur = build_ffn(cur,
-                layer.ff_up_w, layer.ff_up_b,
-                layer.ff_gate_w, layer.ff_gate_b,
-                layer.ff_down_w, layer.ff_down_b,
-                ffn_t, il);
-
-            cb(cur, "ffn_out", il);
-
-            if (layer.ls_2_w) {
-                cur = ggml_mul(ctx0, cur, layer.ls_2_w);
-                cb(cur, "ffn_out_scaled", il);
-            }
-
-            // residual 2
-            cur = ggml_add(ctx0, inpL, cur);
-            cb(cur, "layer_out", il);
-
-            inpL = cur;
-        }
-
-        if (ctx->model.audio_has_avgpool()) {
-            ggml_tensor * cur = inpL;
-            cur = ggml_transpose(ctx0, cur);
-            cur = ggml_cont(ctx0, cur);
-            cur = ggml_pool_1d(ctx0, cur, GGML_OP_POOL_AVG, 2, 2, 0);
-            cur = ggml_transpose(ctx0, cur);
-            cur = ggml_cont(ctx0, cur);
-            inpL = cur;
-        }
-
-        // post-layernorm
-        if (model.post_ln_w) {
-            inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, -1);
-        }
-        return inpL;
+// build the input after conv2d (inp_raw --> patches)
+// returns tensor with shape [n_embd, n_patches]
+ggml_tensor * clip_graph::build_inp() {
+    ggml_tensor * inp_raw = build_inp_raw();
+    ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+    inp = ggml_reshape_2d(ctx0, inp, n_patches, n_embd);
+    inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp));
+    if (model.patch_bias) {
+        inp = ggml_add(ctx0, inp, model.patch_bias);
+        cb(inp, "patch_bias", -1);
     }
+    return inp;
+}
 
-    // build the input after conv2d (inp_raw --> patches)
-    // returns tensor with shape [n_embd, n_patches]
-    ggml_tensor * build_inp() {
-        ggml_tensor * inp_raw = build_inp_raw();
-        ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
-        inp = ggml_reshape_2d(ctx0, inp, n_patches, n_embd);
-        inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp));
-        if (model.patch_bias) {
-            inp = ggml_add(ctx0, inp, model.patch_bias);
-            cb(inp, "patch_bias", -1);
-        }
-        return inp;
-    }
+ggml_tensor * clip_graph::build_inp_raw(int channels) {
+    ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, img.nx, img.ny, channels);
+    ggml_set_name(inp_raw, "inp_raw");
+    ggml_set_input(inp_raw);
+    return inp_raw;
+}
 
-    ggml_tensor * build_inp_raw(int channels = 3) {
-        ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, img.nx, img.ny, channels);
-        ggml_set_name(inp_raw, "inp_raw");
-        ggml_set_input(inp_raw);
-        return inp_raw;
-    }
-
-    ggml_tensor * build_norm(
-            ggml_tensor * cur,
-            ggml_tensor * mw,
-            ggml_tensor * mb,
-            norm_type type,
-            float norm_eps,
-            int il) const {
-
-        cur = type == NORM_TYPE_RMS
-            ? ggml_rms_norm(ctx0, cur, norm_eps)
-            : ggml_norm(ctx0, cur, norm_eps);
-
-        if (mw || mb) {
-            cb(cur, "norm", il);
-        }
-
-        if (mw) {
-            cur = ggml_mul(ctx0, cur, mw);
-            if (mb) {
-                cb(cur, "norm_w", il);
-            }
-        }
-
-        if (mb) {
-            cur = ggml_add(ctx0, cur, mb);
-        }
-
-        return cur;
-    }
-
-    ggml_tensor * build_ffn(
-            ggml_tensor * cur,
-            ggml_tensor * up,
-            ggml_tensor * up_b,
-            ggml_tensor * gate,
-            ggml_tensor * gate_b,
-            ggml_tensor * down,
-            ggml_tensor * down_b,
-            ffn_op_type type_op,
-            int il) const {
-
-        ggml_tensor * tmp = up ? ggml_mul_mat(ctx0, up, cur) : cur;
-        cb(tmp, "ffn_up", il);
-
-        if (up_b) {
-            tmp = ggml_add(ctx0, tmp, up_b);
-            cb(tmp, "ffn_up_b", il);
-        }
-
-        if (gate) {
-            cur = ggml_mul_mat(ctx0, gate, cur);
-            cb(cur, "ffn_gate", il);
-
-            if (gate_b) {
-                cur = ggml_add(ctx0, cur, gate_b);
-                cb(cur, "ffn_gate_b", il);
-            }
-        } else {
-            cur = tmp;
-        }
-
-        // we only support parallel ffn for now
-        switch (type_op) {
-            case FFN_SILU:
-                if (gate) {
-                    cur = ggml_swiglu_split(ctx0, cur, tmp);
-                    cb(cur, "ffn_swiglu", il);
-                } else {
-                    cur = ggml_silu(ctx0, cur);
-                    cb(cur, "ffn_silu", il);
-                } break;
-            case FFN_GELU:
-                if (gate) {
-                    cur = ggml_geglu_split(ctx0, cur, tmp);
-                    cb(cur, "ffn_geglu", il);
-                } else {
-                    cur = ggml_gelu(ctx0, cur);
-                    cb(cur, "ffn_gelu", il);
-                } break;
-            case FFN_GELU_ERF:
-                if (gate) {
-                    cur = ggml_geglu_erf_split(ctx0, cur, tmp);
-                    cb(cur, "ffn_geglu_erf", il);
-                } else {
-                    cur = ggml_gelu_erf(ctx0, cur);
-                    cb(cur, "ffn_gelu_erf", il);
-                } break;
-            case FFN_GELU_QUICK:
-                if (gate) {
-                    cur = ggml_geglu_quick_split(ctx0, cur, tmp);
-                    cb(cur, "ffn_geglu_quick", il);
-                } else {
-                    cur = ggml_gelu_quick(ctx0, cur);
-                    cb(cur, "ffn_gelu_quick", il);
-                } break;
-        }
-
-        if (down) {
-            cur = ggml_mul_mat(ctx0, down, cur);
-        }
-
-        if (down_b) {
-            cb(cur, "ffn_down", il);
-        }
-
-        if (down_b) {
-            cur = ggml_add(ctx0, cur, down_b);
-        }
-
-        return cur;
-    }
-
-    ggml_tensor * build_attn(
-            ggml_tensor * wo,
-            ggml_tensor * wo_b,
-            ggml_tensor * q_cur,
-            ggml_tensor * k_cur,
-            ggml_tensor * v_cur,
-            ggml_tensor * kq_mask,
-            float kq_scale,
-            int il) const {
-        // these nodes are added to the graph together so that they are not reordered
-        // by doing so, the number of splits in the graph is reduced
-        ggml_build_forward_expand(gf, q_cur);
-        ggml_build_forward_expand(gf, k_cur);
-        ggml_build_forward_expand(gf, v_cur);
-
-        ggml_tensor * q = ggml_permute(ctx0, q_cur, 0, 2, 1, 3);
-        //cb(q, "q", il);
-
-        ggml_tensor * k = ggml_permute(ctx0, k_cur, 0, 2, 1, 3);
-        //cb(k, "k", il);
-
-        ggml_tensor * cur;
-
-        if (ctx->flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) {
-            ggml_tensor * v = ggml_permute(ctx0, v_cur, 0, 2, 1, 3);
-
-            k = ggml_cast(ctx0, k, GGML_TYPE_F16);
-            v = ggml_cast(ctx0, v, GGML_TYPE_F16);
-
-            cur = ggml_flash_attn_ext(ctx0, q, k, v, kq_mask, kq_scale, 0.0f, 0.0f);
-            ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
-
-            cur = ggml_reshape_2d(ctx0, cur, cur->ne[0]*cur->ne[1], cur->ne[2]*cur->ne[3]);
-
-        } else {
-            ggml_tensor * v = ggml_permute(ctx0, v_cur, 1, 2, 0, 3);
-            v = ggml_cont(ctx0, v);
-
-            const auto n_tokens = q->ne[1];
-            const auto n_head   = q->ne[2];
-
-            ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
-            // F32 may not needed for vision encoders?
-            // ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
-
-            kq = ggml_soft_max_ext(ctx0, kq, kq_mask, kq_scale, 0.0f);
-
-            ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
-            cur = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
-            cur = ggml_cont_2d(ctx0, cur, cur->ne[0]*n_head, n_tokens);
-        }
-
-        cb(cur, "kqv_out", il);
-
-        if (wo) {
-            cur = ggml_mul_mat(ctx0, wo, cur);
-        }
-
-        if (wo_b) {
-            cur = ggml_add(ctx0, cur, wo_b);
-        }
-
-        return cur;
-    }
-
-    // implementation of the 2D RoPE without adding a new op in ggml
-    // this is not efficient (use double the memory), but works on all backends
-    // TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065
-    static ggml_tensor * build_rope_2d(
-        ggml_context * ctx0,
+ggml_tensor * clip_graph::build_norm(
         ggml_tensor * cur,
-        ggml_tensor * pos_a, // first half
-        ggml_tensor * pos_b, // second half
-        const float freq_base,
-        const bool interleave_freq
-    ) {
-        const int64_t n_dim  = cur->ne[0];
-        const int64_t n_head = cur->ne[1];
-        const int64_t n_pos  = cur->ne[2];
+        ggml_tensor * mw,
+        ggml_tensor * mb,
+        norm_type type,
+        float norm_eps,
+        int il) const {
 
-        // for example, if we have cur tensor of shape (n_dim=8, n_head, n_pos)
-        // we will have a list of 4 inv_freq: 1e-0, 1e-1, 1e-2, 1e-3
-        // first half of cur will use 1e-0, 1e-2 (even)
-        // second half of cur will use 1e-1, 1e-3 (odd)
-        // the trick here is to rotate just half of n_dim, so inv_freq will automatically be even
-        //  ^ don't ask me why, it's math! -2(2i) / n_dim == -2i / (n_dim/2)
-        // then for the second half, we use freq_scale to shift the inv_freq
-        //  ^ why? replace (2i) with (2i+1) in the above equation
-        const float freq_scale_odd = interleave_freq
-                                    ? std::pow(freq_base, (float)-2/n_dim)
-                                    : 1.0;
+    cur = type == NORM_TYPE_RMS
+        ? ggml_rms_norm(ctx0, cur, norm_eps)
+        : ggml_norm(ctx0, cur, norm_eps);
 
-        // first half
-        ggml_tensor * first;
-        {
-            first = ggml_view_3d(ctx0, cur,
-                n_dim/2, n_head, n_pos,
-                ggml_row_size(cur->type, n_dim),
-                ggml_row_size(cur->type, n_dim*n_head),
-                0);
-            first = ggml_rope_ext(
-                ctx0,
-                first,
-                pos_a,      // positions
-                nullptr,    // freq factors
-                n_dim/2,    // n_dims
-                0, 0, freq_base,
-                1.0f, 0.0f, 1.0f, 0.0f, 0.0f
-            );
-        }
-
-        // second half
-        ggml_tensor * second;
-        {
-            second = ggml_view_3d(ctx0, cur,
-                n_dim/2, n_head, n_pos,
-                ggml_row_size(cur->type, n_dim),
-                ggml_row_size(cur->type, n_dim*n_head),
-                n_dim/2 * ggml_element_size(cur));
-            second = ggml_rope_ext(
-                ctx0,
-                second,
-                pos_b,      // positions
-                nullptr,    // freq factors
-                n_dim/2,    // n_dims
-                0, 0, freq_base,
-                freq_scale_odd,
-                0.0f, 1.0f, 0.0f, 0.0f
-            );
-        }
-
-        cur = ggml_concat(ctx0, first, second, 0);
-        return cur;
+    if (mw || mb) {
+        cb(cur, "norm", il);
     }
 
-    // aka pixel_shuffle / pixel_unshuffle / patch_merger (Kimi-VL)
-    // support dynamic resolution
-    ggml_tensor * build_patch_merge_permute(ggml_tensor * cur, int scale_factor) {
-        GGML_ASSERT(scale_factor > 1);
-
-        const int n_embd = cur->ne[0];
-        int width  = img.nx / patch_size;
-        int height = img.ny / patch_size;
-
-        // pad width and height to factor
-        const int64_t pad_width  = CLIP_ALIGN(width,  scale_factor) - width;
-        const int64_t pad_height = CLIP_ALIGN(height, scale_factor) - height;
-        cur = ggml_reshape_3d(ctx0, cur, n_embd, width, height);
-        if (pad_width || pad_height) {
-            cur     = ggml_pad(ctx0, cur, 0, pad_width, pad_height, 0);
-            width  += pad_width;
-            height += pad_height;
+    if (mw) {
+        cur = ggml_mul(ctx0, cur, mw);
+        if (mb) {
+            cb(cur, "norm_w", il);
         }
-
-        // unshuffle h
-        cur = ggml_reshape_3d(ctx0, cur, n_embd * scale_factor, width / scale_factor, height);
-        cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
-
-        // unshuffle w
-        cur = ggml_cont_3d(ctx0, cur, n_embd * scale_factor * scale_factor, height / scale_factor, width / scale_factor);
-        cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
-
-        cur = ggml_cont_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]);
-        cb(cur, "pixel_shuffle", -1);
-
-        return cur;
     }
 
-};
+    if (mb) {
+        cur = ggml_add(ctx0, cur, mb);
+    }
+
+    return cur;
+}
+
+ggml_tensor * clip_graph::build_ffn(
+        ggml_tensor * cur,
+        ggml_tensor * up,
+        ggml_tensor * up_b,
+        ggml_tensor * gate,
+        ggml_tensor * gate_b,
+        ggml_tensor * down,
+        ggml_tensor * down_b,
+        ffn_op_type type_op,
+        int il) const {
+
+    ggml_tensor * tmp = up ? ggml_mul_mat(ctx0, up, cur) : cur;
+    cb(tmp, "ffn_up", il);
+
+    if (up_b) {
+        tmp = ggml_add(ctx0, tmp, up_b);
+        cb(tmp, "ffn_up_b", il);
+    }
+
+    if (gate) {
+        cur = ggml_mul_mat(ctx0, gate, cur);
+        cb(cur, "ffn_gate", il);
+
+        if (gate_b) {
+            cur = ggml_add(ctx0, cur, gate_b);
+            cb(cur, "ffn_gate_b", il);
+        }
+    } else {
+        cur = tmp;
+    }
+
+    // we only support parallel ffn for now
+    switch (type_op) {
+        case FFN_SILU:
+            if (gate) {
+                cur = ggml_swiglu_split(ctx0, cur, tmp);
+                cb(cur, "ffn_swiglu", il);
+            } else {
+                cur = ggml_silu(ctx0, cur);
+                cb(cur, "ffn_silu", il);
+            } break;
+        case FFN_GELU:
+            if (gate) {
+                cur = ggml_geglu_split(ctx0, cur, tmp);
+                cb(cur, "ffn_geglu", il);
+            } else {
+                cur = ggml_gelu(ctx0, cur);
+                cb(cur, "ffn_gelu", il);
+            } break;
+        case FFN_GELU_ERF:
+            if (gate) {
+                cur = ggml_geglu_erf_split(ctx0, cur, tmp);
+                cb(cur, "ffn_geglu_erf", il);
+            } else {
+                cur = ggml_gelu_erf(ctx0, cur);
+                cb(cur, "ffn_gelu_erf", il);
+            } break;
+        case FFN_GELU_QUICK:
+            if (gate) {
+                cur = ggml_geglu_quick_split(ctx0, cur, tmp);
+                cb(cur, "ffn_geglu_quick", il);
+            } else {
+                cur = ggml_gelu_quick(ctx0, cur);
+                cb(cur, "ffn_gelu_quick", il);
+            } break;
+    }
+
+    if (down) {
+        cur = ggml_mul_mat(ctx0, down, cur);
+    }
+
+    if (down_b) {
+        cb(cur, "ffn_down", il);
+    }
+
+    if (down_b) {
+        cur = ggml_add(ctx0, cur, down_b);
+    }
+
+    return cur;
+}
+
+ggml_tensor * clip_graph::build_attn(
+        ggml_tensor * wo,
+        ggml_tensor * wo_b,
+        ggml_tensor * q_cur,
+        ggml_tensor * k_cur,
+        ggml_tensor * v_cur,
+        ggml_tensor * kq_mask,
+        float kq_scale,
+        int il) const {
+    // these nodes are added to the graph together so that they are not reordered
+    // by doing so, the number of splits in the graph is reduced
+    ggml_build_forward_expand(gf, q_cur);
+    ggml_build_forward_expand(gf, k_cur);
+    ggml_build_forward_expand(gf, v_cur);
+
+    ggml_tensor * q = ggml_permute(ctx0, q_cur, 0, 2, 1, 3);
+    //cb(q, "q", il);
+
+    ggml_tensor * k = ggml_permute(ctx0, k_cur, 0, 2, 1, 3);
+    //cb(k, "k", il);
+
+    ggml_tensor * cur;
+
+    if (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) {
+        ggml_tensor * v = ggml_permute(ctx0, v_cur, 0, 2, 1, 3);
+
+        k = ggml_cast(ctx0, k, GGML_TYPE_F16);
+        v = ggml_cast(ctx0, v, GGML_TYPE_F16);
+
+        cur = ggml_flash_attn_ext(ctx0, q, k, v, kq_mask, kq_scale, 0.0f, 0.0f);
+        ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32);
+
+        cur = ggml_reshape_2d(ctx0, cur, cur->ne[0]*cur->ne[1], cur->ne[2]*cur->ne[3]);
+
+    } else {
+        ggml_tensor * v = ggml_permute(ctx0, v_cur, 1, 2, 0, 3);
+        v = ggml_cont(ctx0, v);
+
+        const auto n_tokens = q->ne[1];
+        const auto n_head   = q->ne[2];
+
+        ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+        // F32 may not needed for vision encoders?
+        // ggml_mul_mat_set_prec(kq, GGML_PREC_F32);
+
+        kq = ggml_soft_max_ext(ctx0, kq, kq_mask, kq_scale, 0.0f);
+
+        ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
+        cur = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+        cur = ggml_cont_2d(ctx0, cur, cur->ne[0]*n_head, n_tokens);
+    }
+
+    cb(cur, "kqv_out", il);
+
+    if (wo) {
+        cur = ggml_mul_mat(ctx0, wo, cur);
+    }
+
+    if (wo_b) {
+        cur = ggml_add(ctx0, cur, wo_b);
+    }
+
+    return cur;
+}
+
+// implementation of the 2D RoPE without adding a new op in ggml
+// this is not efficient (use double the memory), but works on all backends
+// TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065
+ggml_tensor * clip_graph::build_rope_2d(
+    ggml_context * ctx0,
+    ggml_tensor * cur,
+    ggml_tensor * pos_a, // first half
+    ggml_tensor * pos_b, // second half
+    const float freq_base,
+    const bool interleave_freq
+) {
+    const int64_t n_dim  = cur->ne[0];
+    const int64_t n_head = cur->ne[1];
+    const int64_t n_pos  = cur->ne[2];
+
+    // for example, if we have cur tensor of shape (n_dim=8, n_head, n_pos)
+    // we will have a list of 4 inv_freq: 1e-0, 1e-1, 1e-2, 1e-3
+    // first half of cur will use 1e-0, 1e-2 (even)
+    // second half of cur will use 1e-1, 1e-3 (odd)
+    // the trick here is to rotate just half of n_dim, so inv_freq will automatically be even
+    //  ^ don't ask me why, it's math! -2(2i) / n_dim == -2i / (n_dim/2)
+    // then for the second half, we use freq_scale to shift the inv_freq
+    //  ^ why? replace (2i) with (2i+1) in the above equation
+    const float freq_scale_odd = interleave_freq
+                                ? std::pow(freq_base, (float)-2/n_dim)
+                                : 1.0;
+
+    // first half
+    ggml_tensor * first;
+    {
+        first = ggml_view_3d(ctx0, cur,
+            n_dim/2, n_head, n_pos,
+            ggml_row_size(cur->type, n_dim),
+            ggml_row_size(cur->type, n_dim*n_head),
+            0);
+        first = ggml_rope_ext(
+            ctx0,
+            first,
+            pos_a,      // positions
+            nullptr,    // freq factors
+            n_dim/2,    // n_dims
+            0, 0, freq_base,
+            1.0f, 0.0f, 1.0f, 0.0f, 0.0f
+        );
+    }
+
+    // second half
+    ggml_tensor * second;
+    {
+        second = ggml_view_3d(ctx0, cur,
+            n_dim/2, n_head, n_pos,
+            ggml_row_size(cur->type, n_dim),
+            ggml_row_size(cur->type, n_dim*n_head),
+            n_dim/2 * ggml_element_size(cur));
+        second = ggml_rope_ext(
+            ctx0,
+            second,
+            pos_b,      // positions
+            nullptr,    // freq factors
+            n_dim/2,    // n_dims
+            0, 0, freq_base,
+            freq_scale_odd,
+            0.0f, 1.0f, 0.0f, 0.0f
+        );
+    }
+
+    cur = ggml_concat(ctx0, first, second, 0);
+    return cur;
+}
+
+// aka pixel_shuffle / pixel_unshuffle / patch_merger (Kimi-VL)
+// support dynamic resolution
+ggml_tensor * clip_graph::build_patch_merge_permute(ggml_tensor * cur, int scale_factor) {
+    GGML_ASSERT(scale_factor > 1);
+
+    const int n_embd = cur->ne[0];
+    int width  = img.nx / patch_size;
+    int height = img.ny / patch_size;
+
+    // pad width and height to factor
+    const int64_t pad_width  = CLIP_ALIGN(width,  scale_factor) - width;
+    const int64_t pad_height = CLIP_ALIGN(height, scale_factor) - height;
+    cur = ggml_reshape_3d(ctx0, cur, n_embd, width, height);
+    if (pad_width || pad_height) {
+        cur     = ggml_pad(ctx0, cur, 0, pad_width, pad_height, 0);
+        width  += pad_width;
+        height += pad_height;
+    }
+
+    // unshuffle h
+    cur = ggml_reshape_3d(ctx0, cur, n_embd * scale_factor, width / scale_factor, height);
+    cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
+
+    // unshuffle w
+    cur = ggml_cont_3d(ctx0, cur, n_embd * scale_factor * scale_factor, height / scale_factor, width / scale_factor);
+    cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
+
+    cur = ggml_cont_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]);
+    cb(cur, "pixel_shuffle", -1);
+
+    return cur;
+}
 
 static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch & imgs) {
     GGML_ASSERT(imgs.entries.size() == 1 && "n_batch > 1 is not supported");
-    clip_graph graph(ctx, *imgs.entries[0]);
 
-    ggml_cgraph * res;
+    const clip_image_f32 & img = *imgs.entries[0];
+    std::unique_ptr<clip_graph> builder;
 
     switch (ctx->proj_type()) {
         case PROJECTOR_TYPE_GEMMA3:
         case PROJECTOR_TYPE_IDEFICS3:
         case PROJECTOR_TYPE_LFM2:
+        case PROJECTOR_TYPE_JANUS_PRO:
             {
-                res = graph.build_siglip();
+                builder = std::make_unique<clip_graph_siglip>(ctx, img);
             } break;
         case PROJECTOR_TYPE_PIXTRAL:
         case PROJECTOR_TYPE_LIGHTONOCR:
             {
-                res = graph.build_pixtral();
+                builder = std::make_unique<clip_graph_pixtral>(ctx, img);
             } break;
         case PROJECTOR_TYPE_QWEN2VL:
         case PROJECTOR_TYPE_QWEN25VL:
             {
-                res = graph.build_qwen2vl();
+                builder = std::make_unique<clip_graph_qwen2vl>(ctx, img);
             } break;
         case PROJECTOR_TYPE_QWEN3VL:
             {
-                res = graph.build_qwen3vl();
+                builder = std::make_unique<clip_graph_qwen3vl>(ctx, img);
             } break;
         case PROJECTOR_TYPE_MINICPMV:
             {
-                res = graph.build_minicpmv();
+                builder = std::make_unique<clip_graph_minicpmv>(ctx, img);
             } break;
         case PROJECTOR_TYPE_INTERNVL:
             {
-                res = graph.build_internvl();
+                builder = std::make_unique<clip_graph_internvl>(ctx, img);
             } break;
         case PROJECTOR_TYPE_LLAMA4:
             {
-                res = graph.build_llama4();
+                builder = std::make_unique<clip_graph_llama4>(ctx, img);
             } break;
         case PROJECTOR_TYPE_ULTRAVOX:
         case PROJECTOR_TYPE_VOXTRAL:
         case PROJECTOR_TYPE_QWEN2A:
             {
-                res = graph.build_whisper_enc();
+                builder = std::make_unique<clip_graph_whisper_enc>(ctx, img);
             } break;
         case PROJECTOR_TYPE_KIMIVL:
             {
-                res = graph.build_kimivl();
-            } break;
-        case PROJECTOR_TYPE_JANUS_PRO:
-            {
-                res = graph.build_siglip();
+                builder = std::make_unique<clip_graph_kimivl>(ctx, img);
             } break;
         case PROJECTOR_TYPE_COGVLM:
             {
-                res = graph.build_cogvlm();
+                builder = std::make_unique<clip_graph_cogvlm>(ctx, img);
+            } break;
+        case PROJECTOR_TYPE_MLP:
+        case PROJECTOR_TYPE_MLP_NORM:
+        case PROJECTOR_TYPE_LDP:
+        case PROJECTOR_TYPE_LDPV2:
+        case PROJECTOR_TYPE_GLM_EDGE:
+            {
+                builder = std::make_unique<clip_graph_llava>(ctx, img);
             } break;
         default:
-            {
-                res = graph.build_llava();
-            } break;
+            GGML_ABORT("missing cgraph builder");
     }
-    return res;
+
+    return builder->build();
 }
 
+//
+// clip_model_loader
+//
+
 struct clip_model_loader {
     ggml_context_ptr ctx_meta;
     gguf_context_ptr ctx_gguf;
diff --git a/tools/mtmd/models/cogvlm.cpp b/tools/mtmd/models/cogvlm.cpp
new file mode 100644
index 0000000000..d5b739c687
--- /dev/null
+++ b/tools/mtmd/models/cogvlm.cpp
@@ -0,0 +1,98 @@
+#include "models.h"
+
+ggml_cgraph * clip_graph_cogvlm::build() {
+    GGML_ASSERT(model.class_embedding != nullptr);
+    GGML_ASSERT(model.position_embeddings != nullptr);
+
+    const int n_pos = n_patches + 1; // +1 for [CLS]
+
+    // build input and concatenate class embedding
+    ggml_tensor * inp = build_inp();
+    inp = ggml_concat(ctx0, inp, model.class_embedding, 1);
+
+    inp = ggml_add(ctx0, inp, model.position_embeddings);
+    cb(inp, "inp_pos", -1);
+
+    ggml_tensor * inpL = inp;
+
+    for (int il = 0; il < n_layer; il++) {
+        auto & layer = model.layers[il];
+        ggml_tensor * cur = inpL;
+
+        cur = ggml_mul_mat(ctx0, layer.qkv_w, cur);
+
+        cur = ggml_add(ctx0, cur, layer.qkv_b);
+
+        ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float),
+            cur->nb[1], 0);
+        ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float),
+            cur->nb[1], n_embd * sizeof(float));
+        ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float),
+            cur->nb[1], 2 * n_embd * sizeof(float));
+
+        cb(Qcur, "Qcur", il);
+        cb(Kcur, "Kcur", il);
+        cb(Vcur, "Vcur", il);
+
+        cur = build_attn(layer.o_w, layer.o_b,
+            Qcur, Kcur, Vcur, nullptr, kq_scale, il);
+        cb(cur, "attn_out", il);
+
+        cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il);
+        cb(cur, "attn_post_norm", il);
+
+        cur = ggml_add(ctx0, cur, inpL);
+        inpL = cur;
+
+        cur = build_ffn(cur,
+            layer.ff_up_w, layer.ff_up_b,
+            layer.ff_gate_w, layer.ff_gate_b,
+            layer.ff_down_w, layer.ff_down_b,
+            hparams.ffn_op, il);
+
+        cb(cur, "ffn_out", il);
+
+        cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il);
+        cb(cur, "ffn_post_norm", il);
+
+        cur = ggml_add(ctx0, cur, inpL);
+        cb(cur, "layer_out", il);
+        inpL = cur;
+
+    }
+
+    // remove CLS token (like build_llama4 does)
+    ggml_tensor * cur = ggml_view_2d(ctx0, inpL,
+        n_embd, n_patches,
+        ggml_row_size(inpL->type, n_embd), 0);
+
+    // Multiply with mm_model_proj
+    cur = ggml_mul_mat(ctx0, model.mm_model_proj, cur);
+
+    // Apply layernorm, weight, bias
+    cur = build_norm(cur, model.mm_post_fc_norm_w, model.mm_post_fc_norm_b, NORM_TYPE_NORMAL, 1e-5, -1);
+
+    // Apply GELU
+    cur = ggml_gelu_inplace(ctx0, cur);
+
+    // Branch 1: multiply with mm_h_to_4h_w
+    ggml_tensor * h_to_4h = ggml_mul_mat(ctx0, model.mm_h_to_4h_w, cur);
+
+    // Branch 2: multiply with mm_gate_w
+    ggml_tensor * gate = ggml_mul_mat(ctx0, model.mm_gate_w, cur);
+
+    // Apply silu
+    gate = ggml_swiglu_split(ctx0, gate, h_to_4h);
+
+    // Apply mm_4h_to_h_w
+    cur = ggml_mul_mat(ctx0, model.mm_4h_to_h_w, gate);
+
+    // Concatenate with boi and eoi
+    cur = ggml_concat(ctx0, model.mm_boi, cur, 1);
+    cur = ggml_concat(ctx0, cur, model.mm_eoi, 1);
+
+    // build the graph
+    ggml_build_forward_expand(gf, cur);
+
+    return gf;
+}
diff --git a/tools/mtmd/models/internvl.cpp b/tools/mtmd/models/internvl.cpp
new file mode 100644
index 0000000000..9aded3b97c
--- /dev/null
+++ b/tools/mtmd/models/internvl.cpp
@@ -0,0 +1,69 @@
+#include "models.h"
+
+ggml_cgraph * clip_graph_internvl::build() {
+    GGML_ASSERT(model.class_embedding != nullptr);
+    GGML_ASSERT(model.position_embeddings != nullptr);
+
+    const int n_pos = n_patches + 1;
+    ggml_tensor * inp = build_inp();
+
+    // add CLS token
+    inp = ggml_concat(ctx0, inp, model.class_embedding, 1);
+
+    // The larger models use a different ViT, which uses RMS norm instead of layer norm
+    // ref: https://github.com/ggml-org/llama.cpp/pull/13443#issuecomment-2869786188
+    norm_type norm_t = (hparams.n_embd == 3200 && hparams.n_layer == 45)
+        ? NORM_TYPE_RMS // 6B ViT (Used by InternVL 2.5/3 - 26B, 38B, 78B)
+        : NORM_TYPE_NORMAL; // 300M ViT (Used by all smaller InternVL models)
+
+    ggml_tensor * cur = build_vit(
+                            inp, n_pos,
+                            norm_t,
+                            hparams.ffn_op,
+                            model.position_embeddings,
+                            nullptr);
+
+    // remove CLS token
+    cur = ggml_view_2d(ctx0, cur,
+        n_embd, n_patches,
+        ggml_row_size(cur->type, n_embd), 0);
+
+    // pixel shuffle
+    {
+        const int scale_factor = model.hparams.n_merge;
+        const int bsz    = 1; // batch size, always 1 for now since we don't support batching
+        const int height = n_patches_y;
+        const int width  = n_patches_x;
+        GGML_ASSERT(scale_factor > 0);
+        cur = ggml_reshape_4d(ctx0, cur, n_embd * scale_factor, height / scale_factor, width, bsz);
+        cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
+        cur = ggml_cont_4d(ctx0, cur,
+            n_embd * scale_factor * scale_factor,
+            height / scale_factor,
+            width / scale_factor,
+            bsz);
+        cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
+        // flatten to 2D
+        cur = ggml_cont_2d(ctx0, cur,
+            n_embd * scale_factor * scale_factor,
+            cur->ne[1] * cur->ne[2]);
+    }
+
+    // projector (always using GELU activation)
+    {
+        // projector LayerNorm uses pytorch's default eps = 1e-5
+        // ref: https://huggingface.co/OpenGVLab/InternVL3-8B-Instruct/blob/a34d3e4e129a5856abfd6aa6de79776484caa14e/modeling_internvl_chat.py#L79
+        cur = build_norm(cur, model.mm_0_w, model.mm_0_b, NORM_TYPE_NORMAL, 1e-5, -1);
+        cur = build_ffn(cur,
+            model.mm_1_w, model.mm_1_b,
+            nullptr, nullptr,
+            model.mm_3_w, model.mm_3_b,
+            FFN_GELU,
+            -1);
+    }
+
+    // build the graph
+    ggml_build_forward_expand(gf, cur);
+
+    return gf;
+}
diff --git a/tools/mtmd/models/kimivl.cpp b/tools/mtmd/models/kimivl.cpp
new file mode 100644
index 0000000000..0a06f5090e
--- /dev/null
+++ b/tools/mtmd/models/kimivl.cpp
@@ -0,0 +1,63 @@
+#include "models.h"
+
+ggml_cgraph * clip_graph_kimivl::build() {
+    // 2D input positions
+    ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
+    ggml_set_name(pos_h, "pos_h");
+    ggml_set_input(pos_h);
+
+    ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
+    ggml_set_name(pos_w, "pos_w");
+    ggml_set_input(pos_w);
+
+    ggml_tensor * learned_pos_embd = resize_position_embeddings();
+
+    // build ViT with 2D position embeddings
+    auto add_pos = [&](ggml_tensor * cur, const clip_layer &) {
+        // first half is X axis and second half is Y axis
+        return build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false);
+    };
+
+    ggml_tensor * inp = build_inp();
+    ggml_tensor * cur = build_vit(
+                            inp, n_patches,
+                            NORM_TYPE_NORMAL,
+                            hparams.ffn_op,
+                            learned_pos_embd,
+                            add_pos);
+
+    cb(cur, "vit_out", -1);
+
+    {
+        // patch_merger
+        const int scale_factor = model.hparams.n_merge;
+        cur = build_patch_merge_permute(cur, scale_factor);
+
+        // projection norm
+        int proj_inp_dim = cur->ne[0];
+        cur = ggml_view_2d(ctx0, cur,
+            n_embd, cur->ne[1] * scale_factor * scale_factor,
+            ggml_row_size(cur->type, n_embd), 0);
+        cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm
+        cur = ggml_mul(ctx0, cur, model.mm_input_norm_w);
+        cur = ggml_add(ctx0, cur, model.mm_input_norm_b);
+        cur = ggml_view_2d(ctx0, cur,
+            proj_inp_dim, cur->ne[1] / scale_factor / scale_factor,
+            ggml_row_size(cur->type, proj_inp_dim), 0);
+        cb(cur, "proj_inp_normed", -1);
+
+        // projection mlp
+        cur = build_ffn(cur,
+            model.mm_1_w, model.mm_1_b,
+            nullptr, nullptr,
+            model.mm_2_w, model.mm_2_b,
+            FFN_GELU,
+            -1);
+        cb(cur, "proj_out", -1);
+    }
+
+    // build the graph
+    ggml_build_forward_expand(gf, cur);
+
+    return gf;
+}
diff --git a/tools/mtmd/models/llama4.cpp b/tools/mtmd/models/llama4.cpp
new file mode 100644
index 0000000000..30d1df5bcd
--- /dev/null
+++ b/tools/mtmd/models/llama4.cpp
@@ -0,0 +1,96 @@
+#include "models.h"
+
+ggml_cgraph * clip_graph_llama4::build() {
+    GGML_ASSERT(model.class_embedding != nullptr);
+    GGML_ASSERT(model.position_embeddings != nullptr);
+
+    const int n_pos = n_patches + 1; // +1 for [CLS]
+
+    // 2D input positions
+    ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos);
+    ggml_set_name(pos_h, "pos_h");
+    ggml_set_input(pos_h);
+
+    ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos);
+    ggml_set_name(pos_w, "pos_w");
+    ggml_set_input(pos_w);
+
+    ggml_tensor * inp = build_inp_raw();
+
+    // Llama4UnfoldConvolution
+    {
+        ggml_tensor * kernel = ggml_reshape_4d(ctx0, model.patch_embeddings_0,
+                                                patch_size, patch_size, 3, n_embd);
+        inp = ggml_im2col(ctx0, kernel, inp, patch_size, patch_size, 0, 0, 1, 1, true, inp->type);
+        inp = ggml_mul_mat(ctx0, model.patch_embeddings_0, inp);
+        inp = ggml_reshape_2d(ctx0, inp, n_embd, n_patches);
+        cb(inp, "patch_conv", -1);
+    }
+
+    // add CLS token
+    inp = ggml_concat(ctx0, inp, model.class_embedding, 1);
+
+    // build ViT with 2D position embeddings
+    auto add_pos = [&](ggml_tensor * cur, const clip_layer &) {
+        // first half is X axis and second half is Y axis
+        // ref: https://github.com/huggingface/transformers/blob/40a493c7ed4f19f08eadb0639cf26d49bfa5e180/src/transformers/models/llama4/modeling_llama4.py#L1312
+        // ref: https://github.com/Blaizzy/mlx-vlm/blob/a57156aa87b33cca6e5ee6cfc14dd4ef8f611be6/mlx_vlm/models/llama4/vision.py#L441
+        return build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false);
+    };
+    ggml_tensor * cur = build_vit(
+                            inp, n_pos,
+                            NORM_TYPE_NORMAL,
+                            hparams.ffn_op,
+                            model.position_embeddings,
+                            add_pos);
+
+    // remove CLS token
+    cur = ggml_view_2d(ctx0, cur,
+        n_embd, n_patches,
+        ggml_row_size(cur->type, n_embd), 0);
+
+    // pixel shuffle
+    // based on Llama4VisionPixelShuffleMLP
+    // https://github.com/huggingface/transformers/blob/2932f318a20d9e54cc7aea052e040164d85de7d6/src/transformers/models/llama4/modeling_llama4.py#L1151
+    {
+        const int scale_factor = model.hparams.n_merge;
+        const int bsz = 1; // batch size, always 1 for now since we don't support batching
+        GGML_ASSERT(scale_factor > 0);
+        GGML_ASSERT(n_patches_x == n_patches_y); // llama4 only supports square images
+        cur = ggml_reshape_4d(ctx0, cur,
+            n_embd * scale_factor,
+            n_patches_x / scale_factor,
+            n_patches_y,
+            bsz);
+        cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
+        cur = ggml_cont_4d(ctx0, cur,
+            n_embd * scale_factor * scale_factor,
+            n_patches_x / scale_factor,
+            n_patches_y / scale_factor,
+            bsz);
+        //cur = ggml_permute(ctx0, cur, 0, 2, 1, 3);
+        // flatten to 2D
+        cur = ggml_cont_2d(ctx0, cur,
+            n_embd * scale_factor * scale_factor,
+            n_patches / scale_factor / scale_factor);
+        cb(cur, "pixel_shuffle", -1);
+    }
+
+    // based on Llama4VisionMLP2 (always uses GELU activation, no bias)
+    {
+        cur = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, cur);
+        cur = ggml_gelu(ctx0, cur);
+        cur = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, cur);
+        cur = ggml_gelu(ctx0, cur);
+        cb(cur, "adapter_mlp", -1);
+    }
+
+    // Llama4MultiModalProjector
+    cur = ggml_mul_mat(ctx0, model.mm_model_proj, cur);
+    cb(cur, "projected", -1);
+
+    // build the graph
+    ggml_build_forward_expand(gf, cur);
+
+    return gf;
+}
diff --git a/tools/mtmd/models/llava.cpp b/tools/mtmd/models/llava.cpp
new file mode 100644
index 0000000000..0bfb5f05f6
--- /dev/null
+++ b/tools/mtmd/models/llava.cpp
@@ -0,0 +1,374 @@
+#include "models.h"
+
+// this graph is used by llava, granite and glm
+// due to having embedding_stack (used by granite), we cannot reuse build_vit
+ggml_cgraph * clip_graph_llava::build() {
+    const int batch_size = 1;
+    const int n_pos = n_patches + (model.class_embedding ? 1 : 0);
+
+    GGML_ASSERT(n_patches_x == n_patches_y && "only square images supported");
+
+    // Calculate the deepest feature layer based on hparams and projector type
+    int max_feature_layer = n_layer;
+    {
+        // Get the index of the second to last layer; this is the default for models that have a llava projector
+        int il_last = hparams.n_layer - 1;
+        int deepest_feature_layer = -1;
+
+        if (proj_type == PROJECTOR_TYPE_MINICPMV || proj_type == PROJECTOR_TYPE_GLM_EDGE) {
+            il_last += 1;
+        }
+
+        // If we set explicit vision feature layers, only go up to the deepest one
+        // NOTE: only used by granite-vision models for now
+        for (const auto & feature_layer : hparams.vision_feature_layer) {
+            if (feature_layer > deepest_feature_layer) {
+                deepest_feature_layer = feature_layer;
+            }
+        }
+        max_feature_layer = deepest_feature_layer < 0 ? il_last : deepest_feature_layer;
+    }
+
+    ggml_tensor * inp = build_inp();
+
+    // concat class_embeddings and patch_embeddings
+    if (model.class_embedding) {
+        inp = ggml_concat(ctx0, inp, model.class_embedding, 1);
+    }
+
+    ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos);
+    ggml_set_name(positions, "positions");
+    ggml_set_input(positions);
+
+    inp = ggml_add(ctx0, inp, ggml_get_rows(ctx0, model.position_embeddings, positions));
+
+    ggml_tensor * inpL = inp;
+
+    // pre-layernorm
+    if (model.pre_ln_w) {
+        inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, NORM_TYPE_NORMAL, eps, -1);
+        cb(inpL, "pre_ln", -1);
+    }
+
+    std::vector<ggml_tensor *> embedding_stack;
+    const auto & vision_feature_layer = hparams.vision_feature_layer;
+
+    // loop over layers
+    for (int il = 0; il < max_feature_layer; il++) {
+        auto & layer = model.layers[il];
+        ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states
+
+        // If this is an embedding feature layer, save the output.
+        // NOTE: 0 index here refers to the input to the encoder.
+        if (vision_feature_layer.find(il) != vision_feature_layer.end()) {
+            embedding_stack.push_back(cur);
+        }
+
+        // layernorm1
+        cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il);
+        cb(cur, "layer_inp_normed", il);
+
+        // self-attention
+        {
+            ggml_tensor * Qcur = ggml_mul_mat(ctx0, layer.q_w, cur);
+            if (layer.q_b) {
+                Qcur = ggml_add(ctx0, Qcur, layer.q_b);
+            }
+
+            ggml_tensor * Kcur = ggml_mul_mat(ctx0, layer.k_w, cur);
+            if (layer.k_b) {
+                Kcur = ggml_add(ctx0, Kcur, layer.k_b);
+            }
+
+            ggml_tensor * Vcur = ggml_mul_mat(ctx0, layer.v_w, cur);
+            if (layer.v_b) {
+                Vcur = ggml_add(ctx0, Vcur, layer.v_b);
+            }
+
+            Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos);
+            Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos);
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(layer.o_w, layer.o_b,
+                Qcur, Kcur, Vcur, nullptr, kq_scale, il);
+            cb(cur, "attn_out", il);
+        }
+
+        // re-add the layer input, e.g., residual
+        cur = ggml_add(ctx0, cur, inpL);
+
+        inpL = cur; // inpL = residual, cur = hidden_states
+
+        cb(cur, "ffn_inp", il);
+
+        // layernorm2
+        cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il);
+        cb(cur, "ffn_inp_normed", il);
+
+        // ffn
+        cur = build_ffn(cur,
+            layer.ff_up_w, layer.ff_up_b,
+            layer.ff_gate_w, layer.ff_gate_b,
+            layer.ff_down_w, layer.ff_down_b,
+            hparams.ffn_op, il);
+
+        cb(cur, "ffn_out", il);
+
+        // residual 2
+        cur = ggml_add(ctx0, inpL, cur);
+        cb(cur, "layer_out", il);
+
+        inpL = cur;
+    }
+
+    // post-layernorm
+    if (model.post_ln_w) {
+        inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, NORM_TYPE_NORMAL, eps, -1);
+    }
+
+    ggml_tensor * embeddings = inpL;
+
+    // process vision feature layers (used by granite)
+    {
+        // final layer is a vision feature layer
+        if (vision_feature_layer.find(max_feature_layer) != vision_feature_layer.end()) {
+            embedding_stack.push_back(inpL);
+        }
+
+        // If feature layers are explicitly set, stack them (if we have multiple)
+        if (!embedding_stack.empty()) {
+            embeddings = embedding_stack[0];
+            for (size_t i = 1; i < embedding_stack.size(); i++) {
+                embeddings = ggml_concat(ctx0, embeddings, embedding_stack[i], 0);
+            }
+        }
+    }
+
+    // llava projector (also used by granite)
+    if (hparams.has_llava_projector) {
+        embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]);
+
+        ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
+        ggml_set_name(patches, "patches");
+        ggml_set_input(patches);
+
+        // shape [1, 576, 1024]
+        // ne is whcn, ne = [1024, 576, 1, 1]
+        embeddings = ggml_get_rows(ctx0, embeddings, patches);
+
+        // print_tensor_info(embeddings, "embeddings");
+
+        // llava projector
+        if (proj_type == PROJECTOR_TYPE_MLP) {
+            embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
+            embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
+
+            embeddings = ggml_gelu(ctx0, embeddings);
+            if (model.mm_2_w) {
+                embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings);
+                embeddings = ggml_add(ctx0, embeddings, model.mm_2_b);
+            }
+        }
+        else if (proj_type == PROJECTOR_TYPE_MLP_NORM) {
+            embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings);
+            embeddings = ggml_add(ctx0, embeddings, model.mm_0_b);
+            // ggml_tensor_printf(embeddings, "mm_0_w",0,true,false);
+            // First LayerNorm
+            embeddings = ggml_norm(ctx0, embeddings, eps);
+            embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_1_w),
+                                model.mm_1_b);
+
+            // GELU activation
+            embeddings = ggml_gelu(ctx0, embeddings);
+
+            // Second linear layer
+            embeddings = ggml_mul_mat(ctx0, model.mm_3_w, embeddings);
+            embeddings = ggml_add(ctx0, embeddings, model.mm_3_b);
+
+            // Second LayerNorm
+            embeddings = ggml_norm(ctx0, embeddings, eps);
+            embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_4_w),
+                                model.mm_4_b);
+        }
+        else if (proj_type == PROJECTOR_TYPE_LDP) {
+            // MobileVLM projector
+            int n_patch = 24;
+            ggml_tensor * mlp_1 = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, embeddings);
+            mlp_1 = ggml_add(ctx0, mlp_1, model.mm_model_mlp_1_b);
+            mlp_1 = ggml_gelu(ctx0, mlp_1);
+            ggml_tensor * mlp_3 = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, mlp_1);
+            mlp_3 = ggml_add(ctx0, mlp_3, model.mm_model_mlp_3_b);
+            // mlp_3 shape = [1, 576, 2048], ne = [2048, 576, 1, 1]
+
+            // block 1
+            ggml_tensor * block_1 = nullptr;
+            {
+                // transpose from [1, 576, 2048] --> [1, 2048, 576] --> [1, 2048, 24, 24]
+                mlp_3 = ggml_permute(ctx0, mlp_3, 1, 0, 2, 3);
+                mlp_3 = ggml_cont_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]);
+                // stride = 1, padding = 1, bias is nullptr
+                block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1);
+
+                // layer norm
+                // // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3));
+                // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1]
+                block_1 = ggml_norm(ctx0, block_1, eps);
+                block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_0_1_w), model.mm_model_block_1_block_0_1_b);
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
+
+                // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
+                // hardswish
+                ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1);
+
+                block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0);
+                // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
+                // pointwise conv
+                block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]);
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc1_w, block_1);
+                block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc1_b);
+                block_1 = ggml_relu(ctx0, block_1);
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc2_w, block_1);
+                block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc2_b);
+                block_1 = ggml_hardsigmoid(ctx0, block_1);
+                // block_1_hw shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1], block_1 shape = [1, 2048], ne = [2048, 1, 1, 1]
+                block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]);
+                block_1 = ggml_mul(ctx0, block_1_hw, block_1);
+
+                int w = block_1->ne[0], h = block_1->ne[1];
+                block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]);
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3));
+
+                // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1]
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_2_0_w, block_1);
+                block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]);
+
+                // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1]
+                block_1 = ggml_norm(ctx0, block_1, eps);
+                block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_2_1_w), model.mm_model_block_1_block_2_1_b);
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
+                // block1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1]
+                // residual
+                block_1 = ggml_add(ctx0, mlp_3, block_1);
+            }
+
+            // block_2
+            {
+                // stride = 2
+                block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1);
+
+                // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1]
+                // layer norm
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3));
+                // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1]
+                block_1 = ggml_norm(ctx0, block_1, eps);
+                block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_0_1_w), model.mm_model_block_2_block_0_1_b);
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3));
+                // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1]
+                // hardswish
+                ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1);
+
+                // not sure the parameters is right for globalAvgPooling
+                block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0);
+                // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
+                // pointwise conv
+                block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]);
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc1_w, block_1);
+                block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc1_b);
+                block_1 = ggml_relu(ctx0, block_1);
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc2_w, block_1);
+                block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc2_b);
+                block_1 = ggml_hardsigmoid(ctx0, block_1);
+
+                // block_1_hw shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1], block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1]
+                block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]);
+                block_1 = ggml_mul(ctx0, block_1_hw, block_1);
+
+                int w = block_1->ne[0], h = block_1->ne[1];
+                block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]);
+                block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3));
+                // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1]
+                block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_2_0_w, block_1);
+                block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]);
+
+
+                // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1]
+                block_1 = ggml_norm(ctx0, block_1, eps);
+                block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_2_1_w), model.mm_model_block_2_block_2_1_b);
+                block_1 = ggml_reshape_3d(ctx0, block_1, block_1->ne[0], block_1->ne[1] * block_1->ne[2], block_1->ne[3]);
+                // block_1 shape = [1, 144, 2048], ne = [2048, 144, 1]
+            }
+            embeddings = block_1;
+        }
+        else if (proj_type == PROJECTOR_TYPE_LDPV2)
+        {
+            int n_patch = 24;
+            ggml_tensor * mlp_0 = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings);
+            mlp_0 = ggml_add(ctx0, mlp_0, model.mm_model_mlp_0_b);
+            mlp_0 = ggml_gelu(ctx0, mlp_0);
+            ggml_tensor * mlp_2 = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, mlp_0);
+            mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b);
+            // mlp_2 ne = [2048, 576, 1, 1]
+            // // AVG Pool Layer 2*2, strides = 2
+            mlp_2 = ggml_permute(ctx0, mlp_2, 1, 0, 2, 3);
+            // mlp_2 ne = [576, 2048, 1, 1]
+            mlp_2 = ggml_cont_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]);
+            // mlp_2 ne [24, 24, 2048, 1]
+            mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0);
+            // weight ne = [3, 3, 2048, 1]
+            ggml_tensor * peg_0 = ggml_conv_2d_dw(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1);
+            peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3));
+            peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b);
+            mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3));
+            peg_0 = ggml_add(ctx0, peg_0, mlp_2);
+            peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]);
+            embeddings = peg_0;
+        }
+        else {
+            GGML_ABORT("fatal error");
+        }
+    }
+
+    // glm projector
+    else if (proj_type == PROJECTOR_TYPE_GLM_EDGE) {
+        size_t gridsz = (size_t)sqrt(embeddings->ne[1]);
+        embeddings = ggml_permute(ctx0,embeddings,1,0,2,3);
+        embeddings = ggml_cont_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]);
+        embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1);
+        embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size);
+        embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings, 1, 0, 2, 3));
+        embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b);
+        // GLU
+        {
+            embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings);
+            embeddings = ggml_norm(ctx0, embeddings, eps);
+            embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b);
+            embeddings = ggml_gelu_inplace(ctx0, embeddings);
+            ggml_tensor * x = embeddings;
+            embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings);
+            x = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w,x);
+            embeddings = ggml_swiglu_split(ctx0, embeddings, x);
+            embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings);
+        }
+        // arrangement of BOI/EOI token embeddings
+        // note: these embeddings are not present in text model, hence we cannot process them as text tokens
+        // see: https://huggingface.co/THUDM/glm-edge-v-2b/blob/main/siglip.py#L53
+        {
+            embeddings = ggml_concat(ctx0, model.mm_boi, embeddings, 1); // BOI
+            embeddings = ggml_concat(ctx0, embeddings, model.mm_eoi, 1); // EOI
+        }
+    }
+
+    else {
+        GGML_ABORT("llava: unknown projector type");
+    }
+
+    // build the graph
+    ggml_build_forward_expand(gf, embeddings);
+
+    return gf;
+}
diff --git a/tools/mtmd/models/minicpmv.cpp b/tools/mtmd/models/minicpmv.cpp
new file mode 100644
index 0000000000..3594ea29fa
--- /dev/null
+++ b/tools/mtmd/models/minicpmv.cpp
@@ -0,0 +1,114 @@
+#include "models.h"
+
+ggml_cgraph * clip_graph_minicpmv::build() {
+    GGML_ASSERT(model.class_embedding == nullptr);
+    const int n_pos       = n_patches;
+    const int n_embd_proj = n_mmproj_embd;
+
+    // position embeddings for the projector (not for ViT)
+    // see: https://huggingface.co/openbmb/MiniCPM-o-2_6/blob/main/resampler.py#L70
+    // base frequency omega
+    ggml_tensor * omega = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_embd_proj / 4);
+    ggml_set_name(omega, "omega");
+    ggml_set_input(omega);
+
+    // 2D input positions (using float for sinusoidal embeddings)
+    ggml_tensor * pos_h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_pos);
+    ggml_set_name(pos_h, "pos_h");
+    ggml_set_input(pos_h);
+    ggml_tensor * pos_w = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_pos);
+    ggml_set_name(pos_w, "pos_w");
+    ggml_set_input(pos_w);
+
+    // for selecting learned pos embd, used by ViT
+    struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos);
+    ggml_set_name(positions, "positions");
+    ggml_set_input(positions);
+
+    ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, model.position_embeddings, positions);
+
+    ggml_tensor * inp = build_inp();
+    ggml_tensor * embeddings = build_vit(
+                            inp, n_pos,
+                            NORM_TYPE_NORMAL,
+                            hparams.ffn_op,
+                            learned_pos_embd,
+                            nullptr);
+
+    // resampler projector (it is just another transformer)
+
+    ggml_tensor * q = model.mm_model_query;
+    ggml_tensor * v = ggml_mul_mat(ctx0, model.mm_model_kv_proj, embeddings);
+
+    // norm
+    q = build_norm(q, model.mm_model_ln_q_w,  model.mm_model_ln_q_b,  NORM_TYPE_NORMAL, eps, -1);
+    v = build_norm(v, model.mm_model_ln_kv_w, model.mm_model_ln_kv_b, NORM_TYPE_NORMAL, eps, -1);
+
+    // calculate sinusoidal pos embd
+    ggml_tensor * pos_embed = nullptr;
+    {
+        // outer product
+        ggml_tensor * omega_b = ggml_repeat_4d(ctx0, omega, omega->ne[0], n_pos, 1, 1); // n_pos rows
+        ggml_tensor * theta_x = ggml_mul(ctx0, omega_b, pos_w);
+        ggml_tensor * theta_y = ggml_mul(ctx0, omega_b, pos_h);
+        // sin and cos
+        ggml_tensor * pos_embd_x = ggml_concat(
+            ctx0,
+            ggml_sin(ctx0, theta_x),
+            ggml_cos(ctx0, theta_x),
+            0 // concat on first dim
+        );
+        ggml_tensor * pos_embd_y = ggml_concat(
+            ctx0,
+            ggml_sin(ctx0, theta_y),
+            ggml_cos(ctx0, theta_y),
+            0 // concat on first dim
+        );
+        pos_embed = ggml_concat(ctx0, pos_embd_x, pos_embd_y, 0);
+    }
+
+    // k = v + pos_embed
+    ggml_tensor * k = ggml_add(ctx0, v, pos_embed);
+
+    // attention
+    {
+        const int d_head = 128;
+        int n_head = n_embd_proj/d_head;
+        // Use actual config value if available, otherwise fall back to hardcoded values
+        int num_query = hparams.minicpmv_query_num;
+        ggml_tensor * Q = ggml_add(ctx0,
+            ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q),
+            model.mm_model_attn_q_b);
+        ggml_tensor * K = ggml_add(ctx0,
+            ggml_mul_mat(ctx0, model.mm_model_attn_k_w, k),
+            model.mm_model_attn_k_b);
+        ggml_tensor * V = ggml_add(ctx0,
+            ggml_mul_mat(ctx0, model.mm_model_attn_v_w, v),
+            model.mm_model_attn_v_b);
+
+        Q = ggml_reshape_3d(ctx0, Q, d_head, n_head, num_query);
+        K = ggml_reshape_3d(ctx0, K, d_head, n_head, n_pos);
+        V = ggml_reshape_3d(ctx0, V, d_head, n_head, n_pos);
+
+        cb(Q, "resampler_Q", -1);
+        cb(K, "resampler_K", -1);
+        cb(V, "resampler_V", -1);
+
+        float resampler_kq_scale = 1.0f/ sqrtf(float(d_head));
+        embeddings = build_attn(
+            model.mm_model_attn_o_w,
+            model.mm_model_attn_o_b,
+            Q, K, V, nullptr, resampler_kq_scale, -1);
+        cb(embeddings, "resampler_attn_out", -1);
+    }
+    // layernorm
+    embeddings = build_norm(embeddings, model.mm_model_ln_post_w, model.mm_model_ln_post_b, NORM_TYPE_NORMAL, eps, -1);
+
+    // projection
+    embeddings = ggml_mul_mat(ctx0, model.mm_model_proj, embeddings);
+
+    // build the graph
+    ggml_build_forward_expand(gf, embeddings);
+
+    return gf;
+}
diff --git a/tools/mtmd/models/models.h b/tools/mtmd/models/models.h
new file mode 100644
index 0000000000..4b35da259c
--- /dev/null
+++ b/tools/mtmd/models/models.h
@@ -0,0 +1,58 @@
+#pragma once
+
+#include "../clip-graph.h"
+
+struct clip_graph_siglip : clip_graph {
+    clip_graph_siglip(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+};
+
+struct clip_graph_pixtral : clip_graph {
+    clip_graph_pixtral(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+};
+
+struct clip_graph_qwen2vl : clip_graph {
+    clip_graph_qwen2vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+};
+
+struct clip_graph_qwen3vl : clip_graph {
+    clip_graph_qwen3vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+};
+
+struct clip_graph_minicpmv : clip_graph {
+    clip_graph_minicpmv(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+};
+
+struct clip_graph_internvl : clip_graph {
+    clip_graph_internvl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+};
+
+struct clip_graph_llama4 : clip_graph {
+    clip_graph_llama4(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+};
+
+struct clip_graph_kimivl : clip_graph {
+    clip_graph_kimivl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+};
+
+struct clip_graph_cogvlm : clip_graph {
+    clip_graph_cogvlm(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+};
+
+struct clip_graph_llava : clip_graph {
+    clip_graph_llava(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+};
+
+struct clip_graph_whisper_enc : clip_graph {
+    clip_graph_whisper_enc(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+};
diff --git a/tools/mtmd/models/pixtral.cpp b/tools/mtmd/models/pixtral.cpp
new file mode 100644
index 0000000000..a849210b53
--- /dev/null
+++ b/tools/mtmd/models/pixtral.cpp
@@ -0,0 +1,86 @@
+#include "models.h"
+
+ggml_cgraph * clip_graph_pixtral::build() {
+    const int n_merge = hparams.n_merge;
+
+    // 2D input positions
+    ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
+    ggml_set_name(pos_h, "pos_h");
+    ggml_set_input(pos_h);
+
+    ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
+    ggml_set_name(pos_w, "pos_w");
+    ggml_set_input(pos_w);
+
+    auto add_pos = [&](ggml_tensor * cur, const clip_layer &) {
+        return build_rope_2d(ctx0, cur, pos_h, pos_w, hparams.rope_theta, true);
+    };
+
+    ggml_tensor * inp = build_inp();
+    ggml_tensor * cur = build_vit(
+                            inp, n_patches,
+                            NORM_TYPE_RMS,
+                            hparams.ffn_op,
+                            nullptr, // no learned pos embd
+                            add_pos);
+
+    // mistral small 3.1 patch merger
+    // ref: https://github.com/huggingface/transformers/blob/7a3e208892c06a5e278144eaf38c8599a42f53e7/src/transformers/models/mistral3/modeling_mistral3.py#L67
+    if (model.mm_patch_merger_w) {
+        GGML_ASSERT(hparams.n_merge > 0);
+
+        cur = ggml_mul(ctx0, ggml_rms_norm(ctx0, cur, eps), model.mm_input_norm_w);
+
+        // reshape image tokens to 2D grid
+        cur = ggml_reshape_3d(ctx0, cur, n_embd, n_patches_x, n_patches_y);
+        cur = ggml_permute(ctx0, cur, 2, 0, 1, 3); // [x, y, n_embd]
+        cur = ggml_cont(ctx0, cur);
+
+        // torch.nn.functional.unfold is just an im2col under the hood
+        // we just need a dummy kernel to make it work
+        ggml_tensor * kernel = ggml_view_3d(ctx0, cur, n_merge, n_merge, cur->ne[2], 0, 0, 0);
+        cur = ggml_im2col(ctx0, kernel, cur, n_merge, n_merge, 0, 0, 1, 1, true, inp->type);
+
+        // project to n_embd
+        cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]);
+        cur = ggml_mul_mat(ctx0, model.mm_patch_merger_w, cur);
+    }
+
+    // LlavaMultiModalProjector (always using GELU activation)
+    {
+        cur = build_ffn(cur,
+            model.mm_1_w, model.mm_1_b,
+            nullptr, nullptr,
+            model.mm_2_w, model.mm_2_b,
+            FFN_GELU,
+            -1);
+    }
+
+    // arrangement of the [IMG_BREAK] token
+    if (model.token_embd_img_break) {
+        // not efficient, but works
+        // the trick is to view the embeddings as a 3D tensor with shape [n_embd, n_patches_per_row, n_rows]
+        // and then concatenate the [IMG_BREAK] token to the end of each row, aka n_patches_per_row dimension
+        // after the concatenation, we have a tensor with shape [n_embd, n_patches_per_row + 1, n_rows]
+
+        const int p_y             = n_merge > 0 ? n_patches_y / n_merge : n_patches_y;
+        const int p_x             = n_merge > 0 ? n_patches_x / n_merge : n_patches_x;
+        const int p_total         = p_x * p_y;
+        const int n_embd_text     = cur->ne[0];
+        const int n_tokens_output = p_total + p_y - 1; // one [IMG_BREAK] per row, except the last row
+
+        ggml_tensor * tmp = ggml_reshape_3d(ctx0, cur, n_embd_text, p_x, p_y);
+        ggml_tensor * tok = ggml_new_tensor_3d(ctx0, tmp->type, n_embd_text, 1, p_y);
+        tok = ggml_scale(ctx0, tok, 0.0); // clear the tensor
+        tok = ggml_add(ctx0, tok, model.token_embd_img_break);
+        tmp = ggml_concat(ctx0, tmp, tok, 1);
+        cur = ggml_view_2d(ctx0, tmp,
+            n_embd_text, n_tokens_output,
+            ggml_row_size(tmp->type, n_embd_text), 0);
+    }
+
+    // build the graph
+    ggml_build_forward_expand(gf, cur);
+
+    return gf;
+}
diff --git a/tools/mtmd/models/qwen2vl.cpp b/tools/mtmd/models/qwen2vl.cpp
new file mode 100644
index 0000000000..85f158bb1c
--- /dev/null
+++ b/tools/mtmd/models/qwen2vl.cpp
@@ -0,0 +1,183 @@
+#include "models.h"
+
+ggml_cgraph * clip_graph_qwen2vl::build() {
+    GGML_ASSERT(model.patch_bias == nullptr);
+    GGML_ASSERT(model.class_embedding == nullptr);
+
+    const int batch_size       = 1;
+    const bool use_window_attn = hparams.n_wa_pattern > 0;
+    const int n_wa_pattern     = hparams.n_wa_pattern;
+    const int n_pos            = n_patches;
+    const int num_position_ids = n_pos * 4; // m-rope requires 4 dim per position
+
+    norm_type norm_t = proj_type == PROJECTOR_TYPE_QWEN25VL
+        ? NORM_TYPE_RMS // qwen 2.5 vl
+        : NORM_TYPE_NORMAL; // qwen 2 vl
+
+    int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
+
+    ggml_tensor * inp_raw = build_inp_raw();
+    ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+
+    GGML_ASSERT(img.nx % (patch_size * 2) == 0);
+    GGML_ASSERT(img.ny % (patch_size * 2) == 0);
+
+    // second conv dimension
+    {
+        auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+        inp = ggml_add(ctx0, inp, inp_1);
+
+        inp = ggml_permute(ctx0, inp, 1, 2, 0, 3);  // [w, h, c, b] -> [c, w, h, b]
+        inp = ggml_cont_4d(
+            ctx0, inp,
+            n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
+        inp = ggml_reshape_4d(
+            ctx0, inp,
+            n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
+        inp = ggml_permute(ctx0, inp, 0, 2, 1, 3);
+        inp = ggml_cont_3d(
+            ctx0, inp,
+            n_embd, n_patches_x * n_patches_y, batch_size);
+    }
+
+    ggml_tensor * inpL           = inp;
+    ggml_tensor * window_mask    = nullptr;
+    ggml_tensor * window_idx     = nullptr;
+    ggml_tensor * inv_window_idx = nullptr;
+
+    ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids);
+    ggml_set_name(positions, "positions");
+    ggml_set_input(positions);
+
+    // pre-layernorm
+    if (model.pre_ln_w) {
+        inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1);
+    }
+
+    if (use_window_attn) {
+        // handle window attention inputs
+        inv_window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4);
+        ggml_set_name(inv_window_idx, "inv_window_idx");
+        ggml_set_input(inv_window_idx);
+        // mask for window attention
+        window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos);
+        ggml_set_name(window_mask, "window_mask");
+        ggml_set_input(window_mask);
+
+        // if flash attn is used, we need to pad the mask and cast to f16
+        if (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) {
+            window_mask = ggml_cast(ctx0, window_mask, GGML_TYPE_F16);
+        }
+
+        // inpL shape: [n_embd, n_patches_x * n_patches_y, batch_size]
+        GGML_ASSERT(batch_size == 1);
+        inpL = ggml_reshape_2d(ctx0, inpL, n_embd * 4, n_patches_x * n_patches_y * batch_size / 4);
+        inpL = ggml_get_rows(ctx0, inpL, inv_window_idx);
+        inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_patches_x * n_patches_y, batch_size);
+    }
+
+    // loop over layers
+    for (int il = 0; il < n_layer; il++) {
+        const auto & layer = model.layers[il];
+        const bool full_attn = use_window_attn ? (il + 1) % n_wa_pattern == 0 : true;
+
+        ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states
+
+        // layernorm1
+        cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il);
+        cb(cur, "ln1", il);
+
+        // self-attention
+        {
+            ggml_tensor * Qcur = ggml_add(ctx0,
+                ggml_mul_mat(ctx0, layer.q_w, cur), layer.q_b);
+            ggml_tensor * Kcur = ggml_add(ctx0,
+                ggml_mul_mat(ctx0, layer.k_w, cur), layer.k_b);
+            ggml_tensor * Vcur = ggml_add(ctx0,
+                ggml_mul_mat(ctx0, layer.v_w, cur), layer.v_b);
+
+            Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_patches);
+            Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_patches);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_patches);
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            // apply M-RoPE
+            Qcur = ggml_rope_multi(
+                ctx0, Qcur, positions, nullptr,
+                d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
+            Kcur = ggml_rope_multi(
+                ctx0, Kcur, positions, nullptr,
+                d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
+
+            cb(Qcur, "Qcur_rope", il);
+            cb(Kcur, "Kcur_rope", il);
+
+            ggml_tensor * attn_mask = full_attn ? nullptr : window_mask;
+
+            cur = build_attn(layer.o_w, layer.o_b,
+                Qcur, Kcur, Vcur, attn_mask, kq_scale, il);
+            cb(cur, "attn_out", il);
+        }
+
+        // re-add the layer input, e.g., residual
+        cur = ggml_add(ctx0, cur, inpL);
+
+        inpL = cur; // inpL = residual, cur = hidden_states
+
+        cb(cur, "ffn_inp", il);
+
+        // layernorm2
+        cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il);
+        cb(cur, "ffn_inp_normed", il);
+
+        // ffn
+        cur = build_ffn(cur,
+            layer.ff_up_w, layer.ff_up_b,
+            layer.ff_gate_w, layer.ff_gate_b,
+            layer.ff_down_w, layer.ff_down_b,
+            hparams.ffn_op, il);
+
+        cb(cur, "ffn_out", il);
+
+        // residual 2
+        cur = ggml_add(ctx0, inpL, cur);
+        cb(cur, "layer_out", il);
+
+        inpL = cur;
+    }
+
+    // post-layernorm
+    if (model.post_ln_w) {
+        inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, n_layer);
+    }
+
+    // multimodal projection
+    ggml_tensor * embeddings = inpL;
+    embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size);
+    embeddings = build_ffn(embeddings,
+                        model.mm_0_w, model.mm_0_b,
+                        nullptr, nullptr,
+                        model.mm_1_w, model.mm_1_b,
+                        FFN_GELU,
+                        -1);
+
+    if (use_window_attn) {
+        window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4);
+        ggml_set_name(window_idx, "window_idx");
+        ggml_set_input(window_idx);
+
+        // embeddings shape: [n_embd, n_patches_x * n_patches_y, batch_size]
+        GGML_ASSERT(batch_size == 1);
+        embeddings = ggml_reshape_2d(ctx0, embeddings, hparams.projection_dim, n_patches_x * n_patches_y / 4);
+        embeddings = ggml_get_rows(ctx0, embeddings, window_idx);
+        embeddings = ggml_reshape_3d(ctx0, embeddings, hparams.projection_dim, n_patches_x * n_patches_y / 4, batch_size);
+    }
+
+    // build the graph
+    ggml_build_forward_expand(gf, embeddings);
+
+    return gf;
+}
diff --git a/tools/mtmd/models/qwen3vl.cpp b/tools/mtmd/models/qwen3vl.cpp
new file mode 100644
index 0000000000..35a42cb84d
--- /dev/null
+++ b/tools/mtmd/models/qwen3vl.cpp
@@ -0,0 +1,191 @@
+#include "models.h"
+
+ggml_cgraph * clip_graph_qwen3vl::build() {
+    GGML_ASSERT(model.patch_bias != nullptr);
+    GGML_ASSERT(model.position_embeddings != nullptr);
+    GGML_ASSERT(model.class_embedding == nullptr);
+
+    const int batch_size       = 1;
+    const int n_pos            = n_patches;
+    const int num_position_ids = n_pos * 4; // m-rope requires 4 dim per position
+
+    norm_type norm_t = NORM_TYPE_NORMAL;
+
+    int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
+
+    ggml_tensor * inp_raw = build_inp_raw();
+    ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+
+    GGML_ASSERT(img.nx % (patch_size * 2) == 0);
+    GGML_ASSERT(img.ny % (patch_size * 2) == 0);
+
+    // second conv dimension
+    {
+        auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+        inp = ggml_add(ctx0, inp, inp_1);
+
+        inp = ggml_permute(ctx0, inp, 1, 2, 0, 3);  // [w, h, c, b] -> [c, w, h, b]
+        inp = ggml_cont_4d(
+            ctx0, inp,
+            n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
+        inp = ggml_reshape_4d(
+            ctx0, inp,
+            n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
+        inp = ggml_permute(ctx0, inp, 0, 2, 1, 3);
+        inp = ggml_cont_3d(
+            ctx0, inp,
+            n_embd, n_patches_x * n_patches_y, batch_size);
+    }
+
+    // add patch bias
+    if (model.patch_bias != nullptr) {
+        inp = ggml_add(ctx0, inp, model.patch_bias);
+        cb(inp, "patch_bias", -1);
+    }
+
+    // calculate absolute position embedding and apply
+    ggml_tensor * learned_pos_embd = resize_position_embeddings();
+    learned_pos_embd = ggml_cont_4d(
+        ctx0, learned_pos_embd,
+        n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
+    learned_pos_embd = ggml_reshape_4d(
+        ctx0, learned_pos_embd,
+        n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
+    learned_pos_embd = ggml_permute(ctx0, learned_pos_embd, 0, 2, 1, 3);
+    learned_pos_embd = ggml_cont_3d(
+        ctx0, learned_pos_embd,
+        n_embd, n_patches_x * n_patches_y, batch_size);
+    inp = ggml_add(ctx0, inp, learned_pos_embd);
+    cb(inp, "inp_pos_emb", -1);
+
+    ggml_tensor * inpL = inp;
+
+    ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids);
+    ggml_set_name(positions, "positions");
+    ggml_set_input(positions);
+
+    // pre-layernorm
+    if (model.pre_ln_w) {
+        inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1);
+    }
+
+    // deepstack features (stack along the feature dimension), [n_embd * len(deepstack_layers), n_patches_x * n_patches_y, batch_size]
+    ggml_tensor * deepstack_features = nullptr;
+    const int merge_factor = hparams.n_merge > 0 ? hparams.n_merge * hparams.n_merge : 4; // default 2x2=4 for qwen3vl
+
+    // loop over layers
+    for (int il = 0; il < n_layer; il++) {
+        auto & layer = model.layers[il];
+
+        ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states
+
+        // layernorm1
+        cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il);
+        cb(cur, "ln1", il);
+
+        // self-attention
+        {
+            cur = ggml_mul_mat(ctx0, layer.qkv_w, cur);
+            cur = ggml_add(ctx0, cur, layer.qkv_b);
+
+            ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
+                    /* nb1    */ ggml_row_size(cur->type, d_head),
+                    /* nb2    */ cur->nb[1],
+                    /* offset */ 0);
+
+            ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
+                    /* nb1    */ ggml_row_size(cur->type, d_head),
+                    /* nb2    */ cur->nb[1],
+                    /* offset */ ggml_row_size(cur->type, n_embd));
+
+            ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos,
+                    /* nb1    */ ggml_row_size(cur->type, d_head),
+                    /* nb2    */ cur->nb[1],
+                    /* offset */ ggml_row_size(cur->type, 2 * n_embd));
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            // apply M-RoPE
+            Qcur = ggml_rope_multi(
+                ctx0, Qcur, positions, nullptr,
+                d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
+            Kcur = ggml_rope_multi(
+                ctx0, Kcur, positions, nullptr,
+                d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
+
+            cb(Qcur, "Qcur_rope", il);
+            cb(Kcur, "Kcur_rope", il);
+
+            cur = build_attn(layer.o_w, layer.o_b,
+                Qcur, Kcur, Vcur, nullptr, kq_scale, il);
+            cb(cur, "attn_out", il);
+        }
+
+        // re-add the layer input, e.g., residual
+        cur = ggml_add(ctx0, cur, inpL);
+
+        inpL = cur; // inpL = residual, cur = hidden_states
+
+        cb(cur, "ffn_inp", il);
+
+        // layernorm2
+        cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il);
+        cb(cur, "ffn_inp_normed", il);
+
+        // ffn
+        cur = build_ffn(cur,
+            layer.ff_up_w, layer.ff_up_b,
+            layer.ff_gate_w, layer.ff_gate_b,
+            layer.ff_down_w, layer.ff_down_b,
+            hparams.ffn_op, il);
+
+        cb(cur, "ffn_out", il);
+
+        // residual 2
+        cur = ggml_add(ctx0, inpL, cur);
+        cb(cur, "layer_out", il);
+
+        if (layer.has_deepstack()) {
+            ggml_tensor * feat = ggml_reshape_3d(ctx0, cur, n_embd * merge_factor, n_pos / merge_factor, batch_size);
+            feat = build_norm(feat, layer.deepstack_norm_w, layer.deepstack_norm_b, norm_t, eps, il);
+            feat = build_ffn(feat,
+                layer.deepstack_fc1_w, layer.deepstack_fc1_b,
+                nullptr, nullptr,
+                layer.deepstack_fc2_w, layer.deepstack_fc2_b,
+                ffn_op_type::FFN_GELU, il);
+
+            if(!deepstack_features) {
+                deepstack_features = feat;
+            } else {
+                // concat along the feature dimension
+                deepstack_features = ggml_concat(ctx0, deepstack_features, feat, 0);
+            }
+        }
+
+        inpL = cur;
+    }
+
+    // post-layernorm
+    if (model.post_ln_w) {
+        inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, n_layer);
+    }
+
+    // multimodal projection
+    ggml_tensor * embeddings = inpL;
+    embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size);
+
+    embeddings = build_ffn(embeddings,
+        model.mm_0_w, model.mm_0_b,
+        nullptr, nullptr,
+        model.mm_1_w, model.mm_1_b,
+        ffn_op_type::FFN_GELU, -1);
+
+    embeddings = ggml_concat(ctx0, embeddings, deepstack_features, 0); // concat along the feature dimension
+
+    // build the graph
+    ggml_build_forward_expand(gf, embeddings);
+
+    return gf;
+}
diff --git a/tools/mtmd/models/siglip.cpp b/tools/mtmd/models/siglip.cpp
new file mode 100644
index 0000000000..ef094cfd0e
--- /dev/null
+++ b/tools/mtmd/models/siglip.cpp
@@ -0,0 +1,81 @@
+#include "models.h"
+
+ggml_cgraph * clip_graph_siglip::build() {
+    ggml_tensor * inp = build_inp();
+
+    ggml_tensor * learned_pos_embd = model.position_embeddings;
+    if (proj_type == PROJECTOR_TYPE_LFM2) {
+        learned_pos_embd = resize_position_embeddings();
+    }
+
+    ggml_tensor * cur = build_vit(
+                            inp, n_patches,
+                            NORM_TYPE_NORMAL,
+                            hparams.ffn_op,
+                            learned_pos_embd,
+                            nullptr);
+
+    if (proj_type == PROJECTOR_TYPE_GEMMA3) {
+        const int batch_size = 1;
+        GGML_ASSERT(n_patches_x == n_patches_y);
+        const int patches_per_image = n_patches_x;
+        const int kernel_size = hparams.n_merge;
+
+        cur = ggml_transpose(ctx0, cur);
+        cur = ggml_cont_4d(ctx0, cur, patches_per_image, patches_per_image, n_embd, batch_size);
+
+        // doing a pool2d to reduce the number of output tokens
+        cur = ggml_pool_2d(ctx0, cur, GGML_OP_POOL_AVG, kernel_size, kernel_size, kernel_size, kernel_size, 0, 0);
+        cur = ggml_reshape_3d(ctx0, cur, cur->ne[0] * cur->ne[0], n_embd, batch_size);
+        cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
+
+        // apply norm before projection
+        cur = ggml_rms_norm(ctx0, cur, eps);
+        cur = ggml_mul(ctx0, cur, model.mm_soft_emb_norm_w);
+
+        // apply projection
+        cur = ggml_mul_mat(ctx0,
+            ggml_cont(ctx0, ggml_transpose(ctx0, model.mm_input_proj_w)),
+            cur);
+
+    } else if (proj_type == PROJECTOR_TYPE_IDEFICS3) {
+        // pixel_shuffle
+        // https://github.com/huggingface/transformers/blob/0a950e0bbe1ed58d5401a6b547af19f15f0c195e/src/transformers/models/idefics3/modeling_idefics3.py#L578
+        const int scale_factor = model.hparams.n_merge;
+        cur = build_patch_merge_permute(cur, scale_factor);
+        cur = ggml_mul_mat(ctx0, model.projection, cur);
+
+    } else if (proj_type == PROJECTOR_TYPE_LFM2) {
+        // pixel unshuffle block
+        const int scale_factor = model.hparams.n_merge;
+        cur = build_patch_merge_permute(cur, scale_factor);
+
+        // projection
+        cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm
+        cur = ggml_mul(ctx0, cur, model.mm_input_norm_w);
+        cur = ggml_add(ctx0, cur, model.mm_input_norm_b);
+
+        cur = build_ffn(cur,
+            model.mm_1_w, model.mm_1_b,
+            nullptr, nullptr,
+            model.mm_2_w, model.mm_2_b,
+            FFN_GELU,
+            -1);
+
+    } else if (proj_type == PROJECTOR_TYPE_JANUS_PRO) {
+        cur = build_ffn(cur,
+            model.mm_0_w, model.mm_0_b,
+            nullptr, nullptr,
+            model.mm_1_w, model.mm_1_b,
+            hparams.ffn_op,
+            -1);
+
+    } else {
+        GGML_ABORT("SigLIP: Unsupported projector type");
+    }
+
+    // build the graph
+    ggml_build_forward_expand(gf, cur);
+
+    return gf;
+}
diff --git a/tools/mtmd/models/whisper-enc.cpp b/tools/mtmd/models/whisper-enc.cpp
new file mode 100644
index 0000000000..07d378b095
--- /dev/null
+++ b/tools/mtmd/models/whisper-enc.cpp
@@ -0,0 +1,107 @@
+#include "models.h"
+
+ggml_cgraph * clip_graph_whisper_enc::build() {
+    const int n_frames = img.nx;
+    const int n_pos    = n_frames / 2;
+    GGML_ASSERT(model.position_embeddings->ne[1] >= n_pos);
+
+    ggml_tensor * inp = build_inp_raw(1);
+
+    // conv1d block
+    {
+        // convolution + gelu
+        ggml_tensor * cur = ggml_conv_1d_ph(ctx0, model.conv1d_1_w, inp, 1, 1);
+        cur = ggml_add(ctx0, cur, model.conv1d_1_b);
+
+        cur = ggml_gelu_erf(ctx0, cur);
+
+        cur = ggml_conv_1d_ph(ctx0, model.conv1d_2_w, cur, 2, 1);
+        cur = ggml_add(ctx0, cur, model.conv1d_2_b);
+
+        cur = ggml_gelu_erf(ctx0, cur);
+        // transpose
+        inp = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
+        cb(inp, "after_conv1d", -1);
+    }
+
+    // sanity check (only check one layer, but it should be the same for all)
+    GGML_ASSERT(model.layers[0].ln_1_w && model.layers[0].ln_1_b);
+    GGML_ASSERT(model.layers[0].ln_2_w && model.layers[0].ln_2_b);
+    GGML_ASSERT(model.layers[0].q_b);
+    GGML_ASSERT(model.layers[0].v_b);
+    GGML_ASSERT(!model.layers[0].k_b); // no bias for k
+    GGML_ASSERT(model.post_ln_w && model.post_ln_b);
+
+    ggml_tensor * pos_embd_selected = ggml_view_2d(
+        ctx0, model.position_embeddings,
+        model.position_embeddings->ne[0], n_pos,
+        model.position_embeddings->nb[1], 0
+    );
+    ggml_tensor * cur = build_vit(
+                            inp, n_pos,
+                            NORM_TYPE_NORMAL,
+                            hparams.ffn_op,
+                            pos_embd_selected,
+                            nullptr);
+
+    cb(cur, "after_transformer", -1);
+
+    if (model.audio_has_stack_frames()) {
+        // StackAudioFrames
+        // https://huggingface.co/fixie-ai/ultravox-v0_5-llama-3_2-1b/blob/main/ultravox_model.py
+        int64_t stride = n_embd * hparams.proj_stack_factor;
+        int64_t padded_len = GGML_PAD(ggml_nelements(cur), stride);
+        int64_t pad = padded_len - ggml_nelements(cur);
+        if (pad > 0) {
+            cur = ggml_view_1d(ctx0, cur, ggml_nelements(cur), 0);
+            cur = ggml_pad(ctx0, cur, pad, 0, 0, 0);
+        }
+        cur = ggml_view_2d(ctx0, cur, stride, padded_len / stride,
+                            ggml_row_size(cur->type, stride), 0);
+        cb(cur, "after_stacked", -1);
+    }
+
+    if (proj_type == PROJECTOR_TYPE_ULTRAVOX) {
+        // UltravoxProjector
+        // pre-norm
+        cur = ggml_rms_norm(ctx0, cur, 1e-6);
+        cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w);
+
+        // ffn in
+        cur = ggml_mul_mat(ctx0, model.mm_1_w, cur);
+
+        // swiglu
+        // see SwiGLU in ultravox_model.py, the second half passed through is silu, not the first half
+        cur = ggml_swiglu_swapped(ctx0, cur);
+
+        // mid-norm
+        cur = ggml_rms_norm(ctx0, cur, 1e-6);
+        cur = ggml_mul(ctx0, cur, model.mm_norm_mid_w);
+
+        // ffn out
+        cur = ggml_mul_mat(ctx0, model.mm_2_w, cur);
+
+    } else if (proj_type == PROJECTOR_TYPE_QWEN2A) {
+        // projector
+        cur = ggml_mul_mat(ctx0, model.mm_fc_w, cur);
+        cur = ggml_add(ctx0, cur, model.mm_fc_b);
+
+    } else if (proj_type == PROJECTOR_TYPE_VOXTRAL) {
+        // projector
+        cur = build_ffn(cur,
+            model.mm_1_w, model.mm_1_b,
+            nullptr, nullptr,
+            model.mm_2_w, model.mm_2_b,
+            FFN_GELU_ERF,
+            -1);
+
+    } else {
+        GGML_ABORT("%s: unknown projector type", __func__);
+    }
+
+    cb(cur, "projected", -1);
+
+    ggml_build_forward_expand(gf, cur);
+
+    return gf;
+}
diff --git a/tools/mtmd/mtmd.h b/tools/mtmd/mtmd.h
index b3df24c299..9f7e861e92 100644
--- a/tools/mtmd/mtmd.h
+++ b/tools/mtmd/mtmd.h
@@ -22,6 +22,11 @@
  *          Issues related to API usage may receive lower priority support.
  *
  * For the usage, see an example in mtmd-cli.cpp
+ *
+ * For contributors:
+ * - Make sure the C API is aligned with the libllama C API (as in llama.h)
+ * - Do not include model name (e.g., qwen, gemma) in the API, use generic terms instead
+ * - Keep the API minimal, do not expose internal details unless necessary
  */
 
 #ifdef LLAMA_SHARED

From 380b4c984e06f8d8381392d15814b3392e39560e Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Fri, 12 Dec 2025 23:58:53 +0100
Subject: [PATCH 075/135] common: support negated args (#17919)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* args: support negated args

* update docs

* fix typo

* add more neg options

* Apply suggestions from code review

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* rm duplicated arg

* fix LLAMA_ARG_NO_HOST

* add test

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---
 common/arg.cpp                 | 290 +++++++++++++++++++--------------
 common/arg.h                   |  13 ++
 common/preset.cpp              |  16 +-
 examples/gen-docs/gen-docs.cpp |   9 +-
 tests/test-arg-parser.cpp      |  20 ++-
 tools/server/README.md         |  48 +++---
 6 files changed, 235 insertions(+), 161 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 5528eeb169..16cb2e03a6 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -105,6 +105,16 @@ bool common_arg::is_exclude(enum llama_example ex) {
 
 bool common_arg::get_value_from_env(std::string & output) const {
     if (env == nullptr) return false;
+    if (!args_neg.empty()) {
+        // for compatibility, we need to check LLAMA_ARG_NO_ env as well
+        std::string neg_env = env;
+        string_replace_all(neg_env, "LLAMA_ARG_", "LLAMA_ARG_NO_");
+        char * neg_value = std::getenv(neg_env.c_str());
+        if (neg_value) {
+            output = "0"; // falsey
+            return true;
+        }
+    }
     char * value = std::getenv(env);
     if (value) {
         output = value;
@@ -114,6 +124,14 @@ bool common_arg::get_value_from_env(std::string & output) const {
 }
 
 bool common_arg::has_value_from_env() const {
+    if (env != nullptr && !args_neg.empty()) {
+        // for compatibility, we need to check LLAMA_ARG_NO_ env as well
+        std::string neg_env = env;
+        string_replace_all(neg_env, "LLAMA_ARG_", "LLAMA_ARG_NO_");
+        if (std::getenv(neg_env.c_str())) {
+            return true;
+        }
+    }
     return env != nullptr && std::getenv(env);
 }
 
@@ -151,9 +169,10 @@ std::string common_arg::to_string() const {
     std::string leading_spaces(n_leading_spaces, ' ');
 
     std::ostringstream ss;
-    for (const auto arg : args) {
-        if (arg == args.front()) {
-            if (args.size() == 1) {
+    auto all_args = get_args(); // also contains args_neg
+    for (const auto & arg : all_args) {
+        if (arg == all_args.front()) {
+            if (all_args.size() == 1) {
                 ss << arg;
             } else {
                 // first arg is usually abbreviation, we need padding to make it more beautiful
@@ -162,7 +181,7 @@ std::string common_arg::to_string() const {
                 ss << tmp << spaces;
             }
         } else {
-            ss << arg << (arg != args.back() ? ", " : "");
+            ss << arg << (arg != all_args.back() ? ", " : "");
         }
     }
     if (value_hint) ss << " " << value_hint;
@@ -181,6 +200,31 @@ std::string common_arg::to_string() const {
     return ss.str();
 }
 
+std::vector<std::string> common_arg::get_args() const {
+    std::vector<std::string> result;
+    for (const auto & arg : args) {
+        result.push_back(std::string(arg));
+    }
+    for (const auto & arg : args_neg) {
+        result.push_back(std::string(arg));
+    }
+    return result;
+}
+
+std::vector<std::string> common_arg::get_env() const {
+    std::vector<std::string> result;
+    if (env) {
+        result.push_back(std::string(env));
+    }
+    if (!args_neg.empty() && env) {
+        // for compatibility, we need to add LLAMA_ARG_NO_ variant
+        std::string neg_env = env;
+        string_replace_all(neg_env, "LLAMA_ARG_", "LLAMA_ARG_NO_");
+        result.push_back(neg_env);
+    }
+    return result;
+}
+
 //
 // utils
 //
@@ -316,6 +360,16 @@ static std::string get_all_kv_cache_types() {
     return msg.str();
 }
 
+static bool parse_bool_value(const std::string & value) {
+    if (is_truthy(value)) {
+        return true;
+    } else if (is_falsey(value)) {
+        return false;
+    } else {
+        throw std::invalid_argument("invalid boolean value");
+    }
+}
+
 //
 // CLI argument parsing functions
 //
@@ -323,10 +377,13 @@ static std::string get_all_kv_cache_types() {
 static bool common_params_parse_ex(int argc, char ** argv, common_params_context & ctx_arg) {
     common_params & params = ctx_arg.params;
 
-    std::unordered_map<std::string, common_arg *> arg_to_options;
+    std::unordered_map<std::string, std::pair<common_arg *, bool>> arg_to_options;
     for (auto & opt : ctx_arg.options) {
         for (const auto & arg : opt.args) {
-            arg_to_options[arg] = &opt;
+            arg_to_options[arg] = {&opt, /* is_positive */ true};
+        }
+        for (const auto & arg : opt.args_neg) {
+            arg_to_options[arg] = {&opt, /* is_positive */ false};
         }
     }
 
@@ -335,12 +392,15 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         std::string value;
         if (opt.get_value_from_env(value)) {
             try {
-                if (opt.handler_void && (value == "1" || value == "true")) {
+                if (opt.handler_void && is_truthy(value)) {
                     opt.handler_void(params);
                 }
                 if (opt.handler_int) {
                     opt.handler_int(params, std::stoi(value));
                 }
+                if (opt.handler_bool) {
+                    opt.handler_bool(params, parse_bool_value(value));
+                }
                 if (opt.handler_string) {
                     opt.handler_string(params, value);
                     continue;
@@ -369,7 +429,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         if (arg_to_options.find(arg) == arg_to_options.end()) {
             throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str()));
         }
-        auto opt = *arg_to_options[arg];
+        auto & tmp = arg_to_options[arg];
+        auto opt = *tmp.first;
+        bool is_positive = tmp.second;
         if (opt.has_value_from_env()) {
             fprintf(stderr, "warn: %s environment variable is set, but will be overwritten by command line argument %s\n", opt.env, arg.c_str());
         }
@@ -378,6 +440,10 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
                 opt.handler_void(params);
                 continue;
             }
+            if (opt.handler_bool) {
+                opt.handler_bool(params, is_positive);
+                continue;
+            }
 
             // arg with single value
             check_arg(i);
@@ -402,7 +468,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
             throw std::invalid_argument(string_format(
                 "error while handling argument \"%s\": %s\n\n"
                 "usage:\n%s\n\nto show complete usage, run with -h",
-                arg.c_str(), e.what(), arg_to_options[arg]->to_string().c_str()));
+                arg.c_str(), e.what(), opt.to_string().c_str()));
         }
     }
 
@@ -750,11 +816,11 @@ static std::string list_builtin_chat_templates() {
 }
 
 bool common_arg_utils::is_truthy(const std::string & value) {
-    return value == "on" || value == "enabled" || value == "1";
+    return value == "on" || value == "enabled" || value == "true" || value == "1";
 }
 
 bool common_arg_utils::is_falsey(const std::string & value) {
-    return value == "off" || value == "disabled" || value == "0";
+    return value == "off" || value == "disabled" || value == "false" || value == "0";
 }
 
 bool common_arg_utils::is_autoy(const std::string & value) {
@@ -839,10 +905,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ));
     add_opt(common_arg(
+        {"--display-prompt"},
         {"--no-display-prompt"},
-        string_format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"),
-        [](common_params & params) {
-            params.display_prompt = false;
+        string_format("whether to print prompt at generation (default: %s)", params.display_prompt ? "true" : "false"),
+        [](common_params & params, bool value) {
+            params.display_prompt = value;
         }
     ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
@@ -1055,18 +1122,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.kv_unified = true;
         }
     ).set_env("LLAMA_ARG_KV_UNIFIED"));
-    add_opt(common_arg(
-        {"--no-context-shift"},
-        string_format("disables context shift on infinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"),
-        [](common_params & params) {
-            params.ctx_shift = false;
-        }
-    ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT"));
     add_opt(common_arg(
         {"--context-shift"},
-        string_format("enables context shift on infinite text generation (default: %s)", params.ctx_shift ? "enabled" : "disabled"),
-        [](common_params & params) {
-            params.ctx_shift = true;
+        {"--no-context-shift"},
+        string_format("whether to use context shift on infinite text generation (default: %s)", params.ctx_shift ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.ctx_shift = value;
         }
     ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_CONTEXT_SHIFT"));
     add_opt(common_arg(
@@ -1106,20 +1167,22 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_DIFFUSION}));
     add_opt(common_arg(
+        {"--perf"},
         {"--no-perf"},
-        string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),
-        [](common_params & params) {
-            params.no_perf = true;
-            params.sampling.no_perf = true;
+        string_format("whether to enable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"),
+        [](common_params & params, bool value) {
+            params.no_perf = !value;
+            params.sampling.no_perf = !value;
         }
-    ).set_env("LLAMA_ARG_NO_PERF"));
+    ).set_env("LLAMA_ARG_PERF"));
     add_opt(common_arg(
+        {"--show-timings"},
         {"--no-show-timings"},
-        string_format("disable timing information after each response (default: %s)", params.show_timings ? "true" : "false"),
-        [](common_params & params) {
-            params.show_timings = false;
+        string_format("whether to show timing information after each response (default: %s)", params.show_timings ? "true" : "false"),
+        [](common_params & params, bool value) {
+            params.show_timings = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_NO_SHOW_TIMINGS"));
+    ).set_examples({LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SHOW_TIMINGS"));
     add_opt(common_arg(
         {"-f", "--file"}, "FNAME",
         "a file containing the prompt (default: none)",
@@ -1171,16 +1234,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_excludes({LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-e", "--escape"},
-        string_format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"),
-        [](common_params & params) {
-            params.escape = true;
-        }
-    ));
-    add_opt(common_arg(
         {"--no-escape"},
-        "do not process escape sequences",
-        [](common_params & params) {
-            params.escape = false;
+        string_format("whether to process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"),
+        [](common_params & params, bool value) {
+            params.escape = value;
         }
     ));
     add_opt(common_arg(
@@ -1227,19 +1284,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"-cnv", "--conversation"},
-        "run in conversation mode:\n"
+        {"-no-cnv", "--no-conversation"},
+        "whether to run in conversation mode:\n"
         "- does not print special tokens and suffix/prefix\n"
         "- interactive mode is also enabled\n"
         "(default: auto enabled if chat template is available)",
-        [](common_params & params) {
-            params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED;
-        }
-    ).set_examples({LLAMA_EXAMPLE_COMPLETION}));
-    add_opt(common_arg(
-        {"-no-cnv", "--no-conversation"},
-        "force disable conversation mode (default: false)",
-        [](common_params & params) {
-            params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED;
+        [](common_params & params, bool value) {
+            params.conversation_mode = value ? COMMON_CONVERSATION_MODE_ENABLED : COMMON_CONVERSATION_MODE_DISABLED;
         }
     ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
@@ -1297,10 +1348,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_COMPLETION}));
     add_opt(common_arg(
+        {"--warmup"},
         {"--no-warmup"},
-        "skip warming up the model with an empty run",
-        [](common_params & params) {
-            params.warmup = false;
+        string_format("whether to perform warmup with an empty run (default: %s)", params.warmup ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.warmup = value;
         }
     ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_PERPLEXITY}));
     add_opt(common_arg(
@@ -1702,19 +1754,21 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_env("LLAMA_ARG_GRP_ATTN_W").set_examples({LLAMA_EXAMPLE_COMPLETION}));
     add_opt(common_arg(
+        {"-kvo", "--kv-offload"},
         {"-nkvo", "--no-kv-offload"},
-        "disable KV offload",
-        [](common_params & params) {
-            params.no_kv_offload = true;
+        string_format("whether to enable KV cache offloading (default: %s)", params.no_kv_offload ? "disabled" : "enabled"),
+        [](common_params & params, bool value) {
+            params.no_kv_offload = !value;
         }
-    ).set_env("LLAMA_ARG_NO_KV_OFFLOAD"));
+    ).set_env("LLAMA_ARG_KV_OFFLOAD"));
     add_opt(common_arg(
+        {"--repack"},
         {"-nr", "--no-repack"},
-        "disable weight repacking",
-        [](common_params & params) {
-            params.no_extra_bufts = true;
+        string_format("whether to enable weight repacking (default: %s)", params.no_extra_bufts ? "disabled" : "enabled"),
+        [](common_params & params, bool value) {
+            params.no_extra_bufts = !value;
         }
-    ).set_env("LLAMA_ARG_NO_REPACK"));
+    ).set_env("LLAMA_ARG_REPACK"));
     add_opt(common_arg(
         {"--no-host"},
         "bypass host buffer allowing extra buffers to be used",
@@ -1843,18 +1897,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_PARALLEL}));
     add_opt(common_arg(
         {"-cb", "--cont-batching"},
-        string_format("enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"),
-        [](common_params & params) {
-            params.cont_batching = true;
+        {"-nocb", "--no-cont-batching"},
+        string_format("whether to enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.cont_batching = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CONT_BATCHING"));
-    add_opt(common_arg(
-        {"-nocb", "--no-cont-batching"},
-        "disable continuous batching",
-        [](common_params & params) {
-            params.cont_batching = false;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONT_BATCHING"));
     add_opt(common_arg(
         {"-mm", "--mmproj"}, "FILE",
         "path to a multimodal projector file. see tools/mtmd/README.md\n"
@@ -1871,19 +1919,21 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_URL"));
     add_opt(common_arg(
-        {"--no-mmproj"},
-        "explicitly disable multimodal projector, useful when using -hf",
-        [](common_params & params) {
-            params.no_mmproj = true;
+        {"--mmproj-auto"},
+        {"--no-mmproj", "--no-mmproj-auto"},
+        string_format("whether to use multimodal projector file (if available), useful when using -hf (default: %s)", params.no_mmproj ? "disabled" : "enabled"),
+        [](common_params & params, bool value) {
+            params.no_mmproj = !value;
         }
-    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_NO_MMPROJ"));
+    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_AUTO"));
     add_opt(common_arg(
+        {"--mmproj-offload"},
         {"--no-mmproj-offload"},
-        "do not offload multimodal projector to GPU",
-        [](common_params & params) {
-            params.mmproj_use_gpu = false;
+        string_format("whether to enable GPU offloading for multimodal projector (default: %s)", params.mmproj_use_gpu ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.mmproj_use_gpu = value;
         }
-    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_NO_MMPROJ_OFFLOAD"));
+    ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_OFFLOAD"));
     add_opt(common_arg(
         {"--image", "--audio"}, "FILE",
         "path to an image or audio file. use with multimodal models, can be repeated if you have multiple files\n",
@@ -1923,12 +1973,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_env("LLAMA_ARG_MLOCK"));
     add_opt(common_arg(
+        {"--mmap"},
         {"--no-mmap"},
-        "do not memory-map model (slower load but may reduce pageouts if not using mlock)",
-        [](common_params & params) {
-            params.use_mmap = false;
+        string_format("whether to memory-map model (if disabled, slower load but may reduce pageouts if not using mlock) (default: %s)", params.use_mmap ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.use_mmap = value;
         }
-    ).set_env("LLAMA_ARG_NO_MMAP"));
+    ).set_env("LLAMA_ARG_MMAP"));
     add_opt(common_arg(
         {"--numa"}, "TYPE",
         "attempt optimizations that help on some NUMA systems\n"
@@ -2116,10 +2167,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ));
     add_opt(common_arg(
+        {"--op-offload"},
         {"--no-op-offload"},
-        string_format("disable offloading host tensor operations to device (default: %s)", params.no_op_offload ? "true" : "false"),
-        [](common_params & params) {
-            params.no_op_offload = true;
+        string_format("whether to offload host tensor operations to device (default: %s)", params.no_op_offload ? "false" : "true"),
+        [](common_params & params, bool value) {
+            params.no_op_offload = !value;
         }
     ));
     add_opt(common_arg(
@@ -2315,10 +2367,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
     add_opt(common_arg(
+        {"--ppl"},
         {"--no-ppl"},
-        string_format("do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"),
-        [](common_params & params) {
-            params.compute_ppl = false;
+        string_format("whether to compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"),
+        [](common_params & params, bool value) {
+            params.compute_ppl = value;
         }
     ).set_examples({LLAMA_EXAMPLE_IMATRIX}));
     add_opt(common_arg(
@@ -2437,12 +2490,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_API_PREFIX"));
     add_opt(common_arg(
+        {"--webui"},
         {"--no-webui"},
-        string_format("Disable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"),
-        [](common_params & params) {
-            params.webui = false;
+        string_format("whether to enable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.webui = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_WEBUI"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI"));
     add_opt(common_arg(
         {"--embedding", "--embeddings"},
         string_format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"),
@@ -2547,18 +2601,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_PROPS"));
     add_opt(common_arg(
         {"--slots"},
-        string_format("enable slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"),
-        [](common_params & params) {
-            params.endpoint_slots = true;
+        {"--no-slots"},
+        string_format("expose slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.endpoint_slots = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_SLOTS"));
-    add_opt(common_arg(
-        {"--no-slots"},
-        "disables slots monitoring endpoint",
-        [](common_params & params) {
-            params.endpoint_slots = false;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_ENDPOINT_SLOTS"));
     add_opt(common_arg(
         {"--slot-save-path"}, "PATH",
         "path to save slot kv cache (default: disabled)",
@@ -2609,26 +2657,21 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_MAX"));
     add_opt(common_arg(
+        {"--models-autoload"},
         {"--no-models-autoload"},
-        "disables automatic loading of models (default: enabled)",
-        [](common_params & params) {
-            params.models_autoload = false;
+        string_format("for router server, whether to automatically load models (default: %s)", params.models_autoload ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.models_autoload = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_MODELS_AUTOLOAD"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_AUTOLOAD"));
     add_opt(common_arg(
         {"--jinja"},
-        string_format("use jinja template for chat (default: %s)", params.use_jinja ? "enabled" : "disabled"),
-        [](common_params & params) {
-            params.use_jinja = true;
+        {"--no-jinja"},
+        string_format("whether to use jinja template engine for chat (default: %s)", params.use_jinja ? "enabled" : "disabled"),
+        [](common_params & params, bool value) {
+            params.use_jinja = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_JINJA"));
-    add_opt(common_arg(
-        {"--no-jinja"},
-        string_format("disable jinja template for chat (default: %s)", params.use_jinja ? "disabled" : "enabled"),
-        [](common_params & params) {
-            params.use_jinja = false;
-        }
-    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_NO_JINJA"));
     add_opt(common_arg(
         {"--reasoning-format"}, "FORMAT",
         "controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n"
@@ -2673,15 +2716,16 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         }
     ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE"));
     add_opt(common_arg(
+        {"--prefill-assistant"},
         {"--no-prefill-assistant"},
         string_format(
             "whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)\n"
             "when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled\n"
         ),
-        [](common_params & params) {
-            params.prefill_assistant = false;
+        [](common_params & params, bool value) {
+            params.prefill_assistant = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_PREFILL_ASSISTANT"));
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_PREFILL_ASSISTANT"));
     add_opt(common_arg(
         {"-sps", "--slot-prompt-similarity"}, "SIMILARITY",
         string_format("how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity),
diff --git a/common/arg.h b/common/arg.h
index 219c115e63..6db38da488 100644
--- a/common/arg.h
+++ b/common/arg.h
@@ -16,6 +16,7 @@ struct common_arg {
     std::set<enum llama_example> examples = {LLAMA_EXAMPLE_COMMON};
     std::set<enum llama_example> excludes = {};
     std::vector<const char *> args;
+    std::vector<const char *> args_neg;  // for negated args like --no-xxx
     const char * value_hint   = nullptr; // help text or example for arg value
     const char * value_hint_2 = nullptr; // for second arg value
     const char * env          = nullptr;
@@ -25,6 +26,7 @@ struct common_arg {
     void (*handler_string) (common_params & params, const std::string &) = nullptr;
     void (*handler_str_str)(common_params & params, const std::string &, const std::string &) = nullptr;
     void (*handler_int)    (common_params & params, int) = nullptr;
+    void (*handler_bool)   (common_params & params, bool) = nullptr;
 
     common_arg() = default;
 
@@ -48,6 +50,13 @@ struct common_arg {
         void (*handler)(common_params & params)
     ) : args(args), help(help), handler_void(handler) {}
 
+    common_arg(
+        const std::initializer_list<const char *> & args,
+        const std::initializer_list<const char *> & args_neg,
+        const std::string & help,
+        void (*handler)(common_params & params, bool)
+    ) : args(args), args_neg(args_neg), help(help), handler_bool(handler) {}
+
     // support 2 values for arg
     common_arg(
         const std::initializer_list<const char *> & args,
@@ -80,6 +89,10 @@ struct common_arg {
         }
         return strcmp(args[0], other.args[0]) == 0;
     }
+
+    // get all args and env vars (including negated args/env)
+    std::vector<std::string> get_args() const;
+    std::vector<std::string> get_env() const;
 };
 
 namespace common_arg_utils {
diff --git a/common/preset.cpp b/common/preset.cpp
index 09ac171b72..729c27f2cf 100644
--- a/common/preset.cpp
+++ b/common/preset.cpp
@@ -23,8 +23,14 @@ std::vector<std::string> common_preset::to_args() const {
         if (opt.value_hint == nullptr && opt.value_hint_2 == nullptr) {
             // flag option, no value
             if (common_arg_utils::is_falsey(value)) {
-                // skip the flag
-                args.pop_back();
+                // use negative arg if available
+                if (!opt.args_neg.empty()) {
+                    args.back() = opt.args_neg.back();
+                } else {
+                    // otherwise, skip the flag
+                    // TODO: maybe throw an error instead?
+                    args.pop_back();
+                }
             }
         }
         if (opt.value_hint != nullptr) {
@@ -141,10 +147,10 @@ static std::map<std::string, std::map<std::string, std::string>> parse_ini_from_
 static std::map<std::string, common_arg> get_map_key_opt(common_params_context & ctx_params) {
     std::map<std::string, common_arg> mapping;
     for (const auto & opt : ctx_params.options) {
-        if (opt.env != nullptr) {
-            mapping[opt.env] = opt;
+        for (const auto & env : opt.get_env()) {
+            mapping[env] = opt;
         }
-        for (const auto & arg : opt.args) {
+        for (const auto & arg : opt.get_args()) {
             mapping[rm_leading_dashes(arg)] = opt;
         }
     }
diff --git a/examples/gen-docs/gen-docs.cpp b/examples/gen-docs/gen-docs.cpp
index 420195f198..e9f7bf9313 100644
--- a/examples/gen-docs/gen-docs.cpp
+++ b/examples/gen-docs/gen-docs.cpp
@@ -14,12 +14,13 @@ static void write_table_header(std::ofstream & file) {
 static void write_table_entry(std::ofstream & file, const common_arg & opt) {
     file << "| `";
     // args
-    for (const auto & arg : opt.args) {
-    if (arg == opt.args.front()) {
+    auto all_args = opt.get_args();
+    for (const auto & arg : all_args) {
+    if (arg == all_args.front()) {
             file << arg;
-            if (opt.args.size() > 1) file << ", ";
+            if (all_args.size() > 1) file << ", ";
         } else {
-            file << arg << (arg != opt.args.back() ? ", " : "");
+            file << arg << (arg != all_args.back() ? ", " : "");
         }
     }
     // value hint
diff --git a/tests/test-arg-parser.cpp b/tests/test-arg-parser.cpp
index a60ca12fe5..90750b20c2 100644
--- a/tests/test-arg-parser.cpp
+++ b/tests/test-arg-parser.cpp
@@ -20,20 +20,20 @@ int main(void) {
             std::unordered_set<std::string> seen_env_vars;
             for (const auto & opt : ctx_arg.options) {
                 // check for args duplications
-                for (const auto & arg : opt.args) {
+                for (const auto & arg : opt.get_args()) {
                     if (seen_args.find(arg) == seen_args.end()) {
                         seen_args.insert(arg);
                     } else {
-                        fprintf(stderr, "test-arg-parser: found different handlers for the same argument: %s", arg);
+                        fprintf(stderr, "test-arg-parser: found different handlers for the same argument: %s", arg.c_str());
                         exit(1);
                     }
                 }
                 // check for env var duplications
-                if (opt.env) {
-                    if (seen_env_vars.find(opt.env) == seen_env_vars.end()) {
-                        seen_env_vars.insert(opt.env);
+                for (const auto & env : opt.get_env()) {
+                    if (seen_env_vars.find(env) == seen_env_vars.end()) {
+                        seen_env_vars.insert(env);
                     } else {
-                        fprintf(stderr, "test-arg-parser: found different handlers for the same env var: %s", opt.env);
+                        fprintf(stderr, "test-arg-parser: found different handlers for the same env var: %s", env.c_str());
                         exit(1);
                     }
                 }
@@ -115,6 +115,14 @@ int main(void) {
     assert(params.model.path == "blah.gguf");
     assert(params.cpuparams.n_threads == 1010);
 
+    printf("test-arg-parser: test negated environment variables\n\n");
+
+    setenv("LLAMA_ARG_MMAP", "0", true);
+    setenv("LLAMA_ARG_NO_PERF", "1", true); // legacy format
+    argv = {"binary_name"};
+    assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
+    assert(params.use_mmap == false);
+    assert(params.no_perf == true);
 
     printf("test-arg-parser: test environment variables being overwritten\n\n");
 
diff --git a/tools/server/README.md b/tools/server/README.md
index 91cafa9425..073bcd2ccd 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -54,9 +54,8 @@ For the ful list of features, please refer to [server's changelog](https://githu
 | `--swa-full` | use full-size SWA cache (default: false)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)<br/>(env: LLAMA_ARG_SWA_FULL) |
 | `--kv-unified, -kvu` | use single unified KV buffer for the KV cache of all sequences (default: false)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/14363)<br/>(env: LLAMA_ARG_KV_UNIFIED) |
 | `-fa, --flash-attn [on\|off\|auto]` | set Flash Attention use ('on', 'off', or 'auto', default: 'auto')<br/>(env: LLAMA_ARG_FLASH_ATTN) |
-| `--no-perf` | disable internal libllama performance timings (default: false)<br/>(env: LLAMA_ARG_NO_PERF) |
-| `-e, --escape` | process escapes sequences (\n, \r, \t, \', \", \\) (default: true) |
-| `--no-escape` | do not process escape sequences |
+| `--perf, --no-perf` | whether to enable internal libllama performance timings (default: false)<br/>(env: LLAMA_ARG_PERF) |
+| `-e, --escape, --no-escape` | whether to process escapes sequences (\n, \r, \t, \', \", \\) (default: true) |
 | `--rope-scaling {none,linear,yarn}` | RoPE frequency scaling method, defaults to linear unless specified by the model<br/>(env: LLAMA_ARG_ROPE_SCALING_TYPE) |
 | `--rope-scale N` | RoPE context scaling factor, expands context by a factor of N<br/>(env: LLAMA_ARG_ROPE_SCALE) |
 | `--rope-freq-base N` | RoPE base frequency, used by NTK-aware scaling (default: loaded from model)<br/>(env: LLAMA_ARG_ROPE_FREQ_BASE) |
@@ -66,15 +65,15 @@ For the ful list of features, please refer to [server's changelog](https://githu
 | `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: -1.0)<br/>(env: LLAMA_ARG_YARN_ATTN_FACTOR) |
 | `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: -1.0)<br/>(env: LLAMA_ARG_YARN_BETA_SLOW) |
 | `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: -1.0)<br/>(env: LLAMA_ARG_YARN_BETA_FAST) |
-| `-nkvo, --no-kv-offload` | disable KV offload<br/>(env: LLAMA_ARG_NO_KV_OFFLOAD) |
-| `-nr, --no-repack` | disable weight repacking<br/>(env: LLAMA_ARG_NO_REPACK) |
-| `--no-host` | bypass host buffer allowing extra buffers to be used<br/>(env: LLAMA_ARG_NO_HOST) |
+| `-kvo, --kv-offload, -nkvo, --no-kv-offload` | whether to enable KV cache offloading (default: enabled)<br/>(env: LLAMA_ARG_KV_OFFLOAD) |
+| `--repack, -nr, --no-repack` | whether to enable weight repacking (default: enabled)<br/>(env: LLAMA_ARG_REPACK) |
+| `--no-host` | bypass host buffer allowing extra buffers to be used<br/>(env: LLAMA_ARG_HOST) |
 | `-ctk, --cache-type-k TYPE` | KV cache data type for K<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
 | `-ctv, --cache-type-v TYPE` | KV cache data type for V<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
 | `-np, --parallel N` | number of parallel sequences to decode (default: 1)<br/>(env: LLAMA_ARG_N_PARALLEL) |
 | `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
-| `--no-mmap` | do not memory-map model (slower load but may reduce pageouts if not using mlock)<br/>(env: LLAMA_ARG_NO_MMAP) |
+| `--mmap, --no-mmap` | whether to memory-map model (if disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
 | `-dev, --device <dev1,dev2,..>` | comma-separated list of devices to use for offloading (none = don't offload)<br/>use --list-devices to see a list of available devices<br/>(env: LLAMA_ARG_DEVICE) |
 | `--list-devices` | print list of available devices and exit |
@@ -87,7 +86,7 @@ For the ful list of features, please refer to [server's changelog](https://githu
 | `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0)<br/>(env: LLAMA_ARG_MAIN_GPU) |
 | `--check-tensors` | check model tensor data for invalid values (default: false) |
 | `--override-kv KEY=TYPE:VALUE` | advanced option to override model metadata by key. may be specified multiple times.<br/>types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false |
-| `--no-op-offload` | disable offloading host tensor operations to device (default: false) |
+| `--op-offload, --no-op-offload` | whether to offload host tensor operations to device (default: true) |
 | `--lora FNAME` | path to LoRA adapter (can be repeated to use multiple adapters) |
 | `--lora-scaled FNAME SCALE` | path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters) |
 | `--control-vector FNAME` | add a control vector<br/>note: this argument can be repeated to add multiple control vectors |
@@ -157,19 +156,18 @@ For the ful list of features, please refer to [server's changelog](https://githu
 | -------- | ----------- |
 | `--ctx-checkpoints, --swa-checkpoints N` | max number of context checkpoints to create per slot (default: 8)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)<br/>(env: LLAMA_ARG_CTX_CHECKPOINTS) |
 | `--cache-ram, -cram N` | set the maximum cache size in MiB (default: 8192, -1 - no limit, 0 - disable)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)<br/>(env: LLAMA_ARG_CACHE_RAM) |
-| `--no-context-shift` | disables context shift on infinite text generation (default: enabled)<br/>(env: LLAMA_ARG_NO_CONTEXT_SHIFT) |
-| `--context-shift` | enables context shift on infinite text generation (default: disabled)<br/>(env: LLAMA_ARG_CONTEXT_SHIFT) |
+| `--context-shift, --no-context-shift` | whether to use context shift on infinite text generation (default: disabled)<br/>(env: LLAMA_ARG_CONTEXT_SHIFT) |
 | `-r, --reverse-prompt PROMPT` | halt generation at PROMPT, return control in interactive mode<br/> |
 | `-sp, --special` | special tokens output enabled (default: false) |
-| `--no-warmup` | skip warming up the model with an empty run |
+| `--warmup, --no-warmup` | whether to perform warmup with an empty run (default: enabled) |
 | `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) |
 | `--pooling {none,mean,cls,last,rank}` | pooling type for embeddings, use model default if unspecified<br/>(env: LLAMA_ARG_POOLING) |
-| `-cb, --cont-batching` | enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
-| `-nocb, --no-cont-batching` | disable continuous batching<br/>(env: LLAMA_ARG_NO_CONT_BATCHING) |
+| `-cb, --cont-batching, -nocb, --no-cont-batching` | whether to enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
+| `-cb, --cont-batching, -nocb, --no-cont-batching` | whether to enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
 | `-mm, --mmproj FILE` | path to a multimodal projector file. see tools/mtmd/README.md<br/>note: if -hf is used, this argument can be omitted<br/>(env: LLAMA_ARG_MMPROJ) |
 | `-mmu, --mmproj-url URL` | URL to a multimodal projector file. see tools/mtmd/README.md<br/>(env: LLAMA_ARG_MMPROJ_URL) |
-| `--no-mmproj` | explicitly disable multimodal projector, useful when using -hf<br/>(env: LLAMA_ARG_NO_MMPROJ) |
-| `--no-mmproj-offload` | do not offload multimodal projector to GPU<br/>(env: LLAMA_ARG_NO_MMPROJ_OFFLOAD) |
+| `--mmproj-auto, --no-mmproj, --no-mmproj-auto` | whether to use multimodal projector file (if available), useful when using -hf (default: enabled)<br/>(env: LLAMA_ARG_MMPROJ_AUTO) |
+| `--mmproj-offload, --no-mmproj-offload` | whether to enable GPU offloading for multimodal projector (default: enabled)<br/>(env: LLAMA_ARG_MMPROJ_OFFLOAD) |
 | `--image-min-tokens N` | minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)<br/>(env: LLAMA_ARG_IMAGE_MIN_TOKENS) |
 | `--image-max-tokens N` | maximum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)<br/>(env: LLAMA_ARG_IMAGE_MAX_TOKENS) |
 | `--override-tensor-draft, -otd <tensor name pattern>=<buffer type>,...` | override tensor buffer type for draft model |
@@ -180,7 +178,7 @@ For the ful list of features, please refer to [server's changelog](https://githu
 | `--port PORT` | port to listen (default: 8080)<br/>(env: LLAMA_ARG_PORT) |
 | `--path PATH` | path to serve static files from (default: )<br/>(env: LLAMA_ARG_STATIC_PATH) |
 | `--api-prefix PREFIX` | prefix path the server serves from, without the trailing slash (default: )<br/>(env: LLAMA_ARG_API_PREFIX) |
-| `--no-webui` | Disable the Web UI (default: enabled)<br/>(env: LLAMA_ARG_NO_WEBUI) |
+| `--webui, --no-webui` | whether to enable the Web UI (default: enabled)<br/>(env: LLAMA_ARG_WEBUI) |
 | `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)<br/>(env: LLAMA_ARG_EMBEDDINGS) |
 | `--reranking, --rerank` | enable reranking endpoint on server (default: disabled)<br/>(env: LLAMA_ARG_RERANKING) |
 | `--api-key KEY` | API key to use for authentication (default: none)<br/>(env: LLAMA_API_KEY) |
@@ -193,20 +191,19 @@ For the ful list of features, please refer to [server's changelog](https://githu
 | `--cache-reuse N` | min chunk size to attempt reusing from the cache via KV shifting (default: 0)<br/>[(card)](https://ggml.ai/f0.png)<br/>(env: LLAMA_ARG_CACHE_REUSE) |
 | `--metrics` | enable prometheus compatible metrics endpoint (default: disabled)<br/>(env: LLAMA_ARG_ENDPOINT_METRICS) |
 | `--props` | enable changing global properties via POST /props (default: disabled)<br/>(env: LLAMA_ARG_ENDPOINT_PROPS) |
-| `--slots` | enable slots monitoring endpoint (default: enabled)<br/>(env: LLAMA_ARG_ENDPOINT_SLOTS) |
-| `--no-slots` | disables slots monitoring endpoint<br/>(env: LLAMA_ARG_NO_ENDPOINT_SLOTS) |
+| `--slots, --no-slots` | expose slots monitoring endpoint (default: enabled)<br/>(env: LLAMA_ARG_ENDPOINT_SLOTS) |
 | `--slot-save-path PATH` | path to save slot kv cache (default: disabled) |
+| `--media-path PATH` | directory for loading local media files; files can be accessed via file:// URLs using relative paths (default: disabled) |
 | `--models-dir PATH` | directory containing models for the router server (default: disabled)<br/>(env: LLAMA_ARG_MODELS_DIR) |
+| `--models-preset PATH` | path to INI file containing model presets for the router server (default: disabled)<br/>(env: LLAMA_ARG_MODELS_PRESET) |
 | `--models-max N` | for router server, maximum number of models to load simultaneously (default: 4, 0 = unlimited)<br/>(env: LLAMA_ARG_MODELS_MAX) |
-| `--models-allow-extra-args` | for router server, allow extra arguments for models; important: some arguments can allow users to access local file system, use with caution (default: disabled)<br/>(env: LLAMA_ARG_MODELS_ALLOW_EXTRA_ARGS) |
-| `--no-models-autoload` | disables automatic loading of models (default: enabled)<br/>(env: LLAMA_ARG_NO_MODELS_AUTOLOAD) |
-| `--jinja` | use jinja template for chat (default: enabled)<br/><br/>(env: LLAMA_ARG_JINJA) |
-| `--no-jinja` | disable jinja template for chat (default: enabled)<br/><br/>(env: LLAMA_ARG_NO_JINJA) |
+| `--models-autoload, --no-models-autoload` | for router server, whether to automatically load models (default: enabled)<br/>(env: LLAMA_ARG_MODELS_AUTOLOAD) |
+| `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: enabled)<br/>(env: LLAMA_ARG_JINJA) |
 | `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:<br/>- none: leaves thoughts unparsed in `message.content`<br/>- deepseek: puts thoughts in `message.reasoning_content`<br/>- deepseek-legacy: keeps `<think>` tags in `message.content` while also populating `message.reasoning_content`<br/>(default: auto)<br/>(env: LLAMA_ARG_THINK) |
 | `--reasoning-budget N` | controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)<br/>(env: LLAMA_ARG_THINK_BUDGET) |
 | `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE) |
 | `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)<br/>if suffix/prefix are specified, template will be disabled<br/>only commonly used templates are accepted (unless --jinja is set before this flag):<br/>list of built-in templates:<br/>bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr<br/>(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) |
-| `--no-prefill-assistant` | whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)<br/>when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled<br/><br/>(env: LLAMA_ARG_NO_PREFILL_ASSISTANT) |
+| `--prefill-assistant, --no-prefill-assistant` | whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)<br/>when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled<br/><br/>(env: LLAMA_ARG_PREFILL_ASSISTANT) |
 | `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.10, 0.0 = disabled)<br/> |
 | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) |
 | `-td, --threads-draft N` | number of threads to use during generation (default: same as --threads) |
@@ -236,6 +233,11 @@ For the ful list of features, please refer to [server's changelog](https://githu
 
 Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var.
 
+For boolean options like `--mmap` or `--kv-offload`, the environment variable is handled as shown in this example:
+- `LLAMA_ARG_MMAP=true` means enabled, other accepted values are: `1`, `on`, `enabled`
+- `LLAMA_ARG_MMAP=false` means disabled, other accepted values are: `0`, `off`, `disabled`
+- If `LLAMA_ARG_NO_MMAP` is present (no matter the value), it means disabling mmap
+
 Example usage of docker compose with environment variables:
 
 ```yml

From fd1085ffb71dabf4490b428512d0fd6fdc637536 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Sat, 13 Dec 2025 08:34:26 +0100
Subject: [PATCH 076/135] model-conversion : use CONVERTED_MODEL value for
 converted model [no ci] (#17984)

* model-conversion : use CONVERTED_MODEL value for converted model [no ci]

This commit updates the model verification scripts to use the
CONVERTED_MODEL environment variable instead of using the MODEL_PATH
(the original model path) as the basis for the converted model file
name.

The motivation for this that currently if the converted model file name
differs from the original model directory/name the verification scripts
will look for the wrong .bin files that were generating when running the
models.
For example, the following steps were not possible:
```console
(venv) $ huggingface-cli download google/gemma-3-270m-it --local-dir ggml-org/gemma-3-270m
(venv) $ python3 convert_hf_to_gguf.py ggml-org/gemma-3-270m --outfile test-bf16.gguf --outtype bf16
(venv) $ cd examples/model-conversion/
(venv) $ export MODEL_PATH=../../ggml-org/gemma-3-270m
(venv) $ export CONVERTED_MODEL=../../test-bf16.gguf
(venv) $ make causal-verify-logits
...
Data saved to data/llamacpp-test-bf16.bin
Data saved to data/llamacpp-test-bf16.txt
Error: llama.cpp logits file not found: data/llamacpp-gemma-3-270m.bin
Please run scripts/run-converted-model.sh first to generate this file.
make: *** [Makefile:62: causal-verify-logits] Error 1
```

With the changes in this commit, the above steps will now work as
expected.
---
 .../scripts/causal/compare-logits.py          | 24 ++++++++-----------
 .../scripts/utils/__init__.py                 |  0
 .../scripts/utils/check-nmse.py               |  7 ++++--
 .../model-conversion/scripts/utils/common.py  | 20 ++++++++++++++++
 pyrightconfig.json                            |  2 +-
 5 files changed, 36 insertions(+), 17 deletions(-)
 create mode 100644 examples/model-conversion/scripts/utils/__init__.py
 create mode 100644 examples/model-conversion/scripts/utils/common.py

diff --git a/examples/model-conversion/scripts/causal/compare-logits.py b/examples/model-conversion/scripts/causal/compare-logits.py
index 2744789099..894302c69e 100755
--- a/examples/model-conversion/scripts/causal/compare-logits.py
+++ b/examples/model-conversion/scripts/causal/compare-logits.py
@@ -1,10 +1,13 @@
 #!/usr/bin/env python3
 
-import numpy as np
 import sys
-import os
+import numpy as np
 from pathlib import Path
 
+# Add utils directory to path for direct script execution
+sys.path.insert(0, str(Path(__file__).parent.parent / "utils"))
+from common import get_model_name_from_env_path  # type: ignore[import-not-found]
+
 def quick_logits_check(pytorch_file, llamacpp_file):
     """Lightweight sanity check before NMSE"""
 
@@ -35,20 +38,13 @@ def quick_logits_check(pytorch_file, llamacpp_file):
     return True
 
 def main():
-    model_path = os.getenv('MODEL_PATH')
-    if not model_path:
-        print("Error: MODEL_PATH environment variable not set")
-        sys.exit(1)
-
-    if not os.path.exists(model_path):
-        print(f"Error: Model file not found: {model_path}")
-        sys.exit(1)
-
-    model_name = os.path.basename(model_path)
+    model_name = get_model_name_from_env_path('MODEL_PATH')
     data_dir = Path("data")
-
     pytorch_file = data_dir / f"pytorch-{model_name}.bin"
-    llamacpp_file = data_dir / f"llamacpp-{model_name}.bin"
+
+    llamacpp_model_name = get_model_name_from_env_path('CONVERTED_MODEL')
+    print(f"Using converted model: {llamacpp_model_name}")
+    llamacpp_file = data_dir / f"llamacpp-{llamacpp_model_name}.bin"
 
     if not pytorch_file.exists():
         print(f"Error: PyTorch logits file not found: {pytorch_file}")
diff --git a/examples/model-conversion/scripts/utils/__init__.py b/examples/model-conversion/scripts/utils/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/examples/model-conversion/scripts/utils/check-nmse.py b/examples/model-conversion/scripts/utils/check-nmse.py
index 939e3153cc..83f63f9ff3 100755
--- a/examples/model-conversion/scripts/utils/check-nmse.py
+++ b/examples/model-conversion/scripts/utils/check-nmse.py
@@ -5,6 +5,7 @@ import sys
 import os
 import argparse
 from pathlib import Path
+from common import get_model_name_from_env_path  # type: ignore[import-not-found]
 
 def calculate_nmse(reference, test):
     mse = np.mean((test - reference) ** 2)
@@ -67,11 +68,13 @@ def main():
     parser.add_argument('-m', '--model-path', required=True,  help='Path to the model directory')
     args = parser.parse_args()
 
-    model_name = os.path.basename(args.model_path)
+    model_name = get_model_name_from_env_path('MODEL_PATH')
     data_dir = Path("data")
 
     pytorch_file = data_dir / f"pytorch-{model_name}.bin"
-    llamacpp_file = data_dir / f"llamacpp-{model_name}.bin"
+
+    llamacpp_model_name = get_model_name_from_env_path('CONVERTED_MODEL')
+    llamacpp_file = data_dir / f"llamacpp-{llamacpp_model_name}.bin"
 
     print(f"Model name: {model_name}")
     print(f"PyTorch logits file: {pytorch_file}")
diff --git a/examples/model-conversion/scripts/utils/common.py b/examples/model-conversion/scripts/utils/common.py
new file mode 100644
index 0000000000..945f9a1a1d
--- /dev/null
+++ b/examples/model-conversion/scripts/utils/common.py
@@ -0,0 +1,20 @@
+#!/usr/bin/env python3
+
+import os
+import sys
+
+def get_model_name_from_env_path(env_path_name):
+    model_path = os.getenv(env_path_name)
+    if not model_path:
+        print(f"Error: {env_path_name} environment variable not set")
+        sys.exit(1)
+
+    if not os.path.exists(model_path):
+        print(f"Error: Model file not found: {model_path}")
+        sys.exit(1)
+
+    name = os.path.basename(os.path.normpath(model_path))
+    if name.endswith(".gguf"):
+        name = name[:-5]
+
+    return name
diff --git a/pyrightconfig.json b/pyrightconfig.json
index 5320fe5864..a7bc007bdc 100644
--- a/pyrightconfig.json
+++ b/pyrightconfig.json
@@ -1,5 +1,5 @@
 {
-  "extraPaths": ["gguf-py"],
+  "extraPaths": ["gguf-py", "examples/model-conversion/scripts"],
   "pythonVersion": "3.9",
   "pythonPlatform": "All",
   "reportUnusedImport": "warning",

From 2bc94e792867497e2f0088b17d3fd3fcebd1f44b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Sat, 13 Dec 2025 08:35:50 +0100
Subject: [PATCH 077/135] add llama-completion to completion-bash executables
 (#17976)

---
 common/arg.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/common/arg.cpp b/common/arg.cpp
index 16cb2e03a6..788a9ab4e6 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -639,6 +639,7 @@ static void common_params_print_completion(common_params_context & ctx_arg) {
         "llama-batched-bench",
         "llama-bench",
         "llama-cli",
+        "llama-completion",
         "llama-convert-llama2c-to-ggml",
         "llama-cvector-generator",
         "llama-embedding",

From 07a10c1090502163ab48cc14a5dfc7059949a2c9 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 13 Dec 2025 01:40:04 -0600
Subject: [PATCH 078/135] vulkan: Allow non-pow2 n_experts in topk_moe (#17872)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 22 ++++++++++++-------
 .../ggml-vulkan/vulkan-shaders/topk_moe.comp  | 12 +++++++---
 tests/test-backend-ops.cpp                    |  4 ++++
 3 files changed, 27 insertions(+), 11 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index c6f5809ccd..52e7e1e7fe 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -757,7 +757,8 @@ struct vk_device_struct {
 
     vk_pipeline pipeline_flash_attn_split_k_reduce;
 
-    vk_pipeline pipeline_topk_moe[num_topk_moe_pipelines][TOPK_MOE_COUNT];
+    // [2] is for whether to take n_experts from spec constant (0) or push constant (1)
+    vk_pipeline pipeline_topk_moe[num_topk_moe_pipelines][TOPK_MOE_COUNT][2];
 
     std::vector<vk_pipeline_ref> all_pipelines;
 
@@ -1149,6 +1150,7 @@ static_assert(sizeof(vk_op_multi_add_push_constants) <= 256);
 
 struct vk_op_topk_moe_push_constants {
     uint32_t n_rows;
+    uint32_t n_experts_push;
     uint32_t n_expert_used;
     float clamp_min;
     float clamp_max;
@@ -4204,10 +4206,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f16_f32, "conv2d_dw_whcn_f16_f32", conv2d_dw_whcn_f16_f32_len, conv2d_dw_whcn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_cwhn_f16_f32, "conv2d_dw_cwhn_f16_f32", conv2d_dw_cwhn_f16_f32_len, conv2d_dw_cwhn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);
 
-    for (uint32_t i = 0; i < num_topk_moe_pipelines; ++i) {
-        ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX],      "topk_moe_f32_early_softmax_"+std::to_string(i),       topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, 0, 0}, 1, true, true, device->subgroup_size);
-        ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX_NORM], "topk_moe_f32_early_softmax_norm"+std::to_string(i),   topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, 1, 0}, 1, true, true, device->subgroup_size);
-        ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_LATE_SOFTMAX],       "topk_moe_f32_late_softmax"+std::to_string(i),         topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, 0, 1}, 1, true, true, device->subgroup_size);
+    for (uint32_t use_push = 0; use_push < 2; ++use_push) {
+        for (uint32_t i = 0; i < num_topk_moe_pipelines; ++i) {
+            ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX][use_push],      "topk_moe_f32_early_softmax_"+std::to_string(i),       topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, 0, 0, use_push}, 1, true, true, device->subgroup_size);
+            ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX_NORM][use_push], "topk_moe_f32_early_softmax_norm"+std::to_string(i),   topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, 1, 0, use_push}, 1, true, true, device->subgroup_size);
+            ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_LATE_SOFTMAX][use_push],       "topk_moe_f32_late_softmax"+std::to_string(i),         topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, 0, 1, use_push}, 1, true, true, device->subgroup_size);
+        }
     }
 
     for (auto &c : compiles) {
@@ -8554,7 +8558,9 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
             uint32_t idx = (uint32_t)ceilf(log2f(float(dst->ne[0])));
             GGML_ASSERT(idx < num_topk_moe_pipelines);
             topk_moe_mode mode = ggml_vk_num_additional_ops_to_topk_moe_mode(ctx->num_additional_fused_ops);
-            return ctx->device->pipeline_topk_moe[idx][mode];
+            // use n_experts from push constant if it's not equal to the power of two spec constant
+            bool use_push = dst->ne[0] != (1u << idx);
+            return ctx->device->pipeline_topk_moe[idx][mode][use_push];
         }
 
         if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) {
@@ -10158,6 +10164,7 @@ static void ggml_vk_topk_moe(ggml_backend_vk_context * ctx, vk_context& subctx,
 
     vk_op_topk_moe_push_constants pc {};
     pc.n_rows = n_rows;
+    pc.n_experts_push = n_experts;
     pc.n_expert_used = n_expert_used;
     if (mode == TOPK_MOE_EARLY_SOFTMAX_NORM) {
         ggml_tensor * clamp = cgraph->nodes[node_idx + 7];
@@ -12832,8 +12839,7 @@ static bool ggml_vk_can_fuse_topk_moe(ggml_backend_vk_context * ctx, const struc
     }
 
     const int n_expert = softmax->ne[0];
-    // n_expert must be a power of 2
-    if (!is_pow2(n_expert) || n_expert > (1 << (num_topk_moe_pipelines-1))) {
+    if (n_expert > (1 << (num_topk_moe_pipelines-1))) {
         return false;
     }
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp b/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp
index 5cd0785d20..b83a2b9d2d 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp
@@ -10,6 +10,7 @@
 layout (push_constant) uniform parameter
 {
     uint n_rows;
+    uint n_experts_push;
     uint n_expert_used;
     float clamp_min;
     float clamp_max;
@@ -18,11 +19,16 @@ layout (push_constant) uniform parameter
 layout(local_size_x_id = 0, local_size_y = 4, local_size_z = 1) in;
 
 layout(constant_id = 0) const uint WARP_SIZE = 32;
-layout(constant_id = 1) const uint n_experts = 512;
+layout(constant_id = 1) const uint n_experts_spec = 512;
 layout(constant_id = 2) const bool with_norm = true;
 layout(constant_id = 3) const bool late_softmax = false;
+layout(constant_id = 4) const bool nexperts_use_push = false;
 
-const uint experts_per_thread = (n_experts > WARP_SIZE) ? n_experts / WARP_SIZE : 1;
+uint n_experts = nexperts_use_push ? n_experts_push : n_experts_spec;
+
+#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b))
+
+const uint experts_per_thread = CEIL_DIV(n_experts_spec, WARP_SIZE);
 
 layout (binding = 0, std430) readonly buffer Logits {float logits[];};
 layout (binding = 1, std430) writeonly buffer Weights {float weights[];};
@@ -94,7 +100,7 @@ void main() {
     }
 
     if (!late_softmax) {
-        softmax_warp_inplace(wt, n_experts, lane, false);
+        softmax_warp_inplace(wt, n_experts, lane, nexperts_use_push);
     }
 
     // at this point, each thread holds a portion of softmax,
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 308e752b1d..f1bed864e3 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -7971,8 +7971,12 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
 
     for (bool with_norm : {false, true}) {
         test_cases.emplace_back(new test_topk_moe({8, 22, 1, 1}, 4, with_norm));
+        test_cases.emplace_back(new test_topk_moe({31, 22, 1, 1}, 8, with_norm));
         test_cases.emplace_back(new test_topk_moe({32, 22, 1, 1}, 8, with_norm));
+        test_cases.emplace_back(new test_topk_moe({40, 22, 1, 1}, 8, with_norm));
+        test_cases.emplace_back(new test_topk_moe({71, 22, 1, 1}, 8, with_norm));
         test_cases.emplace_back(new test_topk_moe({128, 1, 1, 1}, 128, with_norm));
+        test_cases.emplace_back(new test_topk_moe({129, 1, 1, 1}, 128, with_norm));
     }
 
     test_cases.emplace_back(new test_topk_moe({ 8, 22, 1, 1 }, 4, /*with_norm*/ false, /*delayed_softmax*/ true));

From 8e4d678528b2582eb8a9f773309e88b6a167e4cd Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Sat, 13 Dec 2025 08:40:50 +0100
Subject: [PATCH 079/135] common : skip model validation when --completion-bash
 is requested (#17975)

---
 common/arg.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 788a9ab4e6..19f22f883f 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -504,7 +504,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
 
     // model is required (except for server)
     // TODO @ngxson : maybe show a list of available models in CLI in this case
-    if (params.model.path.empty() && ctx_arg.ex != LLAMA_EXAMPLE_SERVER && !params.usage) {
+    if (params.model.path.empty() && ctx_arg.ex != LLAMA_EXAMPLE_SERVER && !params.usage && !params.completion) {
         throw std::invalid_argument("error: --model is required\n");
     }
 

From 3c6391e748d8c00c45fba033811508288580cdc7 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 13 Dec 2025 09:48:34 +0200
Subject: [PATCH 080/135] speculative-simple : free batch on exit (#17985)

---
 examples/speculative-simple/speculative-simple.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/examples/speculative-simple/speculative-simple.cpp b/examples/speculative-simple/speculative-simple.cpp
index a8e53f28eb..0d11d0f803 100644
--- a/examples/speculative-simple/speculative-simple.cpp
+++ b/examples/speculative-simple/speculative-simple.cpp
@@ -255,6 +255,8 @@ int main(int argc, char ** argv) {
     LOG_INF("target:\n\n");
     common_perf_print(ctx_tgt, smpl);
 
+    llama_batch_free(batch_tgt);
+
     common_sampler_free(smpl);
     common_speculative_free(spec);
 

From 303f8615e94d74f140eb4c3947758c1eca933c3a Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 13 Dec 2025 03:04:29 -0600
Subject: [PATCH 081/135] vulkan: Multi-pass softmax for large number of cols
 (#17892)

When the number of cols is large, split each row across multiple workgroups.
There are three phases that communicate partial results through temp buffers:
(1) compute max partials
(2) take max of partials, compute sum(exp(x-max)) partials
(3) sum partials, compute scaled result
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 64 ++++++++++++++-
 .../vulkan-shaders/soft_max_large1.comp       | 62 +++++++++++++++
 .../vulkan-shaders/soft_max_large2.comp       | 79 +++++++++++++++++++
 .../vulkan-shaders/soft_max_large3.comp       | 65 +++++++++++++++
 .../vulkan-shaders/soft_max_large_common.glsl | 53 +++++++++++++
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |  7 ++
 tests/test-backend-ops.cpp                    |  3 +
 7 files changed, 331 insertions(+), 2 deletions(-)
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large1.comp
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large2.comp
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large3.comp
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large_common.glsl

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 52e7e1e7fe..0b4f8c36ad 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -722,6 +722,11 @@ struct vk_device_struct {
     vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16;
     vk_pipeline pipeline_soft_max_f32_wg512, pipeline_soft_max_f32_f16_wg512;
     vk_pipeline pipeline_soft_max_back_f32;
+
+    vk_pipeline pipeline_soft_max_large1_f32, pipeline_soft_max_large1_f32_f16;
+    vk_pipeline pipeline_soft_max_large2_f32, pipeline_soft_max_large2_f32_f16;
+    vk_pipeline pipeline_soft_max_large3_f32, pipeline_soft_max_large3_f32_f16;
+
     vk_pipeline pipeline_rope_norm_f32, pipeline_rope_norm_f16, pipeline_rope_norm_f32_f16;
     vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16, pipeline_rope_neox_f32_f16;
     vk_pipeline pipeline_rope_multi_f32, pipeline_rope_multi_f16;
@@ -3998,6 +4003,13 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16_wg512, "soft_max_f32_f16_wg512", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1);
     ggml_vk_create_pipeline(device, device->pipeline_soft_max_back_f32, "soft_max_back_f32", soft_max_back_f32_len, soft_max_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1, true);
 
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large1_f32,     "soft_max_large1_f32",     soft_max_large1_f32_len,     soft_max_large1_f32_data,     "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large2_f32,     "soft_max_large2_f32",     soft_max_large2_f32_len,     soft_max_large2_f32_data,     "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large3_f32,     "soft_max_large3_f32",     soft_max_large3_f32_len,     soft_max_large3_f32_data,     "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large1_f32_f16, "soft_max_large1_f32_f16", soft_max_large1_f32_f16_len, soft_max_large1_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large2_f32_f16, "soft_max_large2_f32_f16", soft_max_large2_f32_f16_len, soft_max_large2_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_soft_max_large3_f32_f16, "soft_max_large3_f32_f16", soft_max_large3_f32_f16_len, soft_max_large3_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true);
+
     ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32, "rope_norm_f32", rope_norm_f32_len, rope_norm_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_rope_multi_f32, "rope_multi_f32", rope_multi_f32_len, rope_multi_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1);
@@ -10117,7 +10129,7 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx,
     const float m0 = powf(2.0f, -(max_bias       ) / n_head_log2);
     const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2);
 
-    ggml_vk_op_f32<vk_op_soft_max_push_constants>(ctx, subctx, src0, src1, src2, nullptr, dst, GGML_OP_SOFT_MAX, {
+    vk_op_soft_max_push_constants pc {
         ncols,
         src1 != nullptr ? nrows_y : (uint32_t)0,
         (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],
@@ -10128,7 +10140,55 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx,
         n_head_log2,
         nrows_x,
         src2 != nullptr
-    });
+    };
+
+    if (ncols <= 16384) {
+        ggml_vk_op_f32<vk_op_soft_max_push_constants>(ctx, subctx, src0, src1, src2, nullptr, dst, GGML_OP_SOFT_MAX, std::move(pc));
+    } else {
+
+        vk_subbuffer buf_a = ggml_vk_tensor_subbuffer(ctx, src0);
+        vk_subbuffer buf_b = src1 ? ggml_vk_tensor_subbuffer(ctx, src1) : buf_a;
+        vk_subbuffer buf_c = src2 ? ggml_vk_tensor_subbuffer(ctx, src2) : buf_a;
+        vk_subbuffer buf_d = ggml_vk_tensor_subbuffer(ctx, dst);
+
+        uint32_t elems_per_wg = 128 * 4;
+        uint32_t num_wgs = CEIL_DIV(ncols, elems_per_wg);
+        size_t tmp_size = num_wgs * nrows_x * sizeof(float);
+
+        if (ctx->prealloc_size_x < tmp_size) {
+            ctx->prealloc_size_x = tmp_size;
+            ggml_vk_preallocate_buffers(ctx, subctx);
+        }
+        if (ctx->prealloc_size_y < tmp_size) {
+            ctx->prealloc_size_y = tmp_size;
+            ggml_vk_preallocate_buffers(ctx, subctx);
+        }
+        if (ctx->prealloc_x_need_sync || ctx->prealloc_y_need_sync) {
+            ggml_vk_sync_buffers(ctx, subctx);
+        }
+
+        vk_subbuffer buf_x = { ctx->prealloc_x, 0, tmp_size };
+        vk_subbuffer buf_y = { ctx->prealloc_y, 0, tmp_size };
+
+        std::array<uint32_t, 3> elements = { num_wgs, nrows_x, 1 };
+
+        vk_pipeline pipeline1 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large1_f32_f16 : ctx->device->pipeline_soft_max_large1_f32;
+        vk_pipeline pipeline2 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large2_f32_f16 : ctx->device->pipeline_soft_max_large2_f32;
+        vk_pipeline pipeline3 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large3_f32_f16 : ctx->device->pipeline_soft_max_large3_f32;
+
+        ggml_pipeline_request_descriptor_sets(ctx, pipeline1, 1);
+        ggml_pipeline_request_descriptor_sets(ctx, pipeline2, 1);
+        ggml_pipeline_request_descriptor_sets(ctx, pipeline3, 1);
+
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline1, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements);
+        ggml_vk_sync_buffers(ctx, subctx);
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline2, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements);
+        ggml_vk_sync_buffers(ctx, subctx);
+        ggml_vk_dispatch_pipeline(ctx, subctx, pipeline3, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements);
+
+        ctx->prealloc_x_need_sync = true;
+        ctx->prealloc_y_need_sync = true;
+    }
 }
 
 static void ggml_vk_soft_max_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large1.comp
new file mode 100644
index 0000000000..39c4663912
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large1.comp
@@ -0,0 +1,62 @@
+#version 450
+
+#include "soft_max_large_common.glsl"
+
+void main() {
+    const uint tid = gl_LocalInvocationID.x;
+    const uint rowx = gl_WorkGroupID.y;
+    const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters;
+
+    const uint32_t i03 = rowx / (p.ne01 * p.ne02);
+    const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01;
+    const uint32_t i01 = rowx % p.ne01;
+
+    uint rowy_start = 0;
+    if (p.KY > 0) {
+        rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13;
+    }
+
+    if (rowx >= p.nrows_x) {
+        return;
+    }
+
+    float slope = get_slope(rowx);
+
+    // Find max
+    FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02];
+
+    [[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) {
+        const uint col = col0 + tid;
+
+        FLOAT_TYPE a = FLOAT_TYPE(0);
+        if (col < p.KX) {
+            a = data_a[rowx * p.KX + col];
+        }
+
+        FLOAT_TYPE b = FLOAT_TYPE(0);
+        if (p.KY > 0 && col < p.KX) {
+            b = data_b[rowy_start + col];
+        }
+
+        FLOAT_TYPE v = a * p.scale + slope * b;
+
+        if (col < p.KX) {
+            max_val = max(max_val, v);
+        }
+    }
+
+    // reduce across the workgroup
+    vals[tid] = max_val;
+    barrier();
+    [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            vals[tid] = max(vals[tid], vals[tid + s]);
+        }
+        barrier();
+    }
+
+    if (tid == 0) {
+        max_val = vals[0];
+        data_m[rowx * gl_NumWorkGroups.x + gl_WorkGroupID.x] = max_val;
+    }
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large2.comp
new file mode 100644
index 0000000000..69524f5f75
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large2.comp
@@ -0,0 +1,79 @@
+#version 450
+
+#include "soft_max_large_common.glsl"
+
+void main() {
+    const uint tid = gl_LocalInvocationID.x;
+    const uint rowx = gl_WorkGroupID.y;
+    const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters;
+
+    const uint32_t i03 = rowx / (p.ne01 * p.ne02);
+    const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01;
+    const uint32_t i01 = rowx % p.ne01;
+
+    uint rowy_start = 0;
+    if (p.KY > 0) {
+        rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13;
+    }
+
+    if (rowx >= p.nrows_x) {
+        return;
+    }
+
+    float slope = get_slope(rowx);
+
+    // Find max
+    FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02];
+
+    [[unroll]] for (uint i = 0; i < gl_NumWorkGroups.x; i += BLOCK_SIZE) {
+        if (i + tid < gl_NumWorkGroups.x) {
+            max_val = max(max_val, data_m[rowx * gl_NumWorkGroups.x + i + tid]);
+        }
+    }
+
+    // reduce across the workgroup
+    vals[tid] = max_val;
+    barrier();
+    [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            vals[tid] = max(max_val, vals[tid + s]);
+        }
+        barrier();
+    }
+
+    max_val = vals[0];
+    barrier();
+
+    FLOAT_TYPE sum = FLOAT_TYPE(0.0f);
+
+    // Compute sum{exp(x - max)}
+    [[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) {
+        const uint col = col0 + tid;
+
+        if (col >= p.KX) {
+            break;
+        }
+
+        // compute exp(a*scale+b*slope), add it to sum
+        const uint i = rowx * p.KX + col;
+        FLOAT_TYPE val;
+        val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy_start + col]) : FLOAT_TYPE(0.0f)) - max_val);
+        sum += val;
+        data_d[i] = D_TYPE(val);
+    }
+
+    // reduce across the workgroup
+    vals[tid] = sum;
+    barrier();
+    [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            vals[tid] += vals[tid + s];
+        }
+        barrier();
+    }
+
+    if (tid == 0) {
+        sum = vals[0];
+        data_s[rowx * gl_NumWorkGroups.x + gl_WorkGroupID.x] = sum;
+    }
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large3.comp b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large3.comp
new file mode 100644
index 0000000000..06efd7d9fb
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large3.comp
@@ -0,0 +1,65 @@
+#version 450
+
+#include "soft_max_large_common.glsl"
+
+shared FLOAT_TYPE sumsh[BLOCK_SIZE];
+
+void main() {
+    const uint tid = gl_LocalInvocationID.x;
+    const uint rowx = gl_WorkGroupID.y;
+    const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters;
+
+    const uint32_t i03 = rowx / (p.ne01 * p.ne02);
+    const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01;
+    const uint32_t i01 = rowx % p.ne01;
+
+    uint rowy_start = 0;
+    if (p.KY > 0) {
+        rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13;
+    }
+
+    if (rowx >= p.nrows_x) {
+        return;
+    }
+
+    FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02];
+    FLOAT_TYPE sum = FLOAT_TYPE(0.0f);
+
+    [[unroll]] for (uint i = 0; i < gl_NumWorkGroups.x; i += BLOCK_SIZE) {
+        if (i + tid < gl_NumWorkGroups.x) {
+            max_val = max(max_val, data_m[rowx * gl_NumWorkGroups.x + i + tid]);
+            sum += data_s[rowx * gl_NumWorkGroups.x + i + tid];
+        }
+    }
+
+    // reduce across the workgroup
+    vals[tid] = max_val;
+    sumsh[tid] = sum;
+    barrier();
+    [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            vals[tid] = max(max_val, vals[tid + s]);
+            sumsh[tid] += sumsh[tid + s];
+        }
+        barrier();
+    }
+
+    max_val = vals[0];
+    sum = sumsh[0];
+
+    if (p.has_sinks != 0) {
+        sum += FLOAT_TYPE(exp(FLOAT_TYPE(data_c[i02]) - max_val));
+    }
+
+    FLOAT_TYPE rcpdivisor = 1.0/sum;
+
+    [[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) {
+        const uint col = col0 + tid;
+
+        if (col >= p.KX) {
+            continue;
+        }
+
+        data_d[rowx*p.KX + col] *= D_TYPE(rcpdivisor);
+    }
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large_common.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large_common.glsl
new file mode 100644
index 0000000000..6636d1f8de
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large_common.glsl
@@ -0,0 +1,53 @@
+#extension GL_EXT_control_flow_attributes : enable
+
+layout (push_constant) uniform parameter
+{
+    uint KX;
+    uint KY;
+    uint ne00;
+    uint ne01;
+    uint ne02;
+    uint ne12;
+    uint ne13;
+    uint nb11;
+    uint nb12;
+    uint nb13;
+    float scale;
+    float max_bias;
+    float m0;
+    float m1;
+    uint n_head_log2;
+    uint nrows_x;
+    uint has_sinks;
+} p;
+
+#include "types.glsl"
+
+layout(constant_id = 0) const uint BLOCK_SIZE = 128;
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
+layout(constant_id = 1) const uint num_iters = 4;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer Y {B_TYPE data_b[];};
+layout (binding = 2) readonly buffer Z {float data_c[];};
+layout (binding = 3) buffer D {D_TYPE data_d[];};
+layout (binding = 4) buffer M {float data_m[];};
+layout (binding = 5) buffer S {float data_s[];};
+
+shared FLOAT_TYPE vals[BLOCK_SIZE];
+
+float get_slope(uint rowx) {
+    float slope = 1.0f;
+
+    // ALiBi
+    if (p.max_bias > 0.0f) {
+        const uint h = (rowx / p.ne01) % p.ne02; // head index
+
+        const float base = h < p.n_head_log2 ? p.m0 : p.m1;
+        const uint   exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1;
+
+        slope = pow(base, exp);
+    }
+
+    return slope;
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 92bae088b2..72c63e8173 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -899,6 +899,13 @@ void process_shaders() {
     string_to_spv("soft_max_f32_f16", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
     string_to_spv("soft_max_back_f32", "soft_max_back.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
 
+    string_to_spv("soft_max_large1_f32", "soft_max_large1.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
+    string_to_spv("soft_max_large2_f32", "soft_max_large2.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
+    string_to_spv("soft_max_large3_f32", "soft_max_large3.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}}));
+    string_to_spv("soft_max_large1_f32_f16", "soft_max_large1.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
+    string_to_spv("soft_max_large2_f32_f16", "soft_max_large2.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
+    string_to_spv("soft_max_large3_f32_f16", "soft_max_large3.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}}));
+
     string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}});
     string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}});
     string_to_spv("rope_norm_f16_rte", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}});
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index f1bed864e3..416218b5b8 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -7652,6 +7652,9 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  true,  GGML_TYPE_F32, {1, 1}, 0.1f, 8.0f));
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  true,  GGML_TYPE_F16, {1, 1}, 0.1f, 8.0f));
 
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {200001, 2, 3, 1}, true,  true,  GGML_TYPE_F32, {1, 1}, 0.1f, 8.0f));
+    test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {200001, 2, 3, 1}, true,  true,  GGML_TYPE_F16, {1, 1}, 0.1f, 8.0f));
+
     for (float max_bias : {0.0f, 8.0f}) {
         for (float scale : {1.0f, 0.1f}) {
             for (int64_t ne0 : {16, 1024}) {

From 3229a23fa675ab7316fc53d045ded99f90fc6766 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 13 Dec 2025 03:07:49 -0600
Subject: [PATCH 082/135] vulkan: support GGML_OP_DIAG (#17893)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 24 +++++++++++++++
 ggml/src/ggml-vulkan/vulkan-shaders/diag.comp | 29 +++++++++++++++++++
 .../vulkan-shaders/vulkan-shaders-gen.cpp     |  2 ++
 3 files changed, 55 insertions(+)
 create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/diag.comp

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 0b4f8c36ad..ac3841764e 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -659,6 +659,7 @@ struct vk_device_struct {
     vk_pipeline pipeline_cos_f32;
     vk_pipeline pipeline_log[2];
     vk_pipeline pipeline_tri[2];
+    vk_pipeline pipeline_diag[2];
     vk_pipeline pipeline_clamp_f32;
     vk_pipeline pipeline_pad_f32;
     vk_pipeline pipeline_roll_f32;
@@ -3924,6 +3925,9 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_tri[0], "tri_f32", tri_f32_len, tri_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_tri[1], "tri_f16", tri_f16_len, tri_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
+    ggml_vk_create_pipeline(device, device->pipeline_diag[0], "diag_f32", diag_f32_len, diag_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_diag[1], "diag_f16", diag_f16_len, diag_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
+
     ggml_vk_create_pipeline(device, device->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_pad_f32, "pad_f32", pad_f32_len, pad_f32_data, "main", 2, sizeof(vk_op_pad_push_constants), {512, 1, 1}, {}, 1);
@@ -8416,6 +8420,12 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
             return ctx->device->pipeline_tri[dst->type == GGML_TYPE_F16];
         }
         return nullptr;
+    case GGML_OP_DIAG:
+        if (src0->type == dst->type &&
+            (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) {
+            return ctx->device->pipeline_diag[dst->type == GGML_TYPE_F16];
+        }
+        return nullptr;
     case GGML_OP_CLAMP:
         if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
             return ctx->device->pipeline_clamp_f32;
@@ -9109,6 +9119,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
     case GGML_OP_COS:
     case GGML_OP_LOG:
     case GGML_OP_TRI:
+    case GGML_OP_DIAG:
     case GGML_OP_CLAMP:
     case GGML_OP_PAD:
     case GGML_OP_ROLL:
@@ -9796,6 +9807,12 @@ static void ggml_vk_tri(ggml_backend_vk_context * ctx, vk_context& subctx, const
     ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_TRI, std::move(p));
 }
 
+static void ggml_vk_diag(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
+    vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst, ggml_nelements(dst));
+
+    ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_DIAG, std::move(p));
+}
+
 static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
     vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst);
     p.param1 = ggml_get_op_params_f32(dst, 0);
@@ -11924,6 +11941,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
     case GGML_OP_TRI:
         ggml_vk_tri(ctx, compute_ctx, src0, node);
 
+        break;
+    case GGML_OP_DIAG:
+        ggml_vk_diag(ctx, compute_ctx, src0, node);
+
         break;
     case GGML_OP_CLAMP:
         ggml_vk_clamp(ctx, compute_ctx, src0, node);
@@ -14067,6 +14088,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
             return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_LOG:
         case GGML_OP_TRI:
+        case GGML_OP_DIAG:
             return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
                    op->type == op->src[0]->type;
         case GGML_OP_ARGSORT:
@@ -14657,6 +14679,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
             tensor_clone = ggml_log(ggml_ctx, src_clone[0]);
         } else if (tensor->op == GGML_OP_TRI) {
             tensor_clone = ggml_tri(ggml_ctx, src_clone[0], ggml_get_op_params_i32(tensor, 0));
+        } else if (tensor->op == GGML_OP_DIAG) {
+            tensor_clone = ggml_diag(ggml_ctx, src_clone[0]);
         } else if (tensor->op == GGML_OP_CLAMP) {
             const float * params = (const float *)tensor->op_params;
             tensor_clone = ggml_clamp(ggml_ctx, src_clone[0], params[0], params[1]);
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/diag.comp b/ggml/src/ggml-vulkan/vulkan-shaders/diag.comp
new file mode 100644
index 0000000000..cd3f42f491
--- /dev/null
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/diag.comp
@@ -0,0 +1,29 @@
+#version 450
+
+#include "rte.glsl"
+#include "types.glsl"
+#include "generic_unary_head.glsl"
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+void main() {
+    const uint idx = get_idx();
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    const uint i13 = fastdiv(idx, p.ne1_012mp, p.ne1_012L);
+    const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10;
+    const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, p.ne1_01L);
+    const uint i12_offset = i12*p.ne11*p.ne10;
+    const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, p.ne1_0L);
+    const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
+
+    if (i10 == i11) {
+        const float val = float(data_a[get_aoffset() + i13*p.nb03 + i12*p.nb02 + 0*p.nb01 + i10*p.nb00]);
+        data_d[get_doffset() + dst_idx(idx)] = D_TYPE(val);
+    } else {
+        data_d[get_doffset() + dst_idx(idx)] = D_TYPE(0);
+    }
+}
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index 72c63e8173..f606ea1085 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -854,6 +854,8 @@ void process_shaders() {
 
     string_to_spv("tri_f16",        "tri.comp",         {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
     string_to_spv("tri_f32",        "tri.comp",         {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
+    string_to_spv("diag_f16",       "diag.comp",        {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
+    string_to_spv("diag_f32",       "diag.comp",        {{"A_TYPE", "float"},       {"D_TYPE", "float"}});
 
     string_to_spv("softplus_f16",   "softplus.comp",    {{"A_TYPE", "float16_t"},   {"D_TYPE", "float16_t"}});
     string_to_spv("softplus_f32",   "softplus.comp",    {{"A_TYPE", "float"},       {"D_TYPE", "float"}});

From 36255a22681583c3787bce778132c89d74da4e4c Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 13 Dec 2025 03:12:53 -0600
Subject: [PATCH 083/135] vulkan: support get_rows for i32 (#17941)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp                      | 7 +++++++
 ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp         | 4 ++--
 .../src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp | 8 +++++---
 3 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index ac3841764e..34ec09d403 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -3738,6 +3738,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_XS],  "get_rows_iq4_xs",  get_rows_iq4_xs_len,  get_rows_iq4_xs_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_NL],  "get_rows_iq4_nl",  get_rows_iq4_nl_len,  get_rows_iq4_nl_data,  "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_MXFP4],   "get_rows_mxfp4",   get_rows_mxfp4_len,   get_rows_mxfp4_data,   "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_I32],     "get_rows_i32",     get_rows_i32_len,     get_rows_i32_data,     "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32",  get_rows_f32_f32_len,  get_rows_f32_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32",  get_rows_f16_f32_len,  get_rows_f16_f32_data,  "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1);
@@ -8294,6 +8295,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
     switch (op) {
     case GGML_OP_GET_ROWS:
         GGML_ASSERT(src1->type == GGML_TYPE_I32);
+        if (src0->type == GGML_TYPE_I32) {
+            // i32 src only supports i32 result
+            GGML_ASSERT(dst->type == GGML_TYPE_I32);
+            return ctx->device->pipeline_get_rows[src0->type];
+        }
         if (dst->type == GGML_TYPE_F16) {
             return ctx->device->pipeline_get_rows[src0->type];
         }
@@ -13964,6 +13970,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
                     case GGML_TYPE_IQ4_XS:
                     case GGML_TYPE_IQ4_NL:
                     case GGML_TYPE_MXFP4:
+                    case GGML_TYPE_I32:
                         return true;
                     default:
                         return false;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp
index 76d83041ce..e88bdd057e 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp
@@ -26,9 +26,9 @@ void main() {
             const uint d_offset = get_doffset() + i10*p.nb21 + i11*p.nb22 + i12*p.nb23;
 
 #if defined(DATA_A_BF16)
-            FLOAT_TYPE v = FLOAT_TYPE(bf16_to_fp32(data_a[a_offset + i00]));
+            TEMP_TYPE v = TEMP_TYPE(bf16_to_fp32(data_a[a_offset + i00]));
 #else
-            FLOAT_TYPE v = FLOAT_TYPE(data_a[a_offset + i00]);
+            TEMP_TYPE v = TEMP_TYPE(data_a[a_offset + i00]);
 #endif
 #ifndef OPTIMIZATION_ERROR_WORKAROUND
             data_d[d_offset + i00] = D_TYPE(v);
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
index f606ea1085..b0ade078c7 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -704,13 +704,15 @@ void process_shaders() {
         shader = (tname == "f32" || tname == "f16" || tname == "bf16") ? "get_rows.comp" : "get_rows_quant.comp";
 
         if (tname == "f16") {
-            string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}));
+            string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}}));
         } else {
-            string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}}));
+            string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}}));
         }
-        string_to_spv("get_rows_" + tname + "_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}}));
+        string_to_spv("get_rows_" + tname + "_f32", shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}}));
     }
 
+    string_to_spv("get_rows_i32", "get_rows.comp", {{"TEMP_TYPE", "uint"}, {"A_TYPE", "uint"}, {"B_TYPE", "int"}, {"D_TYPE", "uint"}});
+
     string_to_spv("mul_mat_vec_p021_f16_f32_subgroup_add", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"A_TYPE_VEC4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}});
     string_to_spv("mul_mat_vec_p021_f16_f32",              "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"A_TYPE_VEC4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}});
     string_to_spv("mul_mat_vec_nc_f16_f32", "mul_mat_vec_nc.comp", {{"A_TYPE", "float16_t"}, {"A_TYPE_VEC4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}});

From 66ba51252ed1cf08739fbed8a538d3cb3541655d Mon Sep 17 00:00:00 2001
From: Gustavo Rocha Dias <91472747+gustrd@users.noreply.github.com>
Date: Sat, 13 Dec 2025 08:46:36 -0300
Subject: [PATCH 084/135] cmake: correct scope - link ws2_32 for
 MinGW/w64devkit builds in cpp-httplib (#17972)

* fix - w64devkit build

* fix - w64devkit build private scope
---
 vendor/cpp-httplib/CMakeLists.txt | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/vendor/cpp-httplib/CMakeLists.txt b/vendor/cpp-httplib/CMakeLists.txt
index e90e8e2d1b..8f0d15d1fd 100644
--- a/vendor/cpp-httplib/CMakeLists.txt
+++ b/vendor/cpp-httplib/CMakeLists.txt
@@ -11,8 +11,9 @@ endif()
 target_link_libraries  (${TARGET} PRIVATE Threads::Threads)
 
 if (WIN32 AND NOT MSVC)
-    target_link_libraries(${TARGET} PUBLIC ws2_32)
+    target_link_libraries(${TARGET} PRIVATE ws2_32)
 endif()
+
 target_compile_features(${TARGET} PRIVATE cxx_std_17)
 
 target_compile_definitions(${TARGET} PRIVATE

From 4d5ae24c0ac79c4e360773bac58dd2c2a46b7f67 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Sat, 13 Dec 2025 12:53:37 +0100
Subject: [PATCH 085/135] arg: fix common_params_parse not accepting negated
 arg (#17991)

---
 common/arg.cpp                 | 5 ++++-
 common/arg.h                   | 2 +-
 tests/test-arg-parser.cpp      | 4 ++++
 tools/server/server-models.cpp | 2 +-
 4 files changed, 10 insertions(+), 3 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index 19f22f883f..bb2a6840ba 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -724,7 +724,7 @@ static void add_rpc_devices(const std::string & servers) {
     }
 }
 
-bool common_params_parse(int argc, char ** argv, llama_example ex, std::map<common_arg, std::string> & out_map) {
+bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<common_arg, std::string> & out_map) {
     common_params dummy_params;
     common_params_context ctx_arg = common_params_parser_init(dummy_params, ex, nullptr);
 
@@ -733,6 +733,9 @@ bool common_params_parse(int argc, char ** argv, llama_example ex, std::map<comm
         for (const auto & arg : opt.args) {
             arg_to_options[arg] = &opt;
         }
+        for (const auto & arg : opt.args_neg) {
+            arg_to_options[arg] = &opt;
+        }
     }
 
     // TODO @ngxson : find a way to deduplicate this code
diff --git a/common/arg.h b/common/arg.h
index 6db38da488..1321595c1a 100644
--- a/common/arg.h
+++ b/common/arg.h
@@ -115,7 +115,7 @@ bool common_params_parse(int argc, char ** argv, common_params & params, llama_e
 
 // parse input arguments from CLI into a map
 // TODO: support repeated args in the future
-bool common_params_parse(int argc, char ** argv, llama_example ex, std::map<common_arg, std::string> & out_map);
+bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<common_arg, std::string> & out_map);
 
 // initialize argument parser context - used by test-arg-parser and preset
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
diff --git a/tests/test-arg-parser.cpp b/tests/test-arg-parser.cpp
index 90750b20c2..468d325e22 100644
--- a/tests/test-arg-parser.cpp
+++ b/tests/test-arg-parser.cpp
@@ -72,6 +72,10 @@ int main(void) {
     argv = {"binary_name", "--draft", "123"};
     assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_EMBEDDING));
 
+    // negated arg
+    argv = {"binary_name", "--no-mmap"};
+    assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON));
+
 
     printf("test-arg-parser: test valid usage\n\n");
 
diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp
index 6c618a673c..6be5ffbdf0 100644
--- a/tools/server/server-models.cpp
+++ b/tools/server/server-models.cpp
@@ -171,7 +171,7 @@ server_presets::server_presets(int argc, char ** argv, common_params & base_para
     }
 
     // read base args from router's argv
-    common_params_parse(argc, argv, LLAMA_EXAMPLE_SERVER, base_args);
+    common_params_to_map(argc, argv, LLAMA_EXAMPLE_SERVER, base_args);
 
     // remove any router-controlled args from base_args
     for (const auto & cargs : control_args) {

From 5266379bcae74214af397f36aa81b2a08b15d545 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 13 Dec 2025 09:19:51 -0600
Subject: [PATCH 086/135] llama_context: synchronize before reallocating output
 buffer (#17974)

---
 src/llama-context.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 2692297dca..9914b3276b 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -1318,6 +1318,7 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
             // This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark)
             LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
 #endif
+            synchronize();
             buf_output = nullptr;
             logits = nullptr;
             embd = nullptr;

From 4ed2bae50d64dcff7f99cb2b28f737fda314abf5 Mon Sep 17 00:00:00 2001
From: Sergey Fedorov <vital.had@gmail.com>
Date: Sun, 14 Dec 2025 05:02:43 +0800
Subject: [PATCH 087/135] server-models.cpp: add missing <filesystem> (#18000)

Fixes: https://github.com/ggml-org/llama.cpp/issues/17999
---
 tools/server/server-models.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp
index 6be5ffbdf0..3690c0bb82 100644
--- a/tools/server/server-models.cpp
+++ b/tools/server/server-models.cpp
@@ -16,6 +16,7 @@
 #include <atomic>
 #include <chrono>
 #include <queue>
+#include <filesystem>
 
 #ifdef _WIN32
 #include <winsock2.h>

From c00ff929dcfd150234e62f30e863bca4f1337aee Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Sat, 13 Dec 2025 22:33:29 +0100
Subject: [PATCH 088/135] scripts: add script to compare logprobs of llama.cpp
 against other frameworks (#17947)

* scripts: add script to compare logits of llama.cpp against other frameworks

* accept custom prompt file

* fix code style

* clarify endpoint

* fix displaying

* use abs for diff

* fix vllm case

* rm output file

* rename to compare-logprobs

* add "pattern"
---
 scripts/compare-logprobs.py | 281 ++++++++++++++++++++++++++++++++++++
 1 file changed, 281 insertions(+)
 create mode 100644 scripts/compare-logprobs.py

diff --git a/scripts/compare-logprobs.py b/scripts/compare-logprobs.py
new file mode 100644
index 0000000000..63861dd9a4
--- /dev/null
+++ b/scripts/compare-logprobs.py
@@ -0,0 +1,281 @@
+import argparse
+import requests
+import json
+from pathlib import Path
+import logging
+
+logger = logging.getLogger("compare-logprobs")
+logging.basicConfig(level=logging.INFO)
+
+
+DESCRIPTION = """
+Compare logits between llama.cpp and another inference engine using OpenAI-compatible server endpoints.
+
+Unlike compare-logits.py, it allows dumping logits from a hosted API endpoint. Useful when it's not possible to run both models locally.
+
+Example usage:
+    Step 1: Dump logits from two different servers
+        python scripts/compare-logprobs.py dump logits_llama.log http://localhost:8080/v1/completions
+        python scripts/compare-logprobs.py dump logits_other.log http://other-engine:8000/v1/completions
+
+        (optionally, you can add --api-key <key> if the endpoint requires authentication)
+
+    Step 2: Compare the dumped logits
+        python scripts/compare-logprobs.py compare logits_llama.log logits_other.log report.md
+"""
+
+
+def generate_input_prompt(length: int) -> list[str]:
+    CORPUS = """
+    You are an advanced AI assistant capable of using tools to gather information, perform calculations, or execute tasks. Always think step by step before responding. If a user's query requires external data, computation, or actions beyond your internal knowledge, use the appropriate tools via function calls.
+
+    ### Tool Call Format:
+    When you need to use a tool, output the call in this exact XML format. Include the opening and closing tags. Do not escape arguments; they will be parsed as plain text.
+
+    You can make multiple calls in one go by placing them one after another.
+    """
+    words = [w.strip() for w in CORPUS.strip().split(" ")]
+    words = [w for w in words if len(w) > 0]  # filter out empty strings
+    while len(words) < length:
+        words += words
+    return words[:length]
+
+
+def dump_logits(
+    endpoint: str,
+    output_path: Path,
+    input_words: list[str],
+    pattern: list[tuple[bool, int]],
+    api_key=None,
+):
+    logger.info(f"Dumping logits to {output_path} from endpoint {endpoint}...")
+    words = input_words
+    curr_text = ""
+    n_total = sum(n for get, n in pattern if get)
+    n_done = 0
+    i_cur = 0
+    i_total = len(words)
+    with output_path.open("w") as f:
+        for get, n in pattern:
+            if not get:
+                # skip n words
+                for i in range(n):
+                    curr_text += words.pop(0) + " "
+                    i_cur += 1
+                continue
+            # get n words
+            for i in range(n):
+                curr_text += words.pop(0) + " "
+                payload = {
+                    "prompt": curr_text.strip(),
+                    "temperature": 0.0,
+                    "top_k": 1,
+                    "max_tokens": 1,
+                    "logprobs": 1,
+                    "stream": False,
+                }
+                response = requests.post(
+                    endpoint,
+                    json=payload,
+                    headers={"Authorization": f"Bearer {api_key}"} if api_key else {},
+                )
+                response.raise_for_status()
+                data = response.json()
+                data["__index"] = i_cur  # add index for easier debugging later
+                data = json.dumps(data)
+                f.write(f"{data}\n")
+                n_done += 1
+                i_cur += 1
+                logger.info(
+                    f"\n\n{data}\n\n[Step: {n_done}/{n_total} | Word: {i_cur}/{i_total}]"
+                )
+    logger.info(f"Logits dumped to {output_path}")
+
+
+def get_token_logprobs(data: dict):
+    logprobs = data["choices"][0]["logprobs"]
+    if "content" in logprobs:
+        # llama.cpp case
+        top = logprobs["content"][0]["top_logprobs"][0]
+        return top["token"], top["logprob"]
+    else:
+        # vllm case
+        tokens = logprobs["tokens"]
+        token_logprobs = logprobs["token_logprobs"]
+        return tokens[0], token_logprobs[0]
+
+
+def clean_text(text: str) -> str:
+    return (
+        "'"
+        + text.replace("\n", "\\n")
+        .replace("\t", "\\t")
+        .replace("\r", "\\r")
+        .replace("|", "\\|")
+        + "'"
+    )
+
+
+def compare_logits(input1: Path, input2: Path, output_path: Path):
+    with input1.open("r") as f1, input2.open("r") as f2, output_path.open("w") as fout:
+        lines1 = f1.readlines()
+        lines2 = f2.readlines()
+
+        tab_header = [
+            "idx",
+            input1.name,
+            "logprob_1",
+            input2.name,
+            "logprob_2",
+            "diff (abs)",
+        ]
+        tab_entries = []
+        tab_max_widths = [len(h) for h in tab_header]
+
+        assert len(lines1) == len(
+            lines2
+        ), "Input files must have the same number of lines."
+
+        fout.write("# Logits Comparison Report\n\n")
+        for i, (line1, line2) in enumerate(zip(lines1, lines2)):
+            if not line1.strip() or not line2.strip():
+                continue  # skip empty lines
+
+            data1 = json.loads(line1)
+            data2 = json.loads(line2)
+
+            idx1 = data1.get("__index", -1)
+            idx2 = data2.get("__index", -1)
+            if idx1 != idx2:
+                logger.warning(
+                    f"Warning: Mismatched indices at line {i}: {idx1} vs {idx2}"
+                )
+
+            token1, logprob1 = get_token_logprobs(data1)
+            token2, logprob2 = get_token_logprobs(data2)
+
+            token1 = clean_text(token1)
+            token2 = clean_text(token2)
+            abs_diff = abs(logprob1 - logprob2)
+
+            tab_entries.append(
+                (
+                    str(idx1 + 1),
+                    token1,
+                    f"{logprob1:.4f}",
+                    token2,
+                    f"{logprob2:.4f}",
+                    f"{(abs_diff):.4f}",
+                )
+            )
+
+        for i in range(len(tab_entries)):
+            for j in range(len(tab_header)):
+                tab_max_widths[j] = max(tab_max_widths[j], len(tab_entries[i][j]))
+
+        output = ""
+        for j in range(len(tab_header)):
+            output += f"| {tab_header[j]:<{tab_max_widths[j]}} "
+        output += "|\n"
+        for j in range(len(tab_header)):
+            output += f"|{'-' * (tab_max_widths[j] + 2)}"
+        output += "|\n"
+        for entry in tab_entries:
+            for j in range(len(tab_header)):
+                output += f"| {entry[j]:<{tab_max_widths[j]}} "
+            output += "|\n"
+
+        logger.info("\n" + output)
+        fout.write(output)
+        logger.info(f"Report written to {output_path}")
+
+
+def parse_pattern(pattern: str) -> list[tuple[bool, int]]:
+    parts = pattern.split(",")
+    result = []
+    for i, part in enumerate(parts):
+        n = int(part)
+        if i % 2 == 0:
+            result.append((True, n))  # get n words
+        else:
+            result.append((False, n))  # skip n words
+    return result
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description=DESCRIPTION, formatter_class=argparse.RawTextHelpFormatter
+    )
+    subparsers = parser.add_subparsers(
+        dest="verb", required=True, help="action to perform"
+    )
+
+    # dump subcommand
+    parser_dump = subparsers.add_parser("dump", help="dump logits from an endpoint")
+    parser_dump.add_argument(
+        "output", type=Path, help="output path for dumped logits (.log)"
+    )
+    parser_dump.add_argument(
+        "endpoint", type=str, help="OAI-compat /completions endpoint"
+    )
+    parser_dump.add_argument(
+        "--api-key",
+        type=str,
+        default=None,
+        help="API key for authentication (if required)",
+    )
+    parser_dump.add_argument(
+        "--file",
+        type=Path,
+        default=None,
+        help="File containing prompt to use instead of the default",
+    )
+    parser_dump.add_argument(
+        "--pattern",
+        type=str,
+        default="10,1000,10,4000,10",
+        help="Pattern n_get,n_skip,... where n_get is number of words to get and n_skip is number of words to skip (num of words, NOT num of tokens)",
+    )
+
+    # compare subcommand
+    parser_compare = subparsers.add_parser(
+        "compare", help="compare two dumped logits files"
+    )
+    parser_compare.add_argument("input1", type=Path, help="first input file (.log)")
+    parser_compare.add_argument("input2", type=Path, help="second input file (.log)")
+    parser_compare.add_argument(
+        "output", type=Path, help="output path for comparison report (.md)"
+    )
+
+    try:
+        return parser.parse_args()
+    except Exception as e:
+        parser.print_help()
+        raise e
+
+
+def main():
+    args = parse_args()
+
+    if args.verb == "dump":
+        pattern = parse_pattern(args.pattern)
+        input_length = sum(n for _, n in pattern)
+        input_words = generate_input_prompt(input_length)
+        if args.file is not None:
+            with args.file.open("r") as f:
+                input_words = f.read().strip().split(" ")
+                if input_length < sum(n for _, n in pattern):
+                    raise ValueError(
+                        f"Input file has only {input_length} words, but pattern requires at least {input_length} words."
+                    )
+                input_length = len(input_words)
+        logger.info(f"Using {input_length} words")
+        dump_logits(args.endpoint, args.output, input_words, pattern, args.api_key)
+    elif args.verb == "compare":
+        compare_logits(args.input1, args.input2, args.output)
+    else:
+        raise ValueError(f"Unknown verb: {args.verb}")
+
+
+if __name__ == "__main__":
+    main()

From 615655aafe46339f04e2879d14d7d0d68fc2cfcd Mon Sep 17 00:00:00 2001
From: Congcong Cai <congcongcai0907@163.com>
Date: Fri, 12 Dec 2025 22:37:38 +0800
Subject: [PATCH 089/135] cmake : set `CMAKE_RUNTIME_OUTPUT_DIRECTORY` for non
 standalone build (ggml/1394)

Some backend depends on CMAKE_RUNTIME_OUTPUT_DIRECTORY to create temporary file like metal backened.
Missing CMAKE_RUNTIME_OUTPUT_DIRECTORY will cause some cmake error like permission denied (try to copy file to root).
This PR wants to setup a default path for CMAKE_RUNTIME_OUTPUT_DIRECTORY when it does not exist.
---
 ggml/CMakeLists.txt | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt
index ab5b4760e2..a65dcfbe1e 100644
--- a/ggml/CMakeLists.txt
+++ b/ggml/CMakeLists.txt
@@ -54,6 +54,10 @@ if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
     # TODO
 else()
     set(GGML_STANDALONE OFF)
+
+    if (NOT CMAKE_RUNTIME_OUTPUT_DIRECTORY)
+        set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
+    endif()
 endif()
 
 if (EMSCRIPTEN)

From 71fdcf0616fa0f93dab37e4c0d0d77e708e398a9 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 13 Dec 2025 08:04:09 +0200
Subject: [PATCH 090/135] ggml : arm repack fix build (whisper/0)

---
 ggml/src/ggml-cpu/arch/arm/repack.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/ggml/src/ggml-cpu/arch/arm/repack.cpp b/ggml/src/ggml-cpu/arch/arm/repack.cpp
index 683ed8d2df..fc62f1b86e 100644
--- a/ggml/src/ggml-cpu/arch/arm/repack.cpp
+++ b/ggml/src/ggml-cpu/arch/arm/repack.cpp
@@ -24,6 +24,7 @@
 
 #define UNUSED GGML_UNUSED
 
+#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
 static inline void decode_q4_Kx8_scales_mins(const uint8_t * scales_in,
                                              int16x8_t *     out_mins,
                                              int8_t *        out_scales) {
@@ -46,6 +47,7 @@ static inline void decode_q4_Kx8_scales_mins(const uint8_t * scales_in,
     scales_u32[1] = (sm[2] & kmask2) | (((sm[0] >> 6) & kmask3) << 4);
     memcpy(out_scales, scales_u32, 8);
 }
+#endif
 
 void ggml_quantize_mat_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
     assert(QK8_0 == 32);

From 0e59224990e6b535e87fbf10851dcd35aed04f3a Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 13 Dec 2025 10:07:07 +0200
Subject: [PATCH 091/135] sync : ggml

---
 scripts/sync-ggml.last | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index a879940eae..5823efac2d 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-55bc9320a4aae82af18e23eefd5de319a755d7b9
+130bc125a88bb57664b88932c48c38a1cb316fac

From a63cbafbbc5a7afe80b75486cffa651003aaede4 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sat, 13 Dec 2025 22:54:14 +0200
Subject: [PATCH 092/135] ggml : arm repack fix build

---
 ggml/src/ggml-cpu/arch/arm/repack.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ggml/src/ggml-cpu/arch/arm/repack.cpp b/ggml/src/ggml-cpu/arch/arm/repack.cpp
index fc62f1b86e..fb7f074a85 100644
--- a/ggml/src/ggml-cpu/arch/arm/repack.cpp
+++ b/ggml/src/ggml-cpu/arch/arm/repack.cpp
@@ -24,7 +24,7 @@
 
 #define UNUSED GGML_UNUSED
 
-#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8)
+#if defined(__aarch64__) && defined(__ARM_NEON) && (defined(__ARM_FEATURE_MATMUL_INT8) || defined(__ARM_FEATURE_DOTPROD))
 static inline void decode_q4_Kx8_scales_mins(const uint8_t * scales_in,
                                              int16x8_t *     out_mins,
                                              int8_t *        out_scales) {

From 609a2d026897829730ca231285469a9c80967fb7 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 14 Dec 2025 08:34:56 +0200
Subject: [PATCH 093/135] models : fix YaRN regression + consolidate logic
 (#18006)

* models : fix YaRN regression + consolidate logic

* cont : fix the fix

* cont : remove header

* cont : add header
---
 src/llama-context.cpp  | 38 ++++++++++++++++++++++++++++++++++++++
 src/llama-graph.cpp    |  2 +-
 src/llama-hparams.cpp  | 11 -----------
 src/llama-hparams.h    |  7 -------
 src/llama-kv-cache.cpp |  2 +-
 src/llama-model.cpp    | 26 --------------------------
 6 files changed, 40 insertions(+), 46 deletions(-)

diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 9914b3276b..2a17e44ecd 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -9,6 +9,7 @@
 #include "llama-model.h"
 
 #include <cinttypes>
+#include <cmath>
 #include <cstring>
 #include <limits>
 #include <stdexcept>
@@ -72,6 +73,43 @@ llama_context::llama_context(
         cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f;
     }
 
+    if (cparams.yarn_ext_factor != 0) {
+        static auto get_mscale = [](float scale, float mscale) {
+            return scale <= 1.0f ? 1.0f : (0.1f * mscale * logf(scale) + 1.0f);
+        };
+
+        const float factor = 1.0f / cparams.rope_freq_scale;
+
+        // ref: https://github.com/huggingface/transformers/blob/6d00f6b0a5679c36510f203e4226e36f517c3032/src/transformers/modeling_rope_utils.py#L336-L348
+        if (hparams.rope_yarn_log_mul != 0.0f) {
+            // note: here we assume `mscale == 1.0f`
+            // TODO: start reading the actual value of mscale and handle the case where it is not 1.0f
+                  float mscale          = 1.0f;
+            const float mscale_all_dims = hparams.rope_yarn_log_mul;
+
+            // [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
+            // special-case DEEPSEEK v2:
+            // https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat/blob/main/config.json#L42-L43
+            if (model.arch == LLM_ARCH_DEEPSEEK2 && mscale_all_dims != 1.0f) {
+                mscale = mscale_all_dims;
+            }
+
+            cparams.yarn_attn_factor = get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dims);
+
+            LLAMA_LOG_WARN("%s: setting new yarn_attn_factor = %.4f (mscale == %.1f, mscale_all_dim = %.1f)\n",
+                    __func__, cparams.yarn_attn_factor, mscale, mscale_all_dims);
+        } else {
+            cparams.yarn_attn_factor = get_mscale(factor, 1.0f);
+        }
+
+        // when YARN is applied with yarn_ext_factor != 0.0f, we need to cancel this factor:
+        // https://github.com/ggml-org/llama.cpp/blob/a81a569577cc38b32558958b048228150be63eae/ggml/src/ggml-cpu/ops.cpp#L5541-L5544
+        //
+        // ref: https://github.com/ggml-org/llama.cpp/discussions/7416
+        //      https://github.com/ggml-org/llama.cpp/pull/17945
+        cparams.yarn_attn_factor *= 1.0f / (1.0f + 0.1f * logf(factor));
+    }
+
     cparams.yarn_attn_factor *= hparams.rope_attn_factor;
 
     if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) {
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index a1a32494b7..6cf9a883a6 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -574,7 +574,7 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) :
     freq_base        (cparams.rope_freq_base),
     freq_scale       (cparams.rope_freq_scale),
     ext_factor       (cparams.yarn_ext_factor),
-    attn_factor      (llama_hparams::yarn_attn_factor_adjust(cparams.yarn_attn_factor, cparams.rope_freq_scale, cparams.yarn_ext_factor)),
+    attn_factor      (cparams.yarn_attn_factor),
     beta_fast        (cparams.yarn_beta_fast),
     beta_slow        (cparams.yarn_beta_slow),
     norm_eps         (hparams.f_norm_eps),
diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp
index 277d0bcfd3..96c9598c24 100644
--- a/src/llama-hparams.cpp
+++ b/src/llama-hparams.cpp
@@ -3,7 +3,6 @@
 #include "ggml.h"
 
 #include <cassert>
-#include <cmath>
 
 void llama_hparams::set_swa_pattern(uint32_t n_pattern, bool dense_first) {
     if (dense_first) {
@@ -231,13 +230,3 @@ bool llama_hparams::is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama
 
     return false;
 }
-
-float llama_hparams::yarn_attn_factor_adjust(float attn_factor, float freq_scale, float ext_factor) {
-    GGML_ASSERT(ext_factor >= 0.0f);
-
-    if (ext_factor != 0.0f) {
-        attn_factor *= 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale));
-    }
-
-    return attn_factor;
-}
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index c9960e9169..aab319754e 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -268,13 +268,6 @@ struct llama_hparams {
     // TODO: think of a better place for this function
     // TODO: pack the SWA params in a struct?
     static bool is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama_pos p0, llama_pos p1);
-
-    // when YARN is applied with yarn_ext_factor != 0.0f, we need to cancel this factor:
-    // https://github.com/ggml-org/llama.cpp/blob/a81a569577cc38b32558958b048228150be63eae/ggml/src/ggml-cpu/ops.cpp#L5541-L5544
-    //
-    // ref: https://github.com/ggml-org/llama.cpp/discussions/7416
-    //      https://github.com/ggml-org/llama.cpp/pull/17945
-    static float yarn_attn_factor_adjust(float attn_factor, float freq_scale, float ext_factor);
 };
 
 static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index 8f94c8820c..bf3de2f2ef 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -1372,7 +1372,7 @@ ggml_tensor * llama_kv_cache::build_rope_shift(
     const auto & yarn_ext_factor  = cparams.yarn_ext_factor;
     const auto & yarn_beta_fast   = cparams.yarn_beta_fast;
     const auto & yarn_beta_slow   = cparams.yarn_beta_slow;
-    const auto & yarn_attn_factor = llama_hparams::yarn_attn_factor_adjust(cparams.yarn_attn_factor, cparams.rope_freq_scale, cparams.yarn_ext_factor);
+    const auto & yarn_attn_factor = cparams.yarn_attn_factor;
 
     const auto & n_rot     = hparams.n_rot;
     const auto & rope_type = hparams.rope_type == LLAMA_ROPE_TYPE_MROPE || hparams.rope_type == LLAMA_ROPE_TYPE_IMROPE
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index e4808b1e1e..5da1dd6dbb 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -2294,32 +2294,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
         default: throw std::runtime_error("unsupported model architecture");
     }
 
-    // ref: https://github.com/huggingface/transformers/blob/6d00f6b0a5679c36510f203e4226e36f517c3032/src/transformers/modeling_rope_utils.py#L336-L348
-    if (hparams.rope_yarn_log_mul != 0.0f) {
-        const float factor = 1.0f / hparams.rope_freq_scale_train;
-
-        // note: here we assume `mscale == 1.0f`
-        // TODO: start reading the actual value of mscale and handle the case where it is not 1.0f
-              float mscale          = 1.0f;
-        const float mscale_all_dims = hparams.rope_yarn_log_mul;
-
-        // [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
-        // special-case DEEPSEEK v2:
-        // https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat/blob/main/config.json#L42-L43
-        if (arch == LLM_ARCH_DEEPSEEK2 && mscale_all_dims != 1.0f) {
-            mscale = mscale_all_dims;
-        }
-
-        static auto get_mscale = [](float scale, float mscale) {
-            return scale <= 1.0f ? 1.0f : (0.1f * mscale * logf(scale) + 1.0f);
-        };
-
-        hparams.yarn_attn_factor = get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dims);
-
-        LLAMA_LOG_WARN("%s: setting new yarn_attn_factor = %.4f (mscale == %.1f, mscale_all_dim = %.1f)\n",
-                __func__, hparams.yarn_attn_factor, mscale, mscale_all_dims);
-    }
-
     pimpl->n_bytes = ml.n_bytes;
 
     pimpl->desc_str = arch_name() + " " + type_name() + " " + ml.ftype_name();

From 77ad8542bd09c374d65be8abffd88ae4aa1e704d Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 14 Dec 2025 08:58:13 +0200
Subject: [PATCH 094/135] model-conversion : cast logits to float32 (#18009)

---
 examples/model-conversion/scripts/causal/run-org-model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/model-conversion/scripts/causal/run-org-model.py b/examples/model-conversion/scripts/causal/run-org-model.py
index 7d2b80057c..da1132c003 100755
--- a/examples/model-conversion/scripts/causal/run-org-model.py
+++ b/examples/model-conversion/scripts/causal/run-org-model.py
@@ -200,7 +200,7 @@ with torch.no_grad():
     logits = outputs.logits
 
     # Extract logits for the last token (next token prediction)
-    last_logits = logits[0, -1, :].cpu().numpy()
+    last_logits = logits[0, -1, :].float().cpu().numpy()
 
     print(f"Logits shape: {logits.shape}")
     print(f"Last token logits shape: {last_logits.shape}")

From d15d177f43b7dd7f73a965716bd76a3327abb2d6 Mon Sep 17 00:00:00 2001
From: Eve <139727413+netrunnereve@users.noreply.github.com>
Date: Sun, 14 Dec 2025 07:29:37 +0000
Subject: [PATCH 095/135] vulkan: faster q6_k matmul (#17813)

* q6_k faster mul mat

* 8 values

* fix comment

* switch to two at a time

* start ci for .glsl files
---
 .github/workflows/build.yml                         |  6 ++++--
 .gitignore                                          |  1 +
 .../ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl    | 13 ++++++++-----
 3 files changed, 13 insertions(+), 7 deletions(-)

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 9fe1401df4..af4c60be64 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -20,7 +20,8 @@ on:
       '**/*.swift',
       '**/*.m',
       '**/*.metal',
-      '**/*.comp'
+      '**/*.comp',
+      '**/*.glsl'
     ]
 
   pull_request:
@@ -40,7 +41,8 @@ on:
       '**/*.swift',
       '**/*.m',
       '**/*.metal',
-      '**/*.comp'
+      '**/*.comp',
+      '**/*.glsl'
     ]
 
 concurrency:
diff --git a/.gitignore b/.gitignore
index 428f084110..05eb578a82 100644
--- a/.gitignore
+++ b/.gitignore
@@ -54,6 +54,7 @@
 /out/
 /tmp/
 /autogen-*.md
+/common/build-info.cpp
 
 # Deprecated
 
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl
index ee5ded2e8d..58ede04400 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl
@@ -244,17 +244,20 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
             const uint iqs = idx % 128;                 // 0..127
 
             const uint n = iqs / 64;                    // 0,1
-            const uint b = (iqs % 64) / 32;             // 0,1
+            const uint b = ((iqs % 64) / 32) * 4;       // 0,4
             const uint is_b = (iqs % 16) / 8;           // 0,1
             const uint qhshift = ((iqs % 64) / 16) * 2; // 0,2,4,6
             const uint is = 8 * n + qhshift + is_b;     // 0..15
-            const uint qsi = n * 64 + (iqs % 32) * 2;   // 0,2,4..126
-            const uint qhi = n * 32 + (iqs % 16) * 2;   // 0,2,4..62
+            const uint qsi = n * 32 + (iqs % 32);       // 0..63
+            const uint qhi = n * 16 + (iqs % 16);       // 0..31
 
             const float dscale = float(data_a[ib].d) * float(data_a[ib].scales[is]);
 
-            buf_a[buf_idx] = FLOAT_TYPE_VEC2(dscale * float(int8_t(((data_a[ib].ql[qsi    ] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi    ] >> qhshift) & 3) << 4)) - 32),
-                                             dscale * float(int8_t(((data_a[ib].ql[qsi + 1] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi + 1] >> qhshift) & 3) << 4)) - 32));
+            const uint ql = (uint(data_a_packed16[ib].ql[qsi]) >> b) & 0x0F0F;
+            const uint qh = (uint(data_a_packed16[ib].qh[qhi]) >> qhshift) & 0x0303;
+            const vec2 q = (vec2(unpack8(ql | (qh << 4)).xy) - 32) * dscale;
+
+            buf_a[buf_idx] = FLOAT_TYPE_VEC2(q.x, q.y);
 #elif defined(DATA_A_IQ1_S)
             const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row;
             const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2;

From 4722671641fdc29b371d517db05d4b395c6e068c Mon Sep 17 00:00:00 2001
From: lovedheart <6277001+lovedheart@users.noreply.github.com>
Date: Sun, 14 Dec 2025 08:47:49 +0100
Subject: [PATCH 096/135] vulkan: improve mul_mat_vec_iq1_s speed (#17874)

---
 .../vulkan-shaders/mul_mat_vec_iq1_s.comp     | 60 ++++++++++++-------
 1 file changed, 38 insertions(+), 22 deletions(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp
index 0b74b33212..e6b1f20215 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp
@@ -7,34 +7,50 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
 
 FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
 
-void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) {
-    const uint y_idx = i * QUANT_K + 32 * ib32;
+void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i,
+                     const uint num_blocks_per_row, const uint first_row, const uint num_rows) {
+    const uint y_idx_base = i * QUANT_K + 32 * ib32;
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {    
+        const uint base_b_idx = (j * p.batch_stride_b + b_offset + y_idx_base) / 4;
+        [[unroll]] for (uint l = 0; l < 4; ++l) {            
+            const vec4 b_val_0 = vec4(data_b_v4[base_b_idx + 2 * l]);
+            const vec4 b_val_1 = vec4(data_b_v4[base_b_idx + 2 * l + 1]);
 
-    uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i;
-    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
-        const float d = float(data_a[ibi].d);
-        const uint qh = data_a[ibi].qh[ib32];
-        const float dl = d * float(2 * bitfieldExtract(qh, 12, 3) + 1);
-        const float delta = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA;
+            // index for data_a
+            uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i;
 
-        [[unroll]] for (uint l = 0; l < 4; ++l) {
-            const uint qs = data_a[ibi].qs[4 * ib32 + l];
-            const uint idxhi = bitfieldExtract(qh, 3 * int(l), 3);
-            const int16_t grid = int16_t(iq1s_grid[qs | (idxhi << 8)]);
+            [[unroll]] for (uint n = 0; n < num_rows; ++n) {                
+                const float d = float(data_a[ibi].d);
+                const uint qh = data_a[ibi].qh[ib32];
 
-            [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
-                vec4 b0 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 0]);
-                vec4 b4 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 1]);
+                const float dl = d * float(2 * bitfieldExtract(qh, 12, 3) + 1);
+                const uint qs = data_a[ibi].qs[4 * ib32 + l];
+                const uint idxhi = bitfieldExtract(qh, 3 * int(l), 3);              
+                const uint16_t grid = uint16_t(iq1s_grid[qs | (idxhi << 8)]);
 
-                FLOAT_TYPE sum = FLOAT_TYPE(0.0);
-                [[unroll]] for (int k = 0; k < 4; ++k) {
-                    sum = fma(FLOAT_TYPE(b0[k]), bitfieldExtract(grid, 2 * k, 2) + delta,
-                          fma(FLOAT_TYPE(b4[k]), bitfieldExtract(grid, 8 + 2 * k, 2) + delta, sum));
-                }
-                temp[j][n] = fma(dl, sum, temp[j][n]);
+                const float delta_val = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA;
+                const vec4 delta_v = vec4(delta_val);				
+                const vec4 fbits0 = vec4(
+                    float(bitfieldExtract(grid, 0, 2)),
+                    float(bitfieldExtract(grid, 2, 2)),
+                    float(bitfieldExtract(grid, 4, 2)),
+                    float(bitfieldExtract(grid, 6, 2))
+                );				
+                const vec4 fbits1 = vec4(
+                    float(bitfieldExtract(grid, 8, 2)),
+                    float(bitfieldExtract(grid, 10, 2)),
+                    float(bitfieldExtract(grid, 12, 2)),
+                    float(bitfieldExtract(grid, 14, 2))
+                );
+				
+                vec4 sum_v = fma(b_val_0, fbits0 + delta_v, vec4(0.0));
+                sum_v      = fma(b_val_1, fbits1 + delta_v, sum_v);
+				FLOAT_TYPE sum = dot(sum_v, vec4(1.0));
+				
+                temp[j][n] = fma(dl, sum, temp[j][n]);				
+                ibi += num_blocks_per_row;
             }
         }
-        ibi += num_blocks_per_row;
     }
 }
 

From 3238b1400cf4f0f174c39579d92ce1a8ddb68110 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sun, 14 Dec 2025 02:00:00 -0600
Subject: [PATCH 097/135] vulkan: Fix data race/hang in scalar/cm1 flash
 attention (#17887)

---
 ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp     | 3 +++
 ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp | 3 +++
 2 files changed, 6 insertions(+)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
index 4bef48b006..0379e5d502 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp
@@ -256,6 +256,9 @@ void main() {
         barrier();
     }
 
+    // prevent race on tmpsh
+    barrier();
+
     // reduce across threads
 
     [[unroll]] for (uint32_t r = 0; r < Br; ++r) {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
index cd82e4abfa..c995ab140e 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp
@@ -302,6 +302,9 @@ void main() {
         barrier();
     }
 
+    // prevent race on tmpsh
+    barrier();
+
     // reduce across threads
 
     float rowmaxf[rows_per_thread], eMf[rows_per_thread], Moldf[rows_per_thread];

From 254098a279691615438f1a1bbe87f372199e13ea Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Sun, 14 Dec 2025 10:11:13 +0200
Subject: [PATCH 098/135] common : refactor common_sampler + grammar logic
 changes (#17937)

* common : refactor common_sampler + grammar logic changes

* tests : increase max_tokens to get needed response

* batched : fix uninitialized samplers
---
 common/arg.cpp                                |   2 +-
 common/common.cpp                             | 192 ++++++++++++------
 common/common.h                               |  31 ++-
 common/sampling.cpp                           | 183 +++++++++--------
 common/sampling.h                             |  17 +-
 common/speculative.cpp                        |   2 +-
 examples/batched/batched.cpp                  |  30 ++-
 examples/embedding/embedding.cpp              |   6 +-
 examples/eval-callback/eval-callback.cpp      |   6 +-
 examples/lookahead/lookahead.cpp              |   6 +-
 examples/lookup/lookup-create.cpp             |   8 +-
 examples/lookup/lookup-stats.cpp              |   8 +-
 examples/lookup/lookup.cpp                    |   6 +-
 examples/parallel/parallel.cpp                |   6 +-
 examples/retrieval/retrieval.cpp              |   6 +-
 examples/save-load-state/save-load-state.cpp  |   6 +-
 .../speculative-simple/speculative-simple.cpp |  12 +-
 examples/speculative/speculative.cpp          |  16 +-
 examples/training/finetune.cpp                |  17 +-
 tools/completion/completion.cpp               |  20 +-
 tools/cvector-generator/cvector-generator.cpp |   6 +-
 tools/imatrix/imatrix.cpp                     |   6 +-
 tools/mtmd/mtmd-cli.cpp                       |   6 +-
 tools/perplexity/perplexity.cpp               |   6 +-
 tools/server/server-context.cpp               |  47 ++---
 .../tests/unit/test_compat_anthropic.py       |   2 +-
 tools/tts/tts.cpp                             |  12 +-
 27 files changed, 372 insertions(+), 293 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index bb2a6840ba..aaa7b92a2e 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -1415,7 +1415,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.sampling.top_k = value;
             params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_K;
         }
-    ).set_sparam());
+    ).set_sparam().set_env("LLAMA_ARG_TOP_K"));
     add_opt(common_arg(
         {"--top-p"}, "N",
         string_format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sampling.top_p),
diff --git a/common/common.cpp b/common/common.cpp
index 0497f90a28..b76dfa10ea 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1013,31 +1013,40 @@ bool tty_can_use_colors() {
 // Model utils
 //
 
-static inline void common_init_sampler_from_model(
+// TODO: move to common/sampling
+static void common_init_sampler_from_model(
     const llama_model * model,
     common_params_sampling & sparams) {
 
     const uint64_t config = sparams.user_sampling_config;
 
     auto get_int32 = [&](const char * key, int32_t & dst, uint64_t user_config) {
-        if (config & user_config) return;
+        if (config & user_config) {
+            return;
+        }
 
         char buf[64] = {0};
         if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) {
             char * end = nullptr;
             int32_t v = strtol(buf, &end, 10);
-            if (end && end != buf) dst = v;
+            if (end && end != buf) {
+                dst = v;
+            }
         }
     };
 
     auto get_float = [&](const char * key, float & dst, uint64_t user_config) {
-        if (config & user_config) return;
+        if (config & user_config) {
+            return;
+        }
 
         char buf[128] = {0};
         if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) {
             char * end = nullptr;
             float v = strtof(buf, &end);
-            if (end && end != buf) dst = v;
+            if (end && end != buf) {
+                dst = v;
+            }
         }
     };
 
@@ -1065,31 +1074,122 @@ static inline void common_init_sampler_from_model(
     get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA),    sparams.mirostat_eta,    common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA);
 }
 
-struct common_init_result common_init_from_params(common_params & params) {
-    common_init_result iparams;
-    auto mparams = common_model_params_to_llama(params);
+struct common_init_result::impl {
+    impl() = default;
+    ~impl() = default;
+
+    llama_model_ptr   model;
+    llama_context_ptr context;
+
+    std::vector<llama_adapter_lora_ptr> lora;
+
+    std::vector<common_sampler_ptr> samplers;
+};
+
+common_init_result::common_init_result(common_params & params) :
+    pimpl(new impl{}) {
+    const auto mparams = common_model_params_to_llama(params);
 
     llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
     if (model == NULL) {
-        LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
-            __func__, params.model.path.c_str());
-        return iparams;
+        return;
     }
 
-    common_init_sampler_from_model(model, params.sampling);
+    pimpl->model.reset(model);
 
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
+    // updates params.sampling
+    // TODO: fix naming
+    common_init_sampler_from_model(model, params.sampling);
+
     auto cparams = common_context_params_to_llama(params);
 
+    if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
+        LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
+        params.sampling.ignore_eos = false;
+    }
+
+    // initialize once
+    for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
+        if (llama_vocab_is_eog(vocab, i)) {
+            LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(vocab, i).c_str(), -INFINITY);
+            params.sampling.logit_bias_eog.push_back({i, -INFINITY});
+        }
+    }
+
+    if (params.sampling.ignore_eos) {
+        // add EOG biases to the active set of logit biases
+        params.sampling.logit_bias.insert(
+                params.sampling.logit_bias.end(),
+                params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end());
+    }
+
+    //if (params.sampling.penalty_last_n == -1) {
+    //    LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
+    //    params.sampling.penalty_last_n = llama_n_ctx(lctx);
+    //}
+
+    //if (params.sampling.dry_penalty_last_n == -1) {
+    //    LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
+    //    params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
+    //}
+
+    pimpl->samplers.resize(cparams.n_seq_max);
+
+    for (int i = 0; i < (int) cparams.n_seq_max; ++i) {
+        pimpl->samplers[i].reset(common_sampler_init(model, params.sampling));
+    }
+
     llama_context * lctx = llama_init_from_model(model, cparams);
     if (lctx == NULL) {
         LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
-            __func__, params.model.path.c_str());
-        llama_model_free(model);
-        return iparams;
+                __func__, params.model.path.c_str());
+        return;
     }
 
+    pimpl->context.reset(lctx);
+}
+
+llama_model * common_init_result::model() {
+    return pimpl->model.get();
+}
+
+llama_context * common_init_result::context() {
+    return pimpl->context.get();
+}
+
+common_sampler * common_init_result::sampler(llama_seq_id seq_id) {
+    return pimpl->samplers[seq_id].get();
+}
+
+std::vector<llama_adapter_lora_ptr> & common_init_result::lora() {
+    return pimpl->lora;
+}
+
+void common_init_result::free_context() {
+    pimpl->context.reset();
+}
+
+common_init_result_ptr common_init_from_params(common_params & params) {
+    common_init_result_ptr res(new common_init_result(params));
+
+    llama_model * model = res->model();
+    if (model == NULL) {
+        LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
+            __func__, params.model.path.c_str());
+        return res;
+    }
+
+    llama_context * lctx = res->context();
+    if (lctx == NULL) {
+        LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
+            __func__, params.model.path.c_str());
+        return res;
+    }
+
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
     if (params.ctx_shift && !llama_memory_can_shift(llama_get_memory(lctx))) {
         LOG_WRN("%s: KV cache shifting is not supported for this context, disabling KV cache shifting\n", __func__);
         params.ctx_shift = false;
@@ -1101,10 +1201,7 @@ struct common_init_result common_init_from_params(common_params & params) {
 
         const auto cvec = common_control_vector_load(params.control_vectors);
         if (cvec.n_embd == -1) {
-            llama_free(lctx);
-            llama_model_free(model);
-
-            return iparams;
+            return res;
         }
 
         int err = llama_apply_adapter_cvec(
@@ -1115,10 +1212,7 @@ struct common_init_result common_init_from_params(common_params & params) {
                 params.control_vector_layer_start,
                 params.control_vector_layer_end);
         if (err) {
-            llama_free(lctx);
-            llama_model_free(model);
-
-            return iparams;
+            return res;
         }
     }
 
@@ -1142,10 +1236,7 @@ struct common_init_result common_init_from_params(common_params & params) {
         }
 
         if (!ok) {
-            llama_free(lctx);
-            llama_model_free(model);
-
-            return iparams;
+            return res;
         }
     }
 
@@ -1155,9 +1246,7 @@ struct common_init_result common_init_from_params(common_params & params) {
         lora.reset(llama_adapter_lora_init(model, la.path.c_str()));
         if (lora == nullptr) {
             LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str());
-            llama_free(lctx);
-            llama_model_free(model);
-            return iparams;
+            return res;
         }
 
         char buf[1024];
@@ -1166,43 +1255,13 @@ struct common_init_result common_init_from_params(common_params & params) {
         la.task_name = buf;
         llama_adapter_meta_val_str(la.ptr, "adapter.lora.prompt_prefix", buf, sizeof(buf));
         la.prompt_prefix = buf;
-        iparams.lora.emplace_back(std::move(lora)); // copy to list of loaded adapters
+        res->lora().emplace_back(std::move(lora)); // copy to list of loaded adapters
     }
 
     if (!params.lora_init_without_apply) {
         common_set_adapter_lora(lctx, params.lora_adapters);
     }
 
-    if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
-        LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
-        params.sampling.ignore_eos = false;
-    }
-
-    // initialize once
-    for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) {
-        if (llama_vocab_is_eog(vocab, i)) {
-            LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY);
-            params.sampling.logit_bias_eog.push_back({i, -INFINITY});
-        }
-    }
-
-    if (params.sampling.ignore_eos) {
-        // add EOG biases to the active set of logit biases
-        params.sampling.logit_bias.insert(
-                params.sampling.logit_bias.end(),
-                params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end());
-    }
-
-    if (params.sampling.penalty_last_n == -1) {
-        LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
-        params.sampling.penalty_last_n = llama_n_ctx(lctx);
-    }
-
-    if (params.sampling.dry_penalty_last_n == -1) {
-        LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
-        params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
-    }
-
     if (params.warmup) {
         LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__);
 
@@ -1241,12 +1300,11 @@ struct common_init_result common_init_from_params(common_params & params) {
         llama_set_warmup(lctx, false);
     }
 
-    iparams.model.reset(model);
-    iparams.context.reset(lctx);
-
-    return iparams;
+    return res;
 }
 
+common_init_result::~common_init_result() = default;
+
 std::string get_model_endpoint() {
     const char * model_endpoint_env = getenv("MODEL_ENDPOINT");
     // We still respect the use of environment-variable "HF_ENDPOINT" for backward-compatibility.
@@ -1255,7 +1313,9 @@ std::string get_model_endpoint() {
     std::string model_endpoint = "https://huggingface.co/";
     if (endpoint_env) {
         model_endpoint = endpoint_env;
-        if (model_endpoint.back() != '/') model_endpoint += '/';
+        if (model_endpoint.back() != '/') {
+            model_endpoint += '/';
+        }
     }
     return model_endpoint;
 }
diff --git a/common/common.h b/common/common.h
index 2fd83f0cf9..4edb74b706 100644
--- a/common/common.h
+++ b/common/common.h
@@ -195,7 +195,6 @@ struct common_params_sampling {
 
     std::vector<std::string> dry_sequence_breakers = {"\n", ":", "\"", "*"};     // default sequence breakers for DRY
 
-
     std::vector<enum common_sampler_type> samplers = {
         COMMON_SAMPLER_TYPE_PENALTIES,
         COMMON_SAMPLER_TYPE_DRY,
@@ -216,6 +215,10 @@ struct common_params_sampling {
     std::vector<llama_logit_bias> logit_bias;     // logit biases to apply
     std::vector<llama_logit_bias> logit_bias_eog; // pre-calculated logit biases for EOG tokens
 
+    bool has_logit_bias() const {
+        return !logit_bias.empty();
+    }
+
     // print the parameters into a string
     std::string print() const;
 };
@@ -669,15 +672,29 @@ bool tty_can_use_colors();
 // Model utils
 //
 
-// note: defines object's lifetime
-struct common_init_result {
-    llama_model_ptr   model;
-    llama_context_ptr context;
+struct common_sampler;
 
-    std::vector<llama_adapter_lora_ptr> lora;
+// note: defines the model, context, samplers, ets. lifetimes
+struct common_init_result {
+    common_init_result(common_params & params);
+    ~common_init_result();
+
+    llama_model * model();
+    llama_context * context();
+    common_sampler * sampler(llama_seq_id seq_id);
+
+    std::vector<llama_adapter_lora_ptr> & lora();
+
+    void free_context();
+
+private:
+    struct impl;
+    std::unique_ptr<impl> pimpl;
 };
 
-struct common_init_result     common_init_from_params(common_params & params);
+using common_init_result_ptr = std::unique_ptr<common_init_result>;
+
+common_init_result_ptr common_init_from_params(common_params & params);
 
 struct llama_model_params     common_model_params_to_llama  (      common_params & params);
 struct llama_context_params   common_context_params_to_llama(const common_params & params);
diff --git a/common/sampling.cpp b/common/sampling.cpp
index 7a6b7be1e0..6935d84e22 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -104,9 +104,10 @@ struct ring_buffer {
 struct common_sampler {
     common_params_sampling params;
 
-    struct llama_sampler * grmr;
     struct llama_sampler * chain;
 
+    bool grammar;
+
     ring_buffer<llama_token> prev;
 
     std::vector<llama_token_data> cur;
@@ -116,7 +117,6 @@ struct common_sampler {
     void reset() {
         prev.clear();
 
-        llama_sampler_reset(grmr);
         llama_sampler_reset(chain);
     }
 
@@ -167,10 +167,15 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
 
     lparams.no_perf = params.no_perf;
 
-    struct llama_sampler * grmr;
+    llama_sampler * chain = llama_sampler_chain_init(lparams);
+
+    bool grammar = false;
+    std::vector<llama_sampler *> samplers;
+
     if (params.grammar.compare(0, 11, "%llguidance") == 0) {
 #ifdef LLAMA_USE_LLGUIDANCE
-        grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str());
+        samplers.push_back(llama_sampler_init_llg(vocab, "lark", params.grammar.c_str()));
+        grammar = true;
 #else
         GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
 #endif // LLAMA_USE_LLGUIDANCE
@@ -217,30 +222,23 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
             trigger_patterns_c.push_back(regex.c_str());
         }
 
-        grmr = params.grammar_lazy
-             ? llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root",
-                                                        trigger_patterns_c.data(), trigger_patterns_c.size(),
-                                                        trigger_tokens.data(), trigger_tokens.size())
-             :      llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root");
-        if (!grmr) {
-            return nullptr;
+        if (!params.grammar.empty()) {
+             if (params.grammar_lazy) {
+                 samplers.push_back(
+                         llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root",
+                             trigger_patterns_c.data(), trigger_patterns_c.size(),
+                             trigger_tokens.data(),     trigger_tokens.size()));
+             } else {
+                 samplers.push_back(llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root"));
+             }
+
+             grammar = true;
         }
     }
 
-    auto * result = new common_sampler {
-        /* .params = */ params,
-        /* .grmr   = */ grmr,
-        /* .chain  = */ llama_sampler_chain_init(lparams),
-        /* .prev   = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
-        /* .cur    = */ {},
-        /* .cur_p  = */ {},
-    };
-
-    llama_sampler_chain_add(result->chain,
-            llama_sampler_init_logit_bias(
-                llama_vocab_n_tokens(vocab),
-                params.logit_bias.size(),
-                params.logit_bias.data()));
+    if (params.has_logit_bias()) {
+        samplers.push_back(llama_sampler_init_logit_bias(llama_vocab_n_tokens(vocab), params.logit_bias.size(), params.logit_bias.data()));
+    }
 
     if (params.mirostat == 0) {
         for (const auto & cnstr : params.samplers) {
@@ -253,58 +251,70 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
                             c_breakers.push_back(str.c_str());
                         }
 
-                        llama_sampler_chain_add(result->chain, llama_sampler_init_dry      (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
+                        samplers.push_back(llama_sampler_init_dry    (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
                     }
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_K:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_k       (params.top_k));
+                    samplers.push_back(llama_sampler_init_top_k      (params.top_k));
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_P:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_p       (params.top_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_top_p      (params.top_p, params.min_keep));
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_N_SIGMA:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_top_n_sigma (params.top_n_sigma));
+                    samplers.push_back(llama_sampler_init_top_n_sigma(params.top_n_sigma));
                     break;
                 case COMMON_SAMPLER_TYPE_MIN_P:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_min_p       (params.min_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_min_p      (params.min_p, params.min_keep));
                     break;
                 case COMMON_SAMPLER_TYPE_XTC:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_xtc         (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
+                    samplers.push_back(llama_sampler_init_xtc        (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
                     break;
                 case COMMON_SAMPLER_TYPE_TYPICAL_P:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_typical     (params.typ_p, params.min_keep));
+                    samplers.push_back(llama_sampler_init_typical    (params.typ_p, params.min_keep));
                     break;
                 case COMMON_SAMPLER_TYPE_TEMPERATURE:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext    (params.temp, params.dynatemp_range, params.dynatemp_exponent));
+                    samplers.push_back(llama_sampler_init_temp_ext   (params.temp, params.dynatemp_range, params.dynatemp_exponent));
                     break;
                 case COMMON_SAMPLER_TYPE_INFILL:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_infill      (vocab));
+                    samplers.push_back(llama_sampler_init_infill     (vocab));
                     break;
                 case COMMON_SAMPLER_TYPE_PENALTIES:
-                    llama_sampler_chain_add(result->chain, llama_sampler_init_penalties   (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
+                    samplers.push_back(llama_sampler_init_penalties  (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
                     break;
                 default:
                     GGML_ASSERT(false && "unknown sampler type");
             }
         }
-        llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed));
+
+        samplers.push_back(llama_sampler_init_dist(params.seed));
     } else if (params.mirostat == 1) {
-        llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
-        llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
+        samplers.push_back(llama_sampler_init_temp(params.temp));
+        samplers.push_back(llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
     } else if (params.mirostat == 2) {
-        llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp));
-        llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta));
+        samplers.push_back(llama_sampler_init_temp(params.temp));
+        samplers.push_back(llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta));
     } else {
         GGML_ASSERT(false && "unknown mirostat version");
     }
 
+    for (auto * smpl : samplers) {
+        llama_sampler_chain_add(chain, smpl);
+    }
+
+    auto * result = new common_sampler {
+        /* .params  = */ params,
+        /* .chain   = */ chain,
+        /* .grammar = */ grammar,
+        /* .prev    = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
+        /* .cur     = */ {},
+        /* .cur_p   = */ {},
+    };
+
     return result;
 }
 
 void common_sampler_free(struct common_sampler * gsmpl) {
     if (gsmpl) {
-        llama_sampler_free(gsmpl->grmr);
-
         llama_sampler_free(gsmpl->chain);
 
         delete gsmpl;
@@ -314,11 +324,24 @@ void common_sampler_free(struct common_sampler * gsmpl) {
 void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
     const auto tm = gsmpl->tm();
 
-    if (accept_grammar) {
-        llama_sampler_accept(gsmpl->grmr, token);
-    }
+    if (gsmpl->grammar) {
+        const int n_smpl = llama_sampler_chain_n(gsmpl->chain);
 
-    llama_sampler_accept(gsmpl->chain, token);
+        for (int i = 0; i < n_smpl; i++) {
+            auto * smpl = llama_sampler_chain_get(gsmpl->chain, i);
+
+            // the grammar sampler is always the first one
+            if (i == 0) {
+                if (accept_grammar) {
+                    llama_sampler_accept(smpl, token);
+                }
+            } else {
+                llama_sampler_accept(smpl, token);
+            }
+        }
+    } else {
+        llama_sampler_accept(gsmpl->chain, token);
+    }
 
     gsmpl->prev.push_back(token);
 }
@@ -329,12 +352,12 @@ void common_sampler_reset(struct common_sampler * gsmpl) {
 
 struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
     return new common_sampler {
-        /* .params = */ gsmpl->params,
-        /* .grmr   = */ llama_sampler_clone(gsmpl->grmr),
-        /* .chain  = */ llama_sampler_clone(gsmpl->chain),
-        /* .prev   = */ gsmpl->prev,
-        /* .cur    = */ gsmpl->cur,
-        /* .cur_p  = */ gsmpl->cur_p,
+        /* .params  = */ gsmpl->params,
+        /* .chain   = */ llama_sampler_clone(gsmpl->chain),
+        /* .grammar = */ gsmpl->grammar,
+        /* .prev    = */ gsmpl->prev,
+        /* .cur     = */ gsmpl->cur,
+        /* .cur_p   = */ gsmpl->cur_p,
     };
 }
 
@@ -383,58 +406,33 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
     }
 }
 
-llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
+struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl) {
+    return gsmpl->chain;
+}
+
+llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx) {
     llama_synchronize(ctx);
 
     // start measuring sampling time after the llama_context synchronization in order to not measure any ongoing async operations
     const auto tm = gsmpl->tm();
 
-    gsmpl->set_logits(ctx, idx);
+    llama_token id = LLAMA_TOKEN_NULL;
 
-    auto & grmr  = gsmpl->grmr;
     auto & chain = gsmpl->chain;
     auto & cur_p = gsmpl->cur_p; // initialized by set_logits
 
-    if (grammar_first) {
-        llama_sampler_apply(grmr, &cur_p);
-    }
+    gsmpl->set_logits(ctx, idx);
 
     llama_sampler_apply(chain, &cur_p);
 
     GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration");
 
-    const llama_token id = cur_p.data[cur_p.selected].id;
+    id = cur_p.data[cur_p.selected].id;
 
-    if (grammar_first) {
-        return id;
-    }
-
-    // check if it the sampled token fits the grammar
-    {
-        llama_token_data       single_token_data       = { id, 1.0f, 0.0f };
-        llama_token_data_array single_token_data_array = { &single_token_data, 1, -1, false };
-
-        llama_sampler_apply(grmr, &single_token_data_array);
-
-        const bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
-        if (is_valid) {
-            return id;
-        }
-    }
-
-    // resampling:
-    // if the token is not valid, sample again, but first apply the grammar sampler and then the sampling chain
-    gsmpl->set_logits(ctx, idx);
-
-    llama_sampler_apply(grmr,  &cur_p);
-    llama_sampler_apply(chain, &cur_p);
-
-    GGML_ASSERT(cur_p.selected != -1 && "no selected token during re-sampling - check your sampling configuration");
-
-    return cur_p.data[cur_p.selected].id;
+    return id;
 }
 
-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first) {
+std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft) {
     GGML_ASSERT(idxs.size() == draft.size() + 1 && "idxs.size() must be draft.size() + 1");
 
     std::vector<llama_token> result;
@@ -442,7 +440,7 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample
 
     size_t i = 0;
     for (; i < draft.size(); i++) {
-        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
+        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i]);
 
         common_sampler_accept(gsmpl, id, true);
 
@@ -454,7 +452,7 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample
     }
 
     if (i == draft.size()) {
-        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
+        const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i]);
 
         common_sampler_accept(gsmpl, id, true);
 
@@ -464,13 +462,13 @@ std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sample
     return result;
 }
 
-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first) {
+std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft) {
     std::vector<int> idxs(draft.size() + 1);
     for (size_t i = 0; i < idxs.size(); ++i) {
         idxs[i] = i;
     }
 
-    return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft, grammar_first);
+    return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft);
 }
 
 uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) {
@@ -515,7 +513,8 @@ std::string common_sampler_print(const struct common_sampler * gsmpl) {
 
     for (int i = 0; i < llama_sampler_chain_n(gsmpl->chain); i++) {
         const auto * smpl = llama_sampler_chain_get(gsmpl->chain, i);
-        result += std::string("-> ") + llama_sampler_name(smpl) + " ";
+        result += std::string("-> ");
+        result += std::string(llama_sampler_name(smpl)) + " ";
     }
 
     return result;
diff --git a/common/sampling.h b/common/sampling.h
index e198eecda3..ace5d3d020 100644
--- a/common/sampling.h
+++ b/common/sampling.h
@@ -48,6 +48,8 @@ struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl);
 // arguments can be nullptr to skip printing
 void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl);
 
+struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl);
+
 // extended sampling implementation:
 //
 // - set logits
@@ -55,10 +57,7 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
 // - check if the token fits the grammar (if any)
 // - if not: resample by first applying the grammar constraints and then sampling again (slower path)
 //
-// if grammar_first is true, the grammar is applied before the samplers (slower)
-// useful in cases where all the resulting candidates (not just the sampled one) must fit the grammar
-//
-llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false);
+llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx);
 
 // generalized version of common_sampler_sample
 //
@@ -76,10 +75,10 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co
 //
 // returns at least 1 token, up to idxs.size()
 //
-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first = false);
+std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft);
 
 // assume idxs == [ 0, 1, 2, ..., draft.size() ]
-std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first = false);
+std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft);
 
 uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl);
 
@@ -107,3 +106,9 @@ std::vector<enum common_sampler_type> common_sampler_types_from_chars(const std:
 
 llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab,
                 const char * grammar_kind, const char * grammar_data);
+
+struct common_sampler_deleter {
+    void operator()(common_sampler * s) { common_sampler_free(s); }
+};
+
+typedef std::unique_ptr<common_sampler, common_sampler_deleter> common_sampler_ptr;
diff --git a/common/speculative.cpp b/common/speculative.cpp
index 3e83b0964c..1e12383ae6 100644
--- a/common/speculative.cpp
+++ b/common/speculative.cpp
@@ -315,7 +315,7 @@ llama_tokens common_speculative_gen_draft(
     for (int i = 0; i < params.n_draft; ++i) {
         common_batch_clear(batch);
 
-        common_sampler_sample(smpl, ctx_dft, 0, true);
+        common_sampler_sample(smpl, ctx_dft, 0);
 
         const auto * cur_p = common_sampler_get_candidates(smpl, true);
 
diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index 1a5de5928a..36a12d299f 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -2,6 +2,7 @@
 #include "common.h"
 #include "log.h"
 #include "llama.h"
+#include "sampling.h"
 
 #include <algorithm>
 #include <cstdio>
@@ -64,17 +65,23 @@ int main(int argc, char ** argv) {
     ctx_params.n_ctx   = n_kv_req;
     ctx_params.n_batch = std::max(n_predict, n_parallel);
 
-    llama_context * ctx = llama_init_from_model(model, ctx_params);
-
     auto sparams = llama_sampler_chain_default_params();
     sparams.no_perf = false;
 
-    llama_sampler * smpl = llama_sampler_chain_init(sparams);
+    std::vector<llama_sampler *> samplers;
 
-    llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k));
-    llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep));
-    llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp));
-    llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed));
+    for (int32_t i = 0; i < n_parallel; ++i) {
+        llama_sampler * smpl = llama_sampler_chain_init(sparams);
+
+        llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k));
+        llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep));
+        llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp));
+        llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed));
+
+        samplers.push_back(smpl);
+    }
+
+    llama_context * ctx = llama_init_from_model(model, ctx_params);
 
     if (ctx == NULL) {
         LOG_ERR("%s: error: failed to create the llama_context\n" , __func__);
@@ -173,7 +180,7 @@ int main(int argc, char ** argv) {
                 continue;
             }
 
-            const llama_token new_token_id = llama_sampler_sample(smpl, ctx, i_batch[i]);
+            const llama_token new_token_id = llama_sampler_sample(samplers[i], ctx, i_batch[i]);
 
             // is it an end of generation? -> mark the stream as finished
             if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_predict) {
@@ -229,14 +236,17 @@ int main(int argc, char ** argv) {
             __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f));
 
     LOG("\n");
-    llama_perf_sampler_print(smpl);
+    llama_perf_sampler_print(samplers[0]);
     llama_perf_context_print(ctx);
 
     fprintf(stderr, "\n");
 
     llama_batch_free(batch);
 
-    llama_sampler_free(smpl);
+    for (auto & sampler_config : samplers) {
+        llama_sampler_free(sampler_config);
+    }
+
     llama_free(ctx);
     llama_model_free(model);
 
diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp
index fe91b308cd..81111e81b2 100644
--- a/examples/embedding/embedding.cpp
+++ b/examples/embedding/embedding.cpp
@@ -131,10 +131,10 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx = llama_init->context();
 
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n", __func__);
diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp
index 80c693ce61..408338f1af 100644
--- a/examples/eval-callback/eval-callback.cpp
+++ b/examples/eval-callback/eval-callback.cpp
@@ -202,10 +202,10 @@ int main(int argc, char ** argv) {
     params.warmup = false;
 
     // init
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     if (model == nullptr || ctx == nullptr) {
         LOG_ERR("%s : failed to init\n", __func__);
diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp
index 1e26d8221b..f54cfdd77f 100644
--- a/examples/lookahead/lookahead.cpp
+++ b/examples/lookahead/lookahead.cpp
@@ -55,10 +55,10 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // load the target model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     auto * mem = llama_get_memory(ctx);
 
diff --git a/examples/lookup/lookup-create.cpp b/examples/lookup/lookup-create.cpp
index 3da45ed9e0..bb94a8fe06 100644
--- a/examples/lookup/lookup-create.cpp
+++ b/examples/lookup/lookup-create.cpp
@@ -18,16 +18,16 @@ int main(int argc, char ** argv){
     llama_numa_init(params.numa);
 
     // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model_ptr & model = llama_init.model;
-    llama_context_ptr & ctx = llama_init.context;
+    auto * model = llama_init->model();
+    auto * ctx = llama_init->context();
 
     GGML_ASSERT(model != nullptr);
 
     // tokenize the prompt
     std::vector<llama_token> inp;
-    inp = common_tokenize(ctx.get(), params.prompt, true, true);
+    inp = common_tokenize(ctx, params.prompt, true, true);
     fprintf(stderr, "%s: tokenization done\n", __func__);
 
     common_ngram_cache ngram_cache;
diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp
index fcb289abe0..135f6fcab9 100644
--- a/examples/lookup/lookup-stats.cpp
+++ b/examples/lookup/lookup-stats.cpp
@@ -28,13 +28,13 @@ int main(int argc, char ** argv){
     llama_numa_init(params.numa);
 
     // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_context_ptr & ctx = llama_init.context;
+    llama_context * ctx = llama_init->context();
 
     // tokenize the prompt
     std::vector<llama_token> inp;
-    inp = common_tokenize(ctx.get(), params.prompt, true, true);
+    inp = common_tokenize(ctx, params.prompt, true, true);
 
     common_ngram_cache ngram_cache_context;
     common_ngram_cache ngram_cache_dynamic;
@@ -65,7 +65,7 @@ int main(int argc, char ** argv){
     }
 
     const int n_input = inp.size();
-    const int n_ctx = llama_n_ctx(ctx.get());
+    const int n_ctx = llama_n_ctx(ctx);
 
     int n_drafted = 0;
     int n_accept  = 0;
diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp
index 2bfa26b55f..27f159940a 100644
--- a/examples/lookup/lookup.cpp
+++ b/examples/lookup/lookup.cpp
@@ -29,10 +29,10 @@ int main(int argc, char ** argv){
     llama_numa_init(params.numa);
 
     // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     const llama_vocab * vocab = llama_model_get_vocab(model);
 
diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp
index e48f48fc32..c92173ae29 100644
--- a/examples/parallel/parallel.cpp
+++ b/examples/parallel/parallel.cpp
@@ -192,10 +192,10 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // load the target model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     auto * mem = llama_get_memory(ctx);
 
diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp
index 042e12c2bf..2c2143ad10 100644
--- a/examples/retrieval/retrieval.cpp
+++ b/examples/retrieval/retrieval.cpp
@@ -149,10 +149,10 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // load the model
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n", __func__);
diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp
index 4cd3071f76..39d4464663 100644
--- a/examples/save-load-state/save-load-state.cpp
+++ b/examples/save-load-state/save-load-state.cpp
@@ -34,10 +34,10 @@ int main(int argc, char ** argv) {
     std::string result2;
 
     // init
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     if (model == nullptr || ctx == nullptr) {
         fprintf(stderr, "%s : failed to init\n", __func__);
diff --git a/examples/speculative-simple/speculative-simple.cpp b/examples/speculative-simple/speculative-simple.cpp
index 0d11d0f803..8141052a22 100644
--- a/examples/speculative-simple/speculative-simple.cpp
+++ b/examples/speculative-simple/speculative-simple.cpp
@@ -40,10 +40,10 @@ int main(int argc, char ** argv) {
     llama_context * ctx_dft = NULL;
 
     // load the target model
-    common_init_result llama_init_tgt = common_init_from_params(params);
+    auto llama_init_tgt = common_init_from_params(params);
 
-    model_tgt = llama_init_tgt.model.get();
-    ctx_tgt   = llama_init_tgt.context.get();
+    model_tgt = llama_init_tgt->model();
+    ctx_tgt   = llama_init_tgt->context();
 
     const llama_vocab * vocab = llama_model_get_vocab(model_tgt);
 
@@ -61,10 +61,10 @@ int main(int argc, char ** argv) {
     params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
     params.tensor_buft_overrides     = params.speculative.tensor_buft_overrides;
 
-    common_init_result llama_init_dft = common_init_from_params(params);
+    auto llama_init_dft = common_init_from_params(params);
 
-    //model_dft = llama_init_dft.model.get();
-    ctx_dft   = llama_init_dft.context.get();
+    //model_dft = llama_init_dft->model();
+    ctx_dft   = llama_init_dft->context();
 
     if (!common_speculative_are_compatible(ctx_tgt, ctx_dft)) {
         LOG_INF("the draft model '%s' is not compatible with the target model '%s'. tokens will be translated between the draft and target models.\n", params.speculative.model.path.c_str(), params.model.path.c_str());
diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp
index 5f5ac5eb64..2fb7f6374e 100644
--- a/examples/speculative/speculative.cpp
+++ b/examples/speculative/speculative.cpp
@@ -71,10 +71,10 @@ int main(int argc, char ** argv) {
     llama_context * ctx_dft = NULL;
 
     // load the target model
-    common_init_result llama_init_tgt = common_init_from_params(params);
+    auto llama_init_tgt = common_init_from_params(params);
 
-    model_tgt = llama_init_tgt.model.get();
-    ctx_tgt   = llama_init_tgt.context.get();
+    model_tgt = llama_init_tgt->model();
+    ctx_tgt   = llama_init_tgt->context();
 
     // load the draft model
     params.devices = params.speculative.devices;
@@ -87,10 +87,10 @@ int main(int argc, char ** argv) {
     params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads;
     params.tensor_buft_overrides     = params.speculative.tensor_buft_overrides;
 
-    common_init_result llama_init_dft = common_init_from_params(params);
+    auto llama_init_dft = common_init_from_params(params);
 
-    model_dft = llama_init_dft.model.get();
-    ctx_dft   = llama_init_dft.context.get();
+    model_dft = llama_init_dft->model();
+    ctx_dft   = llama_init_dft->context();
 
     const llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt);
     const llama_vocab * vocab_dft = llama_model_get_vocab(model_dft);
@@ -242,7 +242,7 @@ int main(int argc, char ** argv) {
                 bool accept = false;
                 if (params.sampling.temp > 0) {
                     // stochastic verification
-                    common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft], true);
+                    common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft]);
 
                     auto & dist_tgt = *common_sampler_get_candidates(smpl, true);
 
@@ -491,7 +491,7 @@ int main(int argc, char ** argv) {
                     continue;
                 }
 
-                common_sampler_sample(drafts[s].smpl, ctx_dft, drafts[s].i_batch_dft, true);
+                common_sampler_sample(drafts[s].smpl, ctx_dft, drafts[s].i_batch_dft);
 
                 const auto * cur_p = common_sampler_get_candidates(drafts[s].smpl, true);
 
diff --git a/examples/training/finetune.cpp b/examples/training/finetune.cpp
index 416d8d8f6c..c82de8d35d 100644
--- a/examples/training/finetune.cpp
+++ b/examples/training/finetune.cpp
@@ -39,9 +39,10 @@ int main(int argc, char ** argv) {
     llama_backend_init();
     llama_numa_init(params.numa);
     // load the model and apply lora adapter, if any
-    common_init_result   llama_init = common_init_from_params(params);
-    llama_model_ptr    & model      = llama_init.model;
-    llama_context_ptr  & ctx        = llama_init.context;
+    auto llama_init = common_init_from_params(params);
+
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n", __func__);
@@ -54,8 +55,8 @@ int main(int argc, char ** argv) {
         LOG_INF("%s\n", common_params_get_system_info(params).c_str());
     }
 
-    std::vector<llama_token> tokens  = common_tokenize(ctx.get(), params.prompt, true);
-    ggml_opt_dataset_t       dataset = common_opt_dataset_init(ctx.get(), tokens, llama_n_ctx(ctx.get()) / 2);
+    std::vector<llama_token> tokens  = common_tokenize(ctx, params.prompt, true);
+    ggml_opt_dataset_t       dataset = common_opt_dataset_init(ctx, tokens, llama_n_ctx(ctx) / 2);
 
     struct lr_opt & lr = params.lr;
     LOG_INF("-optimizer %s -lr0 %.2g -wd %.2g -lr-min %.2g -min-epochs %.2g -epochs %d -period %.2g -val %.2g\n",
@@ -70,7 +71,7 @@ int main(int argc, char ** argv) {
         /*get_opt_pars_ud =*/&params.lr,
         /*optimizer_type  =*/params.optimizer,
     };
-    llama_opt_init(ctx.get(), model.get(), lopt_params);
+    llama_opt_init(ctx, model, lopt_params);
 
     const int64_t idata_split = ggml_opt_dataset_ndata(dataset) * (1.0f - params.val_split);
 
@@ -78,7 +79,7 @@ int main(int argc, char ** argv) {
     ggml_opt_result_t result_eval  = ggml_opt_result_init();
 
     for (lr.epoch = 0; lr.epoch < lr.epochs; ++lr.epoch) {
-        llama_opt_epoch(ctx.get(), dataset, result_train, result_eval, idata_split,
+        llama_opt_epoch(ctx, dataset, result_train, result_eval, idata_split,
                         ggml_opt_epoch_callback_progress_bar, ggml_opt_epoch_callback_progress_bar);
         fprintf(stderr, "\n");
 
@@ -88,7 +89,7 @@ int main(int argc, char ** argv) {
     ggml_opt_result_free(result_train);
     ggml_opt_result_free(result_eval);
 
-    llama_model_save_to_file(model.get(), params.out_file.c_str());
+    llama_model_save_to_file(model, params.out_file.c_str());
 
     llama_backend_free();
 
diff --git a/tools/completion/completion.cpp b/tools/completion/completion.cpp
index cb2641ae0a..85480f3369 100644
--- a/tools/completion/completion.cpp
+++ b/tools/completion/completion.cpp
@@ -141,13 +141,15 @@ int main(int argc, char ** argv) {
 
     // load the model and apply lora adapter, if any
     LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__);
-    common_init_result llama_init = common_init_from_params(params);
 
-    model = llama_init.model.get();
-    ctx = llama_init.context.get();
+    auto llama_init = common_init_from_params(params);
 
-    if (model == NULL) {
-        LOG_ERR("%s: error: unable to load model\n", __func__);
+    ctx   = llama_init->context();
+    model = llama_init->model();
+    smpl  = llama_init->sampler(0);
+
+    if (ctx == NULL) {
+        LOG_ERR("%s: error: unable to create context\n", __func__);
         return 1;
     }
 
@@ -474,12 +476,6 @@ int main(int argc, char ** argv) {
         }
     }
 
-    smpl = common_sampler_init(model, sparams);
-    if (!smpl) {
-        LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__);
-        return 1;
-    }
-
     LOG_INF("sampler seed: %u\n",     common_sampler_get_seed(smpl));
     LOG_INF("sampler params: \n%s\n", sparams.print().c_str());
     LOG_INF("sampler chain: %s\n",    common_sampler_print(smpl).c_str());
@@ -993,8 +989,6 @@ int main(int argc, char ** argv) {
     LOG("\n\n");
     common_perf_print(ctx, smpl);
 
-    common_sampler_free(smpl);
-
     llama_backend_free();
 
     ggml_threadpool_free_fn(threadpool);
diff --git a/tools/cvector-generator/cvector-generator.cpp b/tools/cvector-generator/cvector-generator.cpp
index d2d97e05ce..3ba7c52950 100644
--- a/tools/cvector-generator/cvector-generator.cpp
+++ b/tools/cvector-generator/cvector-generator.cpp
@@ -419,10 +419,10 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // load the model to get hparams
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     // int n_ctx = llama_n_ctx(ctx);
     int n_layers = llama_model_n_layer(model);
diff --git a/tools/imatrix/imatrix.cpp b/tools/imatrix/imatrix.cpp
index f28a036dee..669de55ddb 100644
--- a/tools/imatrix/imatrix.cpp
+++ b/tools/imatrix/imatrix.cpp
@@ -1265,10 +1265,10 @@ int main(int argc, char ** argv) {
     params.warmup = false;
 
     // init
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     if (model == nullptr || ctx == nullptr) {
         LOG_ERR("%s : failed to init\n", __func__);
diff --git a/tools/mtmd/mtmd-cli.cpp b/tools/mtmd/mtmd-cli.cpp
index 25d24603db..332d2049e5 100644
--- a/tools/mtmd/mtmd-cli.cpp
+++ b/tools/mtmd/mtmd-cli.cpp
@@ -65,7 +65,7 @@ static void sigint_handler(int signo) {
 
 struct mtmd_cli_context {
     mtmd::context_ptr ctx_vision;
-    common_init_result llama_init;
+    common_init_result_ptr llama_init;
 
     llama_model       * model;
     llama_context     * lctx;
@@ -89,8 +89,8 @@ struct mtmd_cli_context {
     llama_pos n_past = 0;
 
     mtmd_cli_context(common_params & params) : llama_init(common_init_from_params(params)) {
-        model = llama_init.model.get();
-        lctx = llama_init.context.get();
+        model = llama_init->model();
+        lctx = llama_init->context();
         vocab = llama_model_get_vocab(model);
         smpl = common_sampler_init(model, params.sampling);
         n_threads = params.cpuparams.n_threads;
diff --git a/tools/perplexity/perplexity.cpp b/tools/perplexity/perplexity.cpp
index caf080e8d1..1ead9c871e 100644
--- a/tools/perplexity/perplexity.cpp
+++ b/tools/perplexity/perplexity.cpp
@@ -2024,10 +2024,10 @@ int main(int argc, char ** argv) {
     llama_numa_init(params.numa);
 
     // load the model and apply lora adapter, if any
-    common_init_result llama_init = common_init_from_params(params);
+    auto llama_init = common_init_from_params(params);
 
-    llama_model * model = llama_init.model.get();
-    llama_context * ctx = llama_init.context.get();
+    auto * model = llama_init->model();
+    auto * ctx   = llama_init->context();
 
     if (model == NULL) {
         LOG_ERR("%s: unable to load model\n", __func__);
diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp
index 5a67f508df..90898b5ec4 100644
--- a/tools/server/server-context.cpp
+++ b/tools/server/server-context.cpp
@@ -153,7 +153,7 @@ struct server_slot {
     // sampling
     json json_schema;
 
-    struct common_sampler * smpl = nullptr;
+    common_sampler_ptr smpl;
 
     llama_token sampled; // in speculative mode, this is the last accepted token
     llama_tokens drafted;
@@ -510,8 +510,8 @@ struct server_context_impl {
     common_params params_base;
 
     // note: keep these alive - they determine the lifetime of the model, context, etc.
-    common_init_result llama_init;
-    common_init_result llama_init_dft;
+    common_init_result_ptr llama_init;
+    common_init_result_ptr llama_init_dft;
 
     llama_model * model = nullptr;
     llama_context * ctx = nullptr;
@@ -557,9 +557,6 @@ struct server_context_impl {
 
         // Clear any sampling context
         for (server_slot & slot : slots) {
-            common_sampler_free(slot.smpl);
-            slot.smpl = nullptr;
-
             llama_free(slot.ctx_dft);
             slot.ctx_dft = nullptr;
 
@@ -580,8 +577,8 @@ struct server_context_impl {
 
         llama_init = common_init_from_params(params_base);
 
-        model = llama_init.model.get();
-        ctx   = llama_init.context.get();
+        model = llama_init->model();
+        ctx   = llama_init->context();
 
         if (model == nullptr) {
             SRV_ERR("failed to load model, '%s'\n", params_base.model.path.c_str());
@@ -613,25 +610,25 @@ struct server_context_impl {
 
             llama_init_dft = common_init_from_params(params_dft);
 
-            model_dft = llama_init_dft.model.get();
+            model_dft = llama_init_dft->model();
 
             if (model_dft == nullptr) {
                 SRV_ERR("failed to load draft model, '%s'\n", params_base.speculative.model.path.c_str());
                 return false;
             }
 
-            vocab_dft_compatible = common_speculative_are_compatible(ctx, llama_init_dft.context.get());
+            vocab_dft_compatible = common_speculative_are_compatible(ctx, llama_init_dft->context());
             if (!vocab_dft_compatible) {
                 SRV_INF("the draft model '%s' is not compatible with the target model '%s'. tokens will be translated between the draft and target models.\n", params_base.speculative.model.path.c_str(), params_base.model.path.c_str());
             }
 
-            const int n_ctx_dft = llama_n_ctx(llama_init_dft.context.get());
+            const int n_ctx_dft = llama_n_ctx(llama_init_dft->context());
 
             cparams_dft = common_context_params_to_llama(params_dft);
             cparams_dft.n_batch = n_ctx_dft;
 
             // the context is not needed - we will create one for each slot
-            llama_init_dft.context.reset();
+            llama_init_dft->free_context();
         }
 
         chat_templates = common_chat_templates_init(model, params_base.chat_template);
@@ -1051,18 +1048,15 @@ struct server_context_impl {
 
         // initialize samplers
         {
-            if (slot.smpl != nullptr) {
-                common_sampler_free(slot.smpl);
-            }
+            slot.smpl.reset(common_sampler_init(model, task.params.sampling));
 
-            slot.smpl = common_sampler_init(model, task.params.sampling);
             if (slot.smpl == nullptr) {
                 // for now, the only error that may happen here is invalid grammar
                 send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST);
                 return false;
             }
 
-            SLT_INF(slot, "sampler chain: %s\n", common_sampler_print(slot.smpl).c_str());
+            SLT_INF(slot, "sampler chain: %s\n", common_sampler_print(slot.smpl.get()).c_str());
         }
 
         // initialize draft batch
@@ -1216,11 +1210,10 @@ struct server_context_impl {
     }
 
     void populate_token_probs(const server_slot & slot, completion_token_output & result, bool post_sampling, bool special, int idx) const {
-        size_t n_probs = slot.task->params.sampling.n_probs;
-        size_t n_vocab = llama_vocab_n_tokens(vocab);
+        const size_t n_probs = slot.task->params.sampling.n_probs;
 
         if (post_sampling) {
-            const auto * cur_p = common_sampler_get_candidates(slot.smpl, true);
+            const auto * cur_p = common_sampler_get_candidates(slot.smpl.get(), true);
             const size_t max_probs = cur_p->size;
 
             // set probability for sampled token
@@ -1245,7 +1238,7 @@ struct server_context_impl {
             std::vector<llama_token_data> cur = get_token_probabilities(ctx, idx);
 
             // set probability for sampled token
-            for (size_t i = 0; i < n_vocab; i++) {
+            for (size_t i = 0; i < cur.size(); i++) {
                 // set probability for sampled token
                 if (cur[i].id == result.tok) {
                     result.prob = cur[i].p;
@@ -1255,7 +1248,7 @@ struct server_context_impl {
 
             // set probability for top n_probs tokens
             result.probs.reserve(n_probs);
-            for (size_t i = 0; i < std::min(n_vocab, n_probs); i++) {
+            for (size_t i = 0; i < std::min(cur.size(), n_probs); i++) {
                 result.probs.push_back({
                     cur[i].id,
                     common_token_to_piece(ctx, cur[i].id, special),
@@ -2301,13 +2294,13 @@ struct server_context_impl {
 
                         GGML_ASSERT(batch.n_tokens > 0);
 
-                        common_sampler_reset(slot.smpl);
+                        common_sampler_reset(slot.smpl.get());
 
                         // Process all prompt tokens through sampler system
                         for (int i = 0; i < slot.task->n_tokens(); ++i) {
                             llama_token id = input_tokens[i];
                             if (id != LLAMA_TOKEN_NULL) {
-                                common_sampler_accept(slot.smpl, id, false);
+                                common_sampler_accept(slot.smpl.get(), id, false);
                             }
                         }
 
@@ -2525,11 +2518,11 @@ struct server_context_impl {
 
                 const int tok_idx = slot.i_batch - i;
 
-                llama_token id = common_sampler_sample(slot.smpl, ctx, tok_idx);
+                llama_token id = common_sampler_sample(slot.smpl.get(), ctx, tok_idx);
 
                 slot.i_batch = -1;
 
-                common_sampler_accept(slot.smpl, id, true);
+                common_sampler_accept(slot.smpl.get(), id, true);
 
                 slot.n_decoded += 1;
 
@@ -2570,7 +2563,7 @@ struct server_context_impl {
                 size_t n_draft = slot.drafted.size();
 
                 // the accepted tokens from the speculation
-                const auto ids = common_sampler_sample_and_accept_n(slot.smpl, ctx, slot.i_batch_dft, slot.drafted);
+                const auto ids = common_sampler_sample_and_accept_n(slot.smpl.get(), ctx, slot.i_batch_dft, slot.drafted);
                 slot.i_batch_dft.clear();
                 slot.drafted.clear();
 
diff --git a/tools/server/tests/unit/test_compat_anthropic.py b/tools/server/tests/unit/test_compat_anthropic.py
index d55dd1d945..e0a003557e 100644
--- a/tools/server/tests/unit/test_compat_anthropic.py
+++ b/tools/server/tests/unit/test_compat_anthropic.py
@@ -684,7 +684,7 @@ def test_anthropic_streaming_content_block_indices():
     # Request that might produce both text and tool use
     res = server.make_stream_request("POST", "/v1/messages", data={
         "model": "test",
-        "max_tokens": 200,
+        "max_tokens": 400,
         "stream": True,
         "tools": [{
             "name": "test_tool",
diff --git a/tools/tts/tts.cpp b/tools/tts/tts.cpp
index eaf56591d9..8c39fce8ba 100644
--- a/tools/tts/tts.cpp
+++ b/tools/tts/tts.cpp
@@ -568,10 +568,10 @@ int main(int argc, char ** argv) {
     llama_context * ctx_ttc = NULL;
     llama_context * ctx_cts = NULL;
 
-    common_init_result llama_init_ttc = common_init_from_params(params);
+    auto llama_init_ttc = common_init_from_params(params);
 
-    model_ttc = llama_init_ttc.model.get();
-    ctx_ttc   = llama_init_ttc.context.get();
+    model_ttc = llama_init_ttc->model();
+    ctx_ttc   = llama_init_ttc->context();
 
     if (model_ttc == nullptr || ctx_ttc == nullptr) {
         return ENOENT;
@@ -583,10 +583,10 @@ int main(int argc, char ** argv) {
     params.embedding = true;
     params.n_ubatch = params.n_batch;
 
-    common_init_result llama_init_cts = common_init_from_params(params);
+    auto llama_init_cts = common_init_from_params(params);
 
-    model_cts = llama_init_cts.model.get();
-    ctx_cts   = llama_init_cts.context.get();
+    model_cts = llama_init_cts->model();
+    ctx_cts   = llama_init_cts->context();
 
     if (model_cts == nullptr || ctx_cts == nullptr) {
         return ENOENT;

From 0759b09c90f9e1bb8beebe74882b9f094b91f7bb Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Sun, 14 Dec 2025 13:05:59 +0100
Subject: [PATCH 099/135] graph: add f_attn_temp_offset (#18025)

---
 src/llama-graph.cpp | 4 ++--
 src/llama-graph.h   | 5 +++--
 src/llama-hparams.h | 1 +
 src/llama-model.cpp | 5 +++++
 4 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 6cf9a883a6..8909bbfb95 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -78,7 +78,7 @@ void llm_graph_input_attn_temp::set_input(const llama_ubatch * ubatch) {
         for (int i = 0; i < n_tokens; ++i) {
             const float pos = ubatch->pos[i];
             attn_scale_data[i] = std::log(
-                std::floor((pos + 1.0f) / n_attn_temp_floor_scale) + 1.0
+                std::floor((pos + f_attn_temp_offset) / n_attn_temp_floor_scale) + 1.0
             ) * f_attn_temp_scale + 1.0;
         }
 
@@ -1203,7 +1203,7 @@ ggml_tensor * llm_graph_context::build_inp_pos() const {
 }
 
 ggml_tensor * llm_graph_context::build_inp_attn_scale() const {
-    auto inp = std::make_unique<llm_graph_input_attn_temp>(hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale);
+    auto inp = std::make_unique<llm_graph_input_attn_temp>(hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale, hparams.f_attn_temp_offset);
 
     auto & cur = inp->attn_scale;
 
diff --git a/src/llama-graph.h b/src/llama-graph.h
index d0c3934f67..e9d387bd7c 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -132,8 +132,8 @@ public:
 // temperature tuning, used by llama4
 class llm_graph_input_attn_temp : public llm_graph_input_i {
 public:
-    llm_graph_input_attn_temp(uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale)
-        : n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale) {}
+    llm_graph_input_attn_temp(uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale, float f_attn_temp_offset)
+        : n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale), f_attn_temp_offset(f_attn_temp_offset) {}
     virtual ~llm_graph_input_attn_temp() = default;
 
     void set_input(const llama_ubatch * ubatch) override;
@@ -142,6 +142,7 @@ public:
 
     const uint32_t n_attn_temp_floor_scale;
     const float    f_attn_temp_scale;
+    const float    f_attn_temp_offset;
 };
 
 class llm_graph_input_pos_bucket : public llm_graph_input_i {
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index aab319754e..a467c64a14 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -165,6 +165,7 @@ struct llama_hparams {
     uint32_t n_no_rope_layer_step    = 4;
     uint32_t n_attn_temp_floor_scale = 0;
     float    f_attn_temp_scale       = 0.0f;
+    float    f_attn_temp_offset      = 0.0f; // offset position index
 
     // gemma3n altup
     uint32_t n_altup      = 4; // altup_num_inputs
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 5da1dd6dbb..28f06b4e61 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -668,6 +668,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     hparams.n_swa                   = 8192;
                     hparams.n_attn_temp_floor_scale = 8192;
                     hparams.f_attn_temp_scale       = 0.1f;
+                    hparams.f_attn_temp_offset      = 1.0f;
                     hparams.set_swa_pattern(4);   // pattern: 3 chunked - 1 full
                 }
 
@@ -1646,6 +1647,8 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE,  hparams.f_attn_temp_scale,       false);
                 ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.n_attn_temp_floor_scale, false);
 
+                hparams.f_attn_temp_offset = 0.0f;
+
                 switch (hparams.n_layer) {
                     case 27: type = LLM_TYPE_16B; break;
                     case 60: type = LLM_TYPE_236B; break;
@@ -2276,6 +2279,8 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow,    false);
                 ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL,   hparams.rope_yarn_log_mul, 0.0f);
 
+                hparams.f_attn_temp_offset = 0.0f;
+
                 // TODO: maybe add n_attn_temp_floor_scale as a separate KV?
                 if (hparams.f_attn_temp_scale != 0.0f) {
                     hparams.n_attn_temp_floor_scale = hparams.n_ctx_orig_yarn;

From 9e6649ecf244a99749dacc28fc4f49f7d6ad6f60 Mon Sep 17 00:00:00 2001
From: Ruben Ortlam <picard12@live.de>
Date: Sun, 14 Dec 2025 14:52:46 +0100
Subject: [PATCH 100/135] vulkan: fix mul_mat_vec_iq1_s formatting (#18026)

---
 .../vulkan-shaders/mul_mat_vec_iq1_s.comp     | 20 +++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp
index e6b1f20215..c5f5e9cbb2 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp
@@ -10,44 +10,44 @@ FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
 void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i,
                      const uint num_blocks_per_row, const uint first_row, const uint num_rows) {
     const uint y_idx_base = i * QUANT_K + 32 * ib32;
-    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {    
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
         const uint base_b_idx = (j * p.batch_stride_b + b_offset + y_idx_base) / 4;
-        [[unroll]] for (uint l = 0; l < 4; ++l) {            
+        [[unroll]] for (uint l = 0; l < 4; ++l) {
             const vec4 b_val_0 = vec4(data_b_v4[base_b_idx + 2 * l]);
             const vec4 b_val_1 = vec4(data_b_v4[base_b_idx + 2 * l + 1]);
 
             // index for data_a
             uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i;
 
-            [[unroll]] for (uint n = 0; n < num_rows; ++n) {                
+            [[unroll]] for (uint n = 0; n < num_rows; ++n) {
                 const float d = float(data_a[ibi].d);
                 const uint qh = data_a[ibi].qh[ib32];
 
                 const float dl = d * float(2 * bitfieldExtract(qh, 12, 3) + 1);
                 const uint qs = data_a[ibi].qs[4 * ib32 + l];
-                const uint idxhi = bitfieldExtract(qh, 3 * int(l), 3);              
+                const uint idxhi = bitfieldExtract(qh, 3 * int(l), 3);
                 const uint16_t grid = uint16_t(iq1s_grid[qs | (idxhi << 8)]);
 
                 const float delta_val = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA;
-                const vec4 delta_v = vec4(delta_val);				
+                const vec4 delta_v = vec4(delta_val);
                 const vec4 fbits0 = vec4(
                     float(bitfieldExtract(grid, 0, 2)),
                     float(bitfieldExtract(grid, 2, 2)),
                     float(bitfieldExtract(grid, 4, 2)),
                     float(bitfieldExtract(grid, 6, 2))
-                );				
+                );
                 const vec4 fbits1 = vec4(
                     float(bitfieldExtract(grid, 8, 2)),
                     float(bitfieldExtract(grid, 10, 2)),
                     float(bitfieldExtract(grid, 12, 2)),
                     float(bitfieldExtract(grid, 14, 2))
                 );
-				
+
                 vec4 sum_v = fma(b_val_0, fbits0 + delta_v, vec4(0.0));
                 sum_v      = fma(b_val_1, fbits1 + delta_v, sum_v);
-				FLOAT_TYPE sum = dot(sum_v, vec4(1.0));
-				
-                temp[j][n] = fma(dl, sum, temp[j][n]);				
+                FLOAT_TYPE sum = dot(sum_v, vec4(1.0));
+
+                temp[j][n] = fma(dl, sum, temp[j][n]);
                 ibi += num_blocks_per_row;
             }
         }

From 37f5a1093bf7f6c58630d916871c54e05a8364d7 Mon Sep 17 00:00:00 2001
From: Haowei Wu <breadcyanide@icloud.com>
Date: Sun, 14 Dec 2025 22:57:52 +0800
Subject: [PATCH 101/135] mtmd: enhance image resizing in llava_uhd (#18014)

---
 tools/mtmd/clip.cpp | 30 +++++++++++++++++-------------
 1 file changed, 17 insertions(+), 13 deletions(-)

diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index bb922e30b4..5cc4e4b1fa 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -2230,7 +2230,14 @@ struct llava_uhd {
         clip_image_size refined_size;  // size of image right before slicing (must be multiple of slice size)
         clip_image_size grid_size;     // grid_size.width * grid_size.height = number of slices
         std::vector<slice_coordinates> slices;
+
+        img_tool::resize_algo interpolation_overview = img_tool::RESIZE_ALGO_BILINEAR;
+        bool padding_overview = false;  // if true, refine image will be padded to the grid size (e.g. llava-1.6)
+        std::array<uint8_t, 3> pad_color_overview = {0, 0, 0};
+
+        img_tool::resize_algo interpolation_refined = img_tool::RESIZE_ALGO_BICUBIC;
         bool padding_refined = false;  // if true, refine image will be padded to the grid size (e.g. llava-1.6)
+        std::array<uint8_t, 3> pad_color_refined = {0, 0, 0};
     };
 
     static slice_instructions get_slice_instructions(struct clip_ctx * ctx, const clip_image_size & original_size) {
@@ -2257,10 +2264,11 @@ struct llava_uhd {
             auto refine_size = llava_uhd::select_best_resolution(
                 original_size,
                 ctx->model.hparams.image_res_candidates);
-            res.overview_size   = clip_image_size{slice_size, slice_size};
-            res.refined_size    = refine_size;
-            res.grid_size       = clip_image_size{0, 0};
-            res.padding_refined = true;
+            res.overview_size         = clip_image_size{slice_size, slice_size};
+            res.refined_size          = refine_size;
+            res.grid_size             = clip_image_size{0, 0};
+            res.padding_refined       = true;
+            res.interpolation_refined = img_tool::RESIZE_ALGO_BILINEAR;  // preserve old behavior when padding
 
             LOG_DBG("%s: using pinpoints for slicing\n", __func__);
             LOG_DBG("%s: original size: %d x %d, overview size: %d x %d, refined size: %d x %d\n",
@@ -2339,12 +2347,13 @@ struct llava_uhd {
 
     static std::vector<clip_image_u8_ptr> slice_image(const clip_image_u8 * img, const slice_instructions & inst) {
         std::vector<clip_image_u8_ptr> output;
-        img_tool::resize_algo interpolation = img_tool::RESIZE_ALGO_BILINEAR; // TODO: make it configurable
 
         // resize to overview size
         clip_image_u8_ptr resized_img(clip_image_u8_init());
-        img_tool::resize(*img, *resized_img, inst.overview_size, interpolation);
+        img_tool::resize(*img, *resized_img, inst.overview_size, inst.interpolation_overview,
+                         inst.padding_overview, inst.pad_color_overview);
         output.push_back(std::move(resized_img));
+
         if (inst.slices.empty()) {
             // no slices, just return the resized image
             return output;
@@ -2352,13 +2361,8 @@ struct llava_uhd {
 
         // resize to refined size
         clip_image_u8_ptr refined_img(clip_image_u8_init());
-        if (inst.padding_refined) {
-            img_tool::resize(*img, *refined_img, inst.refined_size, interpolation);
-        } else {
-            // only algo bicubic preserves the ratio; old models rely on this behavior
-            // TODO: do we need to support other algos here?
-            img_tool::resize(*img, *refined_img, inst.refined_size, img_tool::RESIZE_ALGO_BICUBIC, false);
-        }
+        img_tool::resize(*img, *refined_img, inst.refined_size, inst.interpolation_refined,
+                         inst.padding_refined, inst.pad_color_refined);
 
         // create slices
         for (const auto & slice : inst.slices) {

From 5c8a717128cc98aa9e5b1c44652f5cf458fd426e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Sun, 14 Dec 2025 16:04:37 +0100
Subject: [PATCH 102/135] convert : refactor rope scaling handling (#18013)

* refactor rope scaling handling

* ws--

* missed a couple

* use find_hparam
---
 convert_hf_to_gguf.py | 357 +++++++++++-------------------------------
 1 file changed, 88 insertions(+), 269 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 151608d56b..3f861f2a6a 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -136,11 +136,19 @@ class ModelBase:
         self.remote_hf_model_id = remote_hf_model_id
         self.sentence_transformers_dense_modules = sentence_transformers_dense_modules
         self.hparams = ModelBase.load_hparams(self.dir_model, self.is_mistral_format) if hparams is None else hparams
+        self.rope_parameters = self.hparams.get("rope_parameters", self.hparams.get("rope_scaling")) or {}
         self.model_tensors = self.index_tensors(remote_hf_model_id=remote_hf_model_id)
         self.metadata_override = metadata_override
         self.model_name = model_name
         self.dir_model_card = dir_model  # overridden in convert_lora_to_gguf.py
 
+        # Ensure "rope_theta" and "rope_type" is mirrored in rope_parameters
+        if "full_attention" not in self.rope_parameters and "sliding_attention" not in self.rope_parameters:
+            if "rope_theta" not in self.rope_parameters and (rope_theta := self.find_hparam(["rope_theta", "global_rope_theta", "rotary_emb_base"], optional=True)) is not None:
+                self.rope_parameters["rope_theta"] = rope_theta
+            if "rope_type" not in self.rope_parameters and (rope_type := self.rope_parameters.get("type")) is not None:
+                self.rope_parameters["rope_type"] = rope_type
+
         # Apply heuristics to figure out typical tensor encoding based on first layer tensor encoding type
         if self.ftype == gguf.LlamaFileType.GUESSED:
             # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie.
@@ -795,7 +803,7 @@ class TextModel(ModelBase):
     def set_gguf_parameters(self):
         self.gguf_writer.add_block_count(self.block_count)
 
-        if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions", "max_length"], optional=True)) is not None:
+        if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions", "max_length", "max_sequence_length", "model_max_length"], optional=True)) is not None:
             self.gguf_writer.add_context_length(n_ctx)
             logger.info(f"gguf: context length = {n_ctx}")
 
@@ -815,7 +823,42 @@ class TextModel(ModelBase):
             self.gguf_writer.add_head_count_kv(n_head_kv)
             logger.info(f"gguf: key-value head count = {n_head_kv}")
 
-        if (rope_theta := self.hparams.get("rope_theta")) is not None:
+        rope_params = self.rope_parameters.get("full_attention", self.rope_parameters)
+        if (rope_type := rope_params.get("rope_type")) is not None:
+            rope_factor = rope_params.get("factor")
+            rope_gguf_type = gguf.RopeScalingType.NONE
+            if rope_type == "linear" and rope_factor is not None:
+                rope_gguf_type = gguf.RopeScalingType.LINEAR
+                self.gguf_writer.add_rope_scaling_type(rope_gguf_type)
+                self.gguf_writer.add_rope_scaling_factor(rope_factor)
+            elif rope_type == "yarn" and rope_factor is not None:
+                rope_gguf_type = gguf.RopeScalingType.YARN
+                self.gguf_writer.add_rope_scaling_type(rope_gguf_type)
+                self.gguf_writer.add_rope_scaling_factor(rope_factor)
+                self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_params["original_max_position_embeddings"])
+                if (yarn_ext_factor := rope_params.get("extrapolation_factor")) is not None:
+                    self.gguf_writer.add_rope_scaling_yarn_ext_factor(yarn_ext_factor)
+                if (yarn_attn_factor := rope_params.get("attention_factor", rope_params.get("attn_factor"))) is not None:
+                    self.gguf_writer.add_rope_scaling_yarn_attn_factor(yarn_attn_factor)
+                if (yarn_beta_fast := rope_params.get("beta_fast")) is not None:
+                    self.gguf_writer.add_rope_scaling_yarn_beta_fast(yarn_beta_fast)
+                if (yarn_beta_slow := rope_params.get("beta_slow")) is not None:
+                    self.gguf_writer.add_rope_scaling_yarn_beta_slow(yarn_beta_slow)
+                # self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"])
+            elif rope_type == "su" or rope_type == "longrope":
+                rope_gguf_type = gguf.RopeScalingType.LONGROPE
+                self.gguf_writer.add_rope_scaling_type(rope_gguf_type)
+            elif rope_type == "dynamic":
+                # HunYuan, handled in model class
+                pass
+            elif rope_type.lower() == "llama3":
+                # Handled in generate_extra_tensors
+                pass
+            else:
+                logger.warning(f"Unknown RoPE type: {rope_type}")
+            logger.info(f"gguf: rope scaling type = {rope_gguf_type.name}")
+
+        if (rope_theta := rope_params.get("rope_theta")) is not None:
             self.gguf_writer.add_rope_freq_base(rope_theta)
             logger.info(f"gguf: rope theta = {rope_theta}")
         if (f_rms_eps := self.find_hparam(["rms_norm_eps", "norm_eps"], optional=True)) is not None:
@@ -1966,34 +2009,10 @@ class BaichuanModel(TextModel):
         self._set_vocab_sentencepiece()
 
     def set_gguf_parameters(self):
-        head_count = self.hparams["num_attention_heads"]
-        head_count_kv = self.hparams.get("num_key_value_heads", head_count)
-
-        ctx_length = 0
-        if "max_sequence_length" in self.hparams:
-            ctx_length = self.hparams["max_sequence_length"]
-        elif "max_position_embeddings" in self.hparams:
-            ctx_length = self.hparams["max_position_embeddings"]
-        elif "model_max_length" in self.hparams:
-            ctx_length = self.hparams["model_max_length"]
-        else:
-            raise ValueError("gguf: can not find ctx length parameter.")
+        super().set_gguf_parameters()
 
         self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
-        self.gguf_writer.add_context_length(ctx_length)
-        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
         self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
-        self.gguf_writer.add_head_count(head_count)
-        self.gguf_writer.add_head_count_kv(head_count_kv)
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
-        self.gguf_writer.add_file_type(self.ftype)
-
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         head_count = self.hparams["num_attention_heads"]
@@ -2089,34 +2108,10 @@ class XverseModel(TextModel):
         special_vocab.add_to_gguf(self.gguf_writer)
 
     def set_gguf_parameters(self):
-        head_count = self.hparams["num_attention_heads"]
-        head_count_kv = self.hparams.get("num_key_value_heads", head_count)
-
-        ctx_length = 0
-        if "max_sequence_length" in self.hparams:
-            ctx_length = self.hparams["max_sequence_length"]
-        elif "max_position_embeddings" in self.hparams:
-            ctx_length = self.hparams["max_position_embeddings"]
-        elif "model_max_length" in self.hparams:
-            ctx_length = self.hparams["model_max_length"]
-        else:
-            raise ValueError("gguf: can not find ctx length parameter.")
+        super().set_gguf_parameters()
 
         self.gguf_writer.add_tensor_data_layout("Meta AI original pth")
-        self.gguf_writer.add_context_length(ctx_length)
-        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
         self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
-        self.gguf_writer.add_head_count(head_count)
-        self.gguf_writer.add_head_count_kv(head_count_kv)
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
-        self.gguf_writer.add_file_type(self.ftype)
-
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
@@ -2430,11 +2425,6 @@ class LlamaModel(TextModel):
             rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
         self.gguf_writer.add_rope_dimension_count(rope_dim)
 
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-
     @staticmethod
     def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
         if n_head_kv is not None and n_head != n_head_kv:
@@ -2518,16 +2508,16 @@ class LlamaModel(TextModel):
         return [(self.map_tensor_name(name), data_torch)]
 
     def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
-            if rope_scaling.get("rope_type", '').lower() == "llama3":
-                base = self.hparams.get("rope_theta", 10000.0)
+        if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
+            if rope_params.get("rope_type", '').lower() == "llama3":
+                base = rope_params.get("rope_theta", 10000.0)
                 if (dim := self.hparams.get("head_dim")) is None:
                     dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
                 freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
 
-                factor = rope_scaling.get("factor", 8.0)
-                low_freq_factor = rope_scaling.get("low_freq_factor", 1.0)
-                high_freq_factor = rope_scaling.get("high_freq_factor", 4.0)
+                factor = rope_params.get("factor", 8.0)
+                low_freq_factor = rope_params.get("low_freq_factor", 1.0)
+                high_freq_factor = rope_params.get("high_freq_factor", 4.0)
                 old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
 
                 low_freq_wavelen = old_context_len / low_freq_factor
@@ -2564,11 +2554,6 @@ class ArceeModel(LlamaModel):
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
         self._try_set_pooling_type()
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
 
 
 @ModelBase.register("AfmoeForCausalLM")
@@ -2851,17 +2836,11 @@ class Mistral3Model(LlamaModel):
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
-        rope_params = self.hparams.get("rope_parameters")
+        rope_params = self.rope_parameters
         if self.hparams.get("model_type") == "ministral3":
-            assert rope_params is not None, "ministral3 must have 'rope_parameters' config"
+            assert rope_params, "ministral3 must have 'rope_parameters' config"
             assert rope_params["rope_type"] == "yarn", "ministral3 rope_type must be 'yarn'"
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-            self.gguf_writer.add_rope_scaling_factor(rope_params["factor"])
-            self.gguf_writer.add_rope_scaling_yarn_beta_fast(rope_params["beta_fast"])
-            self.gguf_writer.add_rope_scaling_yarn_beta_slow(rope_params["beta_slow"])
             self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"])
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_params["original_max_position_embeddings"])
-            self.gguf_writer.add_rope_freq_base(rope_params["rope_theta"])
             self.gguf_writer.add_attn_temperature_scale(rope_params["llama_4_scaling_beta"])
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
@@ -2958,7 +2937,7 @@ class DeciModel(TextModel):
             assert self.block_count == len(self._num_kv_heads)
             assert self.block_count == len(self._num_heads)
             assert self.block_count == len(self._ffn_dims)
-            if (rope_theta := self.hparams.get("rope_theta")) is not None:
+            if (rope_theta := self.rope_parameters.get("rope_theta")) is not None:
                 self.gguf_writer.add_rope_freq_base(rope_theta)
             self.gguf_writer.add_head_count_kv(self._num_kv_heads)
             self.gguf_writer.add_head_count(self._num_heads)
@@ -2983,11 +2962,6 @@ class DeciModel(TextModel):
             rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
         self.gguf_writer.add_rope_dimension_count(rope_dim)
 
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-
     @staticmethod
     def permute(weights: Tensor, n_head: int, n_head_kv: int | None):
         if n_head_kv is not None and n_head != n_head_kv:
@@ -3016,16 +2990,16 @@ class DeciModel(TextModel):
         return [(self.map_tensor_name(name), data_torch)]
 
     def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
-            if rope_scaling.get("rope_type", '').lower() == "llama3":
-                base = self.hparams.get("rope_theta", 10000.0)
+        if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
+            if rope_params.get("rope_type", '').lower() == "llama3":
+                base = rope_params.get("rope_theta", 10000.0)
                 if (dim := self.hparams.get("head_dim")) is None:
                     dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
                 freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
 
-                factor = rope_scaling.get("factor", 8.0)
-                low_freq_factor = rope_scaling.get("low_freq_factor", 1.0)
-                high_freq_factor = rope_scaling.get("high_freq_factor", 4.0)
+                factor = rope_params.get("factor", 8.0)
+                low_freq_factor = rope_params.get("low_freq_factor", 1.0)
+                high_freq_factor = rope_params.get("high_freq_factor", 4.0)
                 old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
 
                 low_freq_wavelen = old_context_len / low_freq_factor
@@ -3279,10 +3253,6 @@ class MiniCPMModel(TextModel):
         logit_scale = self.hparams["hidden_size"] / self.hparams["dim_model_base"]
         self.gguf_writer.add_logit_scale(logit_scale)
         logger.info(f"gguf: (minicpm) logit_scale = {logit_scale}")
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "longrope":
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LONGROPE)
-            logger.info(f"gguf: (minicpm) rope_scaling_type = {gguf.RopeScalingType.LONGROPE}")
 
     def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
         rope_dims = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
@@ -3402,17 +3372,6 @@ class QwenModel(TextModel):
     def set_vocab(self):
         self._set_vocab_qwen()
 
-    def set_gguf_parameters(self):
-        self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
-        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
-        self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
-        self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
-        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
-        self.gguf_writer.add_file_type(self.ftype)
-
 
 @ModelBase.register("Qwen2Model", "Qwen2ForCausalLM", "Qwen2AudioForConditionalGeneration")
 class Qwen2Model(TextModel):
@@ -3427,11 +3386,6 @@ class Qwen2Model(TextModel):
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
         self._try_set_pooling_type()
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         if self.hf_arch == "Qwen2Model":
@@ -3499,12 +3453,6 @@ class DreamModel(TextModel):
 
         # Dream models use non-causal attention for diffusion
         self.gguf_writer.add_causal_attention(False)
-        # Handle RoPE scaling similar to Qwen2
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
 
         # Add Dream-specific parameters
         mask_token_id = self.hparams.get("mask_token_id")
@@ -4048,13 +3996,6 @@ class Qwen2MoeModel(TextModel):
         if (shared_expert_intermediate_size := self.hparams.get('shared_expert_intermediate_size')) is not None:
             self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size)
             logger.info(f"gguf: expert shared feed forward length = {shared_expert_intermediate_size}")
-        # YaRN is not enabled by default
-        # To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
 
     _experts: list[dict[str, Tensor]] | None = None
 
@@ -4656,7 +4597,7 @@ class Phi3MiniModel(TextModel):
         self.gguf_writer.add_head_count_kv(n_head_kv)
         self.gguf_writer.add_layer_norm_rms_eps(rms_eps)
         self.gguf_writer.add_rope_dimension_count(rope_dims)
-        self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"]))
+        self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("full_attention", self.rope_parameters)["rope_theta"])
         self.gguf_writer.add_file_type(self.ftype)
         sliding_window = self.hparams.get("sliding_window")
         # use zero value of sliding_window to distinguish Phi-4 from other PHI3 models
@@ -4932,7 +4873,7 @@ class Plamo2Model(TextModel):
         self.gguf_writer.add_value_length(hparams.get("hidden_size_per_head", 128))
         self.gguf_writer.add_block_count(self.block_count)
         self.gguf_writer.add_layer_norm_rms_eps(hparams.get("rms_norm_eps", 1e-06))
-        self.gguf_writer.add_rope_freq_base(hparams.get("rope_theta", 10000))
+        self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("rope_theta", 10000))
 
         # Mamba parameters
         self.gguf_writer.add_ssm_state_size(hparams.get("mamba_d_state", 64))
@@ -5130,21 +5071,6 @@ class InternLM2Model(TextModel):
 
         special_vocab.add_to_gguf(self.gguf_writer)
 
-    def set_gguf_parameters(self):
-        self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
-        self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
-        self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"])
-        self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
-        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"])
-        self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"])
-        self.gguf_writer.add_file_type(self.ftype)
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         num_heads = self.hparams["num_attention_heads"]
         num_kv_heads = self.hparams["num_key_value_heads"]
@@ -5221,11 +5147,6 @@ class InternLM3Model(TextModel):
             rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
         self.gguf_writer.add_rope_dimension_count(rope_dim)
 
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         n_head = self.hparams["num_attention_heads"]
         n_kv_head = self.hparams.get("num_key_value_heads")
@@ -5588,7 +5509,6 @@ class NomicBertModel(BertModel):
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
-        self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
         if self.is_moe:
             self.gguf_writer.add_moe_every_n_layers(self.hparams["moe_every_n_layers"])
             self.gguf_writer.add_expert_count(self.hparams["num_experts"])
@@ -5711,8 +5631,6 @@ class XLMRobertaModel(BertModel):
         super().set_gguf_parameters()
 
         # jina-embeddings-v3
-        if rotary_emb_base := self.hparams.get("rotary_emb_base"):
-            self.gguf_writer.add_rope_freq_base(rotary_emb_base)
         lora_alpha = self.hparams.get("lora_alpha")
         if lora_prompt_prefixes := self.hparams.get("task_instructions"):
             assert self._lora_files and all(lora_name in lora_prompt_prefixes for lora_name in self._lora_files.keys())
@@ -5840,19 +5758,16 @@ class Gemma3Model(TextModel):
             self._set_vocab_gpt2()
 
     def set_gguf_parameters(self):
+        super().set_gguf_parameters()
         hparams = self.hparams
 
         # some default values are not specified in the hparams
         self.gguf_writer.add_context_length(hparams.get("max_position_embeddings", 131072))
-        self.gguf_writer.add_embedding_length(hparams["hidden_size"])
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
         self.gguf_writer.add_head_count(hparams.get("num_attention_heads", 8))
         self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-6))
         self.gguf_writer.add_key_length(hparams.get("head_dim", 256))
         self.gguf_writer.add_value_length(hparams.get("head_dim", 256))
-        self.gguf_writer.add_file_type(self.ftype)
-        self.gguf_writer.add_rope_freq_base(hparams.get("rope_theta", 1_000_000.0)) # for global layers
+        self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("full_attention", self.rope_parameters).get("rope_theta", 1_000_000.0)) # for global layers
         # attn_logit_softcapping is removed in Gemma3
         assert hparams.get("attn_logit_softcapping") is None
         if (final_logit_softcap := hparams.get("final_logit_softcapping")):
@@ -5860,19 +5775,6 @@ class Gemma3Model(TextModel):
         if hparams.get("sliding_window_pattern") != 1:
             self.gguf_writer.add_sliding_window(hparams["sliding_window"])
         self.gguf_writer.add_head_count_kv(hparams.get("num_key_value_heads", 4))
-        if hparams.get("rope_scaling") is not None:
-            rope_scaling = hparams["rope_scaling"]
-            if rope_scaling["rope_type"] == "linear":
-                # important: this rope_scaling is only applied for global layers, and not used by 1B model
-                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
-                self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-            elif rope_scaling["rope_type"] == "yarn":
-                self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-                self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-                self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
-                self.gguf_writer.add_rope_scaling_yarn_ext_factor(rope_scaling["extrapolation_factor"])
-                self.gguf_writer.add_rope_scaling_yarn_beta_fast(rope_scaling["beta_fast"])
-                self.gguf_writer.add_rope_scaling_yarn_beta_slow(rope_scaling["beta_slow"])
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
@@ -6776,13 +6678,6 @@ class Olmo2Model(TextModel):
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
 
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-            self.gguf_writer.add_rope_scaling_attn_factors(rope_scaling["attention_factor"])
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
-
         if "sliding_window" in self.hparams:
             self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
 
@@ -7281,16 +7176,11 @@ class DeepseekV2Model(TextModel):
 
         self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
 
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
-
+        if (rope_mscale_all := self.rope_parameters.get("mscale_all_dim")) is not None:
             # [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX]
             # note: for legacy reasons, this is not consistent with the other usages of self.gguf_writer.add_rope_scaling_yarn_log_mul
             # ref https://github.com/ggml-org/llama.cpp/pull/17945
-            self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * rope_scaling["mscale_all_dim"])
+            self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * rope_mscale_all)
 
     _experts: list[dict[str, Tensor]] | None = None
 
@@ -7898,11 +7788,6 @@ class Glm4Model(TextModel):
         if (rope_dim := self.hparams.get("head_dim")) is None:
             rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
         self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         if name.startswith("model.visual."): # ignore visual part of Glm4v
@@ -8240,50 +8125,26 @@ class ExaoneModel(TextModel):
     model_arch = gguf.MODEL_ARCH.EXAONE
 
     def set_gguf_parameters(self):
+        super().set_gguf_parameters()
         hparams = self.hparams
 
         assert (hparams["activation_function"] == "silu")
 
-        max_position_embeddings = hparams["max_position_embeddings"]
-        embed_dim = hparams["hidden_size"]
-        num_heads = hparams["num_attention_heads"]
-        num_kv_heads = hparams.get("num_key_value_heads", num_heads)
-        layer_norm_eps = hparams["layer_norm_epsilon"]
-        intermediate_size = hparams["intermediate_size"] if "intermediate_size" in hparams else 4 * embed_dim
-        # ignore for now as EXAONE-3.0-7.8B-Instruct attentino_dropout is 0.0
-        # attention_dropout_rate = hparams["attention_dropout"]
-        # ignore for now as EXAONE-3.0-7.8B-Instruct embed_dropout is 0.0
-        # embed_dropout_rate = hparams["embed_dropout"]
-        self.gguf_writer.add_embedding_length(embed_dim)
-        self.gguf_writer.add_head_count(num_heads)
-        self.gguf_writer.add_head_count_kv(num_kv_heads)
-        self.gguf_writer.add_context_length(max_position_embeddings)
-        self.gguf_writer.add_layer_norm_rms_eps(layer_norm_eps)
-        self.gguf_writer.add_feed_forward_length(intermediate_size)
-        self.gguf_writer.add_block_count(self.block_count)
-        self.gguf_writer.add_file_type(self.ftype)
-
-        if (rope_theta := self.hparams.get("rope_theta")) is not None:
-            self.gguf_writer.add_rope_freq_base(rope_theta)
         rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"], optional=True)
         rotary_factor = rotary_factor if rotary_factor is not None else 1.0
         self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"])))
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
 
     def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
-            if rope_scaling.get("rope_type", '').lower() == "llama3":
-                base = self.hparams.get("rope_theta", 10000.0)
+        if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
+            if rope_params.get("rope_type", '').lower() == "llama3":
+                base = self.rope_parameters.get("rope_theta", 10000.0)
                 if (dim := self.hparams.get("head_dim")) is None:
                     dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
                 freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
 
-                factor = rope_scaling.get("factor", 8.0)
-                low_freq_factor = rope_scaling.get("low_freq_factor", 1.0)
-                high_freq_factor = rope_scaling.get("high_freq_factor", 4.0)
+                factor = rope_params.get("factor", 8.0)
+                low_freq_factor = rope_params.get("low_freq_factor", 1.0)
+                high_freq_factor = rope_params.get("high_freq_factor", 4.0)
                 old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
 
                 low_freq_wavelen = old_context_len / low_freq_factor
@@ -8338,22 +8199,17 @@ class Exaone4Model(TextModel):
                 if len(sliding_window_pattern) == hparams["num_hidden_layers"]:
                     self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern)
 
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-
     def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
-        if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
-            if rope_scaling.get("rope_type", '').lower() == "llama3":
-                base = self.hparams.get("rope_theta", 10_000.0)
+        if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters):
+            if rope_params.get("rope_type", '').lower() == "llama3":
+                base = rope_params.get("rope_theta", 10_000.0)
                 if (dim := self.hparams.get("head_dim")) is None:
                     dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
                 freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
 
-                factor = rope_scaling.get("factor", 16.0)
-                low_freq_factor = rope_scaling.get("low_freq_factor", 1.0)
-                high_freq_factor = rope_scaling.get("high_freq_factor", 4.0)
+                factor = rope_params.get("factor", 16.0)
+                low_freq_factor = rope_params.get("low_freq_factor", 1.0)
+                high_freq_factor = rope_params.get("high_freq_factor", 4.0)
                 old_context_len = self.hparams.get("original_max_position_embeddings", 8192)
 
                 low_freq_wavelen = old_context_len / low_freq_factor
@@ -8664,13 +8520,6 @@ class BailingMoeModel(TextModel):
             rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
 
         self.gguf_writer.add_rope_dimension_count(rope_dim)
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
-        else:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
         self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
         self.gguf_writer.add_vocab_size(hparams["vocab_size"])
         self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
@@ -8777,13 +8626,6 @@ class BailingMoeV2Model(TextModel):
             rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
 
         self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
-        else:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
         self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
         self.gguf_writer.add_vocab_size(hparams["vocab_size"])
         self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
@@ -8862,13 +8704,6 @@ class GroveMoeModel(TextModel):
         self.gguf_writer.add_experts_per_group(2)
         # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L376
         self.gguf_writer.add_expert_group_scale(0.05)
-        # YaRN is not enabled by default
-        # To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
 
     _experts: list[dict[str, Tensor]] | None = None
     _chunk_experts: list[dict[str, Tensor]] | None = None
@@ -9178,7 +9013,7 @@ class FalconH1Model(Mamba2Model):
         assert self.d_inner % self.d_head == 0, f"SSM inner size {self.d_inner} not a multiple of head dim {self.d_head}"
 
         # Add any other Falcon Mamba2 specific configuration
-        self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"]))
+        self.gguf_writer.add_rope_freq_base(self.rope_parameters["rope_theta"])
 
 
 @ModelBase.register("HunYuanMoEV1ForCausalLM")
@@ -9256,12 +9091,11 @@ class HunYuanMoEModel(TextModel):
         self.gguf_writer.add_expert_shared_count(moe_shared_expert[0])
 
         # Rope
-        rope_scaling = hparams.get("rope_scaling", {})
-        if rope_scaling.get("type") == "dynamic":
+        if self.rope_parameters.get("rope_type") == "dynamic":
             # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
             # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf)
-            alpha = rope_scaling.get("alpha", 1000)
-            base = hparams.get("rope_theta", 10000.0)
+            alpha = self.rope_parameters.get("alpha", 1000)
+            base = self.rope_parameters.get("rope_theta", 10000.0)
             dim = (hparams["hidden_size"] // hparams["num_attention_heads"]) # 128
             scaled_base = base * (alpha ** (dim / (dim - 2))) # 10000 * (1000 ** (128 / 126)) = 11158839.9251
             self.gguf_writer.add_rope_freq_base(scaled_base)
@@ -9456,12 +9290,11 @@ class HunYuanModel(TextModel):
         hparams = self.hparams
 
         # Rope
-        rope_scaling = hparams.get("rope_scaling", {})
-        if rope_scaling.get("type") == "dynamic":
+        if self.rope_parameters.get("rope_type") == "dynamic":
             # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
             # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf)
-            alpha = rope_scaling.get("alpha", 50)
-            base = hparams.get("rope_theta", 10000.0)
+            alpha = self.rope_parameters.get("alpha", 50)
+            base = self.rope_parameters.get("rope_theta", 10000.0)
             dim = hparams["head_dim"]
             scaled_base = base * (alpha ** (dim / (dim - 2)))
             self.gguf_writer.add_rope_freq_base(scaled_base)
@@ -9612,13 +9445,6 @@ class GptOssModel(TextModel):
         self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
         self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size"])
 
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        rope_type = rope_scaling.get("rope_type", rope_scaling.get("type"))
-        assert rope_type == "yarn", f"GPT-OSS only supports yarn rope scaling, got {rope_type}"
-        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-        self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-        self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling.get("original_max_position_embeddings", 4096))
-
 
 @ModelBase.register("Lfm2ForCausalLM", "LFM2ForCausalLM")
 class LFM2Model(TextModel):
@@ -9791,13 +9617,6 @@ class SmallThinkerModel(TextModel):
             self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
         else:
             self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
-        # YaRN is not enabled by default
-        # To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts
-        rope_scaling = self.hparams.get("rope_scaling") or {}
-        if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
-            self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
-            self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
-            self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
 
         sliding_window_layout = self.hparams.get("sliding_window_layout")
         if sliding_window_layout:

From 52392291b2f3a24b5d3fef4fc0b56f10db358dc1 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Sun, 14 Dec 2025 22:08:10 +0100
Subject: [PATCH 103/135] preset: handle negated arg, reverse the meaning if
 needed (#18041)

---
 common/preset.cpp | 24 ++++++++++++++++++++++--
 1 file changed, 22 insertions(+), 2 deletions(-)

diff --git a/common/preset.cpp b/common/preset.cpp
index 729c27f2cf..60746aad58 100644
--- a/common/preset.cpp
+++ b/common/preset.cpp
@@ -157,6 +157,21 @@ static std::map<std::string, common_arg> get_map_key_opt(common_params_context &
     return mapping;
 }
 
+static bool is_bool_arg(const common_arg & arg) {
+    return !arg.args_neg.empty();
+}
+
+static std::string parse_bool_arg(const common_arg & arg, const std::string & key, const std::string & value) {
+    // if this is a negated arg, we need to reverse the value
+    for (const auto & neg_arg : arg.args_neg) {
+        if (rm_leading_dashes(neg_arg) == key) {
+            return common_arg_utils::is_truthy(value) ? "false" : "true";
+        }
+    }
+    // otherwise, not negated
+    return value;
+}
+
 common_presets common_presets_load(const std::string & path, common_params_context & ctx_params) {
     common_presets out;
     auto key_to_opt = get_map_key_opt(ctx_params);
@@ -173,8 +188,13 @@ common_presets common_presets_load(const std::string & path, common_params_conte
         for (const auto & [key, value] : section.second) {
             LOG_DBG("option: %s = %s\n", key.c_str(), value.c_str());
             if (key_to_opt.find(key) != key_to_opt.end()) {
-                preset.options[key_to_opt[key]] = value;
-                LOG_DBG("accepted option: %s = %s\n", key.c_str(), value.c_str());
+                auto & opt = key_to_opt[key];
+                if (is_bool_arg(opt)) {
+                    preset.options[opt] = parse_bool_arg(opt, key, value);
+                } else {
+                    preset.options[opt] = value;
+                }
+                LOG_DBG("accepted option: %s = %s\n", key.c_str(), preset.options[opt].c_str());
             } else {
                 // TODO: maybe warn about unknown key?
             }

From 745fa0e78b3d3fd3fd12a260c47300dc3869d07e Mon Sep 17 00:00:00 2001
From: piDack <104877312+piDack@users.noreply.github.com>
Date: Mon, 15 Dec 2025 10:18:46 +0800
Subject: [PATCH 104/135] model : add glm-asr support (#17901)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* [model] add glm-asr support

* fix format for ci

* fix convert format for ci

* update glm_asr convert script & use build_ffn for glm_asr clip & use build_stack for padding and review

* check root architecture for convert hf script

* fix conficlt with upstream

* fix convert script for glm asr & format clip-impl

* format

* restore hparams text

* improved conversion

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---
 convert_hf_to_gguf.py             | 86 ++++++++++++++++++++++++++++++-
 gguf-py/gguf/constants.py         |  1 +
 tools/mtmd/clip-graph.h           |  4 ++
 tools/mtmd/clip-impl.h            |  2 +
 tools/mtmd/clip-model.h           |  1 +
 tools/mtmd/clip.cpp               | 60 ++++++++++++++++++++-
 tools/mtmd/models/whisper-enc.cpp | 19 ++++---
 7 files changed, 160 insertions(+), 13 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 3f861f2a6a..ee02cdd91c 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -713,6 +713,9 @@ class ModelBase:
         if "llm_config" in config:
             # rename for InternVL
             config["text_config"] = config["llm_config"]
+        if "lm_config" in config:
+            # rename for GlmASR
+            config["text_config"] = config["lm_config"]
         if "thinker_config" in config:
             # rename for Qwen2.5-Omni
             config["text_config"] = config["thinker_config"]["text_config"]
@@ -1529,6 +1532,21 @@ class TextModel(ModelBase):
                 raise NotImplementedError("Only MEAN, CLS, and LAST pooling types supported")
             self.gguf_writer.add_pooling_type(pooling_type)
 
+    def _set_vocab_glmedge(self):
+        from transformers import AutoTokenizer
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+        special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])
+        special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"])
+        special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"])
+        special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"])
+        special_vocab.add_to_gguf(self.gguf_writer)
+
     def _set_vocab_interns1(self):
         tokens: list[str] = []
         toktypes: list[int] = []
@@ -1658,7 +1676,7 @@ class MmprojModel(ModelBase):
     preprocessor_config: dict[str, Any]
     global_config: dict[str, Any]
 
-    n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth"]
+    n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "encoder_layers"]
 
     has_vision_encoder: bool = True # by default
     has_audio_encoder: bool = False
@@ -1734,7 +1752,8 @@ class MmprojModel(ModelBase):
         return self.global_config.get(config_name)
 
     def get_audio_config(self) -> dict[str, Any] | None:
-        return self.global_config.get("audio_config")
+        mm_config_key = "whisper_config" if "whisper_config" in self.hparams else "audio_config"
+        return self.global_config.get(mm_config_key)
 
     def set_type(self):
         self.gguf_writer.add_type(gguf.GGUFType.MMPROJ)
@@ -2372,8 +2391,13 @@ class LlamaModel(TextModel):
         # fix for SmolVLM2, missing `num_attention_heads` in config.json
         if self.hf_arch == "VLlama3ForCausalLM":
             self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 32)
+        hparams = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
+        self.origin_hf_arch = hparams.get('architectures', [None])[0]
 
     def set_vocab(self):
+        if self.origin_hf_arch == "GlmasrModel":
+            return self._set_vocab_glmedge()
+
         if self.is_mistral_format:
             return self._set_vocab_mistral()
 
@@ -2444,6 +2468,7 @@ class LlamaModel(TextModel):
             "vision_language_adapter.",
             "patch_merger.",
             "pre_mm_projector_norm",
+            "audio_encoder.",
         ]
 
         is_multimodal_tensor = "vision_tower" in name \
@@ -8846,6 +8871,63 @@ class UltravoxModel(TextModel):
         raise NotImplementedError("Ultravox does not have text decoder. Instead, it uses Llama or other models for text. If you want to get the audio encoder, please use --mmproj argument")
 
 
+@ModelBase.register("GlmasrModel")
+class GlmASRWhisperEncoderModel(MmprojModel):
+    has_vision_encoder = False
+    has_audio_encoder = True
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        if "hidden_size" not in self.hparams and "intermediate_size" not in self.hparams:
+            self.hparams["hidden_size"] = self.hparams["d_model"]
+            self.hparams["intermediate_size"] = self.hparams["encoder_ffn_dim"]
+            self.hparams["num_attention_heads"] = self.hparams["encoder_attention_heads"]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GLMA)
+        self.gguf_writer.add_audio_num_mel_bins(self.hparams["num_mel_bins"])
+        self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5))
+        self.gguf_writer.add_audio_stack_factor(self.global_config["merge_factor"])
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        if ".conv" in name and ".weight" in name:
+            return gguf.GGMLQuantizationType.F16
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        if name.startswith("model.") or name.startswith("lm_head."):
+            # skip language model tensors
+            return []
+
+        if name.startswith("audio_encoder.whisper."):
+            name = name.replace("audio_encoder.whisper.","audio_tower.")
+        if "audio_encoder.layer_norm." in name or "audio_encoder.proj." in name:
+            name = name.replace("audio_encoder.", "audio_encoder.adapting.")
+
+        if name.startswith("audio_encoder.audio_bos_eos_token."):
+            return [(self.map_tensor_name("model.vision.boi"), data_torch[0]), (self.map_tensor_name("model.vision.eoi"), data_torch[1])]
+
+        if name.startswith("audio_encoder.adapting."):
+            name = name.replace("audio_encoder.adapting.","audio.multi_modal_projector.")
+            if ".layer_norm." in name:
+                name = name.replace(".layer_norm.", ".ln_pre.")
+            if ".0." in name:
+                name = name.replace(".0.", ".linear_1.")
+            if ".2." in name:
+                name = name.replace(".2.", ".linear_2.")
+            if ".proj." in name:
+                return []
+
+        if "conv1.bias" in name or "conv2.bias" in name:
+            # transpose conv1 and conv2 bias
+            data_torch = data_torch.unsqueeze(-1)
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+
 @ModelBase.register("Qwen2AudioForConditionalGeneration")
 class WhisperEncoderModel(MmprojModel):
     has_vision_encoder = False # no vision encoder
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 2b8489c591..8ef4a23a10 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -3320,6 +3320,7 @@ class VisionProjectorType:
     ULTRAVOX = "ultravox"
     INTERNVL = "internvl"
     QWEN2A = "qwen2a" # audio
+    GLMA = "glma" # audio
     QWEN25O = "qwen2.5o" # omni
     VOXTRAL = "voxtral"
     LFM2 = "lfm2"
diff --git a/tools/mtmd/clip-graph.h b/tools/mtmd/clip-graph.h
index 6d303b4e48..17f90e8aa8 100644
--- a/tools/mtmd/clip-graph.h
+++ b/tools/mtmd/clip-graph.h
@@ -112,4 +112,8 @@ struct clip_graph {
     // aka pixel_shuffle / pixel_unshuffle / patch_merger (Kimi-VL)
     // support dynamic resolution
     ggml_tensor * build_patch_merge_permute(ggml_tensor * cur, int scale_factor);
+
+    // Generic function to stack frames for audio processing
+    // Abstracts out the StackAudioFrames logic used by ultravox
+    ggml_tensor * build_stack(ggml_tensor * cur, int32_t stack_factor, int32_t n_embed);
 };
diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h
index 1726823ec6..9ef0d301e4 100644
--- a/tools/mtmd/clip-impl.h
+++ b/tools/mtmd/clip-impl.h
@@ -157,6 +157,7 @@ enum projector_type {
     PROJECTOR_TYPE_INTERNVL,
     PROJECTOR_TYPE_LLAMA4,
     PROJECTOR_TYPE_QWEN2A,
+    PROJECTOR_TYPE_GLMA,
     PROJECTOR_TYPE_QWEN25O, // will be replaced by QWEN2A or QWEN25VL depending on clip_ctx
     PROJECTOR_TYPE_VOXTRAL,
     PROJECTOR_TYPE_LFM2,
@@ -183,6 +184,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_INTERNVL,  "internvl"},
     { PROJECTOR_TYPE_LLAMA4,    "llama4"},
     { PROJECTOR_TYPE_QWEN2A,    "qwen2a"},
+    { PROJECTOR_TYPE_GLMA,      "glma"},
     { PROJECTOR_TYPE_QWEN25O,   "qwen2.5o"},
     { PROJECTOR_TYPE_VOXTRAL,   "voxtral"},
     { PROJECTOR_TYPE_LFM2,      "lfm2"},
diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h
index 51bcce1ebb..bdd4f63e0f 100644
--- a/tools/mtmd/clip-model.h
+++ b/tools/mtmd/clip-model.h
@@ -256,6 +256,7 @@ struct clip_model {
     ggml_tensor * conv1d_2_w = nullptr;
     ggml_tensor * conv1d_2_b = nullptr;
     ggml_tensor * mm_norm_pre_w = nullptr;
+    ggml_tensor * mm_norm_pre_b = nullptr;
     ggml_tensor * mm_norm_mid_w = nullptr;
 
     // cogvlm
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index 5cc4e4b1fa..c72c6a6381 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -720,6 +720,32 @@ ggml_tensor * clip_graph::build_rope_2d(
     return cur;
 }
 
+// Generic function to stack frames for audio processing
+// Abstracts out the StackAudioFrames logic used by ultravox
+ggml_tensor * clip_graph::build_stack(ggml_tensor * cur, int32_t stack_factor, int32_t n_embed) {
+    if (stack_factor <= 1) {
+        return cur;
+    }
+
+    int64_t total_elements = ggml_nelements(cur);
+    int64_t stride = n_embed * stack_factor;
+
+    // Calculate padded length
+    int64_t padded_len = GGML_PAD(total_elements, stride);
+    int64_t pad = padded_len - total_elements;
+
+    if (pad > 0) {
+        // Pad the tensor to make it divisible by stride
+        cur = ggml_view_1d(ctx0, cur, total_elements, 0);
+        cur = ggml_pad(ctx0, cur, pad, 0, 0, 0);
+    }
+
+    // Reshape to [stride, padded_len / stride]
+    cur = ggml_view_2d(ctx0, cur, stride, padded_len / stride,
+                        ggml_row_size(cur->type, stride), 0);
+    return cur;
+}
+
 // aka pixel_shuffle / pixel_unshuffle / patch_merger (Kimi-VL)
 // support dynamic resolution
 ggml_tensor * clip_graph::build_patch_merge_permute(ggml_tensor * cur, int scale_factor) {
@@ -796,6 +822,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
         case PROJECTOR_TYPE_ULTRAVOX:
         case PROJECTOR_TYPE_VOXTRAL:
         case PROJECTOR_TYPE_QWEN2A:
+        case PROJECTOR_TYPE_GLMA:
             {
                 builder = std::make_unique<clip_graph_whisper_enc>(ctx, img);
             } break;
@@ -1136,10 +1163,12 @@ struct clip_model_loader {
                     } break;
                 case PROJECTOR_TYPE_ULTRAVOX:
                 case PROJECTOR_TYPE_QWEN2A:
+                case PROJECTOR_TYPE_GLMA:
                 case PROJECTOR_TYPE_VOXTRAL:
                     {
                         bool require_stack = model.proj_type == PROJECTOR_TYPE_ULTRAVOX ||
-                                             model.proj_type == PROJECTOR_TYPE_VOXTRAL;
+                                             model.proj_type == PROJECTOR_TYPE_VOXTRAL ||
+                                             model.proj_type == PROJECTOR_TYPE_GLMA;
                         get_u32(KEY_A_PROJ_STACK_FACTOR, hparams.proj_stack_factor, require_stack);
                         if (hparams.n_mel_bins != 128) {
                             throw std::runtime_error(string_format("%s: only 128 mel bins are supported for ultravox\n", __func__));
@@ -1510,6 +1539,21 @@ struct clip_model_loader {
                     model.mm_3_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "weight"));
                     model.mm_3_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "bias"));
                 } break;
+            case PROJECTOR_TYPE_GLMA:
+                {
+                    model.conv1d_1_w = get_tensor(string_format(TN_CONV1D, 1, "weight"));
+                    model.conv1d_1_b = get_tensor(string_format(TN_CONV1D, 1, "bias"));
+                    model.conv1d_2_w = get_tensor(string_format(TN_CONV1D, 2, "weight"));
+                    model.conv1d_2_b = get_tensor(string_format(TN_CONV1D, 2, "bias"));
+                    model.mm_1_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "weight"));
+                    model.mm_1_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "bias"));
+                    model.mm_2_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "weight"));
+                    model.mm_2_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "bias"));
+                    model.mm_norm_pre_w = get_tensor(string_format(TN_MM_NORM_PRE, "weight"));
+                    model.mm_norm_pre_b = get_tensor(string_format(TN_MM_NORM_PRE, "bias"));
+                    model.mm_boi = get_tensor(string_format(TN_TOK_BOI, "weight"));
+                    model.mm_eoi = get_tensor(string_format(TN_TOK_EOI, "weight"));
+                } break;
             case PROJECTOR_TYPE_LLAMA4:
                 {
                     model.mm_model_proj    = get_tensor(TN_MM_PROJECTOR);
@@ -2895,6 +2939,16 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
                     n_patches /= 2;
                 }
             } break;
+        case PROJECTOR_TYPE_GLMA:
+            {
+                n_patches = img->nx;
+                // whisper downscales input token by half after conv1d
+                n_patches /= 2;
+                // reshape by merge_factor
+                n_patches /= ctx->model.hparams.proj_stack_factor;
+                // for BOI and EOI token embeddings
+                n_patches += 2;
+            } break;
         case PROJECTOR_TYPE_COGVLM:
             {
                 n_patches += 2; // for BOI and EOI token embeddings
@@ -3230,6 +3284,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
         case PROJECTOR_TYPE_IDEFICS3:
         case PROJECTOR_TYPE_INTERNVL:
         case PROJECTOR_TYPE_QWEN2A:
+        case PROJECTOR_TYPE_GLMA:
         case PROJECTOR_TYPE_ULTRAVOX:
         case PROJECTOR_TYPE_LFM2:
         case PROJECTOR_TYPE_VOXTRAL:
@@ -3340,6 +3395,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
             return ctx->model.mm_model_proj->ne[1];
         case PROJECTOR_TYPE_QWEN2A:
             return ctx->model.mm_fc_w->ne[1];
+        case PROJECTOR_TYPE_GLMA:
+            return ctx->model.mm_2_w->ne[1];
         case PROJECTOR_TYPE_LFM2:
         case PROJECTOR_TYPE_KIMIVL:
             return ctx->model.mm_2_w->ne[1];
@@ -3386,6 +3443,7 @@ bool clip_has_audio_encoder(const struct clip_ctx * ctx) {
 bool clip_has_whisper_encoder(const struct clip_ctx * ctx) {
     return ctx->proj_type() == PROJECTOR_TYPE_ULTRAVOX
         || ctx->proj_type() == PROJECTOR_TYPE_QWEN2A
+        || ctx->proj_type() == PROJECTOR_TYPE_GLMA
         || ctx->proj_type() == PROJECTOR_TYPE_VOXTRAL;
 }
 
diff --git a/tools/mtmd/models/whisper-enc.cpp b/tools/mtmd/models/whisper-enc.cpp
index 07d378b095..2870d854ab 100644
--- a/tools/mtmd/models/whisper-enc.cpp
+++ b/tools/mtmd/models/whisper-enc.cpp
@@ -30,7 +30,6 @@ ggml_cgraph * clip_graph_whisper_enc::build() {
     GGML_ASSERT(model.layers[0].q_b);
     GGML_ASSERT(model.layers[0].v_b);
     GGML_ASSERT(!model.layers[0].k_b); // no bias for k
-    GGML_ASSERT(model.post_ln_w && model.post_ln_b);
 
     ggml_tensor * pos_embd_selected = ggml_view_2d(
         ctx0, model.position_embeddings,
@@ -49,15 +48,7 @@ ggml_cgraph * clip_graph_whisper_enc::build() {
     if (model.audio_has_stack_frames()) {
         // StackAudioFrames
         // https://huggingface.co/fixie-ai/ultravox-v0_5-llama-3_2-1b/blob/main/ultravox_model.py
-        int64_t stride = n_embd * hparams.proj_stack_factor;
-        int64_t padded_len = GGML_PAD(ggml_nelements(cur), stride);
-        int64_t pad = padded_len - ggml_nelements(cur);
-        if (pad > 0) {
-            cur = ggml_view_1d(ctx0, cur, ggml_nelements(cur), 0);
-            cur = ggml_pad(ctx0, cur, pad, 0, 0, 0);
-        }
-        cur = ggml_view_2d(ctx0, cur, stride, padded_len / stride,
-                            ggml_row_size(cur->type, stride), 0);
+        cur = build_stack(cur, hparams.proj_stack_factor, n_embd);
         cb(cur, "after_stacked", -1);
     }
 
@@ -95,6 +86,14 @@ ggml_cgraph * clip_graph_whisper_enc::build() {
             FFN_GELU_ERF,
             -1);
 
+    } else if (proj_type == PROJECTOR_TYPE_GLMA) {
+            cur = ggml_norm(ctx0, cur, hparams.eps);
+            cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w);
+            cur = ggml_add(ctx0, cur, model.mm_norm_pre_b);
+            cur = build_stack(cur, hparams.proj_stack_factor, n_embd);
+            cur = build_ffn(cur, model.mm_1_w, model.mm_1_b, nullptr, nullptr, model.mm_2_w, model.mm_2_b, hparams.ffn_op, 0);
+            cur = ggml_concat(ctx0, model.mm_boi, cur, 1);
+            cur = ggml_concat(ctx0, cur, model.mm_eoi, 1);
     } else {
         GGML_ABORT("%s: unknown projector type", __func__);
     }

From 4aced7a63156555911157d3002f9d3ddef4a1e55 Mon Sep 17 00:00:00 2001
From: Neo Zhang Jianyu <jianyu.zhang@intel.com>
Date: Mon, 15 Dec 2025 10:35:15 +0800
Subject: [PATCH 105/135] [SYCL] Support gpt-oss by OPs add-id, mul_mat for
 mxfp4, swiglu_oai (#17826)

* support gpt-oss GPU by OP add-id, mul_mat for mxfp4, swiglu_oai, fix warning

* fix fault ut case, update ops.md

* rebase, fix format issue
---
 docs/ops.md                         |   18 +-
 docs/ops/SYCL.csv                   | 1158 ++++++++++++++++++---------
 ggml/src/ggml-sycl/add-id.cpp       |   77 ++
 ggml/src/ggml-sycl/add-id.hpp       |    8 +
 ggml/src/ggml-sycl/common.hpp       |   17 +
 ggml/src/ggml-sycl/convert.cpp      |   15 +
 ggml/src/ggml-sycl/dequantize.hpp   |   18 +
 ggml/src/ggml-sycl/dpct/helper.hpp  |   59 +-
 ggml/src/ggml-sycl/element_wise.cpp |   97 +++
 ggml/src/ggml-sycl/element_wise.hpp |    4 +
 ggml/src/ggml-sycl/ggml-sycl.cpp    |   23 +-
 ggml/src/ggml-sycl/mmvq.cpp         |   22 +
 ggml/src/ggml-sycl/pad.cpp          |   10 +-
 ggml/src/ggml-sycl/ssm_conv.cpp     |    2 +-
 ggml/src/ggml-sycl/vecdotq.hpp      |   58 ++
 15 files changed, 1201 insertions(+), 385 deletions(-)
 create mode 100644 ggml/src/ggml-sycl/add-id.cpp
 create mode 100644 ggml/src/ggml-sycl/add-id.hpp

diff --git a/docs/ops.md b/docs/ops.md
index 43163b39ac..b395d2315c 100644
--- a/docs/ops.md
+++ b/docs/ops.md
@@ -18,12 +18,12 @@ Legend:
 |                              ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                             ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
+|                           ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                           ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
-|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
+|                          ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                             CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                            CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ | ❌ |
 |                           CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ |
 |                          CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ |
@@ -31,7 +31,7 @@ Legend:
 |                          CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                              COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                              COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
 |                      COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
 |               CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
@@ -64,7 +64,7 @@ Legend:
 |                        IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
 |                          L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                       LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
-|                              LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
+|                              LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                             MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                              MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                          MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 |
@@ -98,14 +98,14 @@ Legend:
 |                          SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                             SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                        SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
-|                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                              SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
 |                          SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
 |                         SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
 |                         SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ |
 |                    SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                        SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
-|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
-|                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
+|                              SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ |
+|                             SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ |
 |                         SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                         SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
 |                             STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ |
@@ -113,7 +113,7 @@ Legend:
 |                              SUM | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ |
 |                         SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ |
 |                           SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
-|                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ❌ | ❌ |
+|                       SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |                             TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ |
 |               TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ |
 |                            TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ |
diff --git a/docs/ops/SYCL.csv b/docs/ops/SYCL.csv
index 85a45d6ae0..91b442bde8 100644
--- a/docs/ops/SYCL.csv
+++ b/docs/ops/SYCL.csv
@@ -33,14 +33,14 @@
 "SYCL0","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL"
 "SYCL0","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL"
 "SYCL0","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL"
-"SYCL0","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL"
-"SYCL0","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL"
-"SYCL0","CEIL","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL"
-"SYCL0","CEIL","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL"
-"SYCL0","ROUND","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL"
-"SYCL0","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL"
-"SYCL0","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL"
-"SYCL0","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL"
+"SYCL0","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL"
+"SYCL0","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL"
+"SYCL0","CEIL","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL"
+"SYCL0","CEIL","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL"
+"SYCL0","ROUND","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL"
+"SYCL0","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL"
+"SYCL0","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL"
+"SYCL0","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL"
 "SYCL0","ABS","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL"
 "SYCL0","ABS","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL"
 "SYCL0","SGN","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL"
@@ -287,14 +287,14 @@
 "SYCL0","GEGLU_QUICK","type=f32,ne_a=[5,7,11,13],v=1,swapped=1","support","1","yes","SYCL"
 "SYCL0","GEGLU_QUICK","type=f32,ne_a=[128,2,2,2],v=1,split","support","1","yes","SYCL"
 "SYCL0","GEGLU_QUICK","type=f32,ne_a=[5,7,11,13],v=1,split","support","1","yes","SYCL"
-"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=0.500000,limit=2.000000","support","0","no","SYCL"
-"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=0.500000,limit=7.000000","support","0","no","SYCL"
-"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=1.702000,limit=2.000000","support","0","no","SYCL"
-"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=1.702000,limit=7.000000","support","0","no","SYCL"
-"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=0.500000,limit=2.000000","support","0","no","SYCL"
-"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=0.500000,limit=7.000000","support","0","no","SYCL"
-"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=1.702000,limit=2.000000","support","0","no","SYCL"
-"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=1.702000,limit=7.000000","support","0","no","SYCL"
+"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=0.500000,limit=2.000000","support","1","yes","SYCL"
+"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=0.500000,limit=7.000000","support","1","yes","SYCL"
+"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=1.702000,limit=2.000000","support","1","yes","SYCL"
+"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=1.702000,limit=7.000000","support","1","yes","SYCL"
+"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=0.500000,limit=2.000000","support","1","yes","SYCL"
+"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=0.500000,limit=7.000000","support","1","yes","SYCL"
+"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=1.702000,limit=2.000000","support","1","yes","SYCL"
+"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=1.702000,limit=7.000000","support","1","yes","SYCL"
 "SYCL0","GET_ROWS","type=f32,n=76800,m=5,r=4,be1=1,be2=2,v=0","support","1","yes","SYCL"
 "SYCL0","GET_ROWS","type=f32,n=256,m=80000,r=70000,be1=2,be2=1,v=0","support","1","yes","SYCL"
 "SYCL0","GET_ROWS","type=f32,n=256,m=5,r=4,be1=700,be2=100,v=0","support","1","yes","SYCL"
@@ -4964,6 +4964,7 @@
 "SYCL0","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","1","yes","SYCL"
 "SYCL0","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","SYCL"
 "SYCL0","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","SYCL"
+"SYCL0","CONV_TRANSPOSE_2D","ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","SYCL"
 "SYCL0","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","1","yes","SYCL"
 "SYCL0","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","1","yes","SYCL"
 "SYCL0","ARGMAX","type=f32,ne=[32,1,1,1]","support","1","yes","SYCL"
@@ -5419,17 +5420,45 @@
 "SYCL0","CPY","type_src=f16,type_dst=f16,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","SYCL"
 "SYCL0","CPY","type_src=f32,type_dst=f32,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","SYCL"
 "SYCL0","CPY","type_src=bf16,type_dst=bf16,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","0","no","SYCL"
+"SYCL0","CPY","type_src=i32,type_dst=i32,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","0","no","SYCL"
+"SYCL0","CPY","type_src=i32,type_dst=i32,ne=[256,1,4,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL"
 "SYCL0","CPY","type_src=f32,type_dst=f32,ne=[256,1,4,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL"
-"SYCL0","CONT","type=f32,ne=[10,10,10,1]","support","1","yes","SYCL"
-"SYCL0","CONT","type=f32,ne=[2,1,1,1]","support","1","yes","SYCL"
-"SYCL0","CONT","type=f32,ne=[2,1,3,5]","support","1","yes","SYCL"
-"SYCL0","CONT","type=f32,ne=[2,3,5,7]","support","1","yes","SYCL"
-"SYCL0","CONT","type=f16,ne=[2,1,1,1]","support","1","yes","SYCL"
-"SYCL0","CONT","type=f16,ne=[2,1,3,5]","support","1","yes","SYCL"
-"SYCL0","CONT","type=f16,ne=[2,3,5,7]","support","1","yes","SYCL"
-"SYCL0","CONT","type=bf16,ne=[2,1,1,1]","support","0","no","SYCL"
-"SYCL0","CONT","type=bf16,ne=[2,1,3,5]","support","0","no","SYCL"
-"SYCL0","CONT","type=bf16,ne=[2,3,5,7]","support","0","no","SYCL"
+"SYCL0","CONT","type=f32,ne=[2,1,1,1],use_view_slice=1","support","1","yes","SYCL"
+"SYCL0","CONT","type=f32,ne=[2,1,3,5],use_view_slice=1","support","1","yes","SYCL"
+"SYCL0","CONT","type=f32,ne=[2,3,5,7],use_view_slice=1","support","1","yes","SYCL"
+"SYCL0","CONT","type=f32,ne=[1,4,4,1],use_view_slice=1","support","1","yes","SYCL"
+"SYCL0","CONT","type=f32,ne=[1,8,17,1],use_view_slice=1","support","1","yes","SYCL"
+"SYCL0","CONT","type=f32,ne=[10,10,10,1],use_view_slice=1","support","1","yes","SYCL"
+"SYCL0","CONT","type=f32,ne=[2,1,1,1],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=f32,ne=[2,1,3,5],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=f32,ne=[2,3,5,7],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=f32,ne=[1,4,4,1],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=f32,ne=[1,8,17,1],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=f32,ne=[10,10,10,1],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=i32,ne=[2,1,1,1],use_view_slice=1","support","1","yes","SYCL"
+"SYCL0","CONT","type=i32,ne=[2,1,3,5],use_view_slice=1","support","1","yes","SYCL"
+"SYCL0","CONT","type=i32,ne=[2,3,5,7],use_view_slice=1","support","1","yes","SYCL"
+"SYCL0","CONT","type=i32,ne=[1,4,4,1],use_view_slice=1","support","1","yes","SYCL"
+"SYCL0","CONT","type=i32,ne=[1,8,17,1],use_view_slice=1","support","1","yes","SYCL"
+"SYCL0","CONT","type=i32,ne=[10,10,10,1],use_view_slice=1","support","1","yes","SYCL"
+"SYCL0","CONT","type=i32,ne=[2,1,1,1],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=i32,ne=[2,1,3,5],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=i32,ne=[2,3,5,7],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=i32,ne=[1,4,4,1],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=i32,ne=[1,8,17,1],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=i32,ne=[10,10,10,1],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=f16,ne=[2,1,1,1],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=f16,ne=[2,1,3,5],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=f16,ne=[2,3,5,7],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=f16,ne=[1,4,4,1],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=f16,ne=[1,8,17,1],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=f16,ne=[10,10,10,1],use_view_slice=0","support","1","yes","SYCL"
+"SYCL0","CONT","type=bf16,ne=[2,1,1,1],use_view_slice=0","support","0","no","SYCL"
+"SYCL0","CONT","type=bf16,ne=[2,1,3,5],use_view_slice=0","support","0","no","SYCL"
+"SYCL0","CONT","type=bf16,ne=[2,3,5,7],use_view_slice=0","support","0","no","SYCL"
+"SYCL0","CONT","type=bf16,ne=[1,4,4,1],use_view_slice=0","support","0","no","SYCL"
+"SYCL0","CONT","type=bf16,ne=[1,8,17,1],use_view_slice=0","support","0","no","SYCL"
+"SYCL0","CONT","type=bf16,ne=[10,10,10,1],use_view_slice=0","support","0","no","SYCL"
 "SYCL0","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL"
 "SYCL0","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL"
 "SYCL0","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL"
@@ -5655,6 +5684,7 @@
 "SYCL0","MUL","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL"
 "SYCL0","DIV","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL"
 "SYCL0","ADD1","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL"
+"SYCL0","ADD1","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL"
 "SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=0.000000,inplace=0","support","1","yes","SYCL"
 "SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=0","support","1","yes","SYCL"
 "SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=1","support","1","yes","SYCL"
@@ -5791,15 +5821,15 @@
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -5944,15 +5974,15 @@
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
@@ -5971,15 +6001,15 @@
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6002,15 +6032,15 @@
 "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
@@ -6029,15 +6059,15 @@
 "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6176,15 +6206,15 @@
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6207,15 +6237,15 @@
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6238,15 +6268,15 @@
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6269,15 +6299,15 @@
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6300,15 +6330,15 @@
 "SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6331,15 +6361,15 @@
 "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6362,15 +6392,15 @@
 "SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6393,81 +6423,81 @@
 "SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
@@ -6489,12 +6519,12 @@
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6520,12 +6550,12 @@
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL"
+"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL"
@@ -6807,145 +6837,145 @@
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[2,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[2,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[4,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[4,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[4,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[4,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
 "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL"
-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL"
+"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=16,n_used=16,b=0,m=32,n=1024,k=16","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=2,n_used=2,b=0,m=32,n=8192,k=64","support","1","yes","SYCL"
@@ -6954,7 +6984,7 @@
 "SYCL0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=2,n_used=2,b=1,m=32,n=8192,k=64","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=16,n_used=16,b=1,m=50,n=200,k=64","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=1,n_used=1,b=0,m=8,n=16,k=1","support","1","yes","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=32,n_used=2,b=0,m=2880,n=32,k=2880","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=32,n_used=2,b=0,m=2880,n=32,k=2880","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
@@ -7387,78 +7417,78 @@
 "SYCL0","MUL_MAT_ID","type_a=q4_K,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=q4_K,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=q4_K,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=129,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=1,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=129,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=129,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=1,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=129,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=1,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=129,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=1,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=129,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=1,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=129,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=1,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=129,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=1,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=129,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=1,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=129,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=1,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=129,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=1,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=4,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=5,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=17,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=32,k=256","support","0","no","SYCL"
-"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=129,k=256","support","0","no","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=2,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=4,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=1,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=2,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=0,m=512,n=129,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=1,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=4,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=5,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=17,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=32,k=256","support","1","yes","SYCL"
+"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=129,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","1","yes","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=4,k=256","support","0","no","SYCL"
 "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=5,k=256","support","0","no","SYCL"
@@ -8589,64 +8619,68 @@
 "SYCL0","OUT_PROD","type_a=iq2_xxs,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL"
 "SYCL0","OUT_PROD","type_a=iq2_xxs,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL"
 "SYCL0","OUT_PROD","type_a=iq2_xxs,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=1","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=32","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=129","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=1,n_token=1","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=1,n_token=32","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=1,n_token=129","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=2,n_token=1","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=2,n_token=32","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=2,n_token=129","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=2,n_token=1","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=2,n_token=32","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=2,n_token=129","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=4,n_token=1","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=4,n_token=32","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=4,n_token=129","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=4,n_token=1","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=4,n_token=32","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=4,n_token=129","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=1,n_token=1","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=1,n_token=32","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=1,n_token=129","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=1,n_token=1","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=1,n_token=32","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=1,n_token=129","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=2,n_token=1","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=2,n_token=32","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=2,n_token=129","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=1","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=32","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=129","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=1","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=32","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=129","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=1","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=32","support","0","no","SYCL"
-"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=129","support","0","no","SYCL"
-"SYCL0","SQR","type=f16,ne=[10,5,4,3]","support","0","no","SYCL"
-"SYCL0","SQRT","type=f16,ne=[10,3,3,2]","support","0","no","SYCL"
-"SYCL0","LOG","type=f16,ne=[10,5,4,3]","support","0","no","SYCL"
-"SYCL0","SIN","type=f16,ne=[10,2,2,2]","support","0","no","SYCL"
-"SYCL0","COS","type=f16,ne=[10,2,2,2]","support","0","no","SYCL"
-"SYCL0","CLAMP","type=f16,ne=[10,5,4,3],min=-0.500000,max=0.500000","support","0","no","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=1","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=32","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=129","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=1,n_token=1","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=1,n_token=32","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=1,n_token=129","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=2,n_token=1","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=2,n_token=32","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=2,n_token=129","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=2,n_token=1","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=2,n_token=32","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=2,n_token=129","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=4,n_token=1","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=4,n_token=32","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=4,n_token=129","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=4,n_token=1","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=4,n_token=32","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=4,n_experts_used=4,n_token=129","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=1,n_token=1","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=1,n_token=32","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=1,n_token=129","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=1,n_token=1","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=1,n_token=32","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=1,n_token=129","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=2,n_token=1","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=2,n_token=32","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=2,n_token=129","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=1","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=32","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=2,n_token=129","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=1","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=32","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=8,n_experts_used=4,n_token=129","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=1","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=32","support","1","yes","SYCL"
+"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=129,n_experts=8,n_experts_used=4,n_token=129","support","1","yes","SYCL"
+"SYCL0","SQR","type=f16,ne=[10,5,4,3]","support","1","yes","SYCL"
+"SYCL0","SQRT","type=f16,ne=[10,3,3,2]","support","1","yes","SYCL"
+"SYCL0","LOG","type=f16,ne=[10,5,4,3]","support","1","yes","SYCL"
+"SYCL0","SIN","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL"
+"SYCL0","COS","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL"
+"SYCL0","CLAMP","type=f16,ne=[10,5,4,3],min=-0.500000,max=0.500000","support","1","yes","SYCL"
 "SYCL0","LEAKY_RELU","type=f16,ne_a=[10,5,4,3],negative_slope=0.100000","support","1","yes","SYCL"
-"SYCL0","FLOOR","type=f16,ne=[10,2,2,2]","support","0","no","SYCL"
-"SYCL0","CEIL","type=f16,ne=[10,2,2,2]","support","0","no","SYCL"
-"SYCL0","ROUND","type=f16,ne=[10,2,2,2]","support","0","no","SYCL"
-"SYCL0","TRUNC","type=f16,ne=[10,2,2,2]","support","0","no","SYCL"
-"SYCL0","SQR","type=f16,ne=[7,1,5,3]","support","0","no","SYCL"
-"SYCL0","SQRT","type=f16,ne=[7,1,5,3]","support","0","no","SYCL"
-"SYCL0","LOG","type=f16,ne=[7,1,5,3]","support","0","no","SYCL"
-"SYCL0","SIN","type=f16,ne=[7,1,5,3]","support","0","no","SYCL"
-"SYCL0","COS","type=f16,ne=[7,1,5,3]","support","0","no","SYCL"
-"SYCL0","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","0","no","SYCL"
+"SYCL0","FLOOR","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL"
+"SYCL0","CEIL","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL"
+"SYCL0","ROUND","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL"
+"SYCL0","TRUNC","type=f16,ne=[10,2,2,2]","support","1","yes","SYCL"
+"SYCL0","SQR","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","SQRT","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","LOG","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","SIN","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","COS","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","CLAMP","type=f16,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","SYCL"
 "SYCL0","LEAKY_RELU","type=f16,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","SYCL"
-"SYCL0","FLOOR","type=f16,ne=[7,1,5,3]","support","0","no","SYCL"
-"SYCL0","CEIL","type=f16,ne=[7,1,5,3]","support","0","no","SYCL"
-"SYCL0","ROUND","type=f16,ne=[7,1,5,3]","support","0","no","SYCL"
-"SYCL0","TRUNC","type=f16,ne=[7,1,5,3]","support","0","no","SYCL"
+"SYCL0","FLOOR","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","FLOOR","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL"
+"SYCL0","CEIL","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","CEIL","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL"
+"SYCL0","ROUND","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","ROUND","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL"
+"SYCL0","TRUNC","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","TRUNC","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL"
 "SYCL0","SQR","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL"
 "SYCL0","SQRT","type=f32,ne=[10,3,3,2]","support","1","yes","SYCL"
 "SYCL0","LOG","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL"
@@ -8666,9 +8700,13 @@
 "SYCL0","CLAMP","type=f32,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","SYCL"
 "SYCL0","LEAKY_RELU","type=f32,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","SYCL"
 "SYCL0","FLOOR","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","FLOOR","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL"
 "SYCL0","CEIL","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","CEIL","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL"
 "SYCL0","ROUND","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","ROUND","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL"
 "SYCL0","TRUNC","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL"
+"SYCL0","TRUNC","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL"
 "SYCL0","DIAG_MASK_INF","type=f32,ne=[10,10,1,1],n_past=5","support","1","yes","SYCL"
 "SYCL0","DIAG_MASK_INF","type=f32,ne=[10,10,3,1],n_past=5","support","1","yes","SYCL"
 "SYCL0","DIAG_MASK_INF","type=f32,ne=[10,10,3,2],n_past=5","support","1","yes","SYCL"
@@ -9411,28 +9449,405 @@
 "SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=2,v=3","support","1","yes","SYCL"
 "SYCL0","CONCAT","type=f32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","SYCL"
 "SYCL0","CONCAT","type=i32,ne_a=[11,12,13,14],ne_b_d=7,dim=3,v=3","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[3,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[4,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[7,1,1,1],order=0","support","1","yes","SYCL"
 "SYCL0","ARGSORT","type=f32,ne=[8,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[15,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[16,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[31,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[32,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[63,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[64,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[127,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[128,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[255,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[256,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[511,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[512,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[1023,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[1024,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[2047,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[2048,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[4095,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[4096,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[8191,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[8192,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[16383,1,1,1],order=0","support","1","yes","SYCL"
+"SYCL0","ARGSORT","type=f32,ne=[16384,1,1,1],order=0","support","1","yes","SYCL"
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+"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=15,ties=0","support","0","no","SYCL"
 "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=0","support","1","yes","SYCL"
 "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=1","support","1","yes","SYCL"
 "SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=nearest,flags=none","support","1","yes","SYCL"
@@ -9445,6 +9860,10 @@
 "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bicubic,transpose=1","support","0","no","SYCL"
 "SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic,flags=none","support","0","no","SYCL"
 "SYCL0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bicubic,flags=none","support","0","no","SYCL"
+"SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=513,transpose=0","support","0","no","SYCL"
+"SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=513,transpose=1","support","0","no","SYCL"
+"SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=none","support","0","no","SYCL"
+"SYCL0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear,flags=none","support","0","no","SYCL"
 "SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=align_corners","support","0","no","SYCL"
 "SYCL0","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bilinear,flags=align_corners","support","0","no","SYCL"
 "SYCL0","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bilinear,flags=align_corners","support","0","no","SYCL"
@@ -9479,9 +9898,23 @@
 "SYCL0","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","1","yes","SYCL"
 "SYCL0","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","1","yes","SYCL"
 "SYCL0","ARANGE","type=f32,start=0.000000,stop=10.000000,step=1.000000","support","1","yes","SYCL"
+"SYCL0","ARANGE","type=f32,start=0.000000,stop=1048576.000000,step=1.000000","support","1","yes","SYCL"
 "SYCL0","TIMESTEP_EMBEDDING","type=f32,ne_a=[2,1,1,1],dim=320,max_period=10000","support","1","yes","SYCL"
 "SYCL0","LEAKY_RELU","type=f32,ne_a=[10,5,4,3],negative_slope=0.100000","support","1","yes","SYCL"
 "SYCL0","CUMSUM","type=f32,ne=[10,5,4,3]","support","0","no","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[127,5,4,3]","support","0","no","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[128,5,4,3]","support","0","no","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[128,128,4,4]","support","0","no","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[255,5,4,3]","support","0","no","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[256,5,4,3]","support","0","no","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[511,5,4,3]","support","0","no","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[512,5,4,3]","support","0","no","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[1023,5,4,3]","support","0","no","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[1024,5,4,3]","support","0","no","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[2047,5,4,3]","support","0","no","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[2048,5,4,3]","support","0","no","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[242004,1,1,1]","support","0","no","SYCL"
+"SYCL0","CUMSUM","type=f32,ne=[375960,1,1,1]","support","0","no","SYCL"
 "SYCL0","XIELU","type=f32,ne=[10,5,4,3]","support","0","no","SYCL"
 "SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","0","no","SYCL"
 "SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","0","no","SYCL"
@@ -9490,6 +9923,7 @@
 "SYCL0","FILL","type=f32,ne=[10,10,4,3],c=0.000000","support","0","no","SYCL"
 "SYCL0","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","0","no","SYCL"
 "SYCL0","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","0","no","SYCL"
+"SYCL0","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","0","no","SYCL"
 "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","0","no","SYCL"
 "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","0","no","SYCL"
 "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","0","no","SYCL"
@@ -9497,6 +9931,8 @@
 "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","0","no","SYCL"
 "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","0","no","SYCL"
 "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[100,100,4,4],ne_rhs=[41,100,4,4]","support","0","no","SYCL"
+"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[31,128,4,4]","support","0","no","SYCL"
+"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[300,64,4,4]","support","0","no","SYCL"
 "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0","support","1","yes","SYCL"
 "SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0","support","1","yes","SYCL"
 "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1","support","0","no","SYCL"
diff --git a/ggml/src/ggml-sycl/add-id.cpp b/ggml/src/ggml-sycl/add-id.cpp
new file mode 100644
index 0000000000..00c073cf93
--- /dev/null
+++ b/ggml/src/ggml-sycl/add-id.cpp
@@ -0,0 +1,77 @@
+#include <sycl/sycl.hpp>
+#include "common.hpp"
+#include "add-id.hpp"
+
+static void add_id_kernel(
+    const float* src0,
+    const float* src1,
+    const int32_t* src2,
+    float* dst,
+    int64_t ne0,
+    int64_t ne1,
+    size_t nb01,
+    size_t nb02,
+    size_t nb11,
+    size_t nb21,
+    sycl::nd_item<3> item_ct1) {
+  const int64_t i1 = item_ct1.get_group(2);
+  const int64_t i2 = item_ct1.get_group(1);
+
+  const int i11 =
+      *(const int32_t*)((const char*)src2 + i1 * sizeof(int32_t) + i2 * nb21);
+
+  const size_t nb1 = ne0 * sizeof(float);
+  const size_t nb2 = ne1 * nb1;
+
+  float* dst_row = (float*)((char*)dst + i1 * nb1 + i2 * nb2);
+  const float* src0_row =
+      (const float*)((const char*)src0 + i1 * nb01 + i2 * nb02);
+  const float* src1_row = (const float*)((const char*)src1 + i11 * nb11);
+
+  for (int64_t i0 = item_ct1.get_local_id(2); i0 < ne0;
+       i0 += item_ct1.get_local_range(2)) {
+    dst_row[i0] = src0_row[i0] + src1_row[i0];
+  }
+}
+
+void ggml_sycl_add_id(ggml_backend_sycl_context& ctx, ggml_tensor* dst) {
+  const ggml_tensor* src0 = dst->src[0];
+  const ggml_tensor* src1 = dst->src[1];
+  const ggml_tensor* src2 = dst->src[2];
+
+  GGML_TENSOR_TERNARY_OP_LOCALS
+
+  GGML_ASSERT(dst->type == GGML_TYPE_F32);
+  GGML_ASSERT(src0->type == GGML_TYPE_F32);
+  GGML_ASSERT(src1->type == GGML_TYPE_F32);
+  GGML_ASSERT(src2->type == GGML_TYPE_I32);
+
+  GGML_ASSERT(nb00 == sizeof(float));
+  GGML_ASSERT(nb10 == sizeof(float));
+  GGML_ASSERT(nb20 == sizeof(int32_t));
+
+  const float* src0_d = (const float*)src0->data;
+  const float* src1_d = (const float*)src1->data;
+  const int32_t* src2_d = (const int32_t*)src2->data;
+  float* dst_d = (float*)dst->data;
+
+  int threads = std::min((int)ne00, 768);  // cols
+  ctx.stream()->parallel_for(
+      sycl::nd_range<3>(
+          sycl::range<3>(1, ne02, ne01) * sycl::range<3>(1, 1, threads),
+          sycl::range<3>(1, 1, threads)),
+      [=](sycl::nd_item<3> item_ct1) {
+        add_id_kernel(
+            src0_d,
+            src1_d,
+            src2_d,
+            dst_d,
+            ne0,
+            ne1,
+            nb01,
+            nb02,
+            nb11,
+            nb21,
+            item_ct1);
+      });
+}
diff --git a/ggml/src/ggml-sycl/add-id.hpp b/ggml/src/ggml-sycl/add-id.hpp
new file mode 100644
index 0000000000..e1b09ee8c7
--- /dev/null
+++ b/ggml/src/ggml-sycl/add-id.hpp
@@ -0,0 +1,8 @@
+#ifndef GGML_SYCL_ADD_ID_HPP
+#define GGML_SYCL_ADD_ID_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_add_id(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
+#endif // GGML_SYCL_ADD_ID_HPP
diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp
index 637630c1d2..519638fd41 100644
--- a/ggml/src/ggml-sycl/common.hpp
+++ b/ggml/src/ggml-sycl/common.hpp
@@ -642,5 +642,22 @@ static __dpct_inline__ sycl::uint2 fast_div_modulo(uint32_t n, const sycl::uint3
     return sycl::uint2(div_val, mod_val);
 }
 
+static __dpct_inline__ int ggml_sycl_dp4a(const int a, const int b, int c) {
+    return dpct::dp4a(a, b, c);
+}
+
+static __dpct_inline__ float ggml_sycl_e8m0_to_fp32(uint8_t x) {
+    uint32_t bits;
+    if (x == 0) {
+        bits = 0x00400000;
+    } else {
+        bits = (uint32_t) x << 23;
+    }
+
+    float result;
+    memcpy(&result, &bits, sizeof(float));
+    return result;
+}
+
 
 #endif // GGML_SYCL_COMMON_HPP
diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp
index 7c6ea8a57a..8bdae36458 100644
--- a/ggml/src/ggml-sycl/convert.cpp
+++ b/ggml/src/ggml-sycl/convert.cpp
@@ -472,6 +472,16 @@ static void dequantize_row_iq4_nl_sycl(const void *vx, dst_t *y, const int64_t k
       }
 }
 
+template <typename dst_t>
+static void dequantize_row_mxfp4_sycl(const void * vx, dst_t * y, const int64_t k, dpct::queue_ptr stream) {
+    const int nb = (k + QK_K - 1) / QK_K;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)),
+        [=](sycl::nd_item<3> item_ct1) {
+            dequantize_block_mxfp4(vx, y, item_ct1);
+        });
+}
+
 template <typename src_t, typename dst_t>
 static void convert_unary_nc(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01,
                           const int64_t ne02, const int64_t s01, const int64_t s02, const int64_t s03,
@@ -518,6 +528,7 @@ static void convert_unary_sycl(const void * vx, dst_t * y, const int64_t k, dpct
     convert_unary_nc_sycl<src_t>(vx, y, k, 1, 1, 1, k, k, k, queue);
 }
 
+
 to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) {
     switch (type) {
         case GGML_TYPE_Q4_0:
@@ -571,6 +582,8 @@ to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) {
             return dequantize_row_iq4_xs_sycl;
         case GGML_TYPE_IQ4_NL:
             return dequantize_row_iq4_nl_sycl;
+        case GGML_TYPE_MXFP4:
+            return dequantize_row_mxfp4_sycl;
         case GGML_TYPE_F32:
             return convert_unary_sycl<float>;
 #ifdef GGML_SYCL_HAS_BF16
@@ -636,6 +649,8 @@ to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst) {
             return dequantize_row_iq4_xs_sycl;
         case GGML_TYPE_IQ4_NL:
             return dequantize_row_iq4_nl_sycl;
+        case GGML_TYPE_MXFP4:
+            return dequantize_row_mxfp4_sycl;
         case GGML_TYPE_F16:
             return convert_unary_sycl<sycl::half>;
 #ifdef GGML_SYCL_HAS_BF16
diff --git a/ggml/src/ggml-sycl/dequantize.hpp b/ggml/src/ggml-sycl/dequantize.hpp
index 540539bb22..da2a605daa 100644
--- a/ggml/src/ggml-sycl/dequantize.hpp
+++ b/ggml/src/ggml-sycl/dequantize.hpp
@@ -819,5 +819,23 @@ dequantize_block_iq4_xs(const void *__restrict__ vx, dst_t *__restrict__ yy,
     }
 }
 
+template<typename dst_t>
+static void dequantize_block_mxfp4(const void * __restrict__ vx, dst_t * __restrict__ yy,
+                                   const sycl::nd_item<3> &item_ct1) {
+    // auto                item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
+    const int64_t       i        = item_ct1.get_group(2);
+    const block_mxfp4 * x = (const block_mxfp4 *) vx + i*(QK_K/QK_MXFP4);
+
+    const int64_t    tid = item_ct1.get_local_id(2);
+    const int64_t il = tid/8; // 0...3
+    const int64_t ib = tid%8; // 0...7
+    dst_t * y = yy + i*QK_K + 32*ib + 4*il;
+    const uint8_t  * q4 = x[ib].qs + 4*il;
+    const float d = ggml_sycl_e8m0_to_fp32(x[ib].e);
+    for (int j = 0; j < 4; ++j) {
+        y[j+ 0] = d * kvalues_mxfp4[q4[j] & 0xf]*0.5f;
+        y[j+16] = d * kvalues_mxfp4[q4[j] >>  4]*0.5f;
+    }
+}
 
 #endif // GGML_SYCL_DEQUANTIZE_HPP
diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp
index f93cfa701f..30ec1e8daf 100644
--- a/ggml/src/ggml-sycl/dpct/helper.hpp
+++ b/ggml/src/ggml-sycl/dpct/helper.hpp
@@ -1860,10 +1860,31 @@ namespace dpct
                                            : id);
     }
 
+    template <typename T1, typename T2>
+    using dot_product_acc_t = std::conditional_t<
+        std::is_unsigned_v<T1> && std::is_unsigned_v<T2>,
+        uint32_t,
+        int32_t>;
+
+    template <typename T>
+    sycl::vec<T, 4> extract_and_sign_or_zero_extend4(T val) {
+      return sycl::vec<T, 1>(val)
+          .template as<sycl::vec<
+              std::conditional_t<std::is_signed_v<T>, int8_t, uint8_t>,
+              4>>()
+          .template convert<T>();
+    }
+
     template <typename T1, typename T2, typename T3>
-    inline auto dp4a(T1 a, T2 b, T3 c)
-    {
-        return syclcompat::dp4a(a, b, c);
+    inline auto dp4a(T1 a, T2 b, T3 c) {
+      dot_product_acc_t<T1, T2> res = c;
+      auto va = extract_and_sign_or_zero_extend4(a);
+      auto vb = extract_and_sign_or_zero_extend4(b);
+      res += va[0] * vb[0];
+      res += va[1] * vb[1];
+      res += va[2] * vb[2];
+      res += va[3] * vb[3];
+      return res;
     }
 
     struct sub_sat
@@ -2972,6 +2993,38 @@ namespace dpct
     atomic_fetch_add<T1, addressSpace>(addr, operand, memoryOrder);
     }
 
+    inline unsigned int byte_level_permute(
+        unsigned int a, unsigned int b, unsigned int s) {
+      unsigned int ret;
+      ret = ((((std::uint64_t)b << 32 | a) >> (s & 0x7) * 8) & 0xff) |
+            (((((std::uint64_t)b << 32 | a) >> ((s >> 4) & 0x7) * 8) & 0xff)
+             << 8) |
+            (((((std::uint64_t)b << 32 | a) >> ((s >> 8) & 0x7) * 8) & 0xff)
+             << 16) |
+            (((((std::uint64_t)b << 32 | a) >> ((s >> 12) & 0x7) * 8) & 0xff)
+             << 24);
+      return ret;
+    }
+
+    inline uint32_t byte_level_permute_custom(
+        uint32_t low32, uint32_t high32, uint32_t sel, int mode = 0) {
+      constexpr uint16_t lookup[6][4] = {
+          {0x3210, 0x4321, 0x5432, 0x6543},  // Forward 4-byte extract
+          {0x5670, 0x6701, 0x7012, 0x0123},  // Backward 4-byte extract
+          {0x0000, 0x1111, 0x2222, 0x3333},  // Replicate 8-bit values
+          {0x3210, 0x3211, 0x3222, 0x3333},  // Edge clamp left
+          {0x0000, 0x1110, 0x2210, 0x3210},  // Edge clamp right
+          {0x1010, 0x3232, 0x1010, 0x3232}   // Replicate 16-bit values
+      };
+
+      if (mode >= 1 && mode <= 6) {
+        return byte_level_permute(low32, high32, lookup[mode - 1][sel & 0x3]);
+      } else if (!mode) {
+        return byte_level_permute(low32, high32, sel);
+      }
+      return 0;
+    }
+
 } // COPY from DPCT head files
 
 #endif // GGML_SYCL_DPCT_HELPER_HPP
diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp
index 7d54ce600e..8d83b2446b 100644
--- a/ggml/src/ggml-sycl/element_wise.cpp
+++ b/ggml/src/ggml-sycl/element_wise.cpp
@@ -911,6 +911,98 @@ static inline void ggml_sycl_op_swiglu(ggml_backend_sycl_context & ctx, ggml_ten
         });
 }
 
+__dpct_inline__ float ggml_sycl_op_swiglu_oai_single(float x, float g, float alpha = 1.702f, float limit = 7.0f) {
+    x = sycl::fmin(x, limit);
+    g = sycl::fmax(sycl::fmin(g, limit), -limit);
+
+    float out_glu = x / (1.0f + sycl::native::exp(-x * alpha));
+    out_glu = out_glu * (1.0f + g);
+    return out_glu;
+}
+
+
+template <typename T>
+static void swiglu_oai_kernel(const T * x, const T * g, T * dst, const int64_t k,
+                              const int64_t n, const int64_t o0, const int64_t o1,
+                              float alpha, float limit, sycl::nd_item<3> item_ct1) {
+    const int64_t i = int64_t(item_ct1.get_local_range(2)) * item_ct1.get_group(2) + item_ct1.get_local_id(2);
+
+    if (i >= k) {
+        return;
+    }
+
+    const int64_t j0 = (i / n) * o0 + (i % n);
+    const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n);
+
+    float xi = x[j0];
+    float gi = g[j1];
+
+    dst[i] = ggml_sycl_op_swiglu_oai_single(xi, gi, alpha, limit);
+}
+
+template <typename T>
+static void swiglu_oai_sycl(const T *       x,
+                            const T *       g,
+                            T *             dst,
+                            const int64_t   k,
+                            const int64_t   n,
+                            const int64_t   o0,
+                            const int64_t   o1,
+                            const float     alpha,
+                            const float     limit,
+                            dpct::queue_ptr stream) {
+    const int64_t num_blocks = (k + SYCL_GLU_BLOCK_SIZE - 1) / SYCL_GLU_BLOCK_SIZE;
+    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_GLU_BLOCK_SIZE),
+                                           sycl::range<3>(1, 1, SYCL_GLU_BLOCK_SIZE)),
+                         [=](sycl::nd_item<3> item_ct1) {
+                             swiglu_oai_kernel(x, g, dst, k, n, o0, o1, alpha, limit, item_ct1);
+                         });
+}
+
+void ggml_sycl_op_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+    void * src0_d = src0->data;
+    void * src1_d = src1 ? src1->data : src0->data;
+    const int64_t src0_o = src0->nb[1];
+    const int64_t src1_o = src1 ? src1->nb[1] : src0->nb[1];
+    void * dst_d = dst->data;
+    const int64_t nc = src1 ? src0->ne[0] : src0->ne[0] / 2;
+    dpct::queue_ptr     stream = ctx.stream();
+
+    GGML_ASSERT(ggml_is_contiguous_1(src0));
+    GGML_ASSERT(src0->nb[0] == ggml_element_size(src0));
+    GGML_ASSERT(ggml_is_contiguous(dst));
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(src0->type == dst->type);
+    GGML_ASSERT(dst->ne[0] == nc);
+    GGML_ASSERT(ggml_nrows(dst) == ggml_nrows(src0));
+
+    if (src1) {
+        GGML_ASSERT(ggml_is_contiguous_1(src1));
+        GGML_ASSERT(src1->nb[0] == ggml_element_size(src1));
+        GGML_ASSERT(src1->ne[0] == nc);
+        GGML_ASSERT(src0->type == src1->type);
+    }
+
+    //const int32_t swapped = ((const int32_t *) dst->op_params)[1];
+    const int32_t swapped = ggml_get_op_params_i32(dst, 1);
+    const float alpha = ggml_get_op_params_f32(dst, 2);
+    const float limit = ggml_get_op_params_f32(dst, 3);
+
+    float * src0_p = (float *) src0_d;
+    float * src1_p = (float *) src1_d;
+
+    if (!src1) {
+        src0_p += swapped ? nc : 0;
+        src1_p += swapped ? 0 : nc;
+    }
+
+    swiglu_oai_sycl(src0_p, src1_p, (float *)dst_d, ggml_nelements(dst), nc, src0_o / sizeof(float), src1_o / sizeof(float), alpha, limit, stream);
+}
+
 static inline void ggml_sycl_op_geglu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst,
         [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) {
@@ -1070,6 +1162,11 @@ void ggml_sycl_swiglu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     ggml_sycl_op_swiglu(ctx, dst);
 }
 
+void ggml_sycl_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
+    ggml_sycl_op_swiglu_oai(ctx, dst);
+}
+
 void ggml_sycl_geglu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1);
     ggml_sycl_op_geglu_erf(ctx, dst);
diff --git a/ggml/src/ggml-sycl/element_wise.hpp b/ggml/src/ggml-sycl/element_wise.hpp
index fcf93295cb..0913a2e529 100644
--- a/ggml/src/ggml-sycl/element_wise.hpp
+++ b/ggml/src/ggml-sycl/element_wise.hpp
@@ -5,6 +5,8 @@
 #include "ggml.h"
 #include <limits> // For std::numeric_limits
 
+#define SYCL_GLU_BLOCK_SIZE 256
+
 template <typename T>
 T neg_infinity() {
     return -std::numeric_limits<T>::infinity();
@@ -41,6 +43,8 @@ void ggml_sycl_silu(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
 void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
+void ggml_sycl_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
 void ggml_sycl_gelu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
 
 void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp
index 7449a91609..e996d98be8 100644
--- a/ggml/src/ggml-sycl/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl/ggml-sycl.cpp
@@ -39,6 +39,7 @@
 #include "ggml-impl.h"
 #include "ggml-backend-impl.h"
 
+#include "ggml-sycl/add-id.hpp"
 #include "ggml-sycl/backend.hpp"
 #include "ggml-sycl/common.hpp"
 #include "ggml-sycl/element_wise.hpp"
@@ -3313,6 +3314,7 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
     bool use_mul_mat_q =  ggml_sycl_supports_mmq(src0->type)
         && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
 
+
     // mmvq and mmq need the __dp4a instruction which is available for gen12+
     // Workaround in https://github.com/ggerganov/llama.cpp/commit/95f84d5ce8b449a9b16009434aca800df504a02e
     use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS);
@@ -3320,7 +3322,6 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor
     use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE);
 #endif // SYCL_USE_XMX
 
-
     // mmvq path is faster in the CUDA backend.
     if (!g_ggml_sycl_prioritize_dmmv && (ctx.stream()->get_backend() == sycl::backend::ext_oneapi_cuda
         // Dispatch becomes obscure with the reorder, MMVQ when the reorder optimization
@@ -3711,6 +3712,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
         case GGML_OP_ADD1: // TODO: more efficient implementation
             ggml_sycl_add(ctx, dst);
             break;
+        case GGML_OP_ADD_ID:
+            ggml_sycl_add_id(ctx, dst);
+            break;
         case GGML_OP_SUB:
             ggml_sycl_sub(ctx, dst);
             break;
@@ -3803,6 +3807,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg
                 case GGML_GLU_OP_SWIGLU:
                     ggml_sycl_swiglu(ctx, dst);
                     break;
+                case GGML_GLU_OP_SWIGLU_OAI:
+                    ggml_sycl_swiglu_oai(ctx, dst);
+                    break;
                 case GGML_GLU_OP_GEGLU_ERF:
                     ggml_sycl_geglu_erf(ctx, dst);
                     break;
@@ -4397,6 +4404,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
                 case GGML_GLU_OP_REGLU:
                 case GGML_GLU_OP_GEGLU:
                 case GGML_GLU_OP_SWIGLU:
+                case GGML_GLU_OP_SWIGLU_OAI:
                 case GGML_GLU_OP_GEGLU_ERF:
                 case GGML_GLU_OP_GEGLU_QUICK:
                     return ggml_is_contiguous_1(op->src[0]);
@@ -4424,15 +4432,18 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
                     }
                 }
                 ggml_type src0_type = op->src[0]->type;
-                if (src0_type == GGML_TYPE_BF16 || src0_type == GGML_TYPE_MXFP4) {
-                    // TODO: support MXFP4
+                if (src0_type == GGML_TYPE_BF16 ) {
+                    // TODO: support GGML_TYPE_BF16
                     // FIXME: keep a list of supported types to avoid breaking the backend when a new type is added
                     return false;
                 }
+
                 // TODO: The configuration below needs more work to be supported with oneDNN
-                if (ggml_is_permuted(a) && !ggml_is_contiguous(a) && a->ne[2] > 1 && a->ne[3] > 1) {
-                    return false;
+                if (ggml_is_permuted(a) && !ggml_is_contiguous(a) &&
+                    a->ne[2] > 1 && a->ne[3] > 1 && src0_type == GGML_TYPE_F16) {
+                  return false;
                 }
+
                 // TODO: This specific configuration can fail with oneDNN and needs more debugging
                 if (!ggml_is_permuted(a) && ggml_is_permuted(b) && b->ne[2] > 1 && b->ne[3] > 1 &&
                     a->ne[0] > 128 && a->ne[2] == 1 && src0_type == GGML_TYPE_F16) {
@@ -4553,9 +4564,9 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_VIEW:
         case GGML_OP_PERMUTE:
         case GGML_OP_TRANSPOSE:
-            return true;
         case GGML_OP_ADD:
         case GGML_OP_ADD1:
+        case GGML_OP_ADD_ID:
         case GGML_OP_SUB:
         case GGML_OP_COUNT_EQUAL:
         case GGML_OP_MUL:
diff --git a/ggml/src/ggml-sycl/mmvq.cpp b/ggml/src/ggml-sycl/mmvq.cpp
index 5b7f064074..316aa0d0fb 100644
--- a/ggml/src/ggml-sycl/mmvq.cpp
+++ b/ggml/src/ggml-sycl/mmvq.cpp
@@ -595,6 +595,25 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy,
     }
 }
 
+static void mul_mat_vec_mxfp4_q8_1_sycl(const void * vx, const void * vy, float * dst, const int ncols, const int nrows,
+                                        dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK_MXFP4 == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+
+    {
+        stream->submit([&](sycl::handler & cgh) {
+            cgh.parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
+                             [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                                 mul_mat_vec_q<QK_MXFP4, QI_MXFP4, block_mxfp4, VDR_MXFP4_Q8_1_MMVQ, vec_dot_mxfp4_q8_1>(
+                                     vx, vy, dst, ncols, nrows, item_ct1);
+                             });
+        });
+    }
+}
+
+
 static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy,
                                        float *dst, const int ncols,
                                        const int nrows,
@@ -1123,6 +1142,9 @@ void ggml_sycl_op_mul_mat_vec_q(ggml_backend_sycl_context & ctx, const ggml_tens
             case GGML_TYPE_IQ4_XS:
                 mul_mat_vec_iq4_xs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
                 break;
+            case GGML_TYPE_MXFP4:
+                mul_mat_vec_mxfp4_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+                break;
             default:
                 GGML_ABORT("fatal error");
         }
diff --git a/ggml/src/ggml-sycl/pad.cpp b/ggml/src/ggml-sycl/pad.cpp
index 413712c584..f989c5e4b8 100644
--- a/ggml/src/ggml-sycl/pad.cpp
+++ b/ggml/src/ggml-sycl/pad.cpp
@@ -14,10 +14,10 @@
 #include "pad.hpp"
 
 static void pad_f32(const float * src, float * dst,
-                               const int lp0, const int rp0, const int lp1, const int rp1,
-                               const int lp2, const int rp2, const int lp3, const int rp3,
-                               const int ne0, const int ne1, const int ne2, const int ne3) {
-    auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>();
+                    const int lp0, const int rp0, const int lp1, const int rp1,
+                    const int lp2, const int rp2, const int lp3, const int rp3,
+                    const int ne0, const int ne1, const int ne2, const int ne3,
+                    sycl::nd_item<3> item_ct1) {
     int i0 = item_ct1.get_local_id(2) +
              item_ct1.get_group(2) * item_ct1.get_local_range(2);
     int i1 = item_ct1.get_group(1);
@@ -63,7 +63,7 @@ static void pad_f32_sycl(const float *src, float *dst, const int lp0,
                           sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)),
         [=](sycl::nd_item<3> item_ct1) {
             pad_f32(src, dst, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, ne0, ne1,
-                    ne2, ne3);
+                    ne2, ne3, item_ct1);
         });
 }
 
diff --git a/ggml/src/ggml-sycl/ssm_conv.cpp b/ggml/src/ggml-sycl/ssm_conv.cpp
index 0dc0f71c9a..eea9a73d67 100644
--- a/ggml/src/ggml-sycl/ssm_conv.cpp
+++ b/ggml/src/ggml-sycl/ssm_conv.cpp
@@ -88,7 +88,7 @@ void ggml_sycl_ssm_conv(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
     GGML_ASSERT(src0->nb[0] == sizeof(float));
     GGML_ASSERT(src1->nb[0] == sizeof(float));
 
-    GGML_ASSERT(src0->nb[1] == src0->ne[0] * static_cast<int>(sizeof(float)));
+    GGML_ASSERT(src0->nb[1] == src0->ne[0] * sizeof(float));
 
     const int src_stride_inner = ncs;
     const int src_stride_seq   = ncs * d_inner;
diff --git a/ggml/src/ggml-sycl/vecdotq.hpp b/ggml/src/ggml-sycl/vecdotq.hpp
index 4088ddb54f..43482b3672 100644
--- a/ggml/src/ggml-sycl/vecdotq.hpp
+++ b/ggml/src/ggml-sycl/vecdotq.hpp
@@ -20,6 +20,18 @@
 typedef float (*vec_dot_q_sycl_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1,
                                   const int & iqs);
 
+static __dpct_inline__ int get_int_b1(const void * x, const int & i32) {
+    const uint8_t * x8 = (const uint8_t *) x;
+
+    int x32  = x8[4*i32 + 0] <<  0;
+    x32     |= x8[4*i32 + 1] <<  8;
+    x32     |= x8[4*i32 + 2] << 16;
+    x32     |= x8[4*i32 + 3] << 24;
+
+    return x32;
+}
+
+
 static __dpct_inline__ int get_int_from_int8(const int8_t* x8, const int& i32) {
   const uint16_t* x16 =
       (const uint16_t*)(x8 + sizeof(int) * i32); // assume at least 2 byte
@@ -75,6 +87,28 @@ static __dpct_inline__ void get_int_from_table_16(const uint32_t &q4,
     val2 = v1 | (v2 << 16);
 }
 
+static __dpct_inline__ sycl::int2 get_int_from_table_16(
+    const int& q4, const int8_t* table) {
+  const uint32_t* table32 = (const uint32_t*)table;
+  uint32_t tmp[2];
+  const uint32_t low_high_selection_indices =
+      (0x32103210 | ((q4 & 0x88888888) >> 1));
+#pragma unroll
+  for (uint32_t i = 0; i < 2; ++i) {
+    const uint32_t shift = 16 * i;
+
+    const uint32_t low =
+        dpct::byte_level_permute(table32[0], table32[1], q4 >> shift);
+    const uint32_t high =
+        dpct::byte_level_permute(table32[2], table32[3], q4 >> shift);
+    tmp[i] = dpct::byte_level_permute(
+        low, high, low_high_selection_indices >> shift);
+  }
+  return sycl::int2(
+      dpct::byte_level_permute(tmp[0], tmp[1], 0x6420),
+      dpct::byte_level_permute(tmp[0], tmp[1], 0x7531));
+}
+
 #define VDR_Q2_K_Q8_1_MMVQ 1
 
 // contiguous v/x values
@@ -685,6 +719,30 @@ vec_dot_q4_1_q8_1(const void *__restrict__ vbq,
     return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
 }
 
+#define VDR_MXFP4_Q8_1_MMVQ 2
+#define VDR_MXFP4_Q8_1_MMQ  4
+
+static __dpct_inline__ float vec_dot_mxfp4_q8_1(const void * __restrict__ vbq,
+                                                const block_q8_1 * __restrict__ bq8_1,
+                                                const int & iqs) {
+    const block_mxfp4 * bq4 = (const block_mxfp4 *) vbq;
+
+    const int * q8 = (const int *) bq8_1->qs + iqs;
+
+    int sumi = 0;
+#pragma unroll
+    for (int l = 0; l < VDR_MXFP4_Q8_1_MMVQ; ++l) {
+        const int aux_q4 = get_int_b1(bq4->qs, iqs + l);
+        const sycl::int2 v      = get_int_from_table_16(aux_q4, kvalues_mxfp4);
+        sumi = ggml_sycl_dp4a(v.x(), q8[l + 0], sumi);
+        sumi = ggml_sycl_dp4a(v.y(), q8[l + 4], sumi);
+    }
+
+    const float d = ggml_sycl_e8m0_to_fp32(bq4->e) * 0.5f * (bq8_1->ds)[0];
+    return d * sumi;
+}
+
+
 static __dpct_inline__ float
 vec_dot_q5_0_q8_1(const void *__restrict__ vbq,
                   const block_q8_1 *__restrict__ bq8_1, const int &iqs) {

From b1f3a6e5db7b782ef077bd0e8253ce03283b1f37 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Mon, 15 Dec 2025 09:24:59 +0100
Subject: [PATCH 106/135] llama: automatically set parameters not set by the
 user in such a way that maximizes GPU utilization (#16653)

* llama: automatically fit args to free memory

llama-fit-params tool

* fix CI

* hints for bug reports, ensure no reallocation

* fix segfault with Vulkan

* add llama-fit-params to CI

* fix CI

* fix CI

* fix CI

* minor adjustments

* fix assignment of 1 dense layer

* fix logger not being reset on model load failure

* remove --n-gpu-layer hint on model load failure

* fix llama-fit-params verbosity

* fix edge case

* fix typo [no ci]
---
 .github/ISSUE_TEMPLATE/011-bug-results.yml |   9 +-
 ci/run.sh                                  |   6 +
 common/arg.cpp                             |  33 +-
 common/common.cpp                          |  21 +-
 common/common.h                            |  14 +-
 ggml/include/ggml-alloc.h                  |   9 +
 ggml/include/ggml-backend.h                |   1 +
 ggml/include/ggml.h                        |   3 +-
 ggml/src/ggml-alloc.c                      |  68 ++-
 ggml/src/ggml-backend.cpp                  |  23 +-
 ggml/src/ggml.c                            |   5 +
 include/llama.h                            |  19 +-
 src/llama-context.cpp                      |  69 ++-
 src/llama-context.h                        |  10 +-
 src/llama-hparams.h                        |   1 +
 src/llama-impl.cpp                         |   4 +
 src/llama-kv-cache.cpp                     |  23 +-
 src/llama-model-loader.cpp                 |   2 +
 src/llama-model-loader.h                   |   2 +
 src/llama-model.cpp                        |  38 +-
 src/llama-quant.cpp                        |   2 +-
 src/llama.cpp                              | 650 ++++++++++++++++++++-
 tools/CMakeLists.txt                       |   1 +
 tools/fit-params/CMakeLists.txt            |   8 +
 tools/fit-params/README.md                 |  55 ++
 tools/fit-params/fit-params.cpp            |  62 ++
 26 files changed, 1075 insertions(+), 63 deletions(-)
 create mode 100644 tools/fit-params/CMakeLists.txt
 create mode 100644 tools/fit-params/README.md
 create mode 100644 tools/fit-params/fit-params.cpp

diff --git a/.github/ISSUE_TEMPLATE/011-bug-results.yml b/.github/ISSUE_TEMPLATE/011-bug-results.yml
index c42a14ff83..b815e70a8d 100644
--- a/.github/ISSUE_TEMPLATE/011-bug-results.yml
+++ b/.github/ISSUE_TEMPLATE/011-bug-results.yml
@@ -11,7 +11,7 @@ body:
         (i.e. the generated text) are incorrect or llama.cpp crashes during model evaluation.
         If you encountered the issue while using an external UI (e.g. ollama),
         please reproduce your issue using one of the examples/binaries in this repository.
-        The `llama-cli` binary can be used for simple and reproducible model inference.
+        The `llama-completion` binary can be used for simple and reproducible model inference.
   - type: textarea
     id: version
     attributes:
@@ -74,9 +74,12 @@ body:
         Please give us a summary of the problem and tell us how to reproduce it.
         If you can narrow down the bug to specific hardware, compile flags, or command line arguments,
         that information would be very much appreciated by us.
+
+        If possible, please try to reproduce the issue using `llama-completion` with `-fit off`.
+        If you can only reproduce the issue with `-fit on`, please provide logs both with and without `--verbose`.
       placeholder: >
-        e.g. when I run llama-cli with -ngl 99 I get garbled outputs.
-        When I use -ngl 0 it works correctly.
+        e.g. when I run llama-completion with `-fa on` I get garbled outputs for very long prompts.
+        With short prompts or `-fa off` it works correctly.
         Here are the exact commands that I used: ...
     validations:
       required: true
diff --git a/ci/run.sh b/ci/run.sh
index 0676504b3e..0a4a0e41eb 100755
--- a/ci/run.sh
+++ b/ci/run.sh
@@ -398,6 +398,8 @@ function gg_run_qwen3_0_6b {
     ./bin/llama-quantize ${model_bf16} ${model_q5_k} q5_k $(nproc)
     ./bin/llama-quantize ${model_bf16} ${model_q6_k} q6_k $(nproc)
 
+    (time ./bin/llama-fit-params --model ${model_f16} 2>&1 | tee -a $OUT/${ci}-fp-f16.log)
+
     (time ./bin/llama-completion -no-cnv --model ${model_f16}  -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
     (time ./bin/llama-completion -no-cnv --model ${model_bf16} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-bf16.log
     (time ./bin/llama-completion -no-cnv --model ${model_q8_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
@@ -523,6 +525,8 @@ function gg_run_embd_bge_small {
 
     ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
 
+    (time ./bin/llama-fit-params --model ${model_f16} 2>&1 | tee -a $OUT/${ci}-fp-f16.log)
+
     (time ./bin/llama-embedding --model ${model_f16}  -p "I believe the meaning of life is" -ngl 99 -c 0 --no-op-offload) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
     (time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" -ngl 99 -c 0 --no-op-offload) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
 
@@ -563,6 +567,8 @@ function gg_run_rerank_tiny {
 
     model_f16="${path_models}/ggml-model-f16.gguf"
 
+    (time ./bin/llama-fit-params --model ${model_f16} 2>&1 | tee -a $OUT/${ci}-fp-f16.log)
+
     # for this model, the SEP token is "</s>"
     (time ./bin/llama-embedding --model ${model_f16} -p "what is panda?\thi\nwhat is panda?\tit's a bear\nwhat is panda?\tThe giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --no-op-offload --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log
 
diff --git a/common/arg.cpp b/common/arg.cpp
index aaa7b92a2e..acf4c8f8a8 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -20,6 +20,7 @@
 #include <nlohmann/json.hpp>
 
 #include <algorithm>
+#include <cinttypes>
 #include <climits>
 #include <cstdarg>
 #include <fstream>
@@ -529,7 +530,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context
         params.kv_overrides.back().key[0] = 0;
     }
 
-    if (!params.tensor_buft_overrides.empty()) {
+    // pad tensor_buft_overrides for llama_params_fit:
+    const size_t ntbo = llama_max_tensor_buft_overrides();
+    while (params.tensor_buft_overrides.size() < ntbo) {
         params.tensor_buft_overrides.push_back({nullptr, nullptr});
     }
 
@@ -2153,6 +2156,34 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
         }
     ).set_env("LLAMA_ARG_MAIN_GPU"));
+    add_opt(common_arg(
+        { "-fit", "--fit" }, "[on|off]",
+        string_format("whether to adjust unset arguments to fit in device memory ('on' or 'off', default: '%s')", params.fit_params ? "on" : "off"),
+        [](common_params & params, const std::string & value) {
+            if (is_truthy(value)) {
+                params.fit_params = true;
+            } else if (is_falsey(value)) {
+                params.fit_params = false;
+            } else {
+                throw std::runtime_error(
+                    string_format("error: unkown value for --fit: '%s'\n", value.c_str()));
+            }
+        }
+    ).set_env("LLAMA_ARG_FIT"));
+    add_opt(common_arg(
+        { "-fitt", "--fit-target" }, "MiB",
+        string_format("target margin per device for --fit option, default: %zu", params.fit_params_target/(1024*1024)),
+        [](common_params & params, int value) {
+            params.fit_params_target = value * size_t(1024*1024);
+        }
+    ).set_env("LLAMA_ARG_FIT_TARGET"));
+    add_opt(common_arg(
+        { "-fitc", "--fit-ctx" }, "N",
+        string_format("minimum ctx size that can be set by --fit option, default: %" PRIu32, params.fit_params_min_ctx),
+        [](common_params & params, int value) {
+            params.fit_params_min_ctx = value;
+        }
+    ).set_env("LLAMA_ARG_FIT_CTX"));
     add_opt(common_arg(
         {"--check-tensors"},
         string_format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"),
diff --git a/common/common.cpp b/common/common.cpp
index b76dfa10ea..5a8cf52485 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -1088,7 +1088,15 @@ struct common_init_result::impl {
 
 common_init_result::common_init_result(common_params & params) :
     pimpl(new impl{}) {
-    const auto mparams = common_model_params_to_llama(params);
+    auto mparams = common_model_params_to_llama(params);
+    auto cparams = common_context_params_to_llama(params);
+
+    if (params.fit_params) {
+        LOG_INF("%s: fitting params to device memory, to report bugs during this step use -fit off (or --verbose if you can't)\n", __func__);
+        llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
+            params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target, params.fit_params_min_ctx,
+            params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);
+    }
 
     llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
     if (model == NULL) {
@@ -1103,8 +1111,6 @@ common_init_result::common_init_result(common_params & params) :
     // TODO: fix naming
     common_init_sampler_from_model(model, params.sampling);
 
-    auto cparams = common_context_params_to_llama(params);
-
     if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) {
         LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__);
         params.sampling.ignore_eos = false;
@@ -1143,8 +1149,7 @@ common_init_result::common_init_result(common_params & params) :
 
     llama_context * lctx = llama_init_from_model(model, cparams);
     if (lctx == NULL) {
-        LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
-                __func__, params.model.path.c_str());
+        LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
         return;
     }
 
@@ -1176,15 +1181,13 @@ common_init_result_ptr common_init_from_params(common_params & params) {
 
     llama_model * model = res->model();
     if (model == NULL) {
-        LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
-            __func__, params.model.path.c_str());
+        LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str());
         return res;
     }
 
     llama_context * lctx = res->context();
     if (lctx == NULL) {
-        LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n",
-            __func__, params.model.path.c_str());
+        LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str());
         return res;
     }
 
diff --git a/common/common.h b/common/common.h
index 4edb74b706..d70744840f 100644
--- a/common/common.h
+++ b/common/common.h
@@ -99,6 +99,7 @@ enum llama_example {
     LLAMA_EXAMPLE_TTS,
     LLAMA_EXAMPLE_DIFFUSION,
     LLAMA_EXAMPLE_FINETUNE,
+    LLAMA_EXAMPLE_FIT_PARAMS,
 
     LLAMA_EXAMPLE_COUNT,
 };
@@ -306,8 +307,8 @@ struct lr_opt {
 struct ggml_opt_optimizer_params common_opt_lr_pars(void * userdata);
 
 struct common_params {
-    int32_t n_predict             =    -1; // new tokens to predict
-    int32_t n_ctx                 =  4096; // context size
+    int32_t n_predict             =    -1; // max. number of new tokens to predict, -1 == no limit
+    int32_t n_ctx                 =     0; // context size, 0 == context the model was trained with
     int32_t n_batch               =  2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
     int32_t n_ubatch              =   512; // physical batch size for prompt processing (must be >=32 to use BLAS)
     int32_t n_keep                =     0; // number of tokens to keep from initial prompt
@@ -328,9 +329,12 @@ struct common_params {
     // offload params
     std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
 
-    int32_t n_gpu_layers      = -1;  // number of layers to store in VRAM (-1 - use default)
-    int32_t main_gpu          = 0;   // the GPU that is used for scratch and small tensors
-    float   tensor_split[128] = {0}; // how split tensors should be distributed across GPUs
+    int32_t n_gpu_layers       = -1;               // number of layers to store in VRAM (-1 - use default)
+    int32_t main_gpu           = 0;                // the GPU that is used for scratch and small tensors
+    float   tensor_split[128]  = {0};              // how split tensors should be distributed across GPUs
+    bool    fit_params         = true;             // whether to fit unset model/context parameters to free device memory
+    size_t  fit_params_target  = 1024 * 1024*1024; // margin per device in bytes for fitting parameters to free memory
+    int32_t fit_params_min_ctx = 4096;             // minimum context size to set when trying to reduce memory use
 
     enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
 
diff --git a/ggml/include/ggml-alloc.h b/ggml/include/ggml-alloc.h
index 2cb150fd2a..78aa059dde 100644
--- a/ggml/include/ggml-alloc.h
+++ b/ggml/include/ggml-alloc.h
@@ -53,7 +53,14 @@ GGML_API void           ggml_gallocr_free(ggml_gallocr_t galloc);
 // call with a worst-case graph to avoid buffer reallocations
 // not strictly required for single buffer usage: ggml_gallocr_alloc_graph will reallocate the buffers automatically if needed
 // returns false if the buffer allocation failed
+// ggml_gallocr_resrve_n_size writes the buffer sizes per galloc buffer that would be allocated by ggml_gallocr_reserve_n to sizes
 GGML_API bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph * graph);
+GGML_API void ggml_gallocr_reserve_n_size(
+    ggml_gallocr_t galloc,
+    struct ggml_cgraph * graph,
+    const int * node_buffer_ids,
+    const int * leaf_buffer_ids,
+    size_t * sizes);
 GGML_API bool ggml_gallocr_reserve_n(
     ggml_gallocr_t galloc,
     struct ggml_cgraph * graph,
@@ -68,6 +75,8 @@ GGML_API size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_i
 
 // Utils
 // Create a buffer and allocate all the tensors in a ggml_context
+// ggml_backend_alloc_ctx_tensors_from_buft_size returns the size of the buffer that would be allocated by ggml_backend_alloc_ctx_tensors_from_buft
+GGML_API size_t                       ggml_backend_alloc_ctx_tensors_from_buft_size(struct ggml_context * ctx, ggml_backend_buffer_type_t buft);
 GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft);
 GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend);
 
diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h
index f1b7407859..4ed5f35774 100644
--- a/ggml/include/ggml-backend.h
+++ b/ggml/include/ggml-backend.h
@@ -307,6 +307,7 @@ extern "C" {
     GGML_API void                 ggml_backend_sched_free(ggml_backend_sched_t sched);
 
     // Initialize backend buffers from a measure graph
+    GGML_API void                 ggml_backend_sched_reserve_size(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph, size_t * sizes);
     GGML_API bool                 ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); // returns success
 
     GGML_API int                  ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched);
diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h
index 686da3dbd1..20c912d0e9 100644
--- a/ggml/include/ggml.h
+++ b/ggml/include/ggml.h
@@ -2615,7 +2615,8 @@ extern "C" {
 
     // Set callback for all future logging events.
     // If this is not called, or NULL is supplied, everything is output on stderr.
-    GGML_API void ggml_log_set(ggml_log_callback log_callback, void * user_data);
+    GGML_API void ggml_log_get(ggml_log_callback * log_callback, void ** user_data);
+    GGML_API void ggml_log_set(ggml_log_callback   log_callback, void *  user_data);
 
     GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);
 
diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c
index ec16cbda9f..41419b617b 100644
--- a/ggml/src/ggml-alloc.c
+++ b/ggml/src/ggml-alloc.c
@@ -594,7 +594,9 @@ static bool ggml_gallocr_is_own(ggml_gallocr_t galloc, struct ggml_tensor * t) {
 }
 
 static bool ggml_gallocr_is_allocated(ggml_gallocr_t galloc, struct ggml_tensor * t) {
-    return t->data != NULL || ggml_gallocr_hash_get(galloc, t)->allocated;
+    return t->data != NULL // tensor data already set externally
+        || t->buffer // tensor on external buffer (but not yet allocated)
+        || ggml_gallocr_is_own(galloc, t); // tensor will be allocated by galloc
 }
 
 // free the extra space at the end if the new tensor is smaller
@@ -823,7 +825,8 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr
     }
 }
 
-bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
+static bool ggml_gallocr_reserve_n_impl(
+        ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids, bool no_alloc) {
     size_t min_hash_size = graph->n_nodes + graph->n_leafs;
     // add 25% margin to avoid hash collisions
     min_hash_size += min_hash_size / 4;
@@ -928,16 +931,19 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
                 size_t cur_size = galloc->buffers[i] ? ggml_vbuffer_size(galloc->buffers[i]) : 0;
                 if (cur_size > 0) {
                     GGML_LOG_DEBUG("%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n",
-                        __func__, ggml_backend_buft_name(galloc->bufts[i]),
-                        cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
+                        __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
                 }
             }
 #endif
             ggml_vbuffer_free(galloc->buffers[i]);
-            galloc->buffers[i] = ggml_vbuffer_alloc(galloc->bufts[i], galloc->buf_tallocs[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE);
-            if (galloc->buffers[i] == NULL) {
-                GGML_LOG_ERROR("%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size);
-                return false;
+            if (no_alloc) {
+                galloc->buffers[i] = NULL;
+            } else {
+                galloc->buffers[i] = ggml_vbuffer_alloc(galloc->bufts[i], galloc->buf_tallocs[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE);
+                if (galloc->buffers[i] == NULL) {
+                    GGML_LOG_ERROR("%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size);
+                    return false;
+                }
             }
         }
     }
@@ -945,6 +951,21 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
     return true;
 }
 
+void ggml_gallocr_reserve_n_size(
+        ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids, size_t * sizes) {
+    GGML_ASSERT(ggml_gallocr_reserve_n_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids, /*no_alloc =*/ true));
+    for (int i = 0; i < galloc->n_buffers; i++) {
+        sizes[i] = 0;
+        for (int c = 0; c < galloc->buf_tallocs[i]->n_chunks; c++) {
+            sizes[i] += galloc->buf_tallocs[i]->chunks[c]->max_size;
+        }
+    }
+}
+
+bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
+    return ggml_gallocr_reserve_n_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids, /*no_alloc =*/ false);
+}
+
 bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) {
     return ggml_gallocr_reserve_n(galloc, graph, NULL, NULL);
 }
@@ -1147,7 +1168,8 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
     return true;
 }
 
-ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) {
+static ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft_impl(
+        struct ggml_context * ctx, ggml_backend_buffer_type_t buft, size_t * nbytes_total, bool no_alloc) {
     GGML_ASSERT(ggml_get_no_alloc(ctx) == true);
 
     size_t alignment = ggml_backend_buft_get_alignment(buft);
@@ -1155,6 +1177,7 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte
 
     ggml_backend_buffer_t * buffers = NULL;
     size_t n_buffers = 0;
+    *nbytes_total = 0;
 
     size_t cur_buf_size = 0;
     struct ggml_tensor * first = ggml_get_first_tensor(ctx);
@@ -1166,10 +1189,11 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte
 
         if (cur_buf_size > 0 && (cur_buf_size + this_size) > max_size) {
             // allocate tensors in the current buffer
-            if (!alloc_tensor_range(ctx, first, t, buft, cur_buf_size, &buffers, &n_buffers)) {
+            if (!no_alloc && !alloc_tensor_range(ctx, first, t, buft, cur_buf_size, &buffers, &n_buffers)) {
                 return NULL;
             }
             first = t;
+            *nbytes_total += cur_buf_size;
             cur_buf_size = this_size;
         } else {
             cur_buf_size += this_size;
@@ -1178,15 +1202,21 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte
 
     // allocate remaining tensors
     if (cur_buf_size > 0) {
-        if (!alloc_tensor_range(ctx, first, NULL, buft, cur_buf_size, &buffers, &n_buffers)) {
+        *nbytes_total += cur_buf_size;
+        if (!no_alloc && !alloc_tensor_range(ctx, first, NULL, buft, cur_buf_size, &buffers, &n_buffers)) {
             return NULL;
         }
     }
 
+    if (no_alloc) {
+        return NULL;
+    }
+
     if (n_buffers == 0) {
 #ifndef NDEBUG
         GGML_LOG_DEBUG("%s: all tensors in the context are already allocated\n", __func__);
 #endif
+        GGML_ASSERT(!buffers);
         return NULL;
     }
 
@@ -1196,10 +1226,24 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte
     } else {
         buffer = ggml_backend_multi_buffer_alloc_buffer(buffers, n_buffers);
     }
-    free(buffers);
+    if (buffers) {
+        free(buffers); // can be NULL if context is empty or no_alloc
+    }
     return buffer;
 }
 
+size_t ggml_backend_alloc_ctx_tensors_from_buft_size(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) {
+    size_t nbytes_total = 0;
+    ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft_impl(ctx, buft, &nbytes_total, /*no_alloc=*/ true);
+    GGML_ASSERT(!buf);
+    return nbytes_total;
+}
+
+ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) {
+    size_t nbytes_total = 0;
+    return ggml_backend_alloc_ctx_tensors_from_buft_impl(ctx, buft, &nbytes_total, /*no_alloc =*/ false);
+}
+
 ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend) {
     return ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_get_default_buffer_type(backend));
 }
diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp
index 08681f35e3..8547ecc849 100644
--- a/ggml/src/ggml-backend.cpp
+++ b/ggml/src/ggml-backend.cpp
@@ -36,12 +36,11 @@ const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) {
 }
 
 ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) {
+    GGML_ASSERT(buft);
     if (size == 0) {
         // return a dummy buffer for zero-sized allocations
         return ggml_backend_buffer_init(buft, {}, NULL, 0);
     }
-
-    GGML_ASSERT(buft);
     return buft->iface.alloc_buffer(buft, size);
 }
 
@@ -128,6 +127,12 @@ void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
         return NULL;
     }
 
+    // FIXME JG: a multi_buffer has a non-zero size, according to the above comment get_base is not optional,
+    //     I don't know whether the above comment is correct
+    if (!buffer->iface.get_base) {
+        return NULL;
+    }
+
     void * base = buffer->iface.get_base(buffer);
 
     GGML_ASSERT(base != NULL && "backend buffer base cannot be NULL");
@@ -1727,6 +1732,20 @@ void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
     sched->is_alloc = false;
 }
 
+void ggml_backend_sched_reserve_size(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph, size_t * sizes) {
+    GGML_ASSERT(sched);
+    GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs);
+    GGML_ASSERT(sizes);
+
+    ggml_backend_sched_reset(sched);
+
+    ggml_backend_sched_synchronize(sched);
+
+    ggml_backend_sched_split_graph(sched, measure_graph);
+
+    ggml_gallocr_reserve_n_size(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids, sizes);
+}
+
 bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) {
     GGML_ASSERT(sched);
     GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs);
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index f0913cd359..eb3ae72eaa 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -7566,6 +7566,11 @@ size_t ggml_quantize_chunk(
 
 ////////////////////////////////////////////////////////////////////////////////
 
+void ggml_log_get(ggml_log_callback * log_callback, void ** user_data) {
+    *log_callback = g_logger_state.log_callback;
+    *user_data    = g_logger_state.log_callback_user_data;
+}
+
 void ggml_log_set(ggml_log_callback log_callback, void * user_data) {
     g_logger_state.log_callback = log_callback ? log_callback : ggml_log_callback_default;
     g_logger_state.log_callback_user_data = user_data;
diff --git a/include/llama.h b/include/llama.h
index b52eaacfa7..f862930099 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -313,6 +313,7 @@ extern "C" {
         bool check_tensors;   // validate model tensor data
         bool use_extra_bufts; // use extra buffer types (used for weight repacking)
         bool no_host;         // bypass host buffer allowing extra buffers to be used
+        bool no_alloc;        // only load metadata and simulate memory allocations
     };
 
     // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
@@ -466,10 +467,24 @@ extern "C" {
     // Frees all allocated memory
     LLAMA_API void llama_free(struct llama_context * ctx);
 
+    // fits mparams and cparams to free device memory (assumes system memory is unlimited)
+    // returns true if the parameters could be successfully modified to fit device memory
+    // this function is NOT thread safe because it modifies the global llama logger state
+    LLAMA_API bool llama_params_fit(
+                                   const char   * path_model,
+                    struct llama_model_params   * mparams,
+                    struct llama_context_params * cparams,
+                                          float * tensor_split,          // writable buffer for tensor split, needs at least llama_max_devices elements
+        struct llama_model_tensor_buft_override * tensor_buft_overrides, // writable buffer for overrides, needs at least llama_max_tensor_buft_overrides elements
+                                         size_t   margin,                // margin of memory to leave per device in bytes
+                                       uint32_t   n_ctx_min,             // minimum context size to set when trying to reduce memory use
+                            enum ggml_log_level   log_level);            // minimum log level to print during fitting, lower levels go to debug log
+
     LLAMA_API int64_t llama_time_us(void);
 
     LLAMA_API size_t llama_max_devices(void);
     LLAMA_API size_t llama_max_parallel_sequences(void);
+    LLAMA_API size_t llama_max_tensor_buft_overrides(void);
 
     LLAMA_API bool llama_supports_mmap       (void);
     LLAMA_API bool llama_supports_mlock      (void);
@@ -1354,7 +1369,9 @@ extern "C" {
 
     // Set callback for all future logging events.
     // If this is not called, or NULL is supplied, everything is output on stderr.
-    LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data);
+    // The logger state is global so these functions are NOT thread safe.
+    LLAMA_API void llama_log_get(ggml_log_callback * log_callback, void ** user_data);
+    LLAMA_API void llama_log_set(ggml_log_callback   log_callback, void *  user_data);
 
     //
     // Performance utils
diff --git a/src/llama-context.cpp b/src/llama-context.cpp
index 2a17e44ecd..8786d4ee3e 100644
--- a/src/llama-context.cpp
+++ b/src/llama-context.cpp
@@ -258,6 +258,7 @@ llama_context::llama_context(
 
         backend_buft.clear();
         backend_ptrs.clear();
+        backend_buf_exp_size.clear();
 
         for (auto & backend : backends) {
             auto * buft = ggml_backend_get_default_buffer_type(backend.get());
@@ -274,6 +275,7 @@ llama_context::llama_context(
 
             backend_buft.push_back(buft);
             backend_ptrs.push_back(backend.get());
+            backend_buf_exp_size.push_back(0);
         }
 
         LLAMA_LOG_DEBUG("%s: backend_ptrs.size() = %zu\n", __func__, backend_ptrs.size());
@@ -389,7 +391,8 @@ llama_context::llama_context(
 
         // reserve pp (prompt processing) graph first so that buffers are only allocated once
         {
-            auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
+            auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get(),
+                model.hparams.no_alloc, model.hparams.no_alloc ? backend_buf_exp_size.data() : nullptr);
             if (!gf) {
                 if (pipeline_parallel) {
                     LLAMA_LOG_WARN("%s: compute buffer allocation failed, retrying without pipeline parallelism\n", __func__);
@@ -407,7 +410,7 @@ llama_context::llama_context(
 
         // reserve with tg (token generation) graph to get the number of splits and nodes
         {
-            auto * gf = graph_reserve(n_seqs, n_seqs, n_seqs, mctx.get());
+            auto * gf = graph_reserve(n_seqs, n_seqs, n_seqs, mctx.get(), model.hparams.no_alloc);
             if (!gf) {
                 throw std::runtime_error("failed to allocate compute tg buffers");
             }
@@ -422,7 +425,7 @@ llama_context::llama_context(
             //
             // auto * gf = graph_reserve(n_tokens, 1, n_tokens, mctx.get());
             //
-            auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get());
+            auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get(), model.hparams.no_alloc);
             if (!gf) {
                 throw std::runtime_error("failed to allocate compute pp buffers");
             }
@@ -431,11 +434,13 @@ llama_context::llama_context(
         for (size_t i = 0; i < backend_ptrs.size(); ++i) {
             ggml_backend_t             backend = backend_ptrs[i];
             ggml_backend_buffer_type_t buft    = backend_buft[i];
-            size_t size = ggml_backend_sched_get_buffer_size(sched.get(), backend);
-            if (size > 1) {
+            if (!model.hparams.no_alloc) {
+                backend_buf_exp_size[i] = ggml_backend_sched_get_buffer_size(sched.get(), backend);
+            }
+            if (backend_buf_exp_size[i] > 1) {
                 LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__,
                         ggml_backend_buft_name(buft),
-                        size / 1024.0 / 1024.0);
+                        backend_buf_exp_size[i] / 1024.0 / 1024.0);
             }
         }
 
@@ -454,6 +459,23 @@ llama_context::llama_context(
 }
 
 llama_context::~llama_context() {
+    // FIXME this currently results in a use-after-free bug if the model is freed before the context
+    // if (!model.hparams.no_alloc) {
+    //     for (size_t i = 0; i < backend_ptrs.size(); ++i) {
+    //         ggml_backend_t             backend = backend_ptrs[i];
+    //         ggml_backend_buffer_type_t buft    = backend_buft[i];
+
+    //         const size_t size_exp = backend_buf_exp_size[i];
+    //         const size_t size_act = ggml_backend_sched_get_buffer_size(sched.get(), backend);
+    //         if (size_exp == size_act) {
+    //             LLAMA_LOG_DEBUG("%s: %10s compute buffer size is %8.4f MiB, matches expectation of %8.4f MiB\n",
+    //                 __func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0));
+    //         } else {
+    //             LLAMA_LOG_WARN("%s: %10s compute buffer size of %8.4f MiB, does not match expectation of %8.4f MiB\n",
+    //                 __func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0));
+    //         }
+    //     }
+    // }
     ggml_opt_free(opt_ctx);
 }
 
@@ -1428,7 +1450,8 @@ llm_graph_result * llama_context::get_gf_res_reserve() const {
     return static_cast<llm_graph_result *>(gf_res_reserve.get());
 }
 
-ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only) {
+ggml_cgraph * llama_context::graph_reserve(
+        uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only, size_t * sizes) {
     LLAMA_LOG_DEBUG("%s: reserving a graph for ubatch with n_tokens = %4u, n_seqs = %2u, n_outputs = %4u\n", __func__, n_tokens, n_seqs, n_outputs);
     GGML_ASSERT(n_outputs >= 1);
 
@@ -1465,8 +1488,13 @@ ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, u
 
     // initialize scheduler with the specified graph
     if (split_only) {
-        ggml_backend_sched_split_graph(sched.get(), gf);
+        if (sizes) {
+            ggml_backend_sched_reserve_size(sched.get(), gf, sizes);
+        } else {
+            ggml_backend_sched_split_graph(sched.get(), gf);
+        }
     } else if (!ggml_backend_sched_reserve(sched.get(), gf)) {
+        GGML_ASSERT(!sizes);
         LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__);
         return nullptr;
     }
@@ -2088,15 +2116,26 @@ void llama_context::perf_reset() {
 
 std::map<ggml_backend_buffer_type_t, llama_memory_breakdown_data> llama_context::memory_breakdown() const {
     std::map<ggml_backend_buffer_type_t, llama_memory_breakdown_data> ret;
-    for (const auto & buft_size : model.memory_breakdown()) {
-        ret[buft_size.first].model += buft_size.second;
+    for (const auto & [buft, size] : model.memory_breakdown()) {
+        ret[buft].model += size;
     }
-    for (const auto & buft_size : memory->memory_breakdown()) {
-        ret[buft_size.first].context += buft_size.second;
+    if (memory) {
+        for (const auto & [buft, size] : memory->memory_breakdown()) {
+            ret[buft].context += size;
+        }
     }
-    for (const auto & backend_ptr : backends) {
-        ggml_backend_t backend = backend_ptr.get();
-        ret[ggml_backend_sched_get_buffer_type(sched.get(), backend)].compute += ggml_backend_sched_get_buffer_size(sched.get(), backend);
+    if (model.hparams.no_alloc) {
+        for (size_t i = 0; i < backends.size(); ++i) {
+            ggml_backend_t             backend = backends[i].get();
+            ggml_backend_buffer_type_t buft    = ggml_backend_sched_get_buffer_type(sched.get(), backend);
+            ret[buft].compute += backend_buf_exp_size[i];
+        }
+    } else {
+        for (const auto & backend_ptr : backends) {
+            ggml_backend_t             backend = backend_ptr.get();
+            ggml_backend_buffer_type_t buft    = ggml_backend_sched_get_buffer_type(sched.get(), backend);
+            ret[buft].compute += ggml_backend_sched_get_buffer_size(sched.get(), backend);
+        }
     }
     return ret;
 }
diff --git a/src/llama-context.h b/src/llama-context.h
index cd26eafe18..c31101330e 100644
--- a/src/llama-context.h
+++ b/src/llama-context.h
@@ -26,6 +26,10 @@ struct llama_memory_breakdown_data {
     size_t model   = 0; // memory allocated for the model
     size_t context = 0; // memory allocated for the context
     size_t compute = 0; // memory allocated for temporary compute buffers
+
+    size_t total() const {
+        return model + context + compute;
+    }
 };
 
 struct llama_context {
@@ -206,7 +210,8 @@ public:
     ggml_status graph_compute(ggml_cgraph * gf, bool batched);
 
     // reserve a graph with a dummy ubatch of the specified size
-    ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only = false);
+    ggml_cgraph * graph_reserve(
+        uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only = false, size_t * sizes = nullptr);
 
 private:
     llm_graph_params graph_params(
@@ -281,9 +286,10 @@ private:
 
     std::vector<std::pair<ggml_backend_t, ggml_backend_set_n_threads_t>> set_n_threads_fns;
 
-    // buffer types used for the compute buffer of each backend
+    // pointers and buffer types used for the compute buffer of each backend
     std::vector<ggml_backend_t>             backend_ptrs;
     std::vector<ggml_backend_buffer_type_t> backend_buft;
+    std::vector<size_t>                     backend_buf_exp_size; // expected buffer sizes
 
     llm_graph_result_ptr gf_res_prev;
     llm_graph_result_ptr gf_res_reserve;
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index a467c64a14..cecb476e91 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -34,6 +34,7 @@ struct llama_hparams_convnext {
 
 struct llama_hparams {
     bool vocab_only;
+    bool no_alloc;
     bool rope_finetuned;
     bool use_par_res;
     bool swin_norm;
diff --git a/src/llama-impl.cpp b/src/llama-impl.cpp
index c7a1880aad..8e3e7b223a 100644
--- a/src/llama-impl.cpp
+++ b/src/llama-impl.cpp
@@ -25,6 +25,10 @@ time_meas::~time_meas() {
     }
 }
 
+void llama_log_get(ggml_log_callback * log_callback, void ** user_data) {
+    ggml_log_get(log_callback, user_data);
+}
+
 void llama_log_set(ggml_log_callback log_callback, void * user_data) {
     ggml_log_set(log_callback, user_data);
     g_logger_state.log_callback = log_callback ? log_callback : llama_log_callback_default;
diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index bf3de2f2ef..4cbbca0925 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -175,7 +175,15 @@ llama_kv_cache::llama_kv_cache(
 
     // allocate tensors and initialize the buffers to avoid NaNs in the padding
     for (auto & [buft, ctx] : ctx_map) {
-        ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx.get(), buft);
+        ggml_backend_buffer_t buf;
+        if (model.hparams.no_alloc) {
+            buf = ggml_backend_buft_alloc_buffer(buft, /*size =*/ 0); // dummy buffer
+            for (ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != nullptr; t = ggml_get_next_tensor(ctx.get(), t)) {
+                t->buffer = buf; // set dummy buffer for KV cache so that the backend scheduler won't try to allocate it
+            }
+        } else {
+            buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx.get(), buft); // real buffer
+        }
         if (!buf) {
             throw std::runtime_error("failed to allocate buffer for kv cache");
         }
@@ -482,9 +490,18 @@ llama_pos llama_kv_cache::seq_pos_max(llama_seq_id seq_id) const {
 
 std::map<ggml_backend_buffer_type_t, size_t> llama_kv_cache::memory_breakdown() const {
     std::map<ggml_backend_buffer_type_t, size_t> ret;
-    for (const auto & [_, buf] : ctxs_bufs) {
-        ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get());
+    for (const auto & [ctx, buf] : ctxs_bufs) {
+        ggml_backend_buffer_type_t buft = ggml_backend_buffer_get_type(buf.get());
+
+        if (hparams.no_alloc) {
+            GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) == nullptr);
+            ret[buft] += ggml_backend_alloc_ctx_tensors_from_buft_size(ctx.get(), buft);
+        } else {
+            // GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) != nullptr); // multi_buffer does not have a defined base
+            ret[buft] += ggml_backend_buffer_get_size(buf.get());
+        }
     }
+
     return ret;
 }
 
diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp
index aa3a65f87a..ca2ea2461d 100644
--- a/src/llama-model-loader.cpp
+++ b/src/llama-model-loader.cpp
@@ -473,6 +473,7 @@ llama_model_loader::llama_model_loader(
         std::vector<std::string> & splits,
         bool use_mmap,
         bool check_tensors,
+        bool no_alloc,
         const llama_model_kv_override * param_overrides_p,
         const llama_model_tensor_buft_override * param_tensor_buft_overrides_p) {
     int trace = 0;
@@ -716,6 +717,7 @@ llama_model_loader::llama_model_loader(
 
     this->use_mmap = use_mmap;
     this->check_tensors = check_tensors;
+    this->no_alloc = no_alloc;
 }
 
 std::string llama_model_loader::get_arch_name() const {
diff --git a/src/llama-model-loader.h b/src/llama-model-loader.h
index c9189f6cb4..0380c92fde 100644
--- a/src/llama-model-loader.h
+++ b/src/llama-model-loader.h
@@ -71,6 +71,7 @@ struct llama_model_loader {
 
     bool use_mmap = false;
     bool check_tensors;
+    bool no_alloc;
 
     llama_files files;
     llama_ftype ftype;
@@ -97,6 +98,7 @@ struct llama_model_loader {
         std::vector<std::string> & splits, // optional, only need if the split does not follow naming scheme
         bool use_mmap,
         bool check_tensors,
+        bool no_alloc,
         const llama_model_kv_override * param_overrides_p,
         const llama_model_tensor_buft_override * param_tensor_buft_overrides_p);
 
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 28f06b4e61..e4d2138056 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -6606,9 +6606,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
 
         std::vector<ggml_backend_buffer_ptr> bufs;
         if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft) {
+            GGML_ASSERT(!ml.no_alloc);
             for (uint32_t idx = 0; idx < ml.files.size(); idx++) {
                 // only the mmap region containing the tensors in the model is mapped to the backend buffer
-                // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers
+                // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer,
+                //     then we could just use metal for all layers
                 // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size
                 void * addr = nullptr;
                 size_t first, last; // NOLINT
@@ -6624,9 +6626,16 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                 bufs.emplace_back(buf);
                 buf_map.emplace(idx, buf);
             }
-        }
-        else {
-            ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft);
+        } else {
+            ggml_backend_buffer_t buf;
+            if (ml.no_alloc) {
+                buf = ggml_backend_buft_alloc_buffer(buft, /*size =*/ 0); // dummy buffer
+                for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) {
+                    t->buffer = buf; // set dummy buffer for weights so that the backend scheduler won't try to allocate them
+                }
+            } else {
+                buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); // real buffer
+            }
             if (buf == nullptr) {
                 throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft)));
             }
@@ -6681,6 +6690,10 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
         }
     }
 
+    if (ml.no_alloc) {
+        return true;
+    }
+
     // load tensor data
     for (auto & [ctx, buf_map] : ctx_buf_maps) {
         if (!ml.load_all_data(ctx, buf_map, use_mlock ? &pimpl->mlock_mmaps : NULL, params.progress_callback, params.progress_callback_user_data)) {
@@ -6723,9 +6736,18 @@ size_t llama_model::n_devices() const {
 
 std::map<ggml_backend_buffer_type_t, size_t> llama_model::memory_breakdown() const {
     std::map<ggml_backend_buffer_type_t, size_t> ret;
-    for (const auto & [_, bufs] : pimpl->ctxs_bufs) {
-        for (const auto & buf : bufs) {
-            ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get());
+    for (const auto & [ctx, bufs] : pimpl->ctxs_bufs) {
+        if (hparams.no_alloc) {
+            GGML_ASSERT(bufs.size() == 1);
+            ggml_backend_buffer_t buf = bufs[0].get();
+            GGML_ASSERT(ggml_backend_buffer_get_base(buf) == nullptr);
+            ggml_backend_buffer_type_t buft = ggml_backend_buffer_get_type(buf);
+            ret[buft] += ggml_backend_alloc_ctx_tensors_from_buft_size(ctx.get(), buft);
+        } else {
+            for (const auto & buf : bufs) {
+                // GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) != nullptr); // multi_buffer does not have a defined base
+                ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get());
+            }
         }
     }
     return ret;
@@ -6770,6 +6792,7 @@ void llama_model::print_info() const {
     // hparams
     LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, arch_name().c_str());
     LLAMA_LOG_INFO("%s: vocab_only       = %d\n",     __func__, hparams.vocab_only);
+    LLAMA_LOG_INFO("%s: no_alloc         = %d\n",     __func__, hparams.no_alloc);
 
     if (!hparams.vocab_only) {
         LLAMA_LOG_INFO("%s: n_ctx_train      = %u\n",     __func__, hparams.n_ctx_train);
@@ -7618,6 +7641,7 @@ llama_model_params llama_model_default_params() {
         /*.check_tensors               =*/ false,
         /*.use_extra_bufts             =*/ true,
         /*.no_host                     =*/ false,
+        /*.no_alloc                    =*/ false,
     };
 
     return result;
diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp
index 351dcb7baa..bc4b05c3b5 100644
--- a/src/llama-quant.cpp
+++ b/src/llama-quant.cpp
@@ -596,7 +596,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std::
     }
 
     std::vector<std::string> splits = {};
-    llama_model_loader ml(fname_inp, splits, use_mmap, /*check_tensors*/ true, kv_overrides, nullptr);
+    llama_model_loader ml(fname_inp, splits, use_mmap, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr);
     ml.init_mappings(false); // no prefetching
 
     llama_model model(llama_model_default_params());
diff --git a/src/llama.cpp b/src/llama.cpp
index ab2e9868af..7ed34b80ae 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -1,6 +1,9 @@
+#include "llama.h"
+
 #include "llama-impl.h"
 
 #include "llama-chat.h"
+#include "llama-context.h"
 #include "llama-mmap.h"
 #include "llama-vocab.h"
 #include "llama-model-loader.h"
@@ -11,11 +14,14 @@
 #include "ggml-backend.h"
 
 #include <algorithm>
+#include <cassert>
+#include <cinttypes>
 #include <cstddef>
 #include <cstdint>
 #include <cstdio>
 #include <cstring>
 #include <ctime>
+#include <stdexcept>
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
@@ -37,6 +43,643 @@ const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_ty
     GGML_ABORT("fatal error");
 }
 
+struct llama_device_memory_data {
+    int64_t total;
+    int64_t free;
+    llama_memory_breakdown_data mb;
+};
+
+static std::vector<llama_device_memory_data> llama_get_device_memory_data(
+        const char * path_model, const llama_model_params * mparams, const llama_context_params * cparams,
+        std::vector<ggml_backend_dev_t> & devs, uint32_t & hp_ngl, uint32_t & hp_n_ctx_train, uint32_t & hp_n_expert,
+        const ggml_log_level log_level) {
+    struct user_data_t {
+        struct {
+            ggml_log_callback callback;
+            void * user_data;
+        } original_logger;
+        ggml_log_level min_level; // prints below this log level go to debug log
+    };
+    user_data_t ud;
+    llama_log_get(&ud.original_logger.callback, &ud.original_logger.user_data);
+    ud.min_level = log_level;
+
+    llama_log_set([](ggml_log_level level, const char * text, void * user_data) {
+        const user_data_t * ud = (const user_data_t *) user_data;
+        const ggml_log_level level_eff = level >= ud->min_level ? level : GGML_LOG_LEVEL_DEBUG;
+        ud->original_logger.callback(level_eff, text, ud->original_logger.user_data);
+    }, &ud);
+
+    llama_model_params mparams_copy = *mparams;
+    mparams_copy.no_alloc = true;
+    mparams_copy.use_mmap = false;
+
+    llama_model * model = llama_model_load_from_file(path_model, mparams_copy);
+    if (model == nullptr) {
+        llama_log_set(ud.original_logger.callback, ud.original_logger.user_data);
+        throw std::runtime_error("failed to load model");
+    }
+
+    llama_context * ctx = llama_init_from_model(model, *cparams);
+    if (ctx == nullptr) {
+        llama_model_free(model);
+        llama_log_set(ud.original_logger.callback, ud.original_logger.user_data);
+        throw std::runtime_error("failed to create llama_context from model");
+    }
+
+    std::vector<llama_device_memory_data> ret(model->devices.size());
+
+    std::map<ggml_backend_buffer_type_t, llama_memory_breakdown_data> memory_breakdown = ctx->memory_breakdown();
+
+    for (const auto & [buft, mb] : memory_breakdown) {
+        if (ggml_backend_buft_is_host(buft)) {
+            continue;
+        }
+
+        ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft);
+        if (!dev) {
+            continue;
+        }
+        for (size_t i = 0; i < ret.size(); i++) {
+            if (model->devices[i] == dev) {
+                ret[i].mb.model   += mb.model;
+                ret[i].mb.context += mb.context;
+                ret[i].mb.compute += mb.compute;
+                break;
+            }
+        }
+    }
+    for (size_t i = 0; i < ret.size(); i++) {
+        size_t free, total;
+        ggml_backend_dev_memory(model->devices[i], &free, &total);
+        ret[i].free  = free;
+        ret[i].total = total;
+    }
+
+    devs           = model->devices;
+    hp_ngl         = model->hparams.n_layer;
+    hp_n_ctx_train = model->hparams.n_ctx_train;
+    hp_n_expert    = model->hparams.n_expert;
+
+    llama_memory_breakdown_print(ctx); // goes to debug log
+
+    llama_free(ctx);
+    llama_model_free(model);
+    llama_log_set(ud.original_logger.callback, ud.original_logger.user_data);
+    return ret;
+}
+
+// enum to identify part of a layer for distributing its tensors:
+enum layer_fraction_t {
+    LAYER_FRACTION_NONE = 0, // nothing
+    LAYER_FRACTION_ATTN = 1, // attention
+    LAYER_FRACTION_UP   = 2, // attention + up
+    LAYER_FRACTION_GATE = 3, // attention + up + gate
+    LAYER_FRACTION_MOE  = 4, // everything but sparse MoE weights
+};
+// this enum is only used in llama_params_fit_impl but needs to be defined outside of it to fix a Windows compilation issue
+
+static void llama_params_fit_impl(
+        const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams,
+        float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides,
+        size_t margin_s, uint32_t n_ctx_min, enum ggml_log_level log_level) {
+    constexpr int64_t MiB = 1024*1024;
+    const int64_t margin = margin_s; // this function uses int64_t rather than size_t for memory sizes to more conveniently handle deficits
+    typedef std::vector<llama_device_memory_data> dmds_t;
+    const llama_model_params default_mparams = llama_model_default_params();
+
+    std::vector<ggml_backend_dev_t> devs;
+    uint32_t hp_ngl = 0; // hparams.n_gpu_layers
+    uint32_t hp_nct = 0; // hparams.n_ctx_train
+    uint32_t hp_nex = 0; // hparams.n_expert
+
+    // step 1: get data for default parameters and check whether any changes are necessary in the first place
+
+    LLAMA_LOG_DEBUG("%s: getting device memory data for initial parameters:\n", __func__);
+    const dmds_t dmds_full = llama_get_device_memory_data(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
+    const size_t nd = devs.size(); // number of devices
+    if (nd == 0) {
+        LLAMA_LOG_INFO("%s: no devices with dedicated memory found\n", __func__);
+        return;
+    }
+
+    std::vector<std::string> dev_names;
+    {
+        dev_names.reserve(nd);
+        size_t max_length = 0;
+        for (ggml_backend_dev_t dev : devs) {
+            std::string name = ggml_backend_dev_name(dev);
+            name += " (";
+            name += ggml_backend_dev_description(dev);
+            name += ")";
+            dev_names.push_back(name);
+            max_length = std::max(max_length, name.length());
+        }
+        for (std::string & dn : dev_names) {
+            dn.insert(dn.end(), max_length - dn.length(), ' ');
+        }
+    }
+
+    int64_t sum_total          = 0;
+    int64_t sum_projected_free = 0;
+    int64_t min_projected_free = INT64_MAX;
+    int64_t sum_projected_used = 0;
+    int64_t sum_projected_ctx  = 0;
+
+    if (nd > 1) {
+        LLAMA_LOG_INFO("%s: projected memory use with initial parameters [MiB]:\n", __func__);
+    }
+    for (size_t id = 0; id < nd; id++) {
+        const llama_device_memory_data & dmd = dmds_full[id];
+
+        const int64_t projected_used = dmd.mb.total();
+        const int64_t projected_free = dmd.free - projected_used;
+
+        sum_total          += dmd.total;
+        sum_projected_used += projected_used;
+        sum_projected_free += projected_free;
+        min_projected_free  = std::min(min_projected_free, projected_free);
+        sum_projected_ctx  += dmd.mb.context;
+
+        if (nd > 1) {
+            LLAMA_LOG_INFO("%s:   - %s: %6" PRId64 " total, %6" PRId64 " used, %6" PRId64 " %s\n",
+                __func__, dev_names[id].c_str(), dmd.total/MiB, projected_used/MiB, std::abs(projected_free)/MiB,
+                projected_free >= 0 ? "surplus" : "deficit");
+        }
+    }
+    assert(sum_total >= 0 && sum_projected_used >= 0 && sum_projected_ctx >= 0);
+    assert(sum_projected_used >= sum_projected_ctx);
+    LLAMA_LOG_INFO("%s: projected to use %" PRId64 " MiB of device memory vs. %" PRId64 " MiB of free device memory\n",
+        __func__, sum_projected_used/MiB, sum_total/MiB);
+    if (min_projected_free >= margin) {
+        if (nd == 1) {
+            LLAMA_LOG_INFO("%s: will leave %" PRId64 " >= %" PRId64 " MiB of free device memory, no changes needed\n",
+                __func__, min_projected_free/MiB, margin/MiB);
+            return;
+        }
+        LLAMA_LOG_INFO("%s: will leave at least %" PRId64 " >= %" PRId64 " MiB of free memory on all devices, no changes needed\n",
+            __func__, min_projected_free/MiB, margin/MiB);
+        return;
+    }
+
+    // step 2: try reducing memory use by reducing the context size
+
+    {
+        int64_t global_surplus = sum_projected_free - int64_t(nd)*margin;
+        if (global_surplus < 0) {
+            LLAMA_LOG_INFO(nd == 1 ?
+                "%s: cannot fulfill margin of %" PRId64 " MiB, need to reduce device memory by %" PRId64 " MiB\n" :
+                "%s: cannot fulfill margin of %" PRId64 " MiB on all devices, need to use %" PRId64 " MiB less in total\n",
+                __func__, margin/MiB, -global_surplus/MiB);
+            if (cparams->n_ctx == 0) {
+                if (hp_nct > n_ctx_min) {
+                    const int64_t bytes_per_ctx = sum_projected_ctx / hp_nct;
+                    const uint32_t ctx_reduction = std::min(
+                        uint32_t((-global_surplus + bytes_per_ctx - 1) / bytes_per_ctx), hp_nct - n_ctx_min);
+                    cparams->n_ctx = hp_nct - ctx_reduction;
+                    const int64_t memory_reduction = ctx_reduction * bytes_per_ctx;
+                    global_surplus += memory_reduction;
+                    LLAMA_LOG_INFO("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n",
+                        __func__, hp_nct, cparams->n_ctx, memory_reduction/MiB);
+                } else {
+                    LLAMA_LOG_INFO("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n",
+                        __func__, hp_nct, n_ctx_min);
+                }
+            } else {
+                LLAMA_LOG_INFO("%s: context size set by user to %" PRIu32 " -> no change\n", __func__, cparams->n_ctx);
+            }
+        }
+        if (global_surplus >= 0) {
+            LLAMA_LOG_INFO("%s: entire model can be fit across devices by reducing context\n", __func__);
+            return;
+        }
+    }
+
+    if (mparams->n_gpu_layers != default_mparams.n_gpu_layers) {
+        throw std::runtime_error("n_gpu_layers already set by user to " + std::to_string(mparams->n_gpu_layers) + ", abort");
+    }
+    if (nd > 1) {
+        if (!tensor_split) {
+            throw std::runtime_error("did not provide a buffer to write the tensor_split to, abort");
+        }
+        if (mparams->tensor_split) {
+            for (size_t id = 0; id < nd; id++) {
+                if (mparams->tensor_split[id] != 0.0f) {
+                    throw std::runtime_error("model_params::tensor_split already set by user, abort");
+                }
+            }
+        }
+        if (mparams->split_mode == LLAMA_SPLIT_MODE_ROW) {
+            throw std::runtime_error("changing weight allocation for LLAMA_SPLIT_MODE_ROW not implemented, abort");
+        }
+        if (hp_ngl < 2*nd) {
+            throw std::runtime_error("model has only " + std::to_string(hp_ngl) + " layers but need at least "
+                + std::to_string(2*nd) + " to fit memory for " + std::to_string(nd) + " devices, abort");
+        }
+    }
+    if (!tensor_buft_overrides) {
+        throw std::runtime_error("did not provide buffer to set tensor_buft_overrides, abort");
+    }
+    if (mparams->tensor_buft_overrides && (mparams->tensor_buft_overrides->pattern || mparams->tensor_buft_overrides->buft)) {
+        throw std::runtime_error("model_params::tensor_buft_overrides already set by user, abort");
+    }
+
+    // step 3: iteratively fill the back to front with "dense" layers
+    //   - for a dense model simply fill full layers, giving each device a contiguous slice of the model
+    //   - for a MoE model, same as dense model but with all MoE tensors in system memory
+
+    // utility function that returns a static C string matching the tensors for a specific layer index and layer fraction:
+    auto get_overflow_pattern = [&](const size_t il, const layer_fraction_t lf) -> const char * {
+        constexpr size_t n_strings = 1000;
+        if (il >= n_strings) {
+            throw std::runtime_error("at most " + std::to_string(n_strings) + " model layers are supported");
+        }
+        switch (lf) {
+            case LAYER_FRACTION_ATTN: {
+                static std::array<std::string, n_strings> patterns;
+                if (patterns[il].empty()) {
+                    patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(up|gate|down).*";
+                }
+                return patterns[il].c_str();
+            }
+            case LAYER_FRACTION_UP: {
+                static std::array<std::string, n_strings> patterns;
+                if (patterns[il].empty()) {
+                    patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(gate|down).*";
+                }
+                return patterns[il].c_str();
+            }
+            case LAYER_FRACTION_GATE: {
+                static std::array<std::string, n_strings> patterns;
+                if (patterns[il].empty()) {
+                    patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_down.*";
+                }
+                return patterns[il].c_str();
+            }
+            case LAYER_FRACTION_MOE: {
+                static std::array<std::string, n_strings> patterns;
+                if (patterns[il].empty()) {
+                    patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(up|down|gate)_(ch|)exps";
+                }
+                return patterns[il].c_str();
+            }
+            default:
+                GGML_ABORT("fatal error");
+        }
+    };
+
+    struct ngl_t {
+        uint32_t n_layer = 0; // number of total layers
+        uint32_t n_part  = 0; // number of partial layers, <= n_layer
+
+        // for the first partial layer varying parts can overflow, all further layers use LAYER_FRACTION_MOE:
+        layer_fraction_t overflow_type = LAYER_FRACTION_MOE;
+    };
+
+    const size_t ntbo = llama_max_tensor_buft_overrides();
+
+    // utility function to set n_gpu_layers and tensor_split
+    auto set_ngl_tensor_split_tbo = [&](
+            const std::vector<ngl_t> & ngl_per_device,
+            const std::vector<ggml_backend_buffer_type_t> & overflow_bufts,
+            llama_model_params & mparams,
+            const bool add_nonrepeating) {
+        mparams.n_gpu_layers = 0;
+        for (size_t id = 0; id < nd; id++) {
+            mparams.n_gpu_layers += ngl_per_device[id].n_layer;
+            if (nd > 1) {
+                tensor_split[id] = ngl_per_device[id].n_layer;
+            }
+        }
+        assert(uint32_t(mparams.n_gpu_layers) <= hp_ngl);
+        uint32_t il0 = hp_ngl - mparams.n_gpu_layers; // start index for tensor buft overrides
+
+        if (add_nonrepeating) {
+            mparams.n_gpu_layers += 1;
+            tensor_split[nd - 1] += 1;
+        }
+        mparams.tensor_split = tensor_split;
+
+        size_t itbo = 0;
+        for (size_t id = 0; id < nd; id++) {
+            il0 += ngl_per_device[id].n_layer - ngl_per_device[id].n_part;
+            for (uint32_t il = il0; il < il0 + ngl_per_device[id].n_part; il++) {
+                if (itbo + 1 >= ntbo) {
+                    tensor_buft_overrides[itbo].pattern = nullptr;
+                    tensor_buft_overrides[itbo].buft    = nullptr;
+                    itbo++;
+                    mparams.tensor_buft_overrides = tensor_buft_overrides;
+                    throw std::runtime_error("llama_params_fit_n_tensor_buft_overrides() == "
+                        + std::to_string(ntbo) + " is insufficient for model\n");
+                }
+                tensor_buft_overrides[itbo].pattern = get_overflow_pattern(il, il == il0 ? ngl_per_device[id].overflow_type : LAYER_FRACTION_MOE);
+                tensor_buft_overrides[itbo].buft = overflow_bufts[id];
+                itbo++;
+            }
+            il0 += ngl_per_device[id].n_part;
+        }
+        tensor_buft_overrides[itbo].pattern = nullptr;
+        tensor_buft_overrides[itbo].buft    = nullptr;
+        itbo++;
+        mparams.tensor_buft_overrides = tensor_buft_overrides;
+    };
+
+    // utility function that returns the memory use per device for given numbers of layers per device
+    auto get_memory_for_layers = [&](
+            const char * func_name,
+            const std::vector<ngl_t> & ngl_per_device,
+            const std::vector<ggml_backend_buffer_type_t> & overflow_bufts,
+            const bool add_nonrepeating) -> std::vector<int64_t> {
+        llama_model_params mparams_copy = *mparams;
+        set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, mparams_copy, add_nonrepeating);
+
+        const dmds_t dmd_nl = llama_get_device_memory_data(
+            path_model, &mparams_copy, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
+
+        LLAMA_LOG_DEBUG("%s: memory for test allocation by device:\n", func_name);
+        for (size_t id = 0; id < nd; id++) {
+            const ngl_t & n = ngl_per_device[id];
+            LLAMA_LOG_DEBUG(
+                "%s: id=%zu, n_layer=%2" PRIu32 ", n_part=%2" PRIu32 ", overflow_type=%d, mem=%6" PRId64 " MiB\n",
+                func_name, id, n.n_layer, n.n_part, int(n.overflow_type), dmd_nl[id].mb.total()/MiB);
+        }
+
+        std::vector<int64_t> ret;
+        ret.reserve(nd);
+        for (const llama_device_memory_data & dmd : dmd_nl) {
+            ret.push_back(dmd.mb.total());
+        }
+        return ret;
+    };
+
+    int64_t global_surplus_cpu_moe = 0;
+    if (hp_nex > 0) {
+        const static std::string pattern_moe_all = "blk\\.\\d+\\.ffn_(up|down|gate)_(ch|)exps"; // matches all MoE tensors
+        ggml_backend_buffer_type_t cpu_buft = ggml_backend_cpu_buffer_type();
+        tensor_buft_overrides[0] = {pattern_moe_all.c_str(), cpu_buft};
+        tensor_buft_overrides[1] = {nullptr, nullptr};
+        mparams->tensor_buft_overrides = tensor_buft_overrides;
+
+        LLAMA_LOG_DEBUG("%s: getting device memory data with all MoE tensors moved to system memory:\n", __func__);
+        const dmds_t dmds_cpu_moe = llama_get_device_memory_data(
+            path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level);
+
+        for (const llama_device_memory_data & dmd : dmds_cpu_moe) {
+            global_surplus_cpu_moe += dmd.free;
+            global_surplus_cpu_moe -= int64_t(dmd.mb.total()) + margin;
+        }
+
+        if (global_surplus_cpu_moe > 0) {
+            LLAMA_LOG_INFO("%s: with only dense weights in device memory there is a total surplus of %" PRId64 " MiB\n",
+                __func__, global_surplus_cpu_moe/MiB);
+        } else {
+            LLAMA_LOG_INFO("%s: with only dense weights in device memory there is still a total deficit of %" PRId64 " MiB\n",
+                __func__, -global_surplus_cpu_moe/MiB);
+        }
+
+        // reset
+        tensor_buft_overrides[0] = {nullptr, nullptr};
+        mparams->tensor_buft_overrides = tensor_buft_overrides;
+    }
+
+    std::vector<int64_t> targets; // maximum acceptable memory use per device
+    targets.reserve(nd);
+    for (size_t id = 0; id < nd; id++) {
+        targets.push_back(dmds_full[id].free - margin);
+        LLAMA_LOG_DEBUG("%s: id=%zu, target=%" PRId64 " MiB\n", __func__, id, targets[id]/MiB);
+    }
+
+    // whether for the optimal memory use we expect to load at least some MoE tensors:
+    const bool partial_moe = hp_nex > 0 && global_surplus_cpu_moe > 0;
+
+    std::vector<ggml_backend_buffer_type_t> overflow_bufts; // which bufts the partial layers of a device overflow to:
+    overflow_bufts.reserve(nd);
+    for (size_t id = 0; id < nd - 1; ++id) {
+        overflow_bufts.push_back(ggml_backend_dev_buffer_type(devs[id + 1]));
+    }
+    overflow_bufts.push_back(ggml_backend_cpu_buffer_type());
+
+    std::vector<ngl_t> ngl_per_device(nd);
+    std::vector<int64_t> mem = get_memory_for_layers(__func__, ngl_per_device, overflow_bufts, partial_moe);
+    if (hp_nex > 0) {
+        for (size_t id = 0; id < nd; id++) {
+            ngl_per_device[id].overflow_type = LAYER_FRACTION_MOE;
+        }
+    }
+
+    // optimize the number of layers per device using the method of false position:
+    //   - ngl_per_device has 0 layers for each device, lower bound
+    //   - try a "high" configuration where a device is given all unassigned layers
+    //   - interpolate the memory use / layer between low and high linearly to get a guess where it meets our target
+    //   - check memory use of our guess, replace either the low or high bound
+    //   - once we only have a difference of a single layer, stop and return the lower bound that just barely still fits
+    if (hp_nex == 0) {
+        LLAMA_LOG_INFO("%s: filling dense layers back-to-front:\n", __func__);
+    } else {
+        LLAMA_LOG_INFO("%s: filling dense-only layers back-to-front:\n", __func__);
+    }
+    uint32_t n_unassigned = hp_ngl;
+    for (int id = nd - 1; id >= 0; id--) {
+        std::vector<ngl_t> ngl_per_device_high = ngl_per_device;
+        ngl_per_device_high[id].n_layer = n_unassigned;
+        if (hp_nex > 0) {
+            ngl_per_device_high[id].n_part = ngl_per_device_high[id].n_layer;
+        }
+        if (ngl_per_device_high[id].n_layer > 0) {
+            std::vector<int64_t> mem_high = get_memory_for_layers(__func__, ngl_per_device_high, overflow_bufts, partial_moe);
+            if (mem_high[id] > targets[id]) {
+                uint32_t delta = ngl_per_device_high[id].n_layer - ngl_per_device[id].n_layer;
+                while (delta > 1) {
+                    uint32_t step_size = int64_t(delta) * (targets[id] - mem[id]) / (mem_high[id] - mem[id]);
+                    step_size = std::max(step_size, uint32_t(1));
+                    step_size = std::min(step_size, delta - 1);
+
+                    std::vector<ngl_t> ngl_per_device_test = ngl_per_device;
+                    ngl_per_device_test[id].n_layer += step_size;
+                    if (hp_nex) {
+                        ngl_per_device_test[id].n_part += step_size;
+                    }
+                    const std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe);
+
+                    if (mem_test[id] <= targets[id]) {
+                        ngl_per_device  = ngl_per_device_test;
+                        mem             = mem_test;
+                        n_unassigned   -= ngl_per_device[id].n_layer;
+                        LLAMA_LOG_DEBUG("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer);
+                    } else {
+                        ngl_per_device_high = ngl_per_device_test;
+                        mem_high            = mem_test;
+                        LLAMA_LOG_DEBUG("%s: set ngl_per_device_high[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer);
+                    }
+                    delta = ngl_per_device_high[id].n_layer - ngl_per_device[id].n_layer;
+                }
+            } else {
+                ngl_per_device  = ngl_per_device_high;
+                n_unassigned   -= ngl_per_device[id].n_layer;
+                LLAMA_LOG_DEBUG("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer);
+            }
+        }
+
+        const int64_t projected_margin = dmds_full[id].free - mem[id];
+        LLAMA_LOG_INFO(
+            "%s:   - %s: %2" PRIu32 " layers, %6" PRId64 " MiB used, %6" PRId64 " MiB free\n",
+            __func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, mem[id]/MiB, projected_margin/MiB);
+    }
+    if (hp_nex == 0 || global_surplus_cpu_moe <= 0) {
+        set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, *mparams, partial_moe);
+        return;
+    }
+
+    // step 4: for a MoE model where all dense tensors fit,
+    //     convert the dense-only layers in the back to full layers in the front until all devices are full
+    // essentially the same procedure as for the dense-only layers except front-to-back
+    // also, try fitting at least part of one more layer to reduce waste for "small" GPUs with e.g. 24 GiB VRAM
+
+    size_t id_dense_start = nd;
+    for (int id = nd - 1; id >= 0; id--) {
+        if (ngl_per_device[id].n_layer > 0) {
+            id_dense_start = id;
+            continue;
+        }
+        break;
+    }
+    assert(id_dense_start < nd);
+
+    LLAMA_LOG_INFO("%s: converting dense-only layers to full layers and filling them front-to-back with overflow to next device/system memory:\n", __func__);
+    for (size_t id = 0; id <= id_dense_start; id++) {
+        std::vector<ngl_t> ngl_per_device_high = ngl_per_device;
+        for (size_t jd = id_dense_start; jd < nd; jd++) {
+            const uint32_t n_layer_move = ngl_per_device_high[jd].n_layer;
+            ngl_per_device_high[id].n_layer += n_layer_move;
+            ngl_per_device_high[jd].n_layer -= n_layer_move;
+            ngl_per_device_high[jd].n_part = 0;
+        }
+        size_t id_dense_start_high = nd - 1;
+        std::vector<int64_t> mem_high = get_memory_for_layers(__func__, ngl_per_device_high, overflow_bufts, partial_moe);
+
+        if (mem_high[id] > targets[id]) {
+            assert(ngl_per_device_high[id].n_layer >= ngl_per_device_high[id].n_part);
+            assert(ngl_per_device[id].n_layer >= ngl_per_device[id].n_part);
+            assert((ngl_per_device_high[id].n_layer - ngl_per_device_high[id].n_part)
+                   >= ngl_per_device[id].n_layer - ngl_per_device[id].n_part);
+            uint32_t delta = (ngl_per_device_high[id].n_layer - ngl_per_device_high[id].n_part)
+                - (ngl_per_device[id].n_layer - ngl_per_device[id].n_part);
+            while (delta > 1) {
+                uint32_t step_size = int64_t(delta) * (targets[id] - mem[id]) / (mem_high[id] - mem[id]);
+                step_size = std::max(step_size, uint32_t(1));
+                step_size = std::min(step_size, delta - 1);
+
+                std::vector<ngl_t> ngl_per_device_test = ngl_per_device;
+                size_t id_dense_start_test = id_dense_start;
+                uint32_t n_converted_test = 0;
+                for (;id_dense_start_test < nd; id_dense_start_test++) {
+                    const uint32_t n_convert_jd = std::min(step_size - n_converted_test, ngl_per_device_test[id_dense_start_test].n_part);
+                    ngl_per_device_test[id_dense_start_test].n_layer -= n_convert_jd;
+                    ngl_per_device_test[id_dense_start_test].n_part -= n_convert_jd;
+                    ngl_per_device_test[id].n_layer += n_convert_jd;
+                    n_converted_test += n_convert_jd;
+
+                    if (ngl_per_device_test[id_dense_start_test].n_layer > 0) {
+                        break;
+                    }
+                }
+                const std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe);
+
+                if (mem_test[id] <= targets[id]) {
+                    ngl_per_device = ngl_per_device_test;
+                    mem            = mem_test;
+                    id_dense_start = id_dense_start_test;
+                    LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start=%zu\n",
+                        __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
+                } else {
+                    ngl_per_device_high = ngl_per_device_test;
+                    mem_high            = mem_test;
+                    id_dense_start_high = id_dense_start_test;
+                    LLAMA_LOG_DEBUG("%s: set ngl_per_device_high[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start_high=%zu\n",
+                        __func__, id, ngl_per_device_high[id].n_layer, ngl_per_device_high[id].n_part, id_dense_start_high);
+                }
+                delta = (ngl_per_device_high[id].n_layer - ngl_per_device_high[id].n_part)
+                    - (ngl_per_device[id].n_layer - ngl_per_device[id].n_part);
+            }
+        } else {
+            ngl_per_device = ngl_per_device_high;
+            id_dense_start = id_dense_start_high;
+            LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start=%zu\n",
+                __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
+        }
+
+        // try to fit at least part of one more layer
+        if (ngl_per_device[id_dense_start].n_layer > 0) {
+            std::vector<ngl_t> ngl_per_device_test = ngl_per_device;
+            size_t id_dense_start_test = id_dense_start;
+            ngl_per_device_test[id_dense_start_test].n_layer--;
+            ngl_per_device_test[id_dense_start_test].n_part--;
+            ngl_per_device_test[id].n_layer++;
+            ngl_per_device_test[id].n_part++;
+            if (ngl_per_device_test[id_dense_start_test].n_layer == 0) {
+                id_dense_start_test++;
+            }
+            ngl_per_device_test[id].overflow_type = LAYER_FRACTION_UP;
+            LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_UP\n", __func__);
+            std::vector<int64_t> mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe);
+            if (mem_test[id] < targets[id]) {
+                ngl_per_device = ngl_per_device_test;
+                mem            = mem_test;
+                id_dense_start = id_dense_start_test;
+                LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", UP), id_dense_start=%zu\n",
+                    __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
+
+                ngl_per_device_test[id].overflow_type = LAYER_FRACTION_GATE;
+                LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_GATE\n", __func__);
+                mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe);
+                if (mem_test[id] < targets[id]) {
+                    ngl_per_device = ngl_per_device_test;
+                    mem            = mem_test;
+                    id_dense_start = id_dense_start_test;
+                    LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", GATE), id_dense_start=%zu\n",
+                        __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
+                }
+            } else {
+                ngl_per_device_test[id].overflow_type = LAYER_FRACTION_ATTN;
+                LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_ATTN\n", __func__);
+                mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe);
+                if (mem_test[id] < targets[id]) {
+                    ngl_per_device = ngl_per_device_test;
+                    mem            = mem_test;
+                    id_dense_start = id_dense_start_test;
+                    LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", ATTN), id_dense_start=%zu\n",
+                        __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start);
+                }
+            }
+        }
+
+        const int64_t projected_margin = dmds_full[id].free - mem[id];
+        LLAMA_LOG_INFO(
+            "%s:   - %s: %2" PRIu32 " layers (%2" PRIu32 " overflowing), %6" PRId64 " MiB used, %6" PRId64 " MiB free\n",
+            __func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, ngl_per_device[id].n_part, mem[id]/MiB, projected_margin/MiB);
+    }
+
+    set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, *mparams, partial_moe);
+}
+
+bool llama_params_fit(
+        const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams,
+        float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides,
+        size_t margin_s, uint32_t n_ctx_min, enum ggml_log_level log_level) {
+    const int64_t t0_us = llama_time_us();
+    bool ok = true;
+    try {
+        llama_params_fit_impl(path_model, mparams, cparams, tensor_split, tensor_buft_overrides, margin_s, n_ctx_min, log_level);
+        LLAMA_LOG_INFO("%s: successfully fit params to free device memory\n", __func__);
+    } catch (const std::runtime_error & e) {
+        LLAMA_LOG_WARN("%s: failed to fit params to free device memory: %s\n", __func__, e.what());
+        ok = false;
+    }
+    const int64_t t1_us = llama_time_us();
+    LLAMA_LOG_INFO("%s: fitting params to free memory took %.2f seconds\n", __func__, (t1_us - t0_us) * 1e-6);
+    return ok;
+}
+
 struct llama_sampler_chain_params llama_sampler_chain_default_params() {
     struct llama_sampler_chain_params result = {
         /*.no_perf                     =*/ true,
@@ -49,6 +692,10 @@ size_t llama_max_devices(void) {
     return 16;
 }
 
+size_t llama_max_tensor_buft_overrides() {
+    return 4096;
+}
+
 bool llama_supports_mmap(void) {
     return llama_mmap::SUPPORTED;
 }
@@ -108,11 +755,12 @@ static int llama_model_load(const std::string & fname, std::vector<std::string>
     model.t_start_us = tm.t_start_us;
 
     try {
-        llama_model_loader ml(fname, splits, params.use_mmap, params.check_tensors, params.kv_overrides, params.tensor_buft_overrides);
+        llama_model_loader ml(fname, splits, params.use_mmap, params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides);
 
         ml.print_info();
 
         model.hparams.vocab_only = params.vocab_only;
+        model.hparams.no_alloc   = params.no_alloc;
 
         try {
             model.load_arch(ml);
diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt
index 43a0e81949..8df3f41003 100644
--- a/tools/CMakeLists.txt
+++ b/tools/CMakeLists.txt
@@ -37,4 +37,5 @@ else()
         add_subdirectory(cvector-generator)
         add_subdirectory(export-lora)
     endif()
+    add_subdirectory(fit-params)
 endif()
diff --git a/tools/fit-params/CMakeLists.txt b/tools/fit-params/CMakeLists.txt
new file mode 100644
index 0000000000..34c3373f83
--- /dev/null
+++ b/tools/fit-params/CMakeLists.txt
@@ -0,0 +1,8 @@
+set(TARGET llama-fit-params)
+add_executable(${TARGET} fit-params.cpp)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_17)
+
+if(LLAMA_TOOLS_INSTALL)
+    install(TARGETS ${TARGET} RUNTIME)
+endif()
diff --git a/tools/fit-params/README.md b/tools/fit-params/README.md
new file mode 100644
index 0000000000..8f0c958a2f
--- /dev/null
+++ b/tools/fit-params/README.md
@@ -0,0 +1,55 @@
+# fit-params
+
+llama.cpp binaries can automatically fit the projected memory use of a model to the free device memory available at runtime,
+this is controlled using the CLI arguments starting with `-fit`/`--fit`.
+Internally the code is calling `llama_params_fit` to adjust the `llama_model_params` and `llama_context_params` structs.
+`llama-fit-params` is a simple utility that prints the CLI arguments corresponding to these adjustments to stdout.
+Example usage:
+
+``` bash
+# First, run llama-fit-params and store the results in a file:
+> ./build/bin/llama-fit-params --model /opt/models/qwen_3-30b3a-f16.gguf | tee args.txt
+ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
+ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
+ggml_cuda_init: found 1 CUDA devices:
+  Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
+build: 6895 (4341dc8bc) with cc (GCC) 15.2.1 20250813 for x86_64-pc-linux-gnu
+llama_params_fit_impl: projected to use 61807 MiB of device memory vs. 24077 MiB of free device memory
+llama_params_fit_impl: cannot fulfill margin of 1024 MiB, need to reduce device memory by 42444 MiB
+llama_params_fit_impl: context size reduced from 40960 to 4096 -> need 3456 MiB less memory in total
+llama_params_fit_impl: with only dense weights in device memory there is a total surplus of 16164 MiB
+llama_params_fit_impl: distributing layers across devices with overflow to next device/system memory:
+llama_params_fit_impl:   - CUDA0 (NVIDIA GeForce RTX 4090): 48 layers (34 overflowing),  19187 MiB used,   1199 MiB free
+llama_params_fit: successfully fit params to free device memory
+llama_params_fit: fitting params to free memory took 1.15 seconds
+Printing fitted CLI arguments to stdout...
+-c 4096 -ngl 48 -ot blk\.14\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.15\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.16\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.17\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.18\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.19\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.20\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.21\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.22\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.23\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.24\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.25\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.26\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.27\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.28\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.29\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.30\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.31\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.32\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.33\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.34\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.35\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.36\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.37\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.38\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.39\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.40\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.41\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.42\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.43\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.44\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.45\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.46\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.47\.ffn_(up|down|gate)_(ch|)exps=CPU
+
+# Next, use those results for a llama.cpp binary:
+> cat args.txt | xargs ./build/bin/llama-server --model /opt/models/qwen_3-30b3a-f16.gguf
+ggml_cuda_init: GGML_CUDA_FORCE_MMQ:    no
+ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
+ggml_cuda_init: found 1 CUDA devices:
+  Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
+build: 6895 (4341dc8bc) with cc (GCC) 15.2.1 20250813 for x86_64-pc-linux-gnu
+system info: n_threads = 16, n_threads_batch = 16, total_threads = 32
+
+system_info: n_threads = 16 (n_threads_batch = 16) / 32 | CUDA : ARCHS = 890 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
+
+main: binding port with default address family
+main: HTTP server is listening, hostname: 127.0.0.1, port: 8080, http threads: 31
+main: loading model
+srv    load_model: loading model '/opt/models/qwen_3-30b3a-f16.gguf'
+llama_params_fit_impl: projected to use 19187 MiB of device memory vs. 24077 MiB of free device memory
+llama_params_fit_impl: will leave 1199 >= 1024 MiB of free device memory, no changes needed
+llama_params_fit: successfully fit params to free device memory
+llama_params_fit: fitting params to free memory took 0.28 seconds
+[...]
+main: server is listening on http://127.0.0.1:8080 - starting the main loop
+srv  update_slots: all slots are idle
+^Csrv    operator(): operator(): cleaning up before exit...
+
+llama_memory_breakdown_print: | memory breakdown [MiB] | total   free     self   model   context   compute    unaccounted |
+llama_memory_breakdown_print: |   - CUDA0 (RTX 4090)   | 24077 =  945 + (19187 = 17904 +     384 +     898) +        3945 |
+llama_memory_breakdown_print: |   - Host               |                 58271 = 58259 +       0 +      12                |
+```
diff --git a/tools/fit-params/fit-params.cpp b/tools/fit-params/fit-params.cpp
new file mode 100644
index 0000000000..fbf7a2eb37
--- /dev/null
+++ b/tools/fit-params/fit-params.cpp
@@ -0,0 +1,62 @@
+#include "llama.h"
+
+#include "arg.h"
+#include "common.h"
+#include "log.h"
+
+#include <iostream>
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+int main(int argc, char ** argv) {
+    common_params params;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
+        return 1;
+    }
+
+    common_init();
+    llama_backend_init();
+    llama_numa_init(params.numa);
+    auto mparams = common_model_params_to_llama(params);
+    auto cparams = common_context_params_to_llama(params);
+    llama_params_fit(params.model.path.c_str(), &mparams, &cparams,
+        params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target, params.fit_params_min_ctx,
+        params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR);
+
+    LOG_INF("Printing fitted CLI arguments to stdout...\n");
+    std::cout << "-c "    << cparams.n_ctx;
+    std::cout << " -ngl " << mparams.n_gpu_layers;
+
+    size_t nd = llama_max_devices();
+    while (nd > 1 && mparams.tensor_split[nd - 1] == 0.0f) {
+        nd--;
+    }
+    if (nd > 1) {
+        for (size_t id = 0; id < nd; id++) {
+            if (id == 0) {
+                std::cout << " -ts ";
+            }
+            if (id > 0) {
+                std::cout << ",";
+            }
+            std::cout << mparams.tensor_split[id];
+        }
+    }
+
+    const size_t ntbo = llama_max_tensor_buft_overrides();
+    for (size_t itbo = 0; itbo < ntbo && mparams.tensor_buft_overrides[itbo].pattern != nullptr; itbo++) {
+        if (itbo == 0) {
+            std::cout << " -ot ";
+        }
+        if (itbo > 0) {
+            std::cout << ",";
+        }
+        std::cout << mparams.tensor_buft_overrides[itbo].pattern << "=" << ggml_backend_buft_name(mparams.tensor_buft_overrides[itbo].buft);
+    }
+    std::cout << "\n";
+
+    return 0;
+}

From e73d548659db9297c23a77359776481690b82741 Mon Sep 17 00:00:00 2001
From: Thomas Jarosch <thomas.jarosch@intra2net.com>
Date: Mon, 15 Dec 2025 11:29:29 +0100
Subject: [PATCH 107/135] webui: add "delete all conversations" button to
 import/export tab (#17444)

* webui: add "delete all conversations" button to import/export tab

- Add 'Delete all conversations' functionality with confirmation dialog
- Add Trash icon and destructive styling for clear visual indication
- Redirects to "?new_chat=true#/" by using conversationsStore.deleteAll()

* chore: update webui build output
---
 tools/server/public/index.html.gz             | Bin 1445111 -> 1444280 bytes
 .../ChatSettingsImportExportTab.svelte        |  70 +++++++++++++++++-
 .../src/lib/stores/conversations.svelte.ts    |  26 ++++++-
 3 files changed, 92 insertions(+), 4 deletions(-)

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index 3fd631b77a85820f6fcec9cc3f5e9f60d5ef9fb3..1834d1d91f46fcdaa39f2ccff19e82ee2a2479f3 100644
GIT binary patch
delta 1157796
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diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsImportExportTab.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsImportExportTab.svelte
index 7edce48b9b..1c8b41102a 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsImportExportTab.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsImportExportTab.svelte
@@ -1,9 +1,11 @@
 <script lang="ts">
-	import { Download, Upload } from '@lucide/svelte';
+	import { Download, Upload, Trash2 } from '@lucide/svelte';
 	import { Button } from '$lib/components/ui/button';
 	import { DialogConversationSelection } from '$lib/components/app';
 	import { createMessageCountMap } from '$lib/utils';
 	import { conversationsStore, conversations } from '$lib/stores/conversations.svelte';
+	import { toast } from 'svelte-sonner';
+	import DialogConfirmation from '$lib/components/app/dialogs/DialogConfirmation.svelte';
 
 	let exportedConversations = $state<DatabaseConversation[]>([]);
 	let importedConversations = $state<DatabaseConversation[]>([]);
@@ -18,11 +20,14 @@
 		[]
 	);
 
+	// Delete functionality state
+	let showDeleteDialog = $state(false);
+
 	async function handleExportClick() {
 		try {
 			const allConversations = conversations();
 			if (allConversations.length === 0) {
-				alert('No conversations to export');
+				toast.info('No conversations to export');
 				return;
 			}
 
@@ -145,6 +150,36 @@
 			alert('Failed to import conversations. Please check the file format.');
 		}
 	}
+
+	async function handleDeleteAllClick() {
+		try {
+			const allConversations = conversations();
+
+			if (allConversations.length === 0) {
+				toast.info('No conversations to delete');
+				return;
+			}
+
+			showDeleteDialog = true;
+		} catch (err) {
+			console.error('Failed to load conversations for deletion:', err);
+			toast.error('Failed to load conversations');
+		}
+	}
+
+	async function handleDeleteAllConfirm() {
+		try {
+			await conversationsStore.deleteAll();
+
+			showDeleteDialog = false;
+		} catch (err) {
+			console.error('Failed to delete conversations:', err);
+		}
+	}
+
+	function handleDeleteAllCancel() {
+		showDeleteDialog = false;
+	}
 </script>
 
 <div class="space-y-6">
@@ -229,6 +264,25 @@
 				</div>
 			{/if}
 		</div>
+
+		<div class="grid border-t border-border/30 pt-4">
+			<h4 class="mb-2 text-sm font-medium text-destructive">Delete All Conversations</h4>
+
+			<p class="mb-4 text-sm text-muted-foreground">
+				Permanently delete all conversations and their messages. This action cannot be undone.
+				Consider exporting your conversations first if you want to keep a backup.
+			</p>
+
+			<Button
+				class="text-destructive-foreground w-full justify-start justify-self-start bg-destructive hover:bg-destructive/80 md:w-auto"
+				onclick={handleDeleteAllClick}
+				variant="destructive"
+			>
+				<Trash2 class="mr-2 h-4 w-4" />
+
+				Delete all conversations
+			</Button>
+		</div>
 	</div>
 </div>
 
@@ -249,3 +303,15 @@
 	onCancel={() => (showImportDialog = false)}
 	onConfirm={handleImportConfirm}
 />
+
+<DialogConfirmation
+	bind:open={showDeleteDialog}
+	title="Delete all conversations"
+	description="Are you sure you want to delete all conversations? This action cannot be undone and will permanently remove all your conversations and messages."
+	confirmText="Delete All"
+	cancelText="Cancel"
+	variant="destructive"
+	icon={Trash2}
+	onConfirm={handleDeleteAllConfirm}
+	onCancel={handleDeleteAllCancel}
+/>
diff --git a/tools/server/webui/src/lib/stores/conversations.svelte.ts b/tools/server/webui/src/lib/stores/conversations.svelte.ts
index f766561971..3300eb3113 100644
--- a/tools/server/webui/src/lib/stores/conversations.svelte.ts
+++ b/tools/server/webui/src/lib/stores/conversations.svelte.ts
@@ -385,8 +385,7 @@ class ConversationsStore {
 			this.conversations = this.conversations.filter((c) => c.id !== convId);
 
 			if (this.activeConversation?.id === convId) {
-				this.activeConversation = null;
-				this.activeMessages = [];
+				this.clearActiveConversation();
 				await goto(`?new_chat=true#/`);
 			}
 		} catch (error) {
@@ -394,6 +393,29 @@ class ConversationsStore {
 		}
 	}
 
+	/**
+	 * Deletes all conversations and their messages
+	 */
+	async deleteAll(): Promise<void> {
+		try {
+			const allConversations = await DatabaseService.getAllConversations();
+
+			for (const conv of allConversations) {
+				await DatabaseService.deleteConversation(conv.id);
+			}
+
+			this.clearActiveConversation();
+			this.conversations = [];
+
+			toast.success('All conversations deleted');
+
+			await goto(`?new_chat=true#/`);
+		} catch (error) {
+			console.error('Failed to delete all conversations:', error);
+			toast.error('Failed to delete conversations');
+		}
+	}
+
 	// ─────────────────────────────────────────────────────────────────────────────
 	// Import/Export
 	// ─────────────────────────────────────────────────────────────────────────────

From 4a4f7e6550cf5b327ea0fb241ce7417ab46e1ace Mon Sep 17 00:00:00 2001
From: Andrew Aladjev <aladjev.andrew@gmail.com>
Date: Mon, 15 Dec 2025 13:47:04 +0300
Subject: [PATCH 108/135] cli: fixed dead links to tools/main for cli and
 completion, fixed code owners (#17993)

Co-authored-by: Andrew Aladjev <andrew.aladjev@gmail.com>
---
 CODEOWNERS                          |  3 ++-
 README.md                           |  5 +++--
 docs/development/HOWTO-add-model.md |  3 ++-
 grammars/README.md                  |  6 +++---
 tools/cli/README.md                 |  1 +
 tools/completion/README.md          | 32 ++++++++++++++---------------
 tools/llama-bench/README.md         |  2 +-
 7 files changed, 28 insertions(+), 24 deletions(-)
 create mode 100644 tools/cli/README.md

diff --git a/CODEOWNERS b/CODEOWNERS
index 8e62a36e81..8a0c98c968 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -87,7 +87,8 @@
 /tests/                                 @ggerganov
 /tests/test-chat-.*                     @pwilkin
 /tools/batched-bench/                   @ggerganov
-/tools/main/                            @ggerganov
+/tools/cli/                             @ngxson
+/tools/completion/                      @ggerganov
 /tools/mtmd/                            @ngxson
 /tools/perplexity/                      @ggerganov
 /tools/quantize/                        @ggerganov
diff --git a/README.md b/README.md
index b7d24c9dd7..5f2076d0a3 100644
--- a/README.md
+++ b/README.md
@@ -313,7 +313,7 @@ The Hugging Face platform provides a variety of online tools for converting, qua
 
 To learn more about model quantization, [read this documentation](tools/quantize/README.md)
 
-## [`llama-cli`](tools/main)
+## [`llama-cli`](tools/cli)
 
 #### A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality.
 
@@ -525,7 +525,8 @@ To learn more about model quantization, [read this documentation](tools/quantize
 
 ## Other documentation
 
-- [main (cli)](tools/main/README.md)
+- [cli](tools/cli/README.md)
+- [completion](tools/completion/README.md)
 - [server](tools/server/README.md)
 - [GBNF grammars](grammars/README.md)
 
diff --git a/docs/development/HOWTO-add-model.md b/docs/development/HOWTO-add-model.md
index 5989b873a6..9d1452e3f0 100644
--- a/docs/development/HOWTO-add-model.md
+++ b/docs/development/HOWTO-add-model.md
@@ -9,7 +9,8 @@ Adding a model requires few steps:
 After following these steps, you can open PR.
 
 Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially:
-- [main](/tools/main/)
+- [cli](/tools/cli/)
+- [completion](/tools/completion/)
 - [imatrix](/tools/imatrix/)
 - [quantize](/tools/quantize/)
 - [server](/tools/server/)
diff --git a/grammars/README.md b/grammars/README.md
index 11e3b6dd90..daac7f4d8d 100644
--- a/grammars/README.md
+++ b/grammars/README.md
@@ -1,6 +1,6 @@
 # GBNF Guide
 
-GBNF (GGML BNF) is a format for defining [formal grammars](https://en.wikipedia.org/wiki/Formal_grammar) to constrain model outputs in `llama.cpp`. For example, you can use it to force the model to generate valid JSON, or speak only in emojis. GBNF grammars are supported in various ways in `tools/main` and `tools/server`.
+GBNF (GGML BNF) is a format for defining [formal grammars](https://en.wikipedia.org/wiki/Formal_grammar) to constrain model outputs in `llama.cpp`. For example, you can use it to force the model to generate valid JSON, or speak only in emojis. GBNF grammars are supported in various ways in `tools/cli`, `tools/completion` and `tools/server`.
 
 ## Background
 
@@ -135,7 +135,7 @@ While semantically correct, the syntax `x? x? x?.... x?` (with N repetitions) ma
 You can use GBNF grammars:
 
 - In [llama-server](../tools/server)'s completion endpoints, passed as the `grammar` body field
-- In [llama-cli](../tools/main), passed as the `--grammar` & `--grammar-file` flags
+- In [llama-cli](../tools/cli) and [llama-completion](../tools/completion), passed as the `--grammar` & `--grammar-file` flags
 - With [test-gbnf-validator](../tests/test-gbnf-validator.cpp), to test them against strings.
 
 ## JSON Schemas → GBNF
@@ -145,7 +145,7 @@ You can use GBNF grammars:
 - In [llama-server](../tools/server):
     - For any completion endpoints, passed as the `json_schema` body field
     - For the `/chat/completions` endpoint, passed inside the `response_format` body field (e.g. `{"type", "json_object", "schema": {"items": {}}}` or `{ type: "json_schema", json_schema: {"schema": ...} }`)
-- In [llama-cli](../tools/main), passed as the `--json` / `-j` flag
+- In [llama-cli](../tools/cli) and [llama-completion](../tools/completion), passed as the `--json` / `-j` flag
 - To convert to a grammar ahead of time:
     - in CLI, with [examples/json_schema_to_grammar.py](../examples/json_schema_to_grammar.py)
     - in JavaScript with [json-schema-to-grammar.mjs](../tools/server/public_legacy/json-schema-to-grammar.mjs) (this is used by the [server](../tools/server)'s Web UI)
diff --git a/tools/cli/README.md b/tools/cli/README.md
new file mode 100644
index 0000000000..1333ed77b7
--- /dev/null
+++ b/tools/cli/README.md
@@ -0,0 +1 @@
+TODO
diff --git a/tools/completion/README.md b/tools/completion/README.md
index 54e582de07..57ef394213 100644
--- a/tools/completion/README.md
+++ b/tools/completion/README.md
@@ -1,4 +1,4 @@
-# llama.cpp/tools/main
+# llama.cpp/tools/completion
 
 This example program allows you to use various LLaMA language models easily and efficiently. It is specifically designed to work with the [llama.cpp](https://github.com/ggml-org/llama.cpp) project, which provides a plain C/C++ implementation with optional 4-bit quantization support for faster, lower memory inference, and is optimized for desktop CPUs. This program can be used to perform various inference tasks with LLaMA models, including generating text based on user-provided prompts and chat-like interactions with reverse prompts.
 
@@ -27,64 +27,64 @@ Once downloaded, place your model in the models folder in llama.cpp.
 ##### Input prompt (One-and-done)
 
 ```bash
-./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time"
+./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time"
 ```
 ##### Conversation mode (Allow for continuous interaction with the model)
 
 ```bash
-./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma
+./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma
 ```
 
 ##### Conversation mode using built-in jinja chat template
 
 ```bash
-./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja
+./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja
 ```
 
 ##### One-and-done query using jinja with custom system prompt and a starting prompt
 
 ```bash
-./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello"
+./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello"
 ```
 
 ##### Infinite text from a starting prompt (you can use `Ctrl-C` to stop it):
 ```bash
-./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
+./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 ```
 
 ### Windows:
 
 ##### Input prompt (One-and-done)
 ```powershell
-./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time"
+./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time"
 ```
 ##### Conversation mode (Allow for continuous interaction with the model)
 
 ```powershell
-./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma
+./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma
 ```
 
 ##### Conversation mode using built-in jinja chat template
 
 ```powershell
-./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja
+./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja
 ```
 
 ##### One-and-done query using jinja with custom system prompt and a starting prompt
 
 ```powershell
-./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello"
+./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello"
 ```
 
 #### Infinite text from a starting prompt (you can use `Ctrl-C` to stop it):
 
 ```powershell
-llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
+llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1
 ```
 
 ## Common Options
 
-In this section, we cover the most commonly used options for running the `llama-cli` program with the LLaMA models:
+In this section, we cover the most commonly used options for running the `llama-completion` program with the LLaMA models:
 
 -   `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/gemma-1.1-7b-it.Q4_K_M.gguf`; inferred from `--model-url` if set).
 -   `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g [https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true](https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true)).
@@ -97,7 +97,7 @@ In this section, we cover the most commonly used options for running the `llama-
 
 ## Input Prompts
 
-The `llama-cli` program provides several ways to interact with the LLaMA models using input prompts:
+The `llama-completion` program provides several ways to interact with the LLaMA models using input prompts:
 
 -   `--prompt PROMPT`: Provide a prompt directly as a command-line option.
 -   `--file FNAME`: Provide a file containing a prompt or multiple prompts.
@@ -107,7 +107,7 @@ The `llama-cli` program provides several ways to interact with the LLaMA models
 
 ## Interaction
 
-The `llama-cli` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive` and `--interactive-first`.
+The `llama-completion` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive` and `--interactive-first`.
 
 In interactive mode, users can participate in text generation by injecting their input during the process. Users can press `Ctrl+C` at any time to interject and type their input, followed by pressing `Return` to submit it to the LLaMA model. To submit additional lines without finalizing input, users can end the current line with a backslash (`\`) and continue typing.
 
@@ -136,7 +136,7 @@ To overcome this limitation, you can use the `--in-prefix` flag to add a space o
 The `--in-prefix` flag is used to add a prefix to your input, primarily, this is used to insert a space after the reverse prompt. Here's an example of how to use the `--in-prefix` flag in conjunction with the `--reverse-prompt` flag:
 
 ```sh
-./llama-cli -r "User:" --in-prefix " "
+./llama-completion -r "User:" --in-prefix " "
 ```
 
 ### In-Suffix
@@ -144,7 +144,7 @@ The `--in-prefix` flag is used to add a prefix to your input, primarily, this is
 The `--in-suffix` flag is used to add a suffix after your input. This is useful for adding an "Assistant:" prompt after the user's input. It's added after the new-line character (`\n`) that's automatically added to the end of the user's input. Here's an example of how to use the `--in-suffix` flag in conjunction with the `--reverse-prompt` flag:
 
 ```sh
-./llama-cli -r "User:" --in-prefix " " --in-suffix "Assistant:"
+./llama-completion -r "User:" --in-prefix " " --in-suffix "Assistant:"
 ```
 When --in-prefix or --in-suffix options are enabled the chat template ( --chat-template ) is disabled
 
diff --git a/tools/llama-bench/README.md b/tools/llama-bench/README.md
index 87d9c0a219..c837bb6d26 100644
--- a/tools/llama-bench/README.md
+++ b/tools/llama-bench/README.md
@@ -80,7 +80,7 @@ Each test is repeated the number of times given by `-r`, and the results are ave
 
 Using the `-d <n>` option, each test can be run at a specified context depth, prefilling the KV cache with `<n>` tokens.
 
-For a description of the other options, see the [main example](../main/README.md).
+For a description of the other options, see the [completion example](../completion/README.md).
 
 > [!NOTE]
 > The measurements with `llama-bench` do not include the times for tokenization and for sampling.

From 96a181a933a34e92c316b0603f0f30cb7f30eefa Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Mon, 15 Dec 2025 14:16:52 +0100
Subject: [PATCH 109/135] mtmd: refactor audio preprocessing (#17978)

* mtmd: refactor audio preprocessing

* refactor

Co-authored-by: Tarek <tdakhran@users.noreply.github.com>

* wip

* wip (2)

* improve constructor

* fix use_natural_log

* fix padding for short input

* clean up

* remove need_chunking

---------

Co-authored-by: Tarek <tdakhran@users.noreply.github.com>
---
 tools/mtmd/clip-model.h   |   9 +
 tools/mtmd/clip.cpp       |  19 +-
 tools/mtmd/mtmd-audio.cpp | 872 ++++++++++++++------------------------
 tools/mtmd/mtmd-audio.h   |  43 +-
 tools/mtmd/mtmd.cpp       |  32 +-
 5 files changed, 379 insertions(+), 596 deletions(-)

diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h
index bdd4f63e0f..2f7dbb458e 100644
--- a/tools/mtmd/clip-model.h
+++ b/tools/mtmd/clip-model.h
@@ -65,6 +65,13 @@ struct clip_hparams {
     int32_t n_mel_bins = 0; // whisper preprocessor
     int32_t proj_stack_factor = 0; // ultravox
 
+    // audio-to-mel preprocessor params
+    int32_t audio_chunk_len   = -1; // in seconds
+    int32_t audio_sample_rate = -1;
+    int32_t audio_n_fft       = -1;
+    int32_t audio_window_len  = -1;
+    int32_t audio_hop_len     = -1;
+
     // legacy
     bool has_llava_projector = false;
     int minicpmv_version = 0;
@@ -278,3 +285,5 @@ struct clip_model {
             || proj_type == PROJECTOR_TYPE_VOXTRAL;
     }
 };
+
+const clip_hparams * clip_get_hparams(const struct clip_ctx * ctx);
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index c72c6a6381..fee49e465c 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -1170,11 +1170,15 @@ struct clip_model_loader {
                                              model.proj_type == PROJECTOR_TYPE_VOXTRAL ||
                                              model.proj_type == PROJECTOR_TYPE_GLMA;
                         get_u32(KEY_A_PROJ_STACK_FACTOR, hparams.proj_stack_factor, require_stack);
-                        if (hparams.n_mel_bins != 128) {
-                            throw std::runtime_error(string_format("%s: only 128 mel bins are supported for ultravox\n", __func__));
-                        }
                         hparams.ffn_op = FFN_GELU_ERF;
                         log_ffn_op = "gelu_erf"; // temporary solution for logging
+
+                        // audio preprocessing params
+                        hparams.audio_chunk_len    = 30; // in seconds
+                        hparams.audio_sample_rate  = 16000;
+                        hparams.audio_n_fft        = 400;
+                        hparams.audio_window_len   = 400;
+                        hparams.audio_hop_len      = 160;
                     } break;
                 default:
                     break;
@@ -1212,6 +1216,11 @@ struct clip_model_loader {
                 LOG_INF("\n--- audio hparams ---\n");
                 LOG_INF("%s: n_mel_bins:         %d\n", __func__, hparams.n_mel_bins);
                 LOG_INF("%s: proj_stack_factor:  %d\n", __func__, hparams.proj_stack_factor);
+                LOG_INF("%s: audio_chunk_len:    %d\n", __func__, hparams.audio_chunk_len);
+                LOG_INF("%s: audio_sample_rate:  %d\n", __func__, hparams.audio_sample_rate);
+                LOG_INF("%s: audio_n_fft:        %d\n", __func__, hparams.audio_n_fft);
+                LOG_INF("%s: audio_window_len:   %d\n", __func__, hparams.audio_window_len);
+                LOG_INF("%s: audio_hop_len:      %d\n", __func__, hparams.audio_hop_len);
             }
             LOG_INF("\n");
             LOG_INF("%s: model size:         %.2f MiB\n", __func__, model_size / 1024.0 / 1024.0);
@@ -3478,3 +3487,7 @@ void clip_image_f32_batch_add_mel(struct clip_image_f32_batch * batch, int n_mel
     batch->entries.push_back(clip_image_f32_ptr(audio));
     batch->is_audio = true;
 }
+
+const clip_hparams * clip_get_hparams(const struct clip_ctx * ctx) {
+    return &ctx->model.hparams;
+}
diff --git a/tools/mtmd/mtmd-audio.cpp b/tools/mtmd/mtmd-audio.cpp
index 4d053895cd..f68829a61a 100644
--- a/tools/mtmd/mtmd-audio.cpp
+++ b/tools/mtmd/mtmd-audio.cpp
@@ -11,63 +11,149 @@
 
 // most of the code here is copied from whisper.cpp
 
-// align x to upper multiple of n
-#define _ALIGN(x, n) ((((x) + (n) - 1) / (n)) * (n))
+constexpr bool DEBUG = false;
 
-namespace whisper_preprocessor {
+struct mtmd_audio_mel_filters {
+    int32_t n_mel;
+    int32_t n_fft;
 
-#define SIN_COS_N_COUNT WHISPER_N_FFT
-namespace {
-struct whisper_global_cache {
-    // In FFT, we frequently use sine and cosine operations with the same values.
-    // We can use precalculated values to speed up the process.
-    float sin_vals[SIN_COS_N_COUNT];
-    float cos_vals[SIN_COS_N_COUNT];
+    std::vector<float> data;
+};
 
-    // Hann window (Use cosf to eliminate difference)
-    // ref: https://pytorch.org/docs/stable/generated/torch.hann_window.html
-    // ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L147
-    float hann_window[WHISPER_N_FFT];
+// note: this global cache is shared among all preprocessors
+//       if we want to use multiple preprocessors at the same time,
+//       we will need to enclose it in the preprocessor class in the future
+static struct mtmd_audio_global_cache {
+    // precomputed sin/cos table for FFT
+    std::vector<float> sin_vals;
+    std::vector<float> cos_vals;
 
-    whisper_global_cache() {
-        fill_sin_cos_table();
-        fill_hann_window(sizeof(hann_window)/sizeof(hann_window[0]), true, hann_window);
-    }
+    // hann window
+    std::vector<float> hann_window;
 
-    void fill_sin_cos_table() {
-        for (int i = 0; i < SIN_COS_N_COUNT; i++) {
-            double theta = (2 * M_PI * i) / SIN_COS_N_COUNT;
+    // mel filter bank
+    mtmd_audio_mel_filters filters;
+
+    void fill_sin_cos_table(int n) {
+        sin_vals.resize(n);
+        cos_vals.resize(n);
+        for (int i = 0; i < n; i++) {
+            double theta = (2 * M_PI * i) / n;
             sin_vals[i] = sinf(theta);
             cos_vals[i] = cosf(theta);
         }
     }
 
-    void fill_hann_window(int length, bool periodic, float * output) {
+    void fill_hann_window(int length, bool periodic) {
+        hann_window.resize(length);
         int offset = -1;
         if (periodic) {
             offset = 0;
         }
         for (int i = 0; i < length; i++) {
-            output[i] = 0.5 * (1.0 - cosf((2.0 * M_PI * i) / (length + offset)));
+            hann_window[i] = 0.5 * (1.0 - cosf((2.0 * M_PI * i) / (length + offset)));
         }
     }
-} global_cache;
-}
+
+    // Build mel filterbank matrix [n_mel × n_fft_bins] at runtime.
+    // n_fft_bins must be (N_fft / 2 + 1). Example: if N_fft=512 -> n_fft_bins=257.
+    void fill_mel_filterbank_matrix(
+        int n_mel,
+        int n_fft,
+        int sample_rate,            // e.g. 16000
+        float fmin = 0.0f,          // e.g. 0.0
+        float fmax = -1.0f,         // e.g. sr/2; pass -1 for auto
+        bool slaney_area_norm = true,
+        float scale = 1.0f          // optional extra scaling; use 1.0f/1000.0f to mimic your code
+    ) {
+        GGML_ASSERT(n_mel > 0 && n_fft > 1);
+        if (fmax <= 0.0f) {
+            fmax = 0.5f * sample_rate;
+        }
+
+        // Slaney scale (matches librosa default)
+        const double min_log_hz = 1000.0;
+        const double lin_slope = 3 / 200.;
+        const double min_log_mel = min_log_hz * lin_slope;
+        const double log_step = log(6.4) / 27.0;
+        auto hz_to_mel = [min_log_hz, lin_slope, log_step, min_log_mel](const double f_hz) -> double {
+            return (f_hz < min_log_hz) ? f_hz * lin_slope : min_log_mel + log(f_hz / min_log_hz) / log_step;
+        };
+        auto mel_to_hz = [min_log_hz, lin_slope, log_step, min_log_mel](const double m) -> double {
+            return (m < min_log_mel) ? m / lin_slope : min_log_hz * exp((m - min_log_mel) * log_step);
+        };
+
+        // infer N_fft from n_fft_bins
+        const double bin_hz_step = double(sample_rate) / double(n_fft);
+
+        // mel grid: n_mel + 2 edges
+        const double m_lo = hz_to_mel(fmin);
+        const double m_hi = hz_to_mel(fmax);
+        std::vector<double> mel_pts(n_mel + 2);
+        for (int i = 0; i < n_mel + 2; ++i) {
+            mel_pts[i] = m_lo + (m_hi - m_lo) * (double(i) / (n_mel + 1));
+        }
+
+        // convert to Hz
+        std::vector<double> hz_pts(n_mel + 2);
+        for (int i = 0; i < n_mel + 2; ++i) {
+            hz_pts[i] = mel_to_hz(mel_pts[i]);
+        }
+
+        const int n_fft_bins = n_fft / 2 + 1;
+
+        // filterbank
+        std::vector<float> out(n_mel * n_fft_bins, 0);
+        for (int m = 0; m < n_mel; ++m) {
+            const double f_left   = hz_pts[m];
+            const double f_center = hz_pts[m + 1];
+            const double f_right  = hz_pts[m + 2];
+
+            const double denom_l = std::max(1e-30, f_center - f_left);
+            const double denom_r = std::max(1e-30, f_right  - f_center);
+            const double enorm   = slaney_area_norm ? (2.0 / std::max(1e-30, f_right - f_left)) : 1.0;
+
+            for (int k = 0; k < n_fft_bins; ++k) {
+                const double f = k * bin_hz_step;
+                double w = 0.0;
+                if (f >= f_left && f <= f_center) {
+                    w = (f - f_left) / denom_l;
+                } else if (f > f_center && f <= f_right) {
+                    w = (f_right - f) / denom_r;
+                }
+                out[size_t(m) * size_t(n_fft_bins) + size_t(k)] = float(w * enorm * scale);
+            }
+        }
+
+        filters.n_mel = n_mel;
+        filters.n_fft = n_fft;
+        filters.data  = std::move(out);
+
+        if (DEBUG) { // debug
+            for (size_t i = 0; i < filters.data.size(); ++i) {
+                if (filters.data[i] != 0.0f) {
+                    printf("filters[%zu] = %f\n", i, filters.data[i] * 1000.0f);
+                }
+            }
+        }
+    }
+} g_cache;
 
 // naive Discrete Fourier Transform
 // input is real-valued
 // output is complex-valued
-static void dft(const float* in, int N, float* out) {
-    const int sin_cos_step = SIN_COS_N_COUNT / N;
+static void dft(const float * in, int N, float * out) {
+    const int n_sin_cos_vals = g_cache.sin_vals.size();
+    const int sin_cos_step = n_sin_cos_vals / N;
 
     for (int k = 0; k < N; k++) {
         float re = 0;
         float im = 0;
 
         for (int n = 0; n < N; n++) {
-            int idx = (k * n * sin_cos_step) % (SIN_COS_N_COUNT); // t = 2*M_PI*k*n/N
-            re += in[n]*global_cache.cos_vals[idx]; // cos(t)
-            im -= in[n]*global_cache.sin_vals[idx]; // sin(t)
+            int idx = (k * n * sin_cos_step) % (n_sin_cos_vals); // t = 2*M_PI*k*n/N
+            re += in[n] * g_cache.cos_vals[idx]; // cos(t)
+            im -= in[n] * g_cache.sin_vals[idx]; // sin(t)
         }
 
         out[k*2 + 0] = re;
@@ -79,7 +165,8 @@ static void dft(const float* in, int N, float* out) {
 // poor man's implementation - use something better
 // input is real-valued
 // output is complex-valued
-static void fft(float* in, int N, float* out) {
+static void fft(float * in, int N, float * out) {
+    const int n_sin_cos_vals = g_cache.sin_vals.size();
     if (N == 1) {
         out[0] = in[0];
         out[1] = 0;
@@ -106,11 +193,11 @@ static void fft(float* in, int N, float* out) {
     float* odd_fft = even_fft + N;
     fft(odd, half_N, odd_fft);
 
-    const int sin_cos_step = SIN_COS_N_COUNT / N;
+    const int sin_cos_step = n_sin_cos_vals / N;
     for (int k = 0; k < half_N; k++) {
         int idx = k * sin_cos_step; // t = 2*M_PI*k/N
-        float re = global_cache.cos_vals[idx]; // cos(t)
-        float im = -global_cache.sin_vals[idx]; // sin(t)
+        float re =  g_cache.cos_vals[idx]; // cos(t)
+        float im = -g_cache.sin_vals[idx]; // sin(t)
 
         float re_odd = odd_fft[2*k + 0];
         float im_odd = odd_fft[2*k + 1];
@@ -123,20 +210,34 @@ static void fft(float* in, int N, float* out) {
     }
 }
 
+struct filter_params {
+    int32_t n_mel;
+    int32_t n_fft_bins;
+    int32_t hann_window_size;
+    int32_t hop_length;
+    int32_t sample_rate;
+    bool    center_padding = false;
+    float   preemph = 0.f;
+    bool    use_natural_log = false;
+    bool    norm_per_feature = false;
+};
+
 static void log_mel_spectrogram_worker_thread(int ith, const float * hann, const std::vector<float> & samples,
                                               int n_samples, int frame_size, int frame_step, int n_threads,
-                                              const whisper_filters & filters, whisper_mel & mel) {
+                                              const filter_params & params, mtmd_audio_mel & out) {
     std::vector<float> fft_in(frame_size * 2, 0.0);
     std::vector<float> fft_out(frame_size * 2 * 2 * 2);
 
-    int n_fft = filters.n_fft;
+    int n_fft_bins = params.n_fft_bins;
     int i = ith;
 
-    // make sure n_fft == 1 + (WHISPER_N_FFT / 2), bin_0 to bin_nyquist
-    WHISPER_ASSERT(n_fft == 1 + (frame_size / 2));
+    const auto & filters = g_cache.filters;
 
+    // make sure n_fft == 1 + (WHISPER_N_FFT / 2), bin_0 to bin_nyquist
+    GGML_ASSERT(n_fft_bins == 1 + (frame_size / 2));
+    GGML_ASSERT(g_cache.sin_vals.size() == g_cache.cos_vals.size());
     // calculate FFT only when fft_in are not all zero
-    for (; i < std::min(n_samples / frame_step + 1, mel.n_len); i += n_threads) {
+    for (; i < std::min(n_samples / frame_step + 1, out.n_len); i += n_threads) {
         const int offset = i * frame_step;
 
         // apply Hann window (~10% faster)
@@ -154,36 +255,39 @@ static void log_mel_spectrogram_worker_thread(int ith, const float * hann, const
 
         // Calculate modulus^2 of complex numbers
         // Use pow(fft_out[2 * j + 0], 2) + pow(fft_out[2 * j + 1], 2) causes inference quality problem? Interesting.
-        for (int j = 0; j < n_fft; j++) {
+        for (int j = 0; j < n_fft_bins; j++) {
             fft_out[j] = (fft_out[2 * j + 0] * fft_out[2 * j + 0] + fft_out[2 * j + 1] * fft_out[2 * j + 1]);
         }
 
         // mel spectrogram
-        for (int j = 0; j < mel.n_mel; j++) {
+        for (int j = 0; j < out.n_mel; j++) {
             double sum = 0.0;
             // unroll loop (suggested by GH user @lunixbochs)
             int k = 0;
-            for (k = 0; k < n_fft - 3; k += 4) {
+            for (k = 0; k < n_fft_bins - 3; k += 4) {
+                size_t idx = size_t(j) * size_t(n_fft_bins) + size_t(k);
                 sum +=
-                        fft_out[k + 0] * filters.data[j * n_fft + k + 0] +
-                        fft_out[k + 1] * filters.data[j * n_fft + k + 1] +
-                        fft_out[k + 2] * filters.data[j * n_fft + k + 2] +
-                        fft_out[k + 3] * filters.data[j * n_fft + k + 3];
+                        fft_out[k + 0] * filters.data[idx + 0] +
+                        fft_out[k + 1] * filters.data[idx + 1] +
+                        fft_out[k + 2] * filters.data[idx + 2] +
+                        fft_out[k + 3] * filters.data[idx + 3];
             }
             // handle n_fft remainder
-            for (; k < n_fft; k++) {
-                sum += fft_out[k] * filters.data[j * n_fft + k];
+            for (; k < n_fft_bins; k++) {
+                sum += fft_out[k] * filters.data[j * n_fft_bins + k];
             }
-            sum = log10(std::max(sum, 1e-10));
-            mel.data[j * mel.n_len + i] = sum;
+            sum = params.use_natural_log
+                ? log(sum + 5.960464477539063e-08)
+                : log10(std::max(sum, 1e-10));
+            out.data[j * out.n_len + i] = sum;
         }
     }
 
     // Otherwise fft_out are all zero
-    double sum = log10(1e-10);
-    for (; i < mel.n_len; i += n_threads) {
-        for (int j = 0; j < mel.n_mel; j++) {
-            mel.data[j * mel.n_len + i] = sum;
+    double sum = params.use_natural_log ? log(1e-10) : log10(1e-10);
+    for (; i < out.n_len; i += n_threads) {
+        for (int j = 0; j < out.n_mel; j++) {
+            out.data[j * out.n_len + i] = sum;
         }
     }
 }
@@ -191,115 +295,212 @@ static void log_mel_spectrogram_worker_thread(int ith, const float * hann, const
 // ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L110-L157
 static bool log_mel_spectrogram(
         const float * samples,
-        const int   n_samples,
-        const int   /*sample_rate*/,
-        const int   frame_size,
-        const int   frame_step,
-        const int   n_mel,
-        const int   n_threads,
-        const whisper_filters & filters,
-        const bool   debug,
-        whisper_mel & mel) {
+        const int     n_samples_in,
+        const int     n_threads,
+        const filter_params & params,
+        mtmd_audio_mel & out) {
     //const int64_t t_start_us = ggml_time_us();
 
+    out.n_len_org = n_samples_in;
+    int n_samples = n_samples_in;
+
     // Hann window
-    WHISPER_ASSERT(frame_size == WHISPER_N_FFT && "Unsupported frame_size");
-    const float * hann = global_cache.hann_window;
+    const float * hann = g_cache.hann_window.data();
+    const int frame_size = (params.n_fft_bins - 1) * 2;
+    const int frame_step = params.hop_length;
 
-    // Calculate the length of padding
-    int64_t stage_1_pad = WHISPER_SAMPLE_RATE * 30;
-    int64_t stage_2_pad = frame_size / 2;
-
-    // Initialize a vector and copy data from C array to it.
+    // Padding
     std::vector<float> samples_padded;
-    samples_padded.resize(n_samples + stage_1_pad + stage_2_pad * 2);
-    std::copy(samples, samples + n_samples, samples_padded.begin() + stage_2_pad);
+    if (params.center_padding) {
+        const auto pad_amount = frame_size / 2;
+        samples_padded = std::vector<float>(n_samples + 2 * pad_amount, 0);
+        std::copy(samples, samples + n_samples, samples_padded.data() + pad_amount);
+        samples = samples_padded.data();
+        n_samples = samples_padded.size();
+    } else {
+        // existing padding logic
+        int64_t stage_1_pad = params.sample_rate * 30;
+        int64_t stage_2_pad = frame_size / 2;
+        samples_padded.resize(n_samples + stage_1_pad + stage_2_pad * 2);
+        std::copy(samples, samples + n_samples, samples_padded.begin() + stage_2_pad);
+        // pad 30 seconds of zeros at the end of audio (480,000 samples) + reflective pad 200 samples at the end of audio
+        std::fill(samples_padded.begin() + n_samples + stage_2_pad, samples_padded.begin() + n_samples + stage_1_pad + 2 * stage_2_pad, 0);
+        // reflective pad 200 samples at the beginning of audio
+        if (n_samples < stage_2_pad + 1) {
+            // TODO: Handle short audio differently or return error
+            return false;
+        }
+        std::reverse_copy(samples + 1, samples + 1 + stage_2_pad, samples_padded.begin());
+    }
 
-    // pad 30 seconds of zeros at the end of audio (480,000 samples) + reflective pad 200 samples at the end of audio
-    std::fill(samples_padded.begin() + n_samples + stage_2_pad, samples_padded.begin() + n_samples + stage_1_pad + 2 * stage_2_pad, 0);
+    // preemphasis
+    if (params.preemph) {
+        const int pad_amount = frame_size / 2;
+        const float preemph = 0.97f;
+        float prev = samples_padded[pad_amount];
+        for (int i = pad_amount + 1; i + pad_amount < n_samples; ++i) {
+            float cur = samples_padded[i];
+            samples_padded[i] = cur - preemph * prev;
+            prev = cur;
+        }
+    }
 
-    // reflective pad 200 samples at the beginning of audio
-    std::reverse_copy(samples + 1, samples + 1 + stage_2_pad, samples_padded.begin());
+    // pad hann window if it's smaller than frame_size
+    // TODO: probably unnecessary here? (or better doing it in g_cache?)
+    std::vector<float> hann_window_padded;
+    if (params.hann_window_size < frame_size) {
+        hann_window_padded.resize(frame_size);
+        const int padding = (frame_size - params.hann_window_size) / 2;
+        std::copy(hann, hann + params.hann_window_size, &hann_window_padded[padding]);
+        hann = hann_window_padded.data();
+    }
 
-    mel.n_mel     = n_mel;
-    // https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/SpectralOps.cpp#L936
-    // Calculate number of frames + remove the last frame
-    mel.n_len     = (samples_padded.size() - frame_size) / frame_step;
-    // Calculate semi-padded sample length to ensure compatibility
-    mel.n_len_org = 1 + (n_samples + stage_2_pad - frame_size) / frame_step;
-    mel.data.resize(mel.n_mel * mel.n_len);
+
+    out.n_mel = params.n_mel;
+    out.n_len = (n_samples - frame_size) / frame_step + 1;
+    // TODO: handle these checks better
+    if (out.n_mel > 0 && (unsigned long)out.n_len > SIZE_MAX / out.n_mel) {
+        LOG_ERR("%s: size overflow\n", __func__);
+        return false;
+    }
+    if (n_samples < frame_size) {
+        LOG_ERR("%s: not enough samples after padding\n", __func__);
+        return false;
+    }
+    out.data.resize(out.n_mel * out.n_len);
 
     {
         std::vector<std::thread> workers(n_threads - 1);
         for (int iw = 0; iw < n_threads - 1; ++iw) {
             workers[iw] = std::thread(
                     log_mel_spectrogram_worker_thread, iw + 1, hann, std::cref(samples_padded),
-                    n_samples + stage_2_pad, frame_size, frame_step, n_threads,
-                    std::cref(filters), std::ref(mel));
+                    n_samples, frame_size, frame_step, n_threads,
+                    std::cref(params), std::ref(out));
         }
 
         // main thread
-        log_mel_spectrogram_worker_thread(0, hann, samples_padded, n_samples + stage_2_pad, frame_size, frame_step, n_threads, filters, mel);
-
+        log_mel_spectrogram_worker_thread(0, hann, samples_padded, n_samples, frame_size, frame_step, n_threads, params, out);
         for (int iw = 0; iw < n_threads - 1; ++iw) {
             workers[iw].join();
         }
     }
 
-    // clamping and normalization
-    double mmax = -1e20;
-    for (int i = 0; i < mel.n_mel*mel.n_len; i++) {
-        if (mel.data[i] > mmax) {
-            mmax = mel.data[i];
+    const int effective_n_len = n_samples_in / frame_step;
+    if (params.norm_per_feature) {
+        for (int i = 0; i < out.n_mel; i++) {
+            double mean = 0;
+            for (int j = 0; j < effective_n_len; ++j) {
+                mean += out.data[i * out.n_len + j];
+            }
+            mean /= effective_n_len;
+
+            double var = 0.0;
+            for (int j = 0; j < effective_n_len; ++j) {
+                const double value = out.data[i * out.n_len + j] - mean;
+                var += value * value;
+            }
+            var /= effective_n_len - 1;  // unbiased
+            const double mstd = std::sqrt(var + 1e-5);
+
+            for (int j = 0; j < effective_n_len; ++j) {
+                auto &value = out.data[i * out.n_len + j];
+                value = (value - mean) / mstd;
+            }
+
+            // pad the rest with zeros
+            for (int j = effective_n_len; j < out.n_len; ++j) {
+                out.data[i * out.n_len + j] = 0.0;
+            }
         }
-    }
-
-    mmax -= 8.0;
-
-    for (int i = 0; i < mel.n_mel*mel.n_len; i++) {
-        if (mel.data[i] < mmax) {
-            mel.data[i] = mmax;
+    } else {
+        // clamping and normalization
+        double mmax = -1e20;
+        for (int i = 0; i < out.n_mel*out.n_len; i++) {
+            if (out.data[i] > mmax) {
+                mmax = out.data[i];
+            }
         }
 
-        mel.data[i] = (mel.data[i] + 4.0)/4.0;
+        mmax -= 8.0;
+
+        for (int i = 0; i < out.n_mel*out.n_len; i++) {
+            if (out.data[i] < mmax) {
+                out.data[i] = mmax;
+            }
+            out.data[i] = (out.data[i] + 4.0)/4.0;
+        }
     }
 
     // Dump log_mel_spectrogram
-    if (debug) {
+    if (DEBUG) {
         std::ofstream outFile("log_mel_spectrogram.json");
         outFile << "[";
-        for (uint64_t i = 0; i < mel.data.size() - 1; i++) {
-            outFile << mel.data[i] << ", ";
+        for (uint64_t i = 0; i < out.data.size() - 1; i++) {
+            outFile << out.data[i] << ", ";
         }
-        outFile << mel.data[mel.data.size() - 1] << "]";
+        outFile << out.data[out.data.size() - 1] << "]";
         outFile.close();
     }
 
     return true;
 }
 
-bool preprocess_audio(
+//
+// mtmd_audio_preprocessor_whisper
+//
+
+void mtmd_audio_preprocessor_whisper::initialize() {
+    g_cache.fill_sin_cos_table(hparams.audio_n_fft);
+    g_cache.fill_hann_window(hparams.audio_window_len, true);
+    g_cache.fill_mel_filterbank_matrix(
+        hparams.n_mel_bins,
+        hparams.audio_n_fft,
+        hparams.audio_sample_rate);
+}
+
+bool mtmd_audio_preprocessor_whisper::preprocess(
         const float * samples,
         size_t n_samples,
-        const whisper_filters & filters,
-        std::vector<whisper_mel> & output) {
-
+        std::vector<mtmd_audio_mel> & output) {
     if (n_samples == 0) {
         // empty audio
         return false;
     }
 
-    whisper_mel out_full;
+    std::vector<float> smpl;
+    // if input is too short, pad with zeros
+    // this is to avoid potential issues with stage1/2 padding in log_mel_spectrogram
+    // TODO: maybe handle this better
+    size_t min_samples = (size_t)hparams.audio_sample_rate * (hparams.audio_chunk_len + 1); // +1 second margin
+    if (n_samples < min_samples) {
+        smpl.resize(min_samples, 0.0f);
+        std::memcpy(smpl.data(), samples, n_samples * sizeof(float));
+        samples   = smpl.data();
+        n_samples = smpl.size();
+    }
+
+    filter_params params;
+    params.n_mel            = hparams.n_mel_bins;
+    params.n_fft_bins       = 1 + (hparams.audio_n_fft / 2);
+    params.hann_window_size = hparams.audio_window_len;
+    params.hop_length       = hparams.audio_hop_len;
+    params.sample_rate      = hparams.audio_sample_rate;
+    params.center_padding   = false;
+    params.preemph          = 0.0f; // disabled
+    params.use_natural_log  = false;
+    params.norm_per_feature = false;
+
+    // make sure the global cache is initialized
+    GGML_ASSERT(!g_cache.sin_vals.empty());
+    GGML_ASSERT(!g_cache.cos_vals.empty());
+    GGML_ASSERT(!g_cache.filters.data.empty());
+
+    mtmd_audio_mel out_full;
     bool ok = log_mel_spectrogram(
                 samples,
                 n_samples,
-                COMMON_SAMPLE_RATE,
-                WHISPER_N_FFT,
-                WHISPER_HOP_LENGTH,
-                filters.n_mel,
                 4, // n_threads
-                filters,
-                false, // debug
+                params,
                 out_full);
     if (!ok) {
         return false;
@@ -307,7 +508,9 @@ bool preprocess_audio(
 
     // because the cgraph in clip.cpp only accepts 3000 frames each, we need to split the mel
     // we always expect the mel to have 3000 silent frames at the end
-    // printf("n_len %d\n", out_full.n_len);
+    if (DEBUG) {
+        printf("output: n_mel = %d, n_len = %d\n", out_full.n_mel, out_full.n_len);
+    }
     const size_t frames_per_chunk = 3000;
     GGML_ASSERT((size_t)out_full.n_len > frames_per_chunk);
     for (size_t off = 0; off < (size_t)out_full.n_len; off += frames_per_chunk) {
@@ -316,7 +519,7 @@ bool preprocess_audio(
             break; // last uncomplete chunk will always be a padded chunk, safe to ignore
         }
 
-        whisper_mel out_chunk;
+        mtmd_audio_mel out_chunk;
         out_chunk.n_len     = n_len;
         out_chunk.n_mel     = out_full.n_mel;
         out_chunk.n_len_org = out_full.n_mel; // unused
@@ -332,438 +535,3 @@ bool preprocess_audio(
 
     return true;
 }
-
-} // namespace whisper_preprocessor
-
-
-// precalculated mel filter banks
-// values are multiplied by 1000.0 to save space, and will be divided by 1000.0 in the end of the function
-//
-// generated from python code:
-//
-// from numpy import load
-// data = load('mel_filters.npz')
-// lst = data.files
-// for item in lst:
-//   print(item)
-//   print(data[item].shape)
-//   n_mel = data[item].shape[0]
-//   n_fft = data[item].shape[1]
-//   for i, row in enumerate(data[item]):
-//     for j, val in enumerate(row):
-//       val = val * 1000.0
-//       if val != 0:
-//         print(f"data[{i*n_fft + j}] = {val:.6f};")
-
-namespace whisper_precalc_filters {
-
-whisper_preprocessor::whisper_filters get_128_bins() {
-    whisper_preprocessor::whisper_filters filters;
-    filters.n_mel = 128;
-    filters.n_fft = 201;
-    std::vector data(filters.n_mel * filters.n_fft, 0.0f);
-
-    data[1] = 12.37398665;
-    data[202] = 30.39256483;
-    data[404] = 24.74797331;
-    data[605] = 18.01857911;
-    data[807] = 37.12195903;
-    data[1008] = 5.64459199;
-    data[1009] = 6.72939420;
-    data[1210] = 36.03715822;
-    data[1412] = 19.10337992;
-    data[1613] = 23.66316877;
-    data[1815] = 31.47736564;
-    data[2016] = 11.28918398;
-    data[2017] = 1.08480197;
-    data[2218] = 41.68175161;
-    data[2420] = 13.45878839;
-    data[2621] = 29.30776216;
-    data[2823] = 25.83277412;
-    data[3024] = 16.93377644;
-    data[3226] = 38.20675984;
-    data[3427] = 4.55979025;
-    data[3428] = 7.81419594;
-    data[3629] = 34.95235741;
-    data[3831] = 20.18818259;
-    data[4032] = 22.57836796;
-    data[4234] = 32.56217018;
-    data[4435] = 10.20438317;
-    data[4436] = 2.16960395;
-    data[4637] = 40.59694707;
-    data[4839] = 14.54358920;
-    data[5040] = 28.22295949;
-    data[5242] = 26.91757679;
-    data[5443] = 15.84897563;
-    data[5645] = 39.29156065;
-    data[5846] = 3.47498828;
-    data[5847] = 8.89899861;
-    data[6048] = 33.86755288;
-    data[6250] = 21.27298526;
-    data[6451] = 21.49356715;
-    data[6653] = 33.64697099;
-    data[6854] = 9.11958050;
-    data[6855] = 3.25440569;
-    data[7056] = 39.51214626;
-    data[7258] = 15.62839188;
-    data[7459] = 27.13815868;
-    data[7661] = 28.00237760;
-    data[7862] = 14.76417296;
-    data[8064] = 40.37636518;
-    data[8265] = 2.38068704;
-    data[8266] = 10.20263787;
-    data[8467] = 31.61146119;
-    data[8669] = 24.54700135;
-    data[8870] = 15.32919332;
-    data[8871] = 1.66583748;
-    data[9072] = 36.72905266;
-    data[9274] = 20.09709924;
-    data[9475] = 16.93102531;
-    data[9476] = 2.90265540;
-    data[9677] = 32.84499049;
-    data[9879] = 23.52004871;
-    data[10080] = 11.03894413;
-    data[10081] = 10.72582975;
-    data[10282] = 22.71829173;
-    data[10484] = 32.27872774;
-    data[10685] = 0.11626833;
-    data[10686] = 22.85348251;
-    data[10887] = 8.56344029;
-    data[10888] = 14.97978810;
-    data[11089] = 15.51398356;
-    data[11090] = 8.51490628;
-    data[11291] = 21.10680379;
-    data[11292] = 3.32652032;
-    data[11493] = 25.47064796;
-    data[11695] = 27.35907957;
-    data[11896] = 0.65853616;
-    data[11897] = 23.83812517;
-    data[12098] = 3.44359246;
-    data[12099] = 21.22455277;
-    data[12300] = 5.35842171;
-    data[12301] = 19.42555793;
-    data[12502] = 6.49324711;
-    data[12503] = 18.35542172;
-    data[12704] = 6.93138083;
-    data[12705] = 17.93504693;
-    data[12906] = 6.74968259;
-    data[12907] = 18.09151843;
-    data[13108] = 6.01899112;
-    data[13109] = 18.75767298;
-    data[13310] = 4.80452832;
-    data[13311] = 19.87172849;
-    data[13512] = 3.16627859;
-    data[13513] = 21.37690969;
-    data[13514] = 1.25317345;
-    data[13714] = 1.15934468;
-    data[13715] = 20.80361731;
-    data[13716] = 4.04486805;
-    data[13917] = 17.55363122;
-    data[13918] = 7.08320038;
-    data[14119] = 14.07538634;
-    data[14120] = 10.32655034;
-    data[14321] = 10.40921453;
-    data[14322] = 13.73696327;
-    data[14523] = 6.59187697;
-    data[14524] = 17.27988198;
-    data[14525] = 1.46804214;
-    data[14725] = 2.65681883;
-    data[14726] = 18.09193194;
-    data[14727] = 5.85655728;
-    data[14928] = 13.34277913;
-    data[14929] = 10.28267574;
-    data[15130] = 8.56800377;
-    data[15131] = 14.72230814;
-    data[15132] = 1.04039861;
-    data[15332] = 3.79085587;
-    data[15333] = 17.14678481;
-    data[15334] = 6.11609267;
-    data[15535] = 11.75929047;
-    data[15536] = 11.13393717;
-    data[15737] = 6.43857848;
-    data[15738] = 16.07806236;
-    data[15739] = 4.23917221;
-    data[15939] = 1.19989377;
-    data[15940] = 12.75671553;
-    data[15941] = 9.65298992;
-    data[16142] = 7.06935255;
-    data[16143] = 14.94054683;
-    data[16144] = 4.19024844;
-    data[16344] = 1.51483389;
-    data[16345] = 12.00899947;
-    data[16346] = 9.84823331;
-    data[16547] = 6.10224018;
-    data[16548] = 15.33857174;
-    data[16549] = 5.57676842;
-    data[16749] = 0.36827257;
-    data[16750] = 9.89749376;
-    data[16751] = 11.35340426;
-    data[16752] = 2.05122307;
-    data[16952] = 3.89297144;
-    data[16953] = 12.97352277;
-    data[16954] = 8.06631614;
-    data[17155] = 6.74493238;
-    data[17156] = 13.85874674;
-    data[17157] = 5.41190524;
-    data[17357] = 0.74220158;
-    data[17358] = 8.98779090;
-    data[17359] = 11.37871388;
-    data[17360] = 3.32958088;
-    data[17560] = 2.82313535;
-    data[17561] = 10.68049297;
-    data[17562] = 9.43340641;
-    data[17563] = 1.76325557;
-    data[17763] = 4.39018616;
-    data[17764] = 11.87758986;
-    data[17765] = 7.97005836;
-    data[17766] = 0.66104700;
-    data[17966] = 5.49466675;
-    data[17967] = 12.62953598;
-    data[17968] = 6.93987962;
-    data[18169] = 6.18401915;
-    data[18170] = 12.93473132;
-    data[18171] = 6.29778765;
-    data[18371] = 0.02325210;
-    data[18372] = 6.50206627;
-    data[18373] = 12.32661773;
-    data[18374] = 6.00216538;
-    data[18574] = 0.31548753;
-    data[18575] = 6.48925547;
-    data[18576] = 12.04130240;
-    data[18577] = 6.01462880;
-    data[18777] = 0.29979556;
-    data[18778] = 6.18288014;
-    data[18779] = 12.04272825;
-    data[18780] = 6.29981188;
-    data[18781] = 0.55689598;
-    data[18980] = 0.01120471;
-    data[18981] = 5.61729167;
-    data[18982] = 11.22337859;
-    data[18983] = 6.82516303;
-    data[18984] = 1.35264499;
-    data[19184] = 4.82410006;
-    data[19185] = 10.16623247;
-    data[19186] = 7.56075513;
-    data[19187] = 2.34590308;
-    data[19387] = 3.83235747;
-    data[19388] = 8.92296247;
-    data[19389] = 8.47910438;
-    data[19390] = 3.50978645;
-    data[19590] = 2.66873185;
-    data[19591] = 7.51965167;
-    data[19592] = 9.55500547;
-    data[19593] = 4.81966138;
-    data[19594] = 0.08431751;
-    data[19793] = 1.35767367;
-    data[19794] = 5.98019501;
-    data[19795] = 10.60271543;
-    data[19796] = 6.25298498;
-    data[19797] = 1.74059917;
-    data[19997] = 4.32644226;
-    data[19998] = 8.73131864;
-    data[19999] = 7.78916525;
-    data[20000] = 3.48923868;
-    data[20200] = 2.57835095;
-    data[20201] = 6.77582854;
-    data[20202] = 9.40941647;
-    data[20203] = 5.31194592;
-    data[20204] = 1.21447595;
-    data[20403] = 0.75411191;
-    data[20404] = 4.75395704;
-    data[20405] = 8.75380263;
-    data[20406] = 7.19209015;
-    data[20407] = 3.28754401;
-    data[20607] = 2.68179690;
-    data[20608] = 6.49331464;
-    data[20609] = 9.11457930;
-    data[20610] = 5.39387390;
-    data[20611] = 1.67316827;
-    data[20810] = 0.57394296;
-    data[20811] = 4.20600036;
-    data[20812] = 7.83805829;
-    data[20813] = 7.52023002;
-    data[20814] = 3.97470826;
-    data[20815] = 0.42918732;
-    data[21014] = 1.90464477;
-    data[21015] = 5.36569161;
-    data[21016] = 8.82673822;
-    data[21017] = 6.27609482;
-    data[21018] = 2.89750961;
-    data[21218] = 2.89885257;
-    data[21219] = 6.19694078;
-    data[21220] = 8.56699049;
-    data[21221] = 5.34748193;
-    data[21222] = 2.12797290;
-    data[21421] = 0.44750227;
-    data[21422] = 3.59030394;
-    data[21423] = 6.73310598;
-    data[21424] = 7.77023612;
-    data[21425] = 4.70231380;
-    data[21426] = 1.63439126;
-    data[21625] = 1.01536023;
-    data[21626] = 4.01018746;
-    data[21627] = 7.00501446;
-    data[21628] = 7.23442994;
-    data[21629] = 4.31095669;
-    data[21630] = 1.38748321;
-    data[21829] = 1.33348850;
-    data[21830] = 4.18730825;
-    data[21831] = 7.04112789;
-    data[21832] = 6.93188375;
-    data[21833] = 4.14605811;
-    data[21834] = 1.36023236;
-    data[22033] = 1.42879714;
-    data[22034] = 4.14824858;
-    data[22035] = 6.86769979;
-    data[22036] = 6.83705276;
-    data[22037] = 4.18239459;
-    data[22038] = 1.52773573;
-    data[22237] = 1.32610439;
-    data[22238] = 3.91751388;
-    data[22239] = 6.50892360;
-    data[22240] = 6.92639686;
-    data[22241] = 4.39672917;
-    data[22242] = 1.86706171;
-    data[22441] = 1.04827771;
-    data[22442] = 3.51767405;
-    data[22443] = 5.98707050;
-    data[22444] = 7.17824046;
-    data[22445] = 4.76767914;
-    data[22446] = 2.35711760;
-    data[22645] = 0.61636406;
-    data[22646] = 2.96949223;
-    data[22647] = 5.32262027;
-    data[22648] = 7.57265091;
-    data[22649] = 5.27558755;
-    data[22650] = 2.97852419;
-    data[22651] = 0.68146095;
-    data[22849] = 0.04971400;
-    data[22850] = 2.29204819;
-    data[22851] = 4.53438237;
-    data[22852] = 6.77671656;
-    data[22853] = 5.90240723;
-    data[22854] = 3.71349836;
-    data[22855] = 1.52458926;
-    data[23054] = 1.50285335;
-    data[23055] = 3.63961048;
-    data[23056] = 5.77636715;
-    data[23057] = 6.63159089;
-    data[23058] = 4.54574358;
-    data[23059] = 2.45989650;
-    data[23060] = 0.37404924;
-    data[23258] = 0.61795861;
-    data[23259] = 2.65410915;
-    data[23260] = 4.69025923;
-    data[23261] = 6.72641024;
-    data[23262] = 5.46034705;
-    data[23263] = 3.47270933;
-    data[23264] = 1.48507138;
-    data[23463] = 1.59233576;
-    data[23464] = 3.53261665;
-    data[23465] = 5.47289755;
-    data[23466] = 6.44368259;
-    data[23467] = 4.54962999;
-    data[23468] = 2.65557761;
-    data[23469] = 0.76152512;
-    data[23667] = 0.46749352;
-    data[23668] = 2.31641904;
-    data[23669] = 4.16534441;
-    data[23670] = 6.01426978;
-    data[23671] = 5.67844696;
-    data[23672] = 3.87357362;
-    data[23673] = 2.06870004;
-    data[23674] = 0.26382666;
-    data[23872] = 1.05349103;
-    data[23873] = 2.81536230;
-    data[23874] = 4.57723346;
-    data[23875] = 6.33910485;
-    data[23876] = 5.12815686;
-    data[23877] = 3.40826320;
-    data[23878] = 1.68837002;
-    data[24077] = 1.43350090;
-    data[24078] = 3.11241671;
-    data[24079] = 4.79133241;
-    data[24080] = 6.40943693;
-    data[24081] = 4.77052201;
-    data[24082] = 3.13160778;
-    data[24083] = 1.49269309;
-    data[24281] = 0.02932359;
-    data[24282] = 1.62918994;
-    data[24283] = 3.22905602;
-    data[24284] = 4.82892245;
-    data[24285] = 6.14671456;
-    data[24286] = 4.58496623;
-    data[24287] = 3.02321767;
-    data[24288] = 1.46146910;
-    data[24486] = 0.13601698;
-    data[24487] = 1.66055572;
-    data[24488] = 3.18509457;
-    data[24489] = 4.70963307;
-    data[24490] = 6.04072399;
-    data[24491] = 4.55250870;
-    data[24492] = 3.06429295;
-    data[24493] = 1.57607743;
-    data[24494] = 0.08786193;
-    data[24691] = 0.09328097;
-    data[24692] = 1.54603878;
-    data[24693] = 2.99879676;
-    data[24694] = 4.45155473;
-    data[24695] = 5.90431225;
-    data[24696] = 4.65566106;
-    data[24697] = 3.23751615;
-    data[24698] = 1.81937125;
-    data[24699] = 0.40122634;
-    data[24897] = 1.30262633;
-    data[24898] = 2.68698297;
-    data[24899] = 4.07133950;
-    data[24900] = 5.45569602;
-    data[24901] = 4.87832492;
-    data[24902] = 3.52695142;
-    data[24903] = 2.17557792;
-    data[24904] = 0.82420459;
-    data[25102] = 0.94595028;
-    data[25103] = 2.26512621;
-    data[25104] = 3.58430226;
-    data[25105] = 4.90347855;
-    data[25106] = 5.20569785;
-    data[25107] = 3.91795207;
-    data[25108] = 2.63020652;
-    data[25109] = 1.34246063;
-    data[25110] = 0.05471494;
-    data[25307] = 0.49037894;
-    data[25308] = 1.74744334;
-    data[25309] = 3.00450763;
-    data[25310] = 4.26157191;
-    data[25311] = 5.51863620;
-    data[25312] = 4.39707236;
-    data[25313] = 3.16995848;
-    data[25314] = 1.94284460;
-    data[25315] = 0.71573065;
-    data[25513] = 1.14698056;
-    data[25514] = 2.34485767;
-    data[25515] = 3.54273478;
-    data[25516] = 4.74061165;
-    data[25517] = 4.95198462;
-    data[25518] = 3.78264743;
-    data[25519] = 2.61331047;
-    data[25520] = 1.44397374;
-    data[25521] = 0.27463681;
-    data[25718] = 0.47569509;
-    data[25719] = 1.61717169;
-    data[25720] = 2.75864848;
-    data[25721] = 3.90012516;
-    data[25722] = 5.04160160;
-    data[25723] = 4.45712078;
-    data[25724] = 3.34284059;
-    data[25725] = 2.22856039;
-    data[25726] = 1.11428020;
-
-    for (auto & val : data) {
-        val /= 1000.0f;
-    }
-
-    filters.data = std::move(data);
-    return filters;
-}
-
-} // namespace whisper_precalc_filters
diff --git a/tools/mtmd/mtmd-audio.h b/tools/mtmd/mtmd-audio.h
index 0e552347a0..1b454337cb 100644
--- a/tools/mtmd/mtmd-audio.h
+++ b/tools/mtmd/mtmd-audio.h
@@ -1,6 +1,7 @@
 #pragma once
 
 #include "ggml.h"
+#include "clip-model.h"
 
 #include <cstdint>
 #include <vector>
@@ -8,18 +9,7 @@
 
 #define MTMD_INTERNAL_HEADER
 
-#define WHISPER_ASSERT GGML_ASSERT
-
-#define WHISPER_SAMPLE_RATE 16000
-#define WHISPER_N_FFT       400
-#define WHISPER_HOP_LENGTH  160
-#define WHISPER_CHUNK_SIZE  30
-
-#define COMMON_SAMPLE_RATE 16000
-
-namespace whisper_preprocessor {
-
-struct whisper_mel {
+struct mtmd_audio_mel {
     int n_len;
     int n_len_org;
     int n_mel;
@@ -27,23 +17,18 @@ struct whisper_mel {
     std::vector<float> data;
 };
 
-struct whisper_filters {
-    int32_t n_mel;
-    int32_t n_fft;
+struct mtmd_audio_preprocessor {
+    const clip_hparams & hparams;
 
-    std::vector<float> data;
+    mtmd_audio_preprocessor(const clip_ctx * ctx): hparams(*clip_get_hparams(ctx)) {}
+
+    virtual ~mtmd_audio_preprocessor() = default;
+    virtual void initialize() = 0; // NOT thread-safe
+    virtual bool preprocess(const float * samples, size_t n_samples, std::vector<mtmd_audio_mel> & output) = 0;
 };
 
-bool preprocess_audio(
-        const float * samples,
-        size_t n_samples,
-        const whisper_filters & filters,
-        std::vector<whisper_mel> & output);
-
-} // namespace whisper_preprocessor
-
-namespace whisper_precalc_filters {
-
-whisper_preprocessor::whisper_filters get_128_bins();
-
-} // namespace whisper_precalc_filters
+struct mtmd_audio_preprocessor_whisper : mtmd_audio_preprocessor {
+    mtmd_audio_preprocessor_whisper(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {}
+    void initialize() override;
+    bool preprocess(const float * samples, size_t n_samples, std::vector<mtmd_audio_mel> & output) override;
+};
diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp
index d06fa42e61..c63f299cd9 100644
--- a/tools/mtmd/mtmd.cpp
+++ b/tools/mtmd/mtmd.cpp
@@ -151,8 +151,7 @@ struct mtmd_context {
     // string template for slice image delimiters with row/col (idefics3)
     std::string sli_img_start_tmpl;
 
-    // for whisper, we pre-calculate the mel filter bank
-    whisper_preprocessor::whisper_filters w_filters;
+    std::unique_ptr<mtmd_audio_preprocessor> audio_preproc;
 
     // TODO @ngxson : add timings
 
@@ -317,14 +316,25 @@ struct mtmd_context {
         GGML_ASSERT(ctx_a != nullptr);
         projector_type proj = clip_get_projector_type(ctx_a);
 
-        if (clip_has_whisper_encoder(ctx_a)) {
-            // TODO @ngxson : check if model n_mel is 128 or 80
-            w_filters = whisper_precalc_filters::get_128_bins();
-        }
-
         LOG_WRN("%s: audio input is in experimental stage and may have reduced quality:\n"
                 "    https://github.com/ggml-org/llama.cpp/discussions/13759\n", __func__);
 
+        // set preprocessor
+        switch (proj) {
+            case PROJECTOR_TYPE_QWEN2A:
+            case PROJECTOR_TYPE_QWEN25O:
+            case PROJECTOR_TYPE_ULTRAVOX:
+            case PROJECTOR_TYPE_VOXTRAL:
+                audio_preproc = std::make_unique<mtmd_audio_preprocessor_whisper>(ctx_a);
+                break;
+            default:
+                GGML_ABORT("unsupported audio projector type");
+        }
+
+        // initialize audio preprocessor
+        audio_preproc->initialize();
+
+        // set special tokens
         if (proj == PROJECTOR_TYPE_QWEN2A) {
             // <|audio_bos|> ... (embeddings) ... <|audio_eos|>
             aud_beg = "<|audio_bos|>";
@@ -653,11 +663,10 @@ struct mtmd_tokenizer {
             }
 
             // preprocess audio
-            GGML_ASSERT(ctx->w_filters.n_mel); // make sure we have filter preloaded
-            std::vector<whisper_preprocessor::whisper_mel> mel_spec_chunks;
+            std::vector<mtmd_audio_mel> mel_spec_chunks;
             const float * samples = (const float *)bitmap->data.data();
             size_t n_samples = bitmap->data.size() / sizeof(float);
-            bool ok = whisper_preprocessor::preprocess_audio(samples, n_samples, ctx->w_filters, mel_spec_chunks);
+            bool ok = ctx->audio_preproc->preprocess(samples, n_samples, mel_spec_chunks);
             if (!ok) {
                 LOG_ERR("Unable to preprocess audio\n");
                 return 2;
@@ -863,8 +872,7 @@ int mtmd_get_audio_bitrate(mtmd_context * ctx) {
     if (!ctx->ctx_a) {
         return -1;
     }
-    // for now, we assume that all audio models have the same bitrate
-    return 16000; // 16kHz
+    return clip_get_hparams(ctx->ctx_a)->audio_sample_rate;
 }
 
 //

From 165caaf5fbd359822b176b076f3ef4248309e56d Mon Sep 17 00:00:00 2001
From: Jeremy Demeule <jdemeule@users.noreply.github.com>
Date: Mon, 15 Dec 2025 15:14:49 +0100
Subject: [PATCH 110/135] metal: use shared buffers on eGPU (#17866)

* metal: use shared buffers on eGPU

With #15906, I noticed on important regression when using metal backend on eGPU.
This commit restore the previous behavior and add an option to force its activation.

* metal: use shared buffers on eGPU

* metal: use shared buffers on eGPU
---
 ggml/src/ggml-metal/ggml-metal-device.m | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m
index 7b7d1c1233..f24270bb1c 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.m
+++ b/ggml/src/ggml-metal/ggml-metal-device.m
@@ -769,9 +769,16 @@ ggml_metal_device_t ggml_metal_device_init(void) {
 #endif
 
             dev->props.use_shared_buffers = dev->props.has_unified_memory;
+#if TARGET_OS_OSX
+            // In case of eGPU, shared memory may be preferable.
+            dev->props.use_shared_buffers |= [dev->mtl_device location] == MTLDeviceLocationExternal;
+#endif
             if (getenv("GGML_METAL_SHARED_BUFFERS_DISABLE") != NULL) {
                 dev->props.use_shared_buffers = false;
             }
+            if (getenv("GGML_METAL_SHARED_BUFFERS_ENABLE") != NULL) {
+                dev->props.use_shared_buffers = true;
+            }
 
             dev->props.supports_gpu_family_apple7 = [dev->mtl_device supportsFamily:MTLGPUFamilyApple7];
 

From 0f4f35e7be14d5ef8157aa710b66388e77e99bec Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Mon, 15 Dec 2025 16:34:53 +0100
Subject: [PATCH 111/135] Fix unreadable user markdown colors and truncate long
 texts in deletion dialogs (#17555)

* webui: limit conversation name length in dialogs

* webui: fix unreadable colors on links and table cell hover in user markdown

* webui: keep table borders visible in user markdown

* webui: updating unified exports

* Update tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentThumbnailFile.svelte

Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>

* chore: update webui build output

* chore: update webui build output

* chore: update webui build output

---------

Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
---
 tools/server/public/index.html.gz             | Bin 1444280 -> 1445607 bytes
 .../ChatAttachmentThumbnailFile.svelte        |   2 +-
 .../app/chat/ChatSidebar/ChatSidebar.svelte   |   6 ++++-
 .../app/misc/MarkdownContent.svelte           |  23 ++++++++++++++++++
 .../webui/src/lib/utils/file-preview.ts       |   9 -------
 tools/server/webui/src/lib/utils/index.ts     |   3 ++-
 tools/server/webui/src/lib/utils/text.ts      |   7 ++++++
 7 files changed, 38 insertions(+), 12 deletions(-)
 create mode 100644 tools/server/webui/src/lib/utils/text.ts

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index 1834d1d91f46fcdaa39f2ccff19e82ee2a2479f3..036feff1c3e182484fa80cc64e53e386a4560558 100644
GIT binary patch
delta 1148755
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diff --git a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentThumbnailFile.svelte b/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentThumbnailFile.svelte
index 6fdd857214..908db5894b 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentThumbnailFile.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentThumbnailFile.svelte
@@ -1,6 +1,6 @@
 <script lang="ts">
 	import { RemoveButton } from '$lib/components/app';
-	import { getFileTypeLabel, getPreviewText, formatFileSize, isTextFile } from '$lib/utils';
+	import { formatFileSize, getFileTypeLabel, getPreviewText, isTextFile } from '$lib/utils';
 	import { AttachmentType } from '$lib/enums';
 
 	interface Props {
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte
index 1d313e284e..aa0c27f6d3 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte
@@ -9,6 +9,7 @@
 	import Input from '$lib/components/ui/input/input.svelte';
 	import { conversationsStore, conversations } from '$lib/stores/conversations.svelte';
 	import { chatStore } from '$lib/stores/chat.svelte';
+	import { getPreviewText } from '$lib/utils/text';
 	import ChatSidebarActions from './ChatSidebarActions.svelte';
 
 	const sidebar = Sidebar.useSidebar();
@@ -20,6 +21,9 @@
 	let showEditDialog = $state(false);
 	let selectedConversation = $state<DatabaseConversation | null>(null);
 	let editedName = $state('');
+	let selectedConversationNamePreview = $derived.by(() =>
+		selectedConversation ? getPreviewText(selectedConversation.name) : ''
+	);
 
 	let filteredConversations = $derived.by(() => {
 		if (searchQuery.trim().length > 0) {
@@ -162,7 +166,7 @@
 	bind:open={showDeleteDialog}
 	title="Delete Conversation"
 	description={selectedConversation
-		? `Are you sure you want to delete "${selectedConversation.name}"? This action cannot be undone and will permanently remove all messages in this conversation.`
+		? `Are you sure you want to delete "${selectedConversationNamePreview}"? This action cannot be undone and will permanently remove all messages in this conversation.`
 		: ''}
 	confirmText="Delete"
 	cancelText="Cancel"
diff --git a/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte b/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte
index 9c37bde0d4..2a4a39535e 100644
--- a/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte
+++ b/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte
@@ -504,6 +504,14 @@
 		background: hsl(var(--muted) / 0.1);
 	}
 
+	/* User message markdown should keep table borders visible on light primary backgrounds */
+	div.markdown-user-content :global(table),
+	div.markdown-user-content :global(th),
+	div.markdown-user-content :global(td),
+	div.markdown-user-content :global(.table-wrapper) {
+		border-color: currentColor;
+	}
+
 	/* Horizontal rules */
 	div :global(hr) {
 		border: none;
@@ -642,6 +650,21 @@
 		background: var(--muted);
 	}
 
+	/* Disable hover effects when rendering user messages */
+	.markdown-user-content :global(a),
+	.markdown-user-content :global(a:hover) {
+		color: var(--primary-foreground);
+	}
+
+	.markdown-user-content :global(table:hover) {
+		box-shadow: none;
+	}
+
+	.markdown-user-content :global(th:hover),
+	.markdown-user-content :global(td:hover) {
+		background: inherit;
+	}
+
 	/* Enhanced blockquotes */
 	div :global(blockquote) {
 		transition: all 0.2s ease;
diff --git a/tools/server/webui/src/lib/utils/file-preview.ts b/tools/server/webui/src/lib/utils/file-preview.ts
index 115f8727a9..26a60533ae 100644
--- a/tools/server/webui/src/lib/utils/file-preview.ts
+++ b/tools/server/webui/src/lib/utils/file-preview.ts
@@ -34,12 +34,3 @@ export function getFileTypeLabel(input: string | undefined): string {
 	// Handle AttachmentType or other plain strings
 	return input.toUpperCase();
 }
-
-/**
- * Truncates text content for preview display
- * @param content - The text content to truncate
- * @returns Truncated content with ellipsis if needed
- */
-export function getPreviewText(content: string): string {
-	return content.length > 150 ? content.substring(0, 150) + '...' : content;
-}
diff --git a/tools/server/webui/src/lib/utils/index.ts b/tools/server/webui/src/lib/utils/index.ts
index d8a893ed64..2485e34de3 100644
--- a/tools/server/webui/src/lib/utils/index.ts
+++ b/tools/server/webui/src/lib/utils/index.ts
@@ -43,7 +43,8 @@ export { createMessageCountMap, getMessageCount } from './conversation-utils';
 export { copyToClipboard, copyCodeToClipboard } from './copy';
 
 // File preview utilities
-export { getFileTypeLabel, getPreviewText } from './file-preview';
+export { getFileTypeLabel } from './file-preview';
+export { getPreviewText } from './text';
 
 // File type utilities
 export {
diff --git a/tools/server/webui/src/lib/utils/text.ts b/tools/server/webui/src/lib/utils/text.ts
new file mode 100644
index 0000000000..5c5dd0fe8c
--- /dev/null
+++ b/tools/server/webui/src/lib/utils/text.ts
@@ -0,0 +1,7 @@
+/**
+ * Returns a shortened preview of the provided content capped at the given length.
+ * Appends an ellipsis when the content exceeds the maximum.
+ */
+export function getPreviewText(content: string, max = 150): string {
+	return content.length > max ? content.slice(0, max) + '...' : content;
+}

From 4529c660c89a8675686cbe2bd1839e85f223f3fc Mon Sep 17 00:00:00 2001
From: ssweens <1149151+ssweens@users.noreply.github.com>
Date: Mon, 15 Dec 2025 09:28:35 -0800
Subject: [PATCH 112/135] kv-cache: Fix state restore fragmented cache (#17982)

* kv-cache : fix state restore with fragmented cache (#17527)

Change find_slot to allow non-contiguous allocation during state restore. Fixes 'failed to find available cells in kv cache' error when restoring state to fragmented cache.

* tests : update logic

* cleanup: tightened state_read_meta sig, added is_contiguous case

* fix: state_read_meta arg reorder loose ends

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 src/llama-kv-cache.cpp                  |  91 ++++++++++++------
 src/llama-kv-cache.h                    |  21 +++-
 tests/CMakeLists.txt                    |   8 ++
 tests/test-state-restore-fragmented.cpp | 122 ++++++++++++++++++++++++
 4 files changed, 213 insertions(+), 29 deletions(-)
 create mode 100644 tests/test-state-restore-fragmented.cpp

diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp
index 4cbbca0925..3186242d60 100644
--- a/src/llama-kv-cache.cpp
+++ b/src/llama-kv-cache.cpp
@@ -1561,9 +1561,11 @@ void llama_kv_cache::state_read(llama_io_read_i & io, llama_seq_id seq_id, llama
 
         const uint32_t strm = seq_id == -1 ? s : seq_to_stream[seq_id];
 
+        slot_info sinfo;
+
         bool res = true;
-        res = res && state_read_meta(io, strm, cell_count, seq_id);
-        res = res && state_read_data(io, strm, cell_count);
+        res = res && state_read_meta(io, strm, cell_count, sinfo, seq_id);
+        res = res && state_read_data(io, strm, cell_count, sinfo);
 
         if (!res) {
             if (seq_id == -1) {
@@ -1702,7 +1704,7 @@ void llama_kv_cache::state_write_data(llama_io_write_i & io, const cell_ranges_t
     }
 }
 
-bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, llama_seq_id dest_seq_id) {
+bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, slot_info & sinfo, llama_seq_id dest_seq_id) {
     auto & cells = v_cells[strm];
     auto & head  = v_heads[strm];
 
@@ -1739,7 +1741,7 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32
             ubatch.seq_id[i]   = &dest_seq_id;
         }
 
-        const auto sinfo = find_slot(ubatch, true);
+        sinfo = find_slot(ubatch, false);
         if (sinfo.empty()) {
             LLAMA_LOG_ERROR("%s: failed to find available cells in kv cache\n", __func__);
             return false;
@@ -1749,20 +1751,16 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32
         //       see: https://github.com/ggml-org/llama.cpp/pull/16825#issuecomment-3460868350
         apply_ubatch(sinfo, ubatch);
 
-        const auto head_cur = sinfo.head();
+        LLAMA_LOG_DEBUG("%s: cell_count = %d, dest_seq_id = %d\n", __func__, cell_count, dest_seq_id);
 
-        // keep the head at the old position because we will read the KV data into it in state_read_data()
-        head = head_cur;
-
-        LLAMA_LOG_DEBUG("%s: head_cur = %d, head = %d, cell_count = %d, dest_seq_id = %d\n", __func__, head_cur, head, cell_count, dest_seq_id);
-
-        // DEBUG CHECK: head_cur should be our first cell, head_cur + cell_count - 1 should be our last cell (verify seq_id and pos values)
-        // Assume that this is one contiguous block of cells
-        GGML_ASSERT(head_cur + cell_count <= cells.size());
-        GGML_ASSERT(cells.pos_get(head_cur)                  == ubatch.pos[0]);
-        GGML_ASSERT(cells.pos_get(head_cur + cell_count - 1) == ubatch.pos[cell_count - 1]);
-        GGML_ASSERT(cells.seq_has(head_cur,                  dest_seq_id));
-        GGML_ASSERT(cells.seq_has(head_cur + cell_count - 1, dest_seq_id));
+        // DEBUG CHECK: verify that all cells were allocated and have correct seq_id and pos values
+        GGML_ASSERT(sinfo.n_stream() == 1);
+        GGML_ASSERT(sinfo.idxs[0].size() == cell_count);
+        for (uint32_t i = 0; i < cell_count; ++i) {
+            const uint32_t idx = sinfo.idxs[0][i];
+            GGML_ASSERT(cells.pos_get(idx) == ubatch.pos[i]);
+            GGML_ASSERT(cells.seq_has(idx, dest_seq_id));
+        }
     } else {
         // whole KV cache restore
 
@@ -1795,15 +1793,24 @@ bool llama_kv_cache::state_read_meta(llama_io_read_i & io, uint32_t strm, uint32
             }
         }
 
+        // Create contiguous slot_info for whole cache restore
+        sinfo.s0 = strm;
+        sinfo.s1 = strm;
+        sinfo.resize(1);
+        sinfo.strm[0] = strm;
+        sinfo.idxs[0].resize(cell_count);
+        for (uint32_t i = 0; i < cell_count; ++i) {
+            sinfo.idxs[0][i] = i;
+        }
+
         head = 0;
     }
 
     return true;
 }
 
-bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count) {
+bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, const slot_info & sinfo) {
     auto & cells = v_cells[strm];
-    auto & head  = v_heads[strm];
 
     uint32_t v_trans;
     uint32_t n_layer;
@@ -1853,8 +1860,17 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
         }
 
         if (cell_count) {
-            // Read and set the keys for the whole cell range
-            ggml_backend_tensor_set(k, io.read(cell_count * k_size_row), head * k_size_row, cell_count * k_size_row);
+            if (sinfo.is_contiguous()) {
+                // Fast path: contiguous cells, single memcpy
+                ggml_backend_tensor_set(k, io.read(cell_count * k_size_row), sinfo.head() * k_size_row, cell_count * k_size_row);
+            } else {
+                // Slow path: scatter to non-contiguous positions
+                const void * src = io.read(cell_count * k_size_row);
+                for (uint32_t i = 0; i < cell_count; ++i) {
+                    const size_t dst_offset = sinfo.idxs[0][i] * k_size_row;
+                    ggml_backend_tensor_set(k, (const char*)src + i * k_size_row, dst_offset, k_size_row);
+                }
+            }
         }
     }
 
@@ -1885,8 +1901,17 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
             }
 
             if (cell_count) {
-                // Read and set the values for the whole cell range
-                ggml_backend_tensor_set(v, io.read(cell_count * v_size_row), head * v_size_row, cell_count * v_size_row);
+                if (sinfo.is_contiguous()) {
+                    // Fast path: contiguous cells, single memcpy
+                    ggml_backend_tensor_set(v, io.read(cell_count * v_size_row), sinfo.head() * v_size_row, cell_count * v_size_row);
+                } else {
+                    // Slow path: scatter to non-contiguous positions
+                    const void * src = io.read(cell_count * v_size_row);
+                    for (uint32_t i = 0; i < cell_count; ++i) {
+                        const size_t dst_offset = sinfo.idxs[0][i] * v_size_row;
+                        ggml_backend_tensor_set(v, (const char*)src + i * v_size_row, dst_offset, v_size_row);
+                    }
+                }
             }
         }
     } else {
@@ -1925,10 +1950,22 @@ bool llama_kv_cache::state_read_data(llama_io_read_i & io, uint32_t strm, uint32
             }
 
             if (cell_count) {
-                // For each row in the transposed matrix, read the values for the whole cell range
-                for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
-                    const size_t dst_offset = (head + j * cells.size()) * v_size_el;
-                    ggml_backend_tensor_set(v, io.read(cell_count * v_size_el), dst_offset, cell_count * v_size_el);
+                if (sinfo.is_contiguous()) {
+                    // Fast path: contiguous cells
+                    const uint32_t h = sinfo.head();
+                    for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+                        const size_t dst_offset = (h + j * cells.size()) * v_size_el;
+                        ggml_backend_tensor_set(v, io.read(cell_count * v_size_el), dst_offset, cell_count * v_size_el);
+                    }
+                } else {
+                    // Slow path: scatter to non-contiguous positions
+                    for (uint32_t j = 0; j < n_embd_v_gqa; ++j) {
+                        const void * src = io.read(cell_count * v_size_el);
+                        for (uint32_t i = 0; i < cell_count; ++i) {
+                            const size_t dst_offset = (sinfo.idxs[0][i] + j * cells.size()) * v_size_el;
+                            ggml_backend_tensor_set(v, (const char*)src + i * v_size_el, dst_offset, v_size_el);
+                        }
+                    }
                 }
             }
         }
diff --git a/src/llama-kv-cache.h b/src/llama-kv-cache.h
index bf7821c07c..1868f11857 100644
--- a/src/llama-kv-cache.h
+++ b/src/llama-kv-cache.h
@@ -72,6 +72,23 @@ public:
         void clear() {
             idxs.clear();
         }
+
+        // check if indices are contiguous starting from head()
+        bool is_contiguous() const {
+            if (idxs.empty() || idxs[0].empty()) {
+                return true;
+            }
+            if (idxs.size() > 1) {
+                return false;
+            }
+            const uint32_t h = idxs[0][0];
+            for (size_t i = 0; i < idxs[0].size(); ++i) {
+                if (idxs[0][i] != h + i) {
+                    return false;
+                }
+            }
+            return true;
+        }
     };
 
     using slot_info_vec_t = std::vector<slot_info>;
@@ -264,8 +281,8 @@ private:
     void state_write_meta(llama_io_write_i & io, const cell_ranges_t & cr, llama_seq_id seq_id = -1) const;
     void state_write_data(llama_io_write_i & io, const cell_ranges_t & cr) const;
 
-    bool state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, llama_seq_id dest_seq_id = -1);
-    bool state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count);
+    bool state_read_meta(llama_io_read_i & io, uint32_t strm, uint32_t cell_count,       slot_info & sinfo, llama_seq_id dest_seq_id = -1);
+    bool state_read_data(llama_io_read_i & io, uint32_t strm, uint32_t cell_count, const slot_info & sinfo);
 };
 
 class llama_kv_cache_context : public llama_memory_context_i {
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 9ba559c8df..c3d9f9c324 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -222,6 +222,14 @@ llama_build_and_test(test-backend-ops.cpp)
 llama_build_and_test(test-model-load-cancel.cpp  LABEL "model")
 llama_build_and_test(test-autorelease.cpp        LABEL "model")
 
+# Test for state restore with fragmented KV cache
+# Requires a model, uses same args pattern as test-thread-safety
+if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "s390x")
+    llama_build_and_test(test-state-restore-fragmented.cpp LABEL "model" ARGS -hf ggml-org/models -hff tinyllamas/stories15M-q4_0.gguf)
+else()
+    llama_build_and_test(test-state-restore-fragmented.cpp LABEL "model" ARGS -hf ggml-org/models -hff tinyllamas/stories15M-be.Q4_0.gguf)
+endif()
+
 if (NOT GGML_BACKEND_DL)
     # these tests use the backends directly and cannot be built with dynamic loading
     llama_build_and_test(test-barrier.cpp)
diff --git a/tests/test-state-restore-fragmented.cpp b/tests/test-state-restore-fragmented.cpp
new file mode 100644
index 0000000000..481b39d04c
--- /dev/null
+++ b/tests/test-state-restore-fragmented.cpp
@@ -0,0 +1,122 @@
+// Test for state restore with fragmented KV cache
+// This tests the fix for: https://github.com/ggml-org/llama.cpp/issues/17527
+// The issue was that state restore required contiguous KV cache slots,
+// which fails when the cache is fragmented.
+//
+// The fix changes find_slot(ubatch, true) to find_slot(ubatch, false)
+// in state_read_meta(), allowing non-contiguous slot allocation.
+
+#include "arg.h"
+#include "common.h"
+#include "llama.h"
+
+#include <vector>
+#include <cstdio>
+#include <cstring>
+
+int main(int argc, char ** argv) {
+    common_params params;
+
+    params.sampling.seed = 1234;
+    params.kv_unified = true;
+    params.n_parallel = 3;
+    params.n_ctx = 256;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
+        return 1;
+    }
+
+    common_init();
+
+    // init
+    common_init_result_ptr llama_init = common_init_from_params(params);
+
+    llama_model * model = llama_init->model();
+    llama_context * ctx = llama_init->context();
+
+    if (model == nullptr || ctx == nullptr) {
+        fprintf(stderr, "%s : failed to init\n", __func__);
+        return 1;
+    }
+
+    GGML_UNUSED(model);
+
+    // tokenize prompt
+    std::vector<llama_token> tokens(70, 1);
+
+    // interleave the 3 sequences:
+    // 01201230123...
+    llama_batch batch = llama_batch_init(params.n_parallel*tokens.size(), 0, 1);
+    for (size_t i = 0; i < tokens.size(); i++) {
+        for (int s = 0; s < params.n_parallel; ++s) {
+            common_batch_add(batch, tokens[i], i, {s}, false);
+        }
+    }
+    batch.logits[batch.n_tokens - 1] = true;
+
+    if (llama_decode(ctx, batch)) {
+        fprintf(stderr, "%s : failed to decode seq 0\n", __func__);
+        return 1;
+    }
+
+    fprintf(stderr, "%s : processed prompt on seq 0, 1, 2 (%zu tokens each)\n", __func__, tokens.size());
+
+    // Save state of seq 1
+    std::vector<uint8_t> seq_state(llama_state_seq_get_size(ctx, 1));
+    const size_t ncopy = llama_state_seq_get_data(ctx, seq_state.data(), seq_state.size(), 1);
+    if (ncopy != seq_state.size()) {
+        fprintf(stderr, "%s : failed to save seq 1 state\n", __func__);
+        return 1;
+    }
+    fprintf(stderr, "%s : saved seq 1 state, %zu bytes\n", __func__, ncopy);
+
+    // clear seq 1 to create a "hole" in the KV cache (fragmentation)
+    // 0.20.20.20.2....
+    llama_memory_t mem = llama_get_memory(ctx);
+    llama_memory_seq_rm(mem, 1, -1, -1);
+    fprintf(stderr, "%s : cleared seq 1 to create fragmentation\n", __func__);
+
+    // Now the cache has holes where seq 1 was
+    // This creates fragmentation - there's no contiguous block large enough
+    // for the seq 1 state if we only look for contiguous slots
+
+    // Restore seq 1 state into seq 1 (should work with non-contiguous allocation)
+    // We use seq 1 since it's a valid sequence ID (0 to n_parallel-1)
+    // Before the fix, this would fail with "failed to find available cells in kv cache"
+    const size_t nset = llama_state_seq_set_data(ctx, seq_state.data(), seq_state.size(), 1);
+    if (nset != seq_state.size()) {
+        fprintf(stderr, "%s : FAILED to restore seq state into fragmented cache (got %zu, expected %zu)\n",
+                __func__, nset, seq_state.size());
+        fprintf(stderr, "%s : This is the bug - state restore fails with fragmented KV cache\n", __func__);
+        llama_batch_free(batch);
+        return 1;
+    }
+    fprintf(stderr, "%s : restored state into seq 1, %zu bytes\n", __func__, nset);
+
+    // Verify we can decode with the restored state
+    // Generate one token to verify the restored state is usable
+    auto sparams = llama_sampler_chain_default_params();
+    llama_sampler * smpl = llama_sampler_chain_init(sparams);
+    llama_sampler_chain_add(smpl, llama_sampler_init_dist(params.sampling.seed));
+
+    auto next_token = llama_sampler_sample(smpl, ctx, -1);
+    auto next_token_str = common_token_to_piece(ctx, next_token);
+
+    common_batch_clear(batch);
+    common_batch_add(batch, next_token, (int)tokens.size(), {1}, true);
+
+    if (llama_decode(ctx, batch)) {
+        fprintf(stderr, "%s : failed to decode with restored state\n", __func__);
+        llama_sampler_free(smpl);
+        llama_batch_free(batch);
+        return 1;
+    }
+
+    fprintf(stderr, "%s : successfully decoded with restored state, generated: '%s'\n", __func__, next_token_str.c_str());
+    fprintf(stderr, "%s : SUCCESS - state restore works with fragmented KV cache\n", __func__);
+
+    llama_sampler_free(smpl);
+    llama_batch_free(batch);
+
+    return 0;
+}

From 9d52f17ae33e8df958e20f3f1b13bfec53ab5a1d Mon Sep 17 00:00:00 2001
From: HelloKS <kqwe1859@gmail.com>
Date: Tue, 16 Dec 2025 02:51:43 +0900
Subject: [PATCH 113/135] model : add KORMo model (#18032)

* vocab: add KORMo Tokenizer

* model: add KORMoForCausalLM

* vocab: change pretokenizer to qwen2

* lint: fix unintended line removal

* model: make qwen2 bias tensor optional

* model: use qwen2 architecture for KORMo
---
 convert_hf_to_gguf.py          |  5 ++++-
 convert_hf_to_gguf_update.py   |  1 +
 gguf-py/gguf/tensor_mapping.py |  4 +++-
 src/llama-model.cpp            |  6 +++---
 src/llama-vocab.cpp            |  3 ++-
 src/models/qwen2.cpp           | 15 ++++++++++++---
 6 files changed, 25 insertions(+), 9 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index ee02cdd91c..ad20cb8b88 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -1203,6 +1203,9 @@ class TextModel(ModelBase):
         if chkhsh == "f4f37b6c8eb9ea29b3eac6bb8c8487c5ab7885f8d8022e67edc1c68ce8403e95":
             # ref: https://huggingface.co/MiniMaxAI/MiniMax-M2
             res = "minimax-m2"
+        if chkhsh == "4a2e2abae11ca2b86d570fc5b44be4d5eb5e72cc8f22dd136a94b37da83ab665":
+            # ref: https://huggingface.co/KORMo-Team/KORMo-tokenizer
+            res = "kormo"
 
         if res is None:
             logger.warning("\n")
@@ -3398,7 +3401,7 @@ class QwenModel(TextModel):
         self._set_vocab_qwen()
 
 
-@ModelBase.register("Qwen2Model", "Qwen2ForCausalLM", "Qwen2AudioForConditionalGeneration")
+@ModelBase.register("Qwen2Model", "Qwen2ForCausalLM", "Qwen2AudioForConditionalGeneration", "KORMoForCausalLM")
 class Qwen2Model(TextModel):
     model_arch = gguf.MODEL_ARCH.QWEN2
 
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index b8f694e86c..5e8456a7ea 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -143,6 +143,7 @@ models = [
     {"name": "bailingmoe2",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-mini-base-2.0", },
     {"name": "granite-docling",  "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ibm-granite/granite-docling-258M", },
     {"name": "minimax-m2",       "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2", },
+    {"name": "kormo",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/KORMo-Team/KORMo-tokenizer", },
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index d9c87da194..b320e2b4b2 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -154,7 +154,8 @@ class TensorNameMap:
             "model.layers.{bid}.operator_norm",                     # lfm2
             "model.transformer.blocks.{bid}.attn_norm",             # llada
             "layers.{bid}.input_layernorm",                         # qwen3-embedding
-            "model.layers.{bid}.attention_layernorm"                # apertus
+            "model.layers.{bid}.attention_layernorm",               # apertus
+            "model.layers.{bid}.pre_attention_layernorm",           # kormo
         ),
 
         # Attention norm 2
@@ -342,6 +343,7 @@ class TensorNameMap:
             "model.transformer.blocks.{bid}.ff_norm",                        # llada
             "layers.{bid}.post_attention_layernorm",                         # qwen3-embedding
             "model.layers.{bid}.feedforward_layernorm",                      # apertus
+            "model.layers.{bid}.pre_mlp_layernorm",                          # kormo
         ),
 
         # Pre feed-forward norm
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index e4d2138056..050735afc0 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -3388,9 +3388,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, 0);
 
                         // optional bias tensors
-                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd}, 0);
-                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, 0);
-                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, 0);
+                        layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
+                        layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
+                        layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, TENSOR_NOT_REQUIRED);
 
                         layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
 
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index e2cca66e48..7b01a2edfe 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -1895,7 +1895,8 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                 clean_spaces = false;
             } else if (
                     tokenizer_pre == "qwen2" ||
-                    tokenizer_pre == "deepseek-r1-qwen") {
+                    tokenizer_pre == "deepseek-r1-qwen" ||
+                    tokenizer_pre == "kormo") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_QWEN2;
                 clean_spaces = false;
             } else if (
diff --git a/src/models/qwen2.cpp b/src/models/qwen2.cpp
index 587a932426..3da4dea3c1 100644
--- a/src/models/qwen2.cpp
+++ b/src/models/qwen2.cpp
@@ -31,16 +31,25 @@ llm_build_qwen2::llm_build_qwen2(const llama_model & model, const llm_graph_para
         {
             // compute Q and K and RoPE them
             ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-            Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
             cb(Qcur, "Qcur", il);
+            if (model.layers[il].bq) {
+                Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                cb(Qcur, "Qcur", il);
+            }
 
             ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-            Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
             cb(Kcur, "Kcur", il);
+            if (model.layers[il].bk) {
+                Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                cb(Kcur, "Kcur", il);
+            }
 
             ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-            Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
             cb(Vcur, "Vcur", il);
+            if (model.layers[il].bv) {
+                Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                cb(Vcur, "Vcur", il);
+            }
 
             Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
             Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);

From c45f89d5516221e51eef93e467e3bec6eb811994 Mon Sep 17 00:00:00 2001
From: Shouyu <65317431+joeldushouyu@users.noreply.github.com>
Date: Mon, 15 Dec 2025 13:53:56 -0500
Subject: [PATCH 114/135] ggml-hexagon: mm for mtmd  (#17894)

* feat: add run_mtmd script for hexagon

* fix: fix issue in fp16xfp32 mm

* fix: remove opt_experiment for fp16xfp32 mm

* fix: ggml-hexagon: matmul fp16xfp32 support non-contigious src0

* fix: fix syntax check for run-mtmd.sh for cli
---
 ggml/src/ggml-hexagon/ggml-hexagon.cpp |  3 --
 ggml/src/ggml-hexagon/htp/matmul-ops.c | 65 +++++++++++++++-----------
 scripts/snapdragon/adb/run-mtmd.sh     | 65 ++++++++++++++++++++++++++
 3 files changed, 102 insertions(+), 31 deletions(-)
 create mode 100755 scripts/snapdragon/adb/run-mtmd.sh

diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp
index 72a82a8911..514f086f68 100644
--- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp
+++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp
@@ -1976,9 +1976,6 @@ static bool ggml_hexagon_supported_mul_mat(const struct ggml_hexagon_session * s
             break;
 
         case GGML_TYPE_F16:
-            if (!opt_experimental) {
-                return false;
-            }
             break;
 
         default:
diff --git a/ggml/src/ggml-hexagon/htp/matmul-ops.c b/ggml/src/ggml-hexagon/htp/matmul-ops.c
index c99b6a0d18..346f0bd339 100644
--- a/ggml/src/ggml-hexagon/htp/matmul-ops.c
+++ b/ggml/src/ggml-hexagon/htp/matmul-ops.c
@@ -903,7 +903,7 @@ static void vec_dot_f16_f32(const int n, float * restrict s, const void * restri
         const float * restrict vy  = (const float * restrict) y;
 
         for (uint32_t i = 0; i < n; i++) {
-            rsum += vx[i] * (__fp16) vy[i];
+            rsum += (float)vx[i] * vy[i];
         }
         *s = rsum;
         return;
@@ -917,7 +917,7 @@ static void vec_dot_f16_f32(const int n, float * restrict s, const void * restri
 
     // for some reason we need volatile here so that the compiler doesn't try anything funky
     volatile HVX_Vector rsum = Q6_V_vsplat_R(0);
-
+    float r_sum_scalar = 0.0f;
     uint32_t i = 0;
 
     for (i = 0; i < nv0; i++) {
@@ -926,31 +926,42 @@ static void vec_dot_f16_f32(const int n, float * restrict s, const void * restri
         HVX_Vector     x  = vx[i];
         HVX_VectorPair xp = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(x), Q6_Vh_vsplat_R(0x3C00));  // mul by 1.0
 
-        HVX_Vector hi = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_hi_W(xp)), Q6_V_hi_W(yp));
-        HVX_Vector lo = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_lo_W(xp)), Q6_V_lo_W(yp));
+        //NOTE: need volatile here to prevent compiler optimization
+        // Seem compiler cannot guarantee read-after-write??
+        volatile HVX_Vector hi = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_hi_W(xp)), Q6_V_hi_W(yp));
+        volatile HVX_Vector lo = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_lo_W(xp)), Q6_V_lo_W(yp));
 
         HVX_Vector sum = Q6_Vqf32_vadd_Vqf32Vqf32(hi, lo);
         rsum           = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, sum);
     }
 
     if (nv1) {
-        HVX_VectorPair yp = vy[i];
+        // HVX_VectorPair yp = vy[i];
 
-        HVX_Vector     x  = vx[i];
-        HVX_VectorPair xp = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(x), Q6_Vh_vsplat_R(0x3C00));  // mul by 1.0
+        // HVX_Vector     x  = vx[i];
+        // HVX_VectorPair xp = Q6_Wqf32_vmpy_VhfVhf(Q6_Vh_vshuff_Vh(x), Q6_Vh_vsplat_R(0x3C00));  // mul by 1.0
 
-        if (nv1 >= 32) {
-            HVX_Vector hi = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_hi_W(xp)), Q6_V_hi_W(yp));
-            rsum          = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, hi);
-            nv1 -= 32;
-        }
+        // if (nv1 >= 32) {
+        //     volatile HVX_Vector hi = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_hi_W(xp)), Q6_V_hi_W(yp));
+        //     rsum          = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, hi);
+        //     nv1 -= 32;
+        // }
 
+        // rsum = hvx_vec_qf32_reduce_sum(rsum);
+
+        // if (nv1) {
+        //     volatile HVX_Vector lo  = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_lo_W(xp)), Q6_V_lo_W(yp));
+        //     HVX_Vector sum = hvx_vec_qf32_reduce_sum_n(lo, nv1);
+        //     rsum           = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, sum);
+        // }
+
+        //process the remainder using scalar loop
         rsum = hvx_vec_qf32_reduce_sum(rsum);
+        const __fp16 * restrict sx = (const __fp16 * restrict) x;
+        const float * restrict sy  = (const float * restrict) y;
 
-        if (nv1) {
-            HVX_Vector lo  = Q6_Vqf32_vmpy_VsfVsf(Q6_Vsf_equals_Vqf32(Q6_V_lo_W(xp)), Q6_V_lo_W(yp));
-            HVX_Vector sum = hvx_vec_qf32_reduce_sum_n(lo, nv1);
-            rsum           = Q6_Vqf32_vadd_Vqf32Vqf32(rsum, sum);
+        for (uint32_t i = nv0 * 64; i < n; i++) {
+            r_sum_scalar += (float) sx[i] * sy[i];
         }
 
         // hvx_vec_dump_fp16("X", x);
@@ -961,7 +972,7 @@ static void vec_dot_f16_f32(const int n, float * restrict s, const void * restri
         rsum = hvx_vec_qf32_reduce_sum(rsum);
     }
 
-    *s = hvx_vec_get_fp32(Q6_Vsf_equals_Vqf32(rsum));
+    *s = hvx_vec_get_fp32(Q6_Vsf_equals_Vqf32(rsum)) + r_sum_scalar;
 
 #    ifdef HTP_DEBUG
     {
@@ -1498,9 +1509,6 @@ static void matmul_f16_f32(struct htp_tensor * restrict src0,
     uint64_t t1, t2;
     t1 = HAP_perf_get_qtimer_count();
 
-    const size_t src0_row_size = sizeof(__fp16) * ne00;
-    const size_t src1_row_size = sizeof(float) * ne10;
-
     assert(ne12 % ne02 == 0);
     assert(ne13 % ne03 == 0);
 
@@ -1510,8 +1518,6 @@ static void matmul_f16_f32(struct htp_tensor * restrict src0,
     // This is the size of the rest of the dimensions of the result
     const uint32_t nr1 = ne1 * ne2 * ne3;
 
-    uint32_t chunk_size = 64;
-
     // distribute the thread work across the inner or outer loop based on which one is larger
     uint32_t nchunk0 = nr0 > nr1 ? nth : 1;  // parallelize by src0 rows
     uint32_t nchunk1 = nr0 > nr1 ? 1 : nth;  // parallelize by src1 rows
@@ -1544,11 +1550,11 @@ static void matmul_f16_f32(struct htp_tensor * restrict src0,
     const uint32_t blck_0 = 64;
     const uint32_t blck_1 = 64;
 
-    float tmp[32];
+    __attribute__((aligned(128))) float tmp[64];
 
     for (uint32_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) {
         for (uint32_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) {
-            for (uint32_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ir1++) {
+            for (uint32_t ir1 = iir1; ir1 < MIN(iir1 + blck_1, ir1_end); ir1++) {
                 const uint32_t i13 = (ir1 / (ne12 * ne1));
                 const uint32_t i12 = (ir1 - i13 * ne12 * ne1) / ne1;
                 const uint32_t i11 = (ir1 - i13 * ne12 * ne1 - i12 * ne1);
@@ -1561,13 +1567,16 @@ static void matmul_f16_f32(struct htp_tensor * restrict src0,
                 const uint32_t i2 = i12;
                 const uint32_t i3 = i13;
 
-                const uint8_t * restrict src0_row = (const uint8_t *) src0->data + (0 + i02 * nb02 + i03 * nb03);
+                const uint8_t * restrict src0_base = (const uint8_t *) src0->data + (0 + i02 * nb02 + i03 * nb03);
                 const uint8_t * restrict src1_col =
-                    (const uint8_t *) src1->data + (i11 + i12 * ne11 + i13 * ne12 * ne11) * src1_row_size;
+                    (const uint8_t *) src1->data + (i11 * nb11 + i12 * nb12 + i13 * nb13);
                 float * dst_col = (float *) ((uint8_t * restrict) dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3));
 
-                for (uint32_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ir0++) {
-                    vec_dot_f16_f32(ne00, &tmp[ir0 - iir0], src0_row + ir0 * src0_row_size, src1_col);
+                const uint32_t ir0_block_end = MIN(iir0 + blck_0, ir0_end);
+                for (uint32_t ir0 = iir0; ir0 < ir0_block_end; ir0++) {
+                    // Use nb01 stride for non-contiguous src0 support
+                    const uint8_t * restrict src0_row = src0_base + ir0 * nb01;
+                    vec_dot_f16_f32(ne00, &tmp[ir0 - iir0], src0_row, src1_col);
                 }
 
                 hvx_copy_fp32_ua((uint8_t *) &dst_col[iir0], (uint8_t *) tmp, MIN(iir0 + blck_0, ir0_end) - iir0);
diff --git a/scripts/snapdragon/adb/run-mtmd.sh b/scripts/snapdragon/adb/run-mtmd.sh
new file mode 100755
index 0000000000..91d868278a
--- /dev/null
+++ b/scripts/snapdragon/adb/run-mtmd.sh
@@ -0,0 +1,65 @@
+#!/bin/sh
+#
+
+# Basedir on device
+basedir=/data/local/tmp/llama.cpp
+
+cli_opts=
+
+branch=.
+[ "$B" != "" ] && branch=$B
+
+adbserial=
+[ "$S" != "" ] && adbserial="-s $S"
+
+model="gemma-3-4b-it-Q4_0.gguf"
+[ "$M" != "" ] && model="$M"
+
+mmproj="mmproj-F16.gguf"
+[ "$MMPROJ" != "" ] && mmproj="$MMPROJ"
+
+image=
+[ "$IMG" != "" ] && image="$IMG"
+
+device="HTP0"
+[ "$D" != "" ] && device="$D"
+
+verbose=
+[ "$V" != "" ] && verbose="GGML_HEXAGON_VERBOSE=$V"
+
+experimental="GGML_HEXAGON_EXPERIMENTAL=1"
+[ "$E" != "" ] && experimental="GGML_HEXAGON_EXPERIMENTAL=$E"
+
+sched=
+[ "$SCHED" != "" ] && sched="GGML_SCHED_DEBUG=2" cli_opts="$cli_opts -v"
+
+profile=
+[ "$PROF" != "" ] && profile="GGML_HEXAGON_PROFILE=$PROF GGML_HEXAGON_OPSYNC=1"
+
+opmask=
+[ "$OPMASK" != "" ] && opmask="GGML_HEXAGON_OPMASK=$OPMASK"
+
+nhvx=
+[ "$NHVX" != "" ] && nhvx="GGML_HEXAGON_NHVX=$NHVX"
+
+ndev=
+[ "$NDEV" != "" ] && ndev="GGML_HEXAGON_NDEV=$NDEV"
+
+# MTMD backend device for vision model (defaults to CPU if not set)
+mtmd_backend=
+[ "$MTMD_DEVICE" != "" ] && mtmd_backend="MTMD_BACKEND_DEVICE=$MTMD_DEVICE"
+
+set -x
+
+adb $adbserial shell " \
+  cd $basedir; ulimit -c unlimited;        \
+    LD_LIBRARY_PATH=$basedir/$branch/lib   \
+    ADSP_LIBRARY_PATH=$basedir/$branch/lib \
+    $verbose $experimental $sched $opmask $profile $nhvx $ndev $mtmd_backend       \
+      ./$branch/bin/llama-mtmd-cli --no-mmap -m $basedir/../gguf/$model   \
+         --mmproj $basedir/../gguf/$mmproj \
+         --image $basedir/../gguf/$image \
+         --poll 1000 -t 6 --cpu-mask 0xfc --cpu-strict 1             \
+         --ctx-size 8192 --batch-size 128 -ctk q8_0 -ctv q8_0 -fa on \
+         -ngl 99 --device $device -v $cli_opts $@ \
+"

From d6a1e18c651a46109cbf2ad3b299581f0651128f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Mon, 15 Dec 2025 22:03:16 +0100
Subject: [PATCH 115/135] convert : move rope_parameters to TextModel class
 (#18061)

* make sure to search text_config for rope parameters

* move rope_parameters to TextModel class
---
 convert_hf_to_gguf.py | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index ad20cb8b88..e1c78e3b18 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -136,19 +136,11 @@ class ModelBase:
         self.remote_hf_model_id = remote_hf_model_id
         self.sentence_transformers_dense_modules = sentence_transformers_dense_modules
         self.hparams = ModelBase.load_hparams(self.dir_model, self.is_mistral_format) if hparams is None else hparams
-        self.rope_parameters = self.hparams.get("rope_parameters", self.hparams.get("rope_scaling")) or {}
         self.model_tensors = self.index_tensors(remote_hf_model_id=remote_hf_model_id)
         self.metadata_override = metadata_override
         self.model_name = model_name
         self.dir_model_card = dir_model  # overridden in convert_lora_to_gguf.py
 
-        # Ensure "rope_theta" and "rope_type" is mirrored in rope_parameters
-        if "full_attention" not in self.rope_parameters and "sliding_attention" not in self.rope_parameters:
-            if "rope_theta" not in self.rope_parameters and (rope_theta := self.find_hparam(["rope_theta", "global_rope_theta", "rotary_emb_base"], optional=True)) is not None:
-                self.rope_parameters["rope_theta"] = rope_theta
-            if "rope_type" not in self.rope_parameters and (rope_type := self.rope_parameters.get("type")) is not None:
-                self.rope_parameters["rope_type"] = rope_type
-
         # Apply heuristics to figure out typical tensor encoding based on first layer tensor encoding type
         if self.ftype == gguf.LlamaFileType.GUESSED:
             # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie.
@@ -765,6 +757,15 @@ class TextModel(ModelBase):
         self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"])
         self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
 
+        self.rope_parameters = self.hparams.get("rope_parameters", self.hparams.get("rope_scaling")) or {}
+
+        # Ensure "rope_theta" and "rope_type" is mirrored in rope_parameters
+        if "full_attention" not in self.rope_parameters and "sliding_attention" not in self.rope_parameters:
+            if "rope_theta" not in self.rope_parameters and (rope_theta := self.find_hparam(["rope_theta", "global_rope_theta", "rotary_emb_base"], optional=True)) is not None:
+                self.rope_parameters["rope_theta"] = rope_theta
+            if "rope_type" not in self.rope_parameters and (rope_type := self.rope_parameters.get("type")) is not None:
+                self.rope_parameters["rope_type"] = rope_type
+
     @classmethod
     def __init_subclass__(cls):
         # can't use an abstract property, because overriding it without type errors

From 40d9c394f4523bd5f214e6ea536655c1e9a83672 Mon Sep 17 00:00:00 2001
From: Darius Lukas <dariusjlukas@gmail.com>
Date: Tue, 16 Dec 2025 01:15:49 -0500
Subject: [PATCH 116/135] Webui: Disable attachment button and model selector
 button when prompt textbox is disabled. (#17925)

* Pass disabled state to the file attachments button and the model
selector button.

* Update index.html.gz

* Fix model info card in non-router mode.

* Update index.html.gz
---
 tools/server/public/index.html.gz             | Bin 1445607 -> 1445715 bytes
 .../ChatFormActionFileAttachments.svelte      |   2 +-
 .../ChatFormActions/ChatFormActions.svelte    |   1 +
 .../app/models/ModelsSelector.svelte          | 382 +++++++++---------
 4 files changed, 200 insertions(+), 185 deletions(-)

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index 036feff1c3e182484fa80cc64e53e386a4560558..85be6c4c001e97e8a1c06eae8f032c829379e94e 100644
GIT binary patch
delta 1117507
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zT=(ee#-E)_oR3XqXNWiXx#}y0I-+E?%RHAk^h`lYwP*0WNvM+{_D*-7ZXf3*&$du~
zk=sZ~ks!#QRCvWAD+<te<oAEJS&;VuHrCIiVb*}o&T)LptfMYvP{w>f651MB4ON=9
zAY(e18^w^0>s==7m|3O=tAnNx_0U+tzvqfM`fk-dKVt%tBgP)}fmZt3u@8H~Z}40v
z9w#9gyW%3R6BSFH8qJ}KV#OLbO~K&`C5%Ov=FrVfwgw*maE}j2rFVZ3Z>BiN601^A
z?Z`-l4{vE1>A-)IxQZkhX-bR^s?m=})|bQzDQ@kuH#pcMA#4h>=oZ54W?We>h(9jn
z(g27ii3f!TD!}PDRkZ?wKpfMBkZgTl92_nK7gYZL40P(uf~$IPi&X}@V3Rl+Tp@TQ
ze8dvZF&S*E7#u|G&C`FKS{0N3*76vOT@FOK9{`tzwjB895BuJvUd~pxKI}35c+;~{
z&@*_yA3=Lpz1<qslH!jk_6$PGET+Hr>`qWnm>2+qdFyN+UR5?q0<RUJ!jqGe@A!3n
zy#^X^Q!c8_^@rKdzszU<vr?(9127l2Ik=qH?)iOqfg^Htzqud8FT;y@^@I<@i`i;k
VU!Dvv2=Tc*`3lZ!^%GybKmfo(+`<3=

diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionFileAttachments.svelte b/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionFileAttachments.svelte
index f4aa8a3a3f..127130fb84 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionFileAttachments.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActionFileAttachments.svelte
@@ -35,7 +35,7 @@
 
 <div class="flex items-center gap-1 {className}">
 	<DropdownMenu.Root>
-		<DropdownMenu.Trigger name="Attach files">
+		<DropdownMenu.Trigger name="Attach files" {disabled}>
 			<Tooltip.Root>
 				<Tooltip.Trigger>
 					<Button
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActions.svelte b/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActions.svelte
index 8607e00c02..730c348b30 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActions.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatFormActions/ChatFormActions.svelte
@@ -173,6 +173,7 @@
 	/>
 
 	<ModelsSelector
+		{disabled}
 		bind:this={selectorModelRef}
 		currentModel={conversationModel}
 		forceForegroundText={true}
diff --git a/tools/server/webui/src/lib/components/app/models/ModelsSelector.svelte b/tools/server/webui/src/lib/components/app/models/ModelsSelector.svelte
index ac0937696d..efc9cd4e2f 100644
--- a/tools/server/webui/src/lib/components/app/models/ModelsSelector.svelte
+++ b/tools/server/webui/src/lib/components/app/models/ModelsSelector.svelte
@@ -179,51 +179,37 @@
 		});
 	});
 
+	// Handle changes to the model selector pop-down or the model dialog, depending on if the server is in
+	// router mode or not.
 	function handleOpenChange(open: boolean) {
 		if (loading || updating) return;
 
-		if (open) {
-			isOpen = true;
-			searchTerm = '';
-			highlightedIndex = -1;
+		if (isRouter) {
+			if (open) {
+				isOpen = true;
+				searchTerm = '';
+				highlightedIndex = -1;
 
-			// Focus search input after popover opens
-			tick().then(() => {
-				requestAnimationFrame(() => searchInputRef?.focus());
-			});
+				// Focus search input after popover opens
+				tick().then(() => {
+					requestAnimationFrame(() => searchInputRef?.focus());
+				});
 
-			if (isRouter) {
 				modelsStore.fetchRouterModels().then(() => {
 					modelsStore.fetchModalitiesForLoadedModels();
 				});
+			} else {
+				isOpen = false;
+				searchTerm = '';
+				highlightedIndex = -1;
 			}
 		} else {
-			isOpen = false;
-			searchTerm = '';
-			highlightedIndex = -1;
+			showModelDialog = open;
 		}
 	}
 
-	function handleTriggerClick() {
-		if (loading || updating) return;
-
-		if (!isRouter) {
-			// Single model mode: show dialog instead of popover
-			showModelDialog = true;
-		}
-		// For router mode, the Popover handles open/close
-	}
-
 	export function open() {
-		if (isRouter) {
-			handleOpenChange(true);
-		} else {
-			showModelDialog = true;
-		}
-	}
-
-	function closeMenu() {
-		handleOpenChange(false);
+		handleOpenChange(true);
 	}
 
 	function handleSearchKeyDown(event: KeyboardEvent) {
@@ -292,7 +278,7 @@
 		}
 
 		if (shouldCloseMenu) {
-			closeMenu();
+			handleOpenChange(false);
 
 			// Focus the chat textarea after model selection
 			requestAnimationFrame(() => {
@@ -360,8 +346,181 @@
 	{:else}
 		{@const selectedOption = getDisplayOption()}
 
-		<Popover.Root bind:open={isOpen} onOpenChange={handleOpenChange}>
-			<Popover.Trigger
+		{#if isRouter}
+			<Popover.Root bind:open={isOpen} onOpenChange={handleOpenChange}>
+				<Popover.Trigger
+					class={cn(
+						`inline-flex cursor-pointer items-center gap-1.5 rounded-sm bg-muted-foreground/10 px-1.5 py-1 text-xs transition hover:text-foreground focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60`,
+						!isCurrentModelInCache()
+							? 'bg-red-400/10 !text-red-400 hover:bg-red-400/20 hover:text-red-400'
+							: forceForegroundText
+								? 'text-foreground'
+								: isHighlightedCurrentModelActive
+									? 'text-foreground'
+									: 'text-muted-foreground',
+						isOpen ? 'text-foreground' : ''
+					)}
+					style="max-width: min(calc(100cqw - 6.5rem), 32rem)"
+					disabled={disabled || updating}
+				>
+					<Package class="h-3.5 w-3.5" />
+
+					<span class="truncate font-medium">
+						{selectedOption?.model || 'Select model'}
+					</span>
+
+					{#if updating}
+						<Loader2 class="h-3 w-3.5 animate-spin" />
+					{:else}
+						<ChevronDown class="h-3 w-3.5" />
+					{/if}
+				</Popover.Trigger>
+
+				<Popover.Content
+					class="group/popover-content w-96 max-w-[calc(100vw-2rem)] p-0"
+					align="end"
+					sideOffset={8}
+					collisionPadding={16}
+				>
+					<div class="flex max-h-[50dvh] flex-col overflow-hidden">
+						<div
+							class="order-1 shrink-0 border-b p-4 group-data-[side=top]/popover-content:order-2 group-data-[side=top]/popover-content:border-t group-data-[side=top]/popover-content:border-b-0"
+						>
+							<SearchInput
+								id="model-search"
+								placeholder="Search models..."
+								bind:value={searchTerm}
+								bind:ref={searchInputRef}
+								onClose={() => handleOpenChange(false)}
+								onKeyDown={handleSearchKeyDown}
+							/>
+						</div>
+						<div
+							class="models-list order-2 min-h-0 flex-1 overflow-y-auto group-data-[side=top]/popover-content:order-1"
+						>
+							{#if !isCurrentModelInCache() && currentModel}
+								<!-- Show unavailable model as first option (disabled) -->
+								<button
+									type="button"
+									class="flex w-full cursor-not-allowed items-center bg-red-400/10 px-4 py-2 text-left text-sm text-red-400"
+									role="option"
+									aria-selected="true"
+									aria-disabled="true"
+									disabled
+								>
+									<span class="truncate">{selectedOption?.name || currentModel}</span>
+									<span class="ml-2 text-xs whitespace-nowrap opacity-70">(not available)</span>
+								</button>
+								<div class="my-1 h-px bg-border"></div>
+							{/if}
+							{#if filteredOptions.length === 0}
+								<p class="px-4 py-3 text-sm text-muted-foreground">No models found.</p>
+							{/if}
+							{#each filteredOptions as option, index (option.id)}
+								{@const status = getModelStatus(option.model)}
+								{@const isLoaded = status === ServerModelStatus.LOADED}
+								{@const isLoading = status === ServerModelStatus.LOADING}
+								{@const isSelected = currentModel === option.model || activeId === option.id}
+								{@const isCompatible = isModelCompatible(option)}
+								{@const isHighlighted = index === highlightedIndex}
+								{@const missingModalities = getMissingModalities(option)}
+
+								<div
+									class={cn(
+										'group flex w-full items-center gap-2 px-4 py-2 text-left text-sm transition focus:outline-none',
+										isCompatible
+											? 'cursor-pointer hover:bg-muted focus:bg-muted'
+											: 'cursor-not-allowed opacity-50',
+										isSelected || isHighlighted
+											? 'bg-accent text-accent-foreground'
+											: isCompatible
+												? 'hover:bg-accent hover:text-accent-foreground'
+												: '',
+										isLoaded ? 'text-popover-foreground' : 'text-muted-foreground'
+									)}
+									role="option"
+									aria-selected={isSelected || isHighlighted}
+									aria-disabled={!isCompatible}
+									tabindex={isCompatible ? 0 : -1}
+									onclick={() => isCompatible && handleSelect(option.id)}
+									onmouseenter={() => (highlightedIndex = index)}
+									onkeydown={(e) => {
+										if (isCompatible && (e.key === 'Enter' || e.key === ' ')) {
+											e.preventDefault();
+											handleSelect(option.id);
+										}
+									}}
+								>
+									<span class="min-w-0 flex-1 truncate">{option.model}</span>
+
+									{#if missingModalities}
+										<span class="flex shrink-0 items-center gap-1 text-muted-foreground/70">
+											{#if missingModalities.vision}
+												<Tooltip.Root>
+													<Tooltip.Trigger>
+														<EyeOff class="h-3.5 w-3.5" />
+													</Tooltip.Trigger>
+													<Tooltip.Content class="z-[9999]">
+														<p>No vision support</p>
+													</Tooltip.Content>
+												</Tooltip.Root>
+											{/if}
+											{#if missingModalities.audio}
+												<Tooltip.Root>
+													<Tooltip.Trigger>
+														<MicOff class="h-3.5 w-3.5" />
+													</Tooltip.Trigger>
+													<Tooltip.Content class="z-[9999]">
+														<p>No audio support</p>
+													</Tooltip.Content>
+												</Tooltip.Root>
+											{/if}
+										</span>
+									{/if}
+
+									{#if isLoading}
+										<Tooltip.Root>
+											<Tooltip.Trigger>
+												<Loader2 class="h-4 w-4 shrink-0 animate-spin text-muted-foreground" />
+											</Tooltip.Trigger>
+											<Tooltip.Content class="z-[9999]">
+												<p>Loading model...</p>
+											</Tooltip.Content>
+										</Tooltip.Root>
+									{:else if isLoaded}
+										<Tooltip.Root>
+											<Tooltip.Trigger>
+												<button
+													type="button"
+													class="relative ml-2 flex h-4 w-4 shrink-0 items-center justify-center"
+													onclick={(e) => {
+														e.stopPropagation();
+														modelsStore.unloadModel(option.model);
+													}}
+												>
+													<span
+														class="mr-2 h-2 w-2 rounded-full bg-green-500 transition-opacity group-hover:opacity-0"
+													></span>
+													<Power
+														class="absolute mr-2 h-4 w-4 text-red-500 opacity-0 transition-opacity group-hover:opacity-100 hover:text-red-600"
+													/>
+												</button>
+											</Tooltip.Trigger>
+											<Tooltip.Content class="z-[9999]">
+												<p>Unload model</p>
+											</Tooltip.Content>
+										</Tooltip.Root>
+									{:else}
+										<span class="mx-2 h-2 w-2 rounded-full bg-muted-foreground/50"></span>
+									{/if}
+								</div>
+							{/each}
+						</div>
+					</div>
+				</Popover.Content>
+			</Popover.Root>
+		{:else}
+			<button
 				class={cn(
 					`inline-flex cursor-pointer items-center gap-1.5 rounded-sm bg-muted-foreground/10 px-1.5 py-1 text-xs transition hover:text-foreground focus:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 disabled:cursor-not-allowed disabled:opacity-60`,
 					!isCurrentModelInCache()
@@ -374,165 +533,20 @@
 					isOpen ? 'text-foreground' : ''
 				)}
 				style="max-width: min(calc(100cqw - 6.5rem), 32rem)"
-				onclick={handleTriggerClick}
-				disabled={disabled || updating || !isRouter}
+				onclick={() => handleOpenChange(true)}
+				disabled={disabled || updating}
 			>
 				<Package class="h-3.5 w-3.5" />
 
 				<span class="truncate font-medium">
-					{selectedOption?.model || 'Select model'}
+					{selectedOption?.model}
 				</span>
 
 				{#if updating}
 					<Loader2 class="h-3 w-3.5 animate-spin" />
-				{:else if isRouter}
-					<ChevronDown class="h-3 w-3.5" />
 				{/if}
-			</Popover.Trigger>
-
-			<Popover.Content
-				class="group/popover-content w-96 max-w-[calc(100vw-2rem)] p-0"
-				align="end"
-				sideOffset={8}
-				collisionPadding={16}
-			>
-				<div class="flex max-h-[50dvh] flex-col overflow-hidden">
-					<div
-						class="order-1 shrink-0 border-b p-4 group-data-[side=top]/popover-content:order-2 group-data-[side=top]/popover-content:border-t group-data-[side=top]/popover-content:border-b-0"
-					>
-						<SearchInput
-							id="model-search"
-							placeholder="Search models..."
-							bind:value={searchTerm}
-							bind:ref={searchInputRef}
-							onClose={closeMenu}
-							onKeyDown={handleSearchKeyDown}
-						/>
-					</div>
-					<div
-						class="models-list order-2 min-h-0 flex-1 overflow-y-auto group-data-[side=top]/popover-content:order-1"
-					>
-						{#if !isCurrentModelInCache() && currentModel}
-							<!-- Show unavailable model as first option (disabled) -->
-							<button
-								type="button"
-								class="flex w-full cursor-not-allowed items-center bg-red-400/10 px-4 py-2 text-left text-sm text-red-400"
-								role="option"
-								aria-selected="true"
-								aria-disabled="true"
-								disabled
-							>
-								<span class="truncate">{selectedOption?.name || currentModel}</span>
-								<span class="ml-2 text-xs whitespace-nowrap opacity-70">(not available)</span>
-							</button>
-							<div class="my-1 h-px bg-border"></div>
-						{/if}
-						{#if filteredOptions.length === 0}
-							<p class="px-4 py-3 text-sm text-muted-foreground">No models found.</p>
-						{/if}
-						{#each filteredOptions as option, index (option.id)}
-							{@const status = getModelStatus(option.model)}
-							{@const isLoaded = status === ServerModelStatus.LOADED}
-							{@const isLoading = status === ServerModelStatus.LOADING}
-							{@const isSelected = currentModel === option.model || activeId === option.id}
-							{@const isCompatible = isModelCompatible(option)}
-							{@const isHighlighted = index === highlightedIndex}
-							{@const missingModalities = getMissingModalities(option)}
-
-							<div
-								class={cn(
-									'group flex w-full items-center gap-2 px-4 py-2 text-left text-sm transition focus:outline-none',
-									isCompatible
-										? 'cursor-pointer hover:bg-muted focus:bg-muted'
-										: 'cursor-not-allowed opacity-50',
-									isSelected || isHighlighted
-										? 'bg-accent text-accent-foreground'
-										: isCompatible
-											? 'hover:bg-accent hover:text-accent-foreground'
-											: '',
-									isLoaded ? 'text-popover-foreground' : 'text-muted-foreground'
-								)}
-								role="option"
-								aria-selected={isSelected || isHighlighted}
-								aria-disabled={!isCompatible}
-								tabindex={isCompatible ? 0 : -1}
-								onclick={() => isCompatible && handleSelect(option.id)}
-								onmouseenter={() => (highlightedIndex = index)}
-								onkeydown={(e) => {
-									if (isCompatible && (e.key === 'Enter' || e.key === ' ')) {
-										e.preventDefault();
-										handleSelect(option.id);
-									}
-								}}
-							>
-								<span class="min-w-0 flex-1 truncate">{option.model}</span>
-
-								{#if missingModalities}
-									<span class="flex shrink-0 items-center gap-1 text-muted-foreground/70">
-										{#if missingModalities.vision}
-											<Tooltip.Root>
-												<Tooltip.Trigger>
-													<EyeOff class="h-3.5 w-3.5" />
-												</Tooltip.Trigger>
-												<Tooltip.Content class="z-[9999]">
-													<p>No vision support</p>
-												</Tooltip.Content>
-											</Tooltip.Root>
-										{/if}
-										{#if missingModalities.audio}
-											<Tooltip.Root>
-												<Tooltip.Trigger>
-													<MicOff class="h-3.5 w-3.5" />
-												</Tooltip.Trigger>
-												<Tooltip.Content class="z-[9999]">
-													<p>No audio support</p>
-												</Tooltip.Content>
-											</Tooltip.Root>
-										{/if}
-									</span>
-								{/if}
-
-								{#if isLoading}
-									<Tooltip.Root>
-										<Tooltip.Trigger>
-											<Loader2 class="h-4 w-4 shrink-0 animate-spin text-muted-foreground" />
-										</Tooltip.Trigger>
-										<Tooltip.Content class="z-[9999]">
-											<p>Loading model...</p>
-										</Tooltip.Content>
-									</Tooltip.Root>
-								{:else if isLoaded}
-									<Tooltip.Root>
-										<Tooltip.Trigger>
-											<button
-												type="button"
-												class="relative ml-2 flex h-4 w-4 shrink-0 items-center justify-center"
-												onclick={(e) => {
-													e.stopPropagation();
-													modelsStore.unloadModel(option.model);
-												}}
-											>
-												<span
-													class="mr-2 h-2 w-2 rounded-full bg-green-500 transition-opacity group-hover:opacity-0"
-												></span>
-												<Power
-													class="absolute mr-2 h-4 w-4 text-red-500 opacity-0 transition-opacity group-hover:opacity-100 hover:text-red-600"
-												/>
-											</button>
-										</Tooltip.Trigger>
-										<Tooltip.Content class="z-[9999]">
-											<p>Unload model</p>
-										</Tooltip.Content>
-									</Tooltip.Root>
-								{:else}
-									<span class="mx-2 h-2 w-2 rounded-full bg-muted-foreground/50"></span>
-								{/if}
-							</div>
-						{/each}
-					</div>
-				</div>
-			</Popover.Content>
-		</Popover.Root>
+			</button>
+		{/if}
 	{/if}
 </div>
 

From 2995341730f18deb64faa4538bda113328fd791f Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Tue, 16 Dec 2025 07:19:26 +0100
Subject: [PATCH 117/135] llama : add support for NVIDIA Nemotron 3 Nano
 (#18058)

* llama : add support for NVIDIA Nemotron Nano 3

This commit adds support for the NVIDIA Nemotron Nano 3 model, enabling
the conversion and running of this model.

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 convert_hf_to_gguf.py          | 116 +++++++++++++++++++++++++++++++--
 gguf-py/gguf/constants.py      |  29 +++++++++
 gguf-py/gguf/tensor_mapping.py |   9 ++-
 src/llama-arch.cpp             |  35 ++++++++++
 src/llama-arch.h               |   1 +
 src/llama-graph.cpp            |   9 +++
 src/llama-model.cpp            |  49 +++++++++++---
 src/llama-model.h              |   1 +
 src/models/nemotron-h.cpp      |  41 ++++++++++--
 9 files changed, 267 insertions(+), 23 deletions(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index e1c78e3b18..01b15e4b9a 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -8490,8 +8490,18 @@ class GraniteHybridModel(Mamba2Model, GraniteMoeModel):
 class NemotronHModel(GraniteHybridModel):
     """Hybrid mamba2/attention model from NVIDIA"""
     model_arch = gguf.MODEL_ARCH.NEMOTRON_H
+    is_moe: bool = False
 
     def __init__(self, *args, **kwargs):
+        # We have to determine the correct model architecture (MoE vs non-MoE) before
+        # calling the parent __init__. This is because the parent constructor
+        # uses self.model_arch to build the tensor name map, and all MoE-specific
+        # mappings would be missed if it were called with the default non-MoE arch.
+        hparams = ModelBase.load_hparams(args[0], self.is_mistral_format)
+        if "num_experts_per_tok" in hparams:
+            self.model_arch = gguf.MODEL_ARCH.NEMOTRON_H_MOE
+            self.is_moe = True
+
         super().__init__(*args, **kwargs)
 
         # Save the top-level head_dim for later
@@ -8503,9 +8513,11 @@ class NemotronHModel(GraniteHybridModel):
 
         # Update the ssm / attn / mlp layers
         # M: Mamba2, *: Attention, -: MLP
+        # MoE:
+        # M: Mamba2, *: Attention, E: Expert
         hybrid_override_pattern = self.hparams["hybrid_override_pattern"]
         self._ssm_layers = [i for i, val in enumerate(hybrid_override_pattern) if val == "M"]
-        self._mlp_layers = [i for i, val in enumerate(hybrid_override_pattern) if val == "-"]
+        self._mlp_layers = [i for i, val in enumerate(hybrid_override_pattern) if val == ("E" if self.is_moe else "-")]
 
     def get_attn_layers(self):
         hybrid_override_pattern = self.hparams["hybrid_override_pattern"]
@@ -8521,10 +8533,28 @@ class NemotronHModel(GraniteHybridModel):
         # Set feed_forward_length
         # NOTE: This will trigger an override warning. This is preferrable to
         #   duplicating all the parent logic
-        n_ff = self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"])
-        self.gguf_writer.add_feed_forward_length([
-            n_ff if i in self._mlp_layers else 0 for i in range(self.block_count)
-        ])
+        if not self.is_moe:
+            n_ff = self.find_hparam(["intermediate_size", "n_inner", "hidden_dim"])
+            self.gguf_writer.add_feed_forward_length([
+                n_ff if i in self._mlp_layers else 0 for i in range(self.block_count)
+            ])
+        else:
+            moe_intermediate_size = self.hparams["moe_intermediate_size"]
+            self.gguf_writer.add_feed_forward_length([
+                moe_intermediate_size if i in self._mlp_layers else 0 for i in range(self.block_count)
+            ])
+            self.gguf_writer.add_expert_used_count(self.hparams["num_experts_per_tok"])
+            self.gguf_writer.add_expert_feed_forward_length(self.hparams["moe_intermediate_size"])
+            self.gguf_writer.add_expert_shared_feed_forward_length(self.hparams["moe_shared_expert_intermediate_size"])
+            self.gguf_writer.add_expert_count(self.hparams["n_routed_experts"])
+            self.gguf_writer.add_expert_shared_count(self.hparams["n_shared_experts"])
+            self.gguf_writer.add_expert_weights_norm(self.hparams["norm_topk_prob"])
+            self.gguf_writer.add_expert_weights_scale(self.hparams["routed_scaling_factor"])
+            self.gguf_writer.add_expert_group_count(self.hparams["n_group"])
+
+            # number of experts used per token (top-k)
+            if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None:
+                self.gguf_writer.add_expert_used_count(n_experts_used)
 
     def set_vocab(self):
         super().set_vocab()
@@ -8532,7 +8562,81 @@ class NemotronHModel(GraniteHybridModel):
         # The tokenizer _does_ add a BOS token (via post_processor type
         # TemplateProcessing) but does not set add_bos_token to true in the
         # config, so we need to explicitly override it here.
-        self.gguf_writer.add_add_bos_token(True)
+        if not self.is_moe:
+            self.gguf_writer.add_add_bos_token(True)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if self.is_moe and bid is not None:
+            if name.endswith("mixer.gate.e_score_correction_bias"):
+                new_name = name.replace("e_score_correction_bias", "e_score_correction.bias")
+                mapped_name = self.map_tensor_name(new_name)
+                return [(mapped_name, data_torch)]
+
+            if name.endswith("mixer.dt_bias"):
+                new_name = name.replace("dt_bias", "dt.bias")
+                mapped_name = self.map_tensor_name(new_name)
+                return [(mapped_name, data_torch)]
+
+            if name.endswith("mixer.conv1d.weight"):
+                squeezed_data = data_torch.squeeze()
+                mapped_name = self.map_tensor_name(name)
+                return [(mapped_name, squeezed_data)]
+
+            if name.endswith("mixer.A_log"):
+                transformed_data = -torch.exp(data_torch)
+                reshaped_data = transformed_data.squeeze().reshape(-1, 1)
+                mapped_name = self.map_tensor_name(name)
+                return [(mapped_name, reshaped_data)]
+
+            if name.endswith("mixer.D"):
+                reshaped_data = data_torch.squeeze().reshape(-1, 1)
+                mapped_name = self.map_tensor_name(name)
+                return [(mapped_name, reshaped_data)]
+
+            if name.endswith("mixer.norm.weight"):
+                reshaped_data = data_torch.reshape(8, 512)
+                mapped_name = self.map_tensor_name(name)
+                return [(mapped_name, reshaped_data)]
+
+            if name.find("mixer.experts") != -1:
+                n_experts = self.hparams["n_routed_experts"]
+                assert bid is not None
+
+                if self._experts is None:
+                    self._experts = [{} for _ in range(self.block_count)]
+
+                self._experts[bid][name] = data_torch
+
+                if len(self._experts[bid]) >= n_experts * 2:
+                    # merge the experts into a single tensor
+                    tensors: list[tuple[str, Tensor]] = []
+                    for w_name in ["down_proj", "up_proj"]:
+                        datas: list[Tensor] = []
+
+                        for xid in range(n_experts):
+                            ename = f"backbone.layers.{bid}.mixer.experts.{xid}.{w_name}.weight"
+                            datas.append(self._experts[bid][ename])
+                            del self._experts[bid][ename]
+
+                        data_torch = torch.stack(datas, dim=0)
+                        merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
+                        new_name = self.map_tensor_name(merged_name)
+                        tensors.append((new_name, data_torch))
+
+                    return tensors
+                else:
+                    return []
+
+        return super().modify_tensors(data_torch, name, bid)
+
+    def prepare_tensors(self):
+        super().prepare_tensors()
+
+        if self._experts is not None:
+            # flatten `list[dict[str, Tensor]]` into `list[str]`
+            experts = [k for d in self._experts for k in d.keys()]
+            if len(experts) > 0:
+                raise ValueError(f"Unprocessed experts: {experts}")
 
 
 @ModelBase.register("BailingMoeForCausalLM")
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 8ef4a23a10..5ca4efd043 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -413,6 +413,7 @@ class MODEL_ARCH(IntEnum):
     JAIS             = auto()
     NEMOTRON         = auto()
     NEMOTRON_H       = auto()
+    NEMOTRON_H_MOE   = auto()
     EXAONE           = auto()
     EXAONE4          = auto()
     GRANITE          = auto()
@@ -786,6 +787,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.JAIS:             "jais",
     MODEL_ARCH.NEMOTRON:         "nemotron",
     MODEL_ARCH.NEMOTRON_H:       "nemotron_h",
+    MODEL_ARCH.NEMOTRON_H_MOE:   "nemotron_h_moe",
     MODEL_ARCH.EXAONE:           "exaone",
     MODEL_ARCH.EXAONE4:          "exaone4",
     MODEL_ARCH.GRANITE:          "granite",
@@ -2529,6 +2531,33 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.NEMOTRON_H_MOE: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.SSM_IN,
+        MODEL_TENSOR.SSM_CONV1D,
+        MODEL_TENSOR.SSM_DT,
+        MODEL_TENSOR.SSM_A,
+        MODEL_TENSOR.SSM_D,
+        MODEL_TENSOR.SSM_NORM,
+        MODEL_TENSOR.SSM_OUT,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        # experts
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        # shared expert
+        MODEL_TENSOR.FFN_DOWN_SHEXP,
+        MODEL_TENSOR.FFN_UP_SHEXP,
+        MODEL_TENSOR.FFN_EXP_PROBS_B,
+    ],
     MODEL_ARCH.EXAONE: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index b320e2b4b2..610227231f 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -379,6 +379,7 @@ class TensorNameMap:
             "model.layers.{bid}.feed_forward.gate",               # lfm2moe
             "model.layers.{bid}.mlp.router.gate",               # afmoe
             "layers.{bid}.gate",                                # mistral-large
+            "backbone.layers.{bid}.mixer.gate",                 # nemotron-h-moe
         ),
 
         MODEL_TENSOR.FFN_GATE_INP_SHEXP: (
@@ -392,6 +393,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.expert_bias",                           # afmoe
             "model.layers.{bid}.feed_forward.expert_bias",                  # lfm2moe
             "model.layers.{bid}.block_sparse_moe.e_score_correction",       # minimax-m2
+            "backbone.layers.{bid}.mixer.gate.e_score_correction"           # nemotron-h-moe
         ),
 
         # Feed-forward up
@@ -440,7 +442,7 @@ class TensorNameMap:
             "layers.{bid}.feed_forward.experts.w3",                 # mixtral (merged)
             "transformer.decoder_layer.{bid}.moe.linear_v",         # Grok (merged)
             "transformer.blocks.{bid}.ffn.experts.mlp.v1",          # dbrx
-            "model.layers.{bid}.mlp.experts.up_proj",               # qwen2moe olmoe (merged) ernie4.5-moe
+            "model.layers.{bid}.mlp.experts.up_proj",               # qwen2moe olmoe (merged) ernie4.5-moe, nemotron-h-moe (merged)
             "model.layers.{bid}.block_sparse_moe.experts.w3",       # phimoe (merged)
             "model.layers.{bid}.feed_forward.experts.up_proj",      # llama4
             "encoder.layers.{bid}.mlp.experts.mlp.w1",              # nomic-bert-moe
@@ -454,6 +456,7 @@ class TensorNameMap:
             "model.layers.{bid}.feed_forward.down_proj",
             "model.layers.{bid}.mlp.shared_mlp.up_proj",             # hunyuan
             "layers.{bid}.shared_experts.w3",                        # mistral-large
+            "backbone.layers.{bid}.mixer.shared_experts.up_proj",    # nemotron-h-moe
         ),
 
         MODEL_TENSOR.FFN_UP_CHEXP: (
@@ -548,7 +551,7 @@ class TensorNameMap:
             "layers.{bid}.feed_forward.experts.w2",                 # mixtral (merged)
             "transformer.decoder_layer.{bid}.moe.linear_1",         # Grok (merged)
             "transformer.blocks.{bid}.ffn.experts.mlp.w2",          # dbrx
-            "model.layers.{bid}.mlp.experts.down_proj",             # qwen2moe olmoe (merged) ernie4.5-moe
+            "model.layers.{bid}.mlp.experts.down_proj",             # qwen2moe olmoe (merged) ernie4.5-moe nemotron-h-moe (merged)
             "model.layers.{bid}.block_sparse_moe.output_linear",    # granitemoe
             "model.layers.{bid}.block_sparse_moe.experts.w2",       # phimoe (merged)
             "model.layers.{bid}.feed_forward.experts.down_proj",    # llama4
@@ -563,6 +566,7 @@ class TensorNameMap:
             "model.layers.{bid}.shared_mlp.output_linear",             # granitemoe
             "model.layers.{bid}.mlp.shared_mlp.down_proj",             # hunyuan
             "layers.{bid}.shared_experts.w2",                          # mistral-large
+            "backbone.layers.{bid}.mixer.shared_experts.down_proj",    # nemotron-h-moe
         ),
 
         MODEL_TENSOR.FFN_DOWN_CHEXP: (
@@ -706,6 +710,7 @@ class TensorNameMap:
             "model.layers.{bid}.mamba.dt_proj",         # jamba falcon-h1 granite-hybrid
             "model.layers.layers.{bid}.mixer.dt_proj",  # plamo2
             "model.layers.{bid}.linear_attn.dt_proj",   # qwen3next
+            "backbone.layers.{bid}.mixer.dt",           # nemotron-h-moe
         ),
 
         MODEL_TENSOR.SSM_DT_NORM: (
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 64ad1b7769..05b12a6072 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -75,6 +75,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_JAIS,             "jais"             },
     { LLM_ARCH_NEMOTRON,         "nemotron"         },
     { LLM_ARCH_NEMOTRON_H,       "nemotron_h"       },
+    { LLM_ARCH_NEMOTRON_H_MOE,   "nemotron_h_moe"   },
     { LLM_ARCH_EXAONE,           "exaone"           },
     { LLM_ARCH_EXAONE4,          "exaone4"          },
     { LLM_ARCH_RWKV6,            "rwkv6"            },
@@ -1763,6 +1764,39 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { LLM_TENSOR_FFN_UP,         "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_NEMOTRON_H_MOE,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,     "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,    "output_norm" },
+            { LLM_TENSOR_OUTPUT,         "output" },
+            { LLM_TENSOR_ATTN_NORM,      "blk.%d.attn_norm" },
+            // mamba(2) ssm layers
+            { LLM_TENSOR_SSM_IN,         "blk.%d.ssm_in" },
+            { LLM_TENSOR_SSM_CONV1D,     "blk.%d.ssm_conv1d" },
+            { LLM_TENSOR_SSM_DT,         "blk.%d.ssm_dt" },
+            { LLM_TENSOR_SSM_A,          "blk.%d.ssm_a" },
+            { LLM_TENSOR_SSM_D,          "blk.%d.ssm_d" },
+            { LLM_TENSOR_SSM_NORM,       "blk.%d.ssm_norm" },
+            { LLM_TENSOR_SSM_OUT,        "blk.%d.ssm_out" },
+            // attention layers
+            { LLM_TENSOR_ATTN_Q,         "blk.%d.attn_q" },
+            { LLM_TENSOR_ATTN_K,         "blk.%d.attn_k" },
+            { LLM_TENSOR_ATTN_V,         "blk.%d.attn_v" },
+            { LLM_TENSOR_ATTN_OUT,       "blk.%d.attn_output" },
+            // dense FFN
+            { LLM_TENSOR_FFN_DOWN,       "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,         "blk.%d.ffn_up" },
+            // MoE FFN (for MoE layers)
+            { LLM_TENSOR_FFN_GATE_INP,   "blk.%d.ffn_gate_inp" },
+            { LLM_TENSOR_FFN_UP_EXPS,    "blk.%d.ffn_up_exps" },
+            { LLM_TENSOR_FFN_DOWN_EXPS,  "blk.%d.ffn_down_exps" },
+            { LLM_TENSOR_FFN_EXP_PROBS_B,"blk.%d.exp_probs_b" },
+            // MoE shared expert layer
+            { LLM_TENSOR_FFN_DOWN_SHEXP,  "blk.%d.ffn_down_shexp" },
+            { LLM_TENSOR_FFN_UP_SHEXP,    "blk.%d.ffn_up_shexp" },
+        },
+    },
     {
         LLM_ARCH_EXAONE,
         {
@@ -2817,6 +2851,7 @@ bool llm_arch_is_hybrid(const llm_arch & arch) {
         case LLM_ARCH_LFM2:
         case LLM_ARCH_LFM2MOE:
         case LLM_ARCH_NEMOTRON_H:
+        case LLM_ARCH_NEMOTRON_H_MOE:
         case LLM_ARCH_QWEN3NEXT:
             return true;
         default:
diff --git a/src/llama-arch.h b/src/llama-arch.h
index e113180024..455658f5dc 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -79,6 +79,7 @@ enum llm_arch {
     LLM_ARCH_JAIS,
     LLM_ARCH_NEMOTRON,
     LLM_ARCH_NEMOTRON_H,
+    LLM_ARCH_NEMOTRON_H_MOE,
     LLM_ARCH_EXAONE,
     LLM_ARCH_EXAONE4,
     LLM_ARCH_RWKV6,
diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index 8909bbfb95..b096b93a9a 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -1089,6 +1089,15 @@ ggml_tensor * llm_graph_context::build_moe_ffn(
                 cur = ggml_relu(ctx0, cur);
                 cb(cur, "ffn_moe_relu", il);
             } break;
+        case LLM_FFN_RELU_SQR:
+            if (gate_exps) {
+                // TODO: add support for gated squared relu
+                GGML_ABORT("fatal error: gated squared relu not implemented");
+            } else {
+                cur = ggml_relu(ctx0, cur);
+                cur = ggml_sqr(ctx0, cur);
+                cb(cur, "ffn_moe_relu_sqr", il);
+            } break;
         default:
             GGML_ABORT("fatal error");
     }
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 050735afc0..15547403b4 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -120,6 +120,7 @@ const char * llm_type_name(llm_type type) {
         case LLM_TYPE_16B_A1B:       return "16B.A1B";
         case LLM_TYPE_21B_A3B:       return "21B.A3B";
         case LLM_TYPE_30B_A3B:       return "30B.A3B";
+        case LLM_TYPE_31B_A3_5B:     return "31B.A3.5B";
         case LLM_TYPE_80B_A3B:       return "80B.A3B";
         case LLM_TYPE_100B_A6B:      return "100B.A6B";
         case LLM_TYPE_106B_A12B:     return "106B.A12B";
@@ -1797,6 +1798,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 }
             } break;
         case LLM_ARCH_NEMOTRON_H:
+        case LLM_ARCH_NEMOTRON_H_MOE:
             {
                 ml.get_key(LLM_KV_SSM_CONV_KERNEL,    hparams.ssm_d_conv);
                 ml.get_key(LLM_KV_SSM_INNER_SIZE,     hparams.ssm_d_inner);
@@ -1812,7 +1814,14 @@ void llama_model::load_hparams(llama_model_loader & ml) {
 
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,        hparams.n_ff_exp,        false);
+                ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp,      false);
+                ml.get_key(LLM_KV_EXPERT_SHARED_COUNT,               hparams.n_expert_shared, false);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM,               hparams.expert_weights_norm, false);
+                ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE,              hparams.expert_weights_scale, false);
+
                 switch (hparams.n_layer) {
+                    case 52: type = LLM_TYPE_31B_A3_5B; break; // Nemotron-H_MOE 31B
                     case 56: type = LLM_TYPE_9B; break;
                     default: type = LLM_TYPE_UNKNOWN;
                 }
@@ -5159,6 +5168,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                     }
                 } break;
             case LLM_ARCH_NEMOTRON_H:
+            case LLM_ARCH_NEMOTRON_H_MOE:
                 {
                     // mamba2 Mixer SSM params
                     // NOTE: int64_t for tensor dimensions
@@ -5169,6 +5179,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                     const int64_t n_group    = hparams.ssm_n_group;
                     const int64_t d_in_proj  = 2*d_inner + 2*n_group*d_state + n_ssm_head;
 
+                    const int64_t n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / n_expert_used;
+                    const int64_t n_ff_shexp = hparams.n_ff_shexp;
+
                     // embeddings
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
 
@@ -5218,12 +5231,26 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                             layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "bias",   i), {n_embd_k_gqa_i}, TENSOR_NOT_REQUIRED);
                             layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "bias",   i), {n_embd_v_gqa_i}, TENSOR_NOT_REQUIRED);
                             layer.bo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "bias",   i), {n_embd},         TENSOR_NOT_REQUIRED);
-                        } else {
-                            // mlp layers
-                            layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  hparams.n_ff(i), n_embd}, 0);
-                            layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   hparams.n_ff(i)}, 0);
-                            layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
-                            layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias",   i), {hparams.n_ff(i)}, TENSOR_NOT_REQUIRED);
+                        }  else {
+                            if (n_expert != 0) {
+                                layer.ffn_gate_inp    = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP,  "weight", i), { n_embd, n_expert}, 0);
+                                layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert         }, 0);
+
+                                // MoE branch
+                                layer.ffn_down_exps   = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert}, 0);
+                                layer.ffn_up_exps     = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert}, 0);
+
+                                // Shared expert branch
+                                layer.ffn_down_shexp  = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd}, 0);
+                                layer.ffn_up_shexp    = create_tensor(tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp}, 0);
+
+                            } else {
+                                // mlp layers
+                                layer.ffn_down   = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  hparams.n_ff(i), n_embd}, 0);
+                                layer.ffn_up     = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   hparams.n_ff(i)}, 0);
+                                layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias",   i), {n_embd}, TENSOR_NOT_REQUIRED);
+                                layer.ffn_up_b   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "bias",   i), {hparams.n_ff(i)}, TENSOR_NOT_REQUIRED);
+                            }
                         }
                     }
                 } break;
@@ -6850,7 +6877,8 @@ void llama_model::print_info() const {
         arch == LLM_ARCH_PLAMO2 ||
         arch == LLM_ARCH_GRANITE_HYBRID ||
         arch == LLM_ARCH_QWEN3NEXT ||
-        arch == LLM_ARCH_NEMOTRON_H) {
+        arch == LLM_ARCH_NEMOTRON_H ||
+        arch == LLM_ARCH_NEMOTRON_H_MOE) {
         LLAMA_LOG_INFO("%s: ssm_d_conv       = %u\n",     __func__, hparams.ssm_d_conv);
         LLAMA_LOG_INFO("%s: ssm_d_inner      = %u\n",     __func__, hparams.ssm_d_inner);
         LLAMA_LOG_INFO("%s: ssm_d_state      = %u\n",     __func__, hparams.ssm_d_state);
@@ -6905,7 +6933,8 @@ void llama_model::print_info() const {
     if (arch == LLM_ARCH_MINICPM ||
         arch == LLM_ARCH_GRANITE ||
         arch == LLM_ARCH_GRANITE_MOE ||
-        arch == LLM_ARCH_GRANITE_HYBRID) {
+        arch == LLM_ARCH_GRANITE_HYBRID ||
+        arch == LLM_ARCH_NEMOTRON_H_MOE) {
         LLAMA_LOG_INFO("%s: f_embedding_scale = %f\n", __func__, hparams.f_embedding_scale);
         LLAMA_LOG_INFO("%s: f_residual_scale  = %f\n", __func__, hparams.f_residual_scale);
         LLAMA_LOG_INFO("%s: f_attention_scale = %f\n", __func__, hparams.f_attention_scale);
@@ -7086,7 +7115,7 @@ llama_memory_i * llama_model::create_memory(const llama_memory_params & params,
                     if (arch == LLM_ARCH_FALCON_H1) {
                         filter_attn = [&](int32_t) { return true; };
                         filter_recr = [&](int32_t) { return true; };
-                    } else if (arch == LLM_ARCH_NEMOTRON_H) {
+                    } else if (arch == LLM_ARCH_NEMOTRON_H || arch == LLM_ARCH_NEMOTRON_H_MOE) {
                         filter_attn = [&](int32_t il) {
                             return !hparams.is_recurrent(il) && hparams.n_ff(il) == 0;
                         };
@@ -7457,6 +7486,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
                 llm = std::make_unique<llm_build_nemotron>(*this, params);
             } break;
         case LLM_ARCH_NEMOTRON_H:
+        case LLM_ARCH_NEMOTRON_H_MOE:
             {
                 llm = std::make_unique<llm_build_nemotron_h>(*this, params);
             } break;
@@ -7741,6 +7771,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_ARWKV7:
         case LLM_ARCH_WAVTOKENIZER_DEC:
         case LLM_ARCH_NEMOTRON_H:
+        case LLM_ARCH_NEMOTRON_H_MOE:
             return LLAMA_ROPE_TYPE_NONE;
 
         // use what we call a normal RoPE, operating on pairs of consecutive head values
diff --git a/src/llama-model.h b/src/llama-model.h
index f8342cf2cb..c6eb953188 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -113,6 +113,7 @@ enum llm_type {
     LLM_TYPE_16B_A1B,
     LLM_TYPE_21B_A3B, // Ernie MoE small
     LLM_TYPE_30B_A3B,
+    LLM_TYPE_31B_A3_5B,
     LLM_TYPE_80B_A3B, // Qwen3 Next
     LLM_TYPE_100B_A6B,
     LLM_TYPE_106B_A12B, // GLM-4.5-Air
diff --git a/src/models/nemotron-h.cpp b/src/models/nemotron-h.cpp
index 5414348888..eb135e63f1 100644
--- a/src/models/nemotron-h.cpp
+++ b/src/models/nemotron-h.cpp
@@ -107,12 +107,41 @@ ggml_tensor * llm_build_nemotron_h::build_attention_layer(ggml_tensor *
 }
 
 ggml_tensor * llm_build_nemotron_h::build_ffn_layer(ggml_tensor * cur, const llama_model & model, const int il) {
-    cur = build_ffn(cur,
-            model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
-            NULL, NULL, NULL,
-            model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
-            NULL, LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
-    cb(cur, "ffn_out", il);
+    if (model.layers[il].ffn_gate_inp == nullptr) {
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                NULL,                      NULL,                        NULL,
+                model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                NULL,
+                LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+    } else {
+        ggml_tensor * ffn_inp = cur;
+        ggml_tensor * moe_out =
+            build_moe_ffn(ffn_inp,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    nullptr, // no gate
+                    model.layers[il].ffn_down_exps,
+                    model.layers[il].ffn_exp_probs_b,
+                    n_expert, n_expert_used,
+                    LLM_FFN_RELU_SQR, hparams.expert_weights_norm,
+                    true, hparams.expert_weights_scale,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID,
+                    il);
+        cb(moe_out, "ffn_moe_out", il);
+
+        ggml_tensor * ffn_shexp = build_ffn(ffn_inp,
+                    model.layers[il].ffn_up_shexp,  NULL, NULL,
+                    NULL /* no gate */           ,  NULL, NULL,
+                    model.layers[il].ffn_down_shexp, NULL, NULL,
+                    NULL,
+                    LLM_FFN_RELU_SQR, LLM_FFN_PAR, il);
+        cb(ffn_shexp, "ffn_shexp", il);
+
+        cur = ggml_add(ctx0, moe_out, ffn_shexp);
+        cb(cur, "ffn_out", il);
+    }
 
     cur = build_cvec(cur, il);
     cb(cur, "l_out", il);

From a20979d433e3c8b63a6ba20ed5956ced16f65058 Mon Sep 17 00:00:00 2001
From: Aleksander Grygier <aleksander.grygier@gmail.com>
Date: Tue, 16 Dec 2025 07:31:37 +0100
Subject: [PATCH 118/135] webui: Add setting to always show sidebar on Desktop
 (#17809)

* feat: Add setting to always show Sidebar on Desktop

* chore: update webui build output

* feat: Add auto-show sidebar setting

* fix: Mobile settings dialog UI

* chore: update webui build output

* feat: UI label update

* chore: update webui build output

* chore: update webui build output

* chore: update webui build output

* refactor: Cleanup

* chore: update webui build output
---
 tools/server/public/index.html.gz             | Bin 1445715 -> 1446032 bytes
 .../app/chat/ChatSettings/ChatSettings.svelte |  12 ++++++-
 .../src/lib/constants/settings-config.ts      |   6 ++++
 tools/server/webui/src/routes/+layout.svelte  |  31 +++++++++++++-----
 4 files changed, 40 insertions(+), 9 deletions(-)

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index 85be6c4c001e97e8a1c06eae8f032c829379e94e..a58285a1a2c0103fafc517f2e851913dd774d277 100644
GIT binary patch
delta 1116460
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diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte
index 45640e42a0..6dfae59950 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte
@@ -109,6 +109,16 @@
 					key: 'disableAutoScroll',
 					label: 'Disable automatic scroll',
 					type: 'checkbox'
+				},
+				{
+					key: 'alwaysShowSidebarOnDesktop',
+					label: 'Always show sidebar on desktop',
+					type: 'checkbox'
+				},
+				{
+					key: 'autoShowSidebarOnNewChat',
+					label: 'Auto-show sidebar on new chat',
+					type: 'checkbox'
 				}
 			]
 		},
@@ -404,7 +414,7 @@
 	</div>
 
 	<!-- Mobile Header with Horizontal Scrollable Menu -->
-	<div class="flex flex-col md:hidden">
+	<div class="flex flex-col pt-6 md:hidden">
 		<div class="border-b border-border/30 py-4">
 			<!-- Horizontal Scrollable Category Menu with Navigation -->
 			<div class="relative flex items-center" style="scroll-padding: 1rem;">
diff --git a/tools/server/webui/src/lib/constants/settings-config.ts b/tools/server/webui/src/lib/constants/settings-config.ts
index 3764a2856b..8024be92e1 100644
--- a/tools/server/webui/src/lib/constants/settings-config.ts
+++ b/tools/server/webui/src/lib/constants/settings-config.ts
@@ -15,6 +15,8 @@ export const SETTING_CONFIG_DEFAULT: Record<string, string | number | boolean> =
 	pdfAsImage: false,
 	disableAutoScroll: false,
 	renderUserContentAsMarkdown: false,
+	alwaysShowSidebarOnDesktop: false,
+	autoShowSidebarOnNewChat: true,
 	autoMicOnEmpty: false,
 	// make sure these default values are in sync with `common.h`
 	samplers: 'top_k;typ_p;top_p;min_p;temperature',
@@ -96,6 +98,10 @@ export const SETTING_CONFIG_INFO: Record<string, string> = {
 	disableAutoScroll:
 		'Disable automatic scrolling while messages stream so you can control the viewport position manually.',
 	renderUserContentAsMarkdown: 'Render user messages using markdown formatting in the chat.',
+	alwaysShowSidebarOnDesktop:
+		'Always keep the sidebar visible on desktop instead of auto-hiding it.',
+	autoShowSidebarOnNewChat:
+		'Automatically show sidebar when starting a new chat. Disable to keep the sidebar hidden until you click on it.',
 	autoMicOnEmpty:
 		'Automatically show microphone button instead of send button when textarea is empty for models with audio modality support.',
 	pyInterpreterEnabled:
diff --git a/tools/server/webui/src/routes/+layout.svelte b/tools/server/webui/src/routes/+layout.svelte
index 27dfac19c3..17e13e9f33 100644
--- a/tools/server/webui/src/routes/+layout.svelte
+++ b/tools/server/webui/src/routes/+layout.svelte
@@ -14,6 +14,7 @@
 	import { goto } from '$app/navigation';
 	import { modelsStore } from '$lib/stores/models.svelte';
 	import { TOOLTIP_DELAY_DURATION } from '$lib/constants/tooltip-config';
+	import { IsMobile } from '$lib/hooks/is-mobile.svelte';
 
 	let { children } = $props();
 
@@ -21,6 +22,10 @@
 	let isHomeRoute = $derived(page.route.id === '/');
 	let isNewChatMode = $derived(page.url.searchParams.get('new_chat') === 'true');
 	let showSidebarByDefault = $derived(activeMessages().length > 0 || isLoading());
+	let alwaysShowSidebarOnDesktop = $derived(config().alwaysShowSidebarOnDesktop);
+	let autoShowSidebarOnNewChat = $derived(config().autoShowSidebarOnNewChat);
+	let isMobile = new IsMobile();
+	let isDesktop = $derived(!isMobile.current);
 	let sidebarOpen = $state(false);
 	let innerHeight = $state<number | undefined>();
 	let chatSidebar:
@@ -76,6 +81,11 @@
 	}
 
 	$effect(() => {
+		if (alwaysShowSidebarOnDesktop && isDesktop) {
+			sidebarOpen = true;
+			return;
+		}
+
 		if (isHomeRoute && !isNewChatMode) {
 			// Auto-collapse sidebar when navigating to home route (but not in new chat mode)
 			sidebarOpen = false;
@@ -83,8 +93,11 @@
 			// Keep sidebar open in new chat mode
 			sidebarOpen = true;
 		} else if (isChatRoute) {
-			// On chat routes, show sidebar by default
-			sidebarOpen = true;
+			// On chat routes, only auto-show sidebar if setting is enabled
+			if (autoShowSidebarOnNewChat) {
+				sidebarOpen = true;
+			}
+			// If setting is disabled, don't change sidebar state - let user control it manually
 		} else {
 			// Other routes follow default behavior
 			sidebarOpen = showSidebarByDefault;
@@ -190,12 +203,14 @@
 				<ChatSidebar bind:this={chatSidebar} />
 			</Sidebar.Root>
 
-			<Sidebar.Trigger
-				class="transition-left absolute left-0 z-[900] h-8 w-8 duration-200 ease-linear {sidebarOpen
-					? 'md:left-[var(--sidebar-width)]'
-					: ''}"
-				style="translate: 1rem 1rem;"
-			/>
+			{#if !(alwaysShowSidebarOnDesktop && isDesktop)}
+				<Sidebar.Trigger
+					class="transition-left absolute left-0 z-[900] h-8 w-8 duration-200 ease-linear {sidebarOpen
+						? 'md:left-[var(--sidebar-width)]'
+						: ''}"
+					style="translate: 1rem 1rem;"
+				/>
+			{/if}
 
 			<Sidebar.Inset class="flex flex-1 flex-col overflow-hidden">
 				{@render children?.()}

From 3034836d36bfd4ba1d1cc9173c9bef2c70ffd136 Mon Sep 17 00:00:00 2001
From: Aleksander Grygier <aleksander.grygier@gmail.com>
Date: Tue, 16 Dec 2025 07:38:46 +0100
Subject: [PATCH 119/135] webui: Improve copy to clipboard with text
 attachments (#17969)

* feat: Create copy/paste user message including "pasted text" attachments

* chore: update webui build output

* chore: update webui static output

* fix: UI issues

* chore: update webui static output

* fix: Decode HTML entities using `DOMParser`

* chore: update webui build output

* chore: update webui static output
---
 tools/server/public/index.html.gz             | Bin 1446032 -> 1446569 bytes
 tools/server/webui/README.md                  |  11 +-
 tools/server/webui/package.json               |   5 +-
 .../ChatAttachmentPreview.svelte              |   2 +-
 .../app/chat/ChatForm/ChatForm.svelte         |  28 +-
 .../app/chat/ChatMessages/ChatMessage.svelte  |   7 +-
 .../app/chat/ChatSettings/ChatSettings.svelte |   5 +
 .../app/misc/SyntaxHighlightedCode.svelte     |   5 +-
 .../src/lib/constants/settings-config.ts      |   3 +
 tools/server/webui/src/lib/utils/clipboard.ts | 262 +++++++++++
 tools/server/webui/src/lib/utils/copy.ts      |  71 ---
 tools/server/webui/src/lib/utils/index.ts     |  10 +-
 tools/server/webui/tests/server/demo.spec.ts  |   7 -
 .../server/webui/tests/unit/clipboard.test.ts | 423 ++++++++++++++++++
 .../unit}/latex-protection.test.ts            |   2 +-
 .../utils => tests/unit}/model-names.test.ts  |   2 +-
 tools/server/webui/vite.config.ts             |   4 +-
 17 files changed, 749 insertions(+), 98 deletions(-)
 create mode 100644 tools/server/webui/src/lib/utils/clipboard.ts
 delete mode 100644 tools/server/webui/src/lib/utils/copy.ts
 delete mode 100644 tools/server/webui/tests/server/demo.spec.ts
 create mode 100644 tools/server/webui/tests/unit/clipboard.test.ts
 rename tools/server/webui/{src/lib/utils => tests/unit}/latex-protection.test.ts (99%)
 rename tools/server/webui/{src/lib/utils => tests/unit}/model-names.test.ts (95%)

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index a58285a1a2c0103fafc517f2e851913dd774d277..2ff90e800a9cdf1d5715382876a4fe760411cfc1 100644
GIT binary patch
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z#3w4(424CDR8REDkulU9!P6+aG!wlLZ*a9TNU&8@j$e2{-BAFJwZ`Ju-1ZBTFxvL(
zqPmw!;C$`waD9>gQcOH|F8eGs(3ue{)!sx<q!jEv2|j;=3r%xsAsz{Rz<P!483I&r
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zd#A_ShnIgvL1$a2zDP2nq{t=6AEfY#MOGA`ABo@FWkJpd*jPWKiWvhsJ;U)Wv5wl1
zK?(B#NodQm8Y(w!L2Nph8^w^0d%MK7V`7;eRjsBFydhh{zh{a$`l%Y6ol*djBl;fY
zfmV8G-}^n`|L|N#9w#9g+w20b6DgKjHJY}9LdAa?I8DLf@;OXJm!>VTm92rt-=}dG
zQgJH6n<);m+*PSoJMyN&U2ka_Y0rO>dld;X(v%n-m6IQjEHAn%BynpSy}`mB3Bsl@
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zZn1xou?r@MBjXCeBcUdi`y7s7W5nPfV$ZERH!3FgYiY)OmjY4lN5G|_E-nB3VLzPJ
ztDEY<U3<*0?}p|U^c3Fj7tr3-+iqF4B)^|y&mg4CeEQqa>;wgci4I`QTYvj}P~ON1
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diff --git a/tools/server/webui/README.md b/tools/server/webui/README.md
index d995271fc4..98b01fdcd7 100644
--- a/tools/server/webui/README.md
+++ b/tools/server/webui/README.md
@@ -619,11 +619,12 @@ flowchart TB
 
 ### Test Types
 
-| Type          | Tool               | Location                         | Command             |
-| ------------- | ------------------ | -------------------------------- | ------------------- |
-| **E2E**       | Playwright         | `tests/e2e/`                     | `npm run test:e2e`  |
-| **Unit**      | Vitest             | `tests/client/`, `tests/server/` | `npm run test:unit` |
-| **UI/Visual** | Storybook + Vitest | `tests/stories/`                 | `npm run test:ui`   |
+| Type          | Tool               | Location         | Command             |
+| ------------- | ------------------ | ---------------- | ------------------- |
+| **Unit**      | Vitest             | `tests/unit/`    | `npm run test:unit` |
+| **UI/Visual** | Storybook + Vitest | `tests/stories/` | `npm run test:ui`   |
+| **E2E**       | Playwright         | `tests/e2e/`     | `npm run test:e2e`  |
+| **Client**    | Vitest             | `tests/client/`. | `npm run test:unit` |
 
 ### Running Tests
 
diff --git a/tools/server/webui/package.json b/tools/server/webui/package.json
index c20ab3cfde..1c970ae7a8 100644
--- a/tools/server/webui/package.json
+++ b/tools/server/webui/package.json
@@ -13,12 +13,11 @@
 		"reset": "rm -rf .svelte-kit node_modules",
 		"format": "prettier --write .",
 		"lint": "prettier --check . && eslint .",
-		"test": "npm run test:ui -- --run && npm run test:client -- --run && npm run test:server -- --run && npm run test:e2e",
+		"test": "npm run test:ui -- --run && npm run test:client -- --run && npm run test:unit -- --run && npm run test:e2e",
 		"test:e2e": "playwright test",
 		"test:client": "vitest --project=client",
-		"test:server": "vitest --project=server",
+		"test:unit": "vitest --project=unit",
 		"test:ui": "vitest --project=ui",
-		"test:unit": "vitest",
 		"storybook": "storybook dev -p 6006",
 		"build-storybook": "storybook build",
 		"cleanup": "rm -rf .svelte-kit build node_modules test-results"
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentPreview.svelte b/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentPreview.svelte
index b5fe3fa9c4..0b0bf52ad9 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentPreview.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatAttachments/ChatAttachmentPreview.svelte
@@ -241,7 +241,7 @@
 				</div>
 			{/if}
 		{:else if (isText || (isPdf && pdfViewMode === 'text')) && displayTextContent}
-			<SyntaxHighlightedCode code={displayTextContent} {language} maxWidth="69rem" />
+			<SyntaxHighlightedCode code={displayTextContent} {language} maxWidth="calc(69rem - 2rem)" />
 		{:else if isAudio}
 			<div class="flex items-center justify-center p-8">
 				<div class="w-full max-w-md text-center">
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatForm.svelte b/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
index 78cc1c47da..3ad14ed3ab 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatForm/ChatForm.svelte
@@ -24,7 +24,7 @@
 		MimeTypeImage,
 		MimeTypeText
 	} from '$lib/enums';
-	import { isIMEComposing } from '$lib/utils';
+	import { isIMEComposing, parseClipboardContent } from '$lib/utils';
 	import {
 		AudioRecorder,
 		convertToWav,
@@ -191,7 +191,6 @@
 
 			if ((!message.trim() && uploadedFiles.length === 0) || disabled || isLoading) return;
 
-			// Check if model is selected first
 			if (!checkModelSelected()) return;
 
 			const messageToSend = message.trim();
@@ -228,6 +227,31 @@
 
 		const text = event.clipboardData.getData(MimeTypeText.PLAIN);
 
+		if (text.startsWith('"')) {
+			const parsed = parseClipboardContent(text);
+
+			if (parsed.textAttachments.length > 0) {
+				event.preventDefault();
+
+				message = parsed.message;
+
+				const attachmentFiles = parsed.textAttachments.map(
+					(att) =>
+						new File([att.content], att.name, {
+							type: MimeTypeText.PLAIN
+						})
+				);
+
+				onFileUpload?.(attachmentFiles);
+
+				setTimeout(() => {
+					textareaRef?.focus();
+				}, 10);
+
+				return;
+			}
+		}
+
 		if (
 			text.length > 0 &&
 			pasteLongTextToFileLength > 0 &&
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte
index 96ed56a775..0969a937ed 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte
@@ -1,6 +1,7 @@
 <script lang="ts">
 	import { chatStore } from '$lib/stores/chat.svelte';
-	import { copyToClipboard, isIMEComposing } from '$lib/utils';
+	import { config } from '$lib/stores/settings.svelte';
+	import { copyToClipboard, isIMEComposing, formatMessageForClipboard } from '$lib/utils';
 	import ChatMessageAssistant from './ChatMessageAssistant.svelte';
 	import ChatMessageUser from './ChatMessageUser.svelte';
 	import ChatMessageSystem from './ChatMessageSystem.svelte';
@@ -87,7 +88,9 @@
 	}
 
 	async function handleCopy() {
-		await copyToClipboard(message.content, 'Message copied to clipboard');
+		const asPlainText = Boolean(config().copyTextAttachmentsAsPlainText);
+		const clipboardContent = formatMessageForClipboard(message.content, message.extra, asPlainText);
+		await copyToClipboard(clipboardContent, 'Message copied to clipboard');
 		onCopy?.(message);
 	}
 
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte
index 6dfae59950..4ec9b478fd 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte
@@ -57,6 +57,11 @@
 					label: 'Paste long text to file length',
 					type: 'input'
 				},
+				{
+					key: 'copyTextAttachmentsAsPlainText',
+					label: 'Copy text attachments as plain text',
+					type: 'checkbox'
+				},
 				{
 					key: 'enableContinueGeneration',
 					label: 'Enable "Continue" button',
diff --git a/tools/server/webui/src/lib/components/app/misc/SyntaxHighlightedCode.svelte b/tools/server/webui/src/lib/components/app/misc/SyntaxHighlightedCode.svelte
index f36a9a20b9..bc42f9dd1e 100644
--- a/tools/server/webui/src/lib/components/app/misc/SyntaxHighlightedCode.svelte
+++ b/tools/server/webui/src/lib/components/app/misc/SyntaxHighlightedCode.svelte
@@ -72,9 +72,10 @@
 
 <div
 	class="code-preview-wrapper overflow-auto rounded-lg border border-border bg-muted {className}"
-	style="max-height: {maxHeight};"
+	style="max-height: {maxHeight}; max-width: {maxWidth};"
 >
-	<pre class="m-0 overflow-x-auto p-4 max-w-[{maxWidth}]"><code class="hljs text-sm leading-relaxed"
+	<!-- Needs to be formatted as single line for proper rendering -->
+	<pre class="m-0 overflow-x-auto p-4"><code class="hljs text-sm leading-relaxed"
 			>{@html highlightedHtml}</code
 		></pre>
 </div>
diff --git a/tools/server/webui/src/lib/constants/settings-config.ts b/tools/server/webui/src/lib/constants/settings-config.ts
index 8024be92e1..f9584d01d7 100644
--- a/tools/server/webui/src/lib/constants/settings-config.ts
+++ b/tools/server/webui/src/lib/constants/settings-config.ts
@@ -12,6 +12,7 @@ export const SETTING_CONFIG_DEFAULT: Record<string, string | number | boolean> =
 	showMessageStats: true,
 	askForTitleConfirmation: false,
 	pasteLongTextToFileLen: 2500,
+	copyTextAttachmentsAsPlainText: false,
 	pdfAsImage: false,
 	disableAutoScroll: false,
 	renderUserContentAsMarkdown: false,
@@ -52,6 +53,8 @@ export const SETTING_CONFIG_INFO: Record<string, string> = {
 		'Choose the color theme for the interface. You can choose between System (follows your device settings), Light, or Dark.',
 	pasteLongTextToFileLen:
 		'On pasting long text, it will be converted to a file. You can control the file length by setting the value of this parameter. Value 0 means disable.',
+	copyTextAttachmentsAsPlainText:
+		'When copying a message with text attachments, combine them into a single plain text string instead of a special format that can be pasted back as attachments.',
 	samplers:
 		'The order at which samplers are applied, in simplified way. Default is "top_k;typ_p;top_p;min_p;temperature": top_k->typ_p->top_p->min_p->temperature',
 	temperature:
diff --git a/tools/server/webui/src/lib/utils/clipboard.ts b/tools/server/webui/src/lib/utils/clipboard.ts
new file mode 100644
index 0000000000..91e8ea75ae
--- /dev/null
+++ b/tools/server/webui/src/lib/utils/clipboard.ts
@@ -0,0 +1,262 @@
+import { toast } from 'svelte-sonner';
+import { AttachmentType } from '$lib/enums';
+import type {
+	DatabaseMessageExtra,
+	DatabaseMessageExtraTextFile,
+	DatabaseMessageExtraLegacyContext
+} from '$lib/types/database';
+
+/**
+ * Copy text to clipboard with toast notification
+ * Uses modern clipboard API when available, falls back to legacy method for non-secure contexts
+ * @param text - Text to copy to clipboard
+ * @param successMessage - Custom success message (optional)
+ * @param errorMessage - Custom error message (optional)
+ * @returns Promise<boolean> - True if successful, false otherwise
+ */
+export async function copyToClipboard(
+	text: string,
+	successMessage = 'Copied to clipboard',
+	errorMessage = 'Failed to copy to clipboard'
+): Promise<boolean> {
+	try {
+		// Try modern clipboard API first (secure contexts only)
+		if (navigator.clipboard && navigator.clipboard.writeText) {
+			await navigator.clipboard.writeText(text);
+			toast.success(successMessage);
+			return true;
+		}
+
+		// Fallback for non-secure contexts
+		const textArea = document.createElement('textarea');
+		textArea.value = text;
+		textArea.style.position = 'fixed';
+		textArea.style.left = '-999999px';
+		textArea.style.top = '-999999px';
+		document.body.appendChild(textArea);
+		textArea.focus();
+		textArea.select();
+
+		const successful = document.execCommand('copy');
+		document.body.removeChild(textArea);
+
+		if (successful) {
+			toast.success(successMessage);
+			return true;
+		} else {
+			throw new Error('execCommand failed');
+		}
+	} catch (error) {
+		console.error('Failed to copy to clipboard:', error);
+		toast.error(errorMessage);
+		return false;
+	}
+}
+
+/**
+ * Copy code with HTML entity decoding and toast notification
+ * @param rawCode - Raw code string that may contain HTML entities
+ * @param successMessage - Custom success message (optional)
+ * @param errorMessage - Custom error message (optional)
+ * @returns Promise<boolean> - True if successful, false otherwise
+ */
+export async function copyCodeToClipboard(
+	rawCode: string,
+	successMessage = 'Code copied to clipboard',
+	errorMessage = 'Failed to copy code'
+): Promise<boolean> {
+	const doc = new DOMParser().parseFromString(rawCode, 'text/html');
+	const decodedCode = doc.body.textContent ?? rawCode;
+
+	return copyToClipboard(decodedCode, successMessage, errorMessage);
+}
+
+/**
+ * Format for text attachments when copied to clipboard
+ */
+export interface ClipboardTextAttachment {
+	type: typeof AttachmentType.TEXT;
+	name: string;
+	content: string;
+}
+
+/**
+ * Parsed result from clipboard content
+ */
+export interface ParsedClipboardContent {
+	message: string;
+	textAttachments: ClipboardTextAttachment[];
+}
+
+/**
+ * Formats a message with text attachments for clipboard copying.
+ *
+ * Default format (asPlainText = false):
+ * ```
+ * "Text message content"
+ * [
+ *   {"type":"TEXT","name":"filename.txt","content":"..."},
+ *   {"type":"TEXT","name":"another.txt","content":"..."}
+ * ]
+ * ```
+ *
+ * Plain text format (asPlainText = true):
+ * ```
+ * Text message content
+ *
+ * file content here
+ *
+ * another file content
+ * ```
+ *
+ * @param content - The message text content
+ * @param extras - Optional array of message attachments
+ * @param asPlainText - If true, format as plain text without JSON structure
+ * @returns Formatted string for clipboard
+ */
+export function formatMessageForClipboard(
+	content: string,
+	extras?: DatabaseMessageExtra[],
+	asPlainText: boolean = false
+): string {
+	// Filter only text attachments (TEXT type and legacy CONTEXT type)
+	const textAttachments =
+		extras?.filter(
+			(extra): extra is DatabaseMessageExtraTextFile | DatabaseMessageExtraLegacyContext =>
+				extra.type === AttachmentType.TEXT || extra.type === AttachmentType.LEGACY_CONTEXT
+		) ?? [];
+
+	if (textAttachments.length === 0) {
+		return content;
+	}
+
+	if (asPlainText) {
+		const parts = [content];
+		for (const att of textAttachments) {
+			parts.push(att.content);
+		}
+		return parts.join('\n\n');
+	}
+
+	const clipboardAttachments: ClipboardTextAttachment[] = textAttachments.map((att) => ({
+		type: AttachmentType.TEXT,
+		name: att.name,
+		content: att.content
+	}));
+
+	return `${JSON.stringify(content)}\n${JSON.stringify(clipboardAttachments, null, 2)}`;
+}
+
+/**
+ * Parses clipboard content to extract message and text attachments.
+ * Supports both plain text and the special format with attachments.
+ *
+ * @param clipboardText - Raw text from clipboard
+ * @returns Parsed content with message and attachments
+ */
+export function parseClipboardContent(clipboardText: string): ParsedClipboardContent {
+	const defaultResult: ParsedClipboardContent = {
+		message: clipboardText,
+		textAttachments: []
+	};
+
+	if (!clipboardText.startsWith('"')) {
+		return defaultResult;
+	}
+
+	try {
+		let stringEndIndex = -1;
+		let escaped = false;
+
+		for (let i = 1; i < clipboardText.length; i++) {
+			const char = clipboardText[i];
+
+			if (escaped) {
+				escaped = false;
+				continue;
+			}
+
+			if (char === '\\') {
+				escaped = true;
+				continue;
+			}
+
+			if (char === '"') {
+				stringEndIndex = i;
+				break;
+			}
+		}
+
+		if (stringEndIndex === -1) {
+			return defaultResult;
+		}
+
+		const jsonStringPart = clipboardText.substring(0, stringEndIndex + 1);
+		const remainingPart = clipboardText.substring(stringEndIndex + 1).trim();
+
+		const message = JSON.parse(jsonStringPart) as string;
+
+		if (!remainingPart || !remainingPart.startsWith('[')) {
+			return {
+				message,
+				textAttachments: []
+			};
+		}
+
+		const attachments = JSON.parse(remainingPart) as unknown[];
+
+		const validAttachments: ClipboardTextAttachment[] = [];
+
+		for (const att of attachments) {
+			if (isValidTextAttachment(att)) {
+				validAttachments.push({
+					type: AttachmentType.TEXT,
+					name: att.name,
+					content: att.content
+				});
+			}
+		}
+
+		return {
+			message,
+			textAttachments: validAttachments
+		};
+	} catch {
+		return defaultResult;
+	}
+}
+
+/**
+ * Type guard to validate a text attachment object
+ * @param obj The object to validate
+ * @returns true if the object is a valid text attachment
+ */
+function isValidTextAttachment(
+	obj: unknown
+): obj is { type: string; name: string; content: string } {
+	if (typeof obj !== 'object' || obj === null) {
+		return false;
+	}
+
+	const record = obj as Record<string, unknown>;
+
+	return (
+		(record.type === AttachmentType.TEXT || record.type === 'TEXT') &&
+		typeof record.name === 'string' &&
+		typeof record.content === 'string'
+	);
+}
+
+/**
+ * Checks if clipboard content contains our special format with attachments
+ * @param clipboardText - Raw text from clipboard
+ * @returns true if the clipboard content contains our special format with attachments
+ */
+export function hasClipboardAttachments(clipboardText: string): boolean {
+	if (!clipboardText.startsWith('"')) {
+		return false;
+	}
+
+	const parsed = parseClipboardContent(clipboardText);
+	return parsed.textAttachments.length > 0;
+}
diff --git a/tools/server/webui/src/lib/utils/copy.ts b/tools/server/webui/src/lib/utils/copy.ts
deleted file mode 100644
index 16a4bbd45d..0000000000
--- a/tools/server/webui/src/lib/utils/copy.ts
+++ /dev/null
@@ -1,71 +0,0 @@
-import { toast } from 'svelte-sonner';
-
-/**
- * Copy text to clipboard with toast notification
- * Uses modern clipboard API when available, falls back to legacy method for non-secure contexts
- * @param text - Text to copy to clipboard
- * @param successMessage - Custom success message (optional)
- * @param errorMessage - Custom error message (optional)
- * @returns Promise<boolean> - True if successful, false otherwise
- */
-export async function copyToClipboard(
-	text: string,
-	successMessage = 'Copied to clipboard',
-	errorMessage = 'Failed to copy to clipboard'
-): Promise<boolean> {
-	try {
-		// Try modern clipboard API first (secure contexts only)
-		if (navigator.clipboard && navigator.clipboard.writeText) {
-			await navigator.clipboard.writeText(text);
-			toast.success(successMessage);
-			return true;
-		}
-
-		// Fallback for non-secure contexts
-		const textArea = document.createElement('textarea');
-		textArea.value = text;
-		textArea.style.position = 'fixed';
-		textArea.style.left = '-999999px';
-		textArea.style.top = '-999999px';
-		document.body.appendChild(textArea);
-		textArea.focus();
-		textArea.select();
-
-		const successful = document.execCommand('copy');
-		document.body.removeChild(textArea);
-
-		if (successful) {
-			toast.success(successMessage);
-			return true;
-		} else {
-			throw new Error('execCommand failed');
-		}
-	} catch (error) {
-		console.error('Failed to copy to clipboard:', error);
-		toast.error(errorMessage);
-		return false;
-	}
-}
-
-/**
- * Copy code with HTML entity decoding and toast notification
- * @param rawCode - Raw code string that may contain HTML entities
- * @param successMessage - Custom success message (optional)
- * @param errorMessage - Custom error message (optional)
- * @returns Promise<boolean> - True if successful, false otherwise
- */
-export async function copyCodeToClipboard(
-	rawCode: string,
-	successMessage = 'Code copied to clipboard',
-	errorMessage = 'Failed to copy code'
-): Promise<boolean> {
-	// Decode HTML entities
-	const decodedCode = rawCode
-		.replace(/&amp;/g, '&')
-		.replace(/&lt;/g, '<')
-		.replace(/&gt;/g, '>')
-		.replace(/&quot;/g, '"')
-		.replace(/&#39;/g, "'");
-
-	return copyToClipboard(decodedCode, successMessage, errorMessage);
-}
diff --git a/tools/server/webui/src/lib/utils/index.ts b/tools/server/webui/src/lib/utils/index.ts
index 2485e34de3..ab60061991 100644
--- a/tools/server/webui/src/lib/utils/index.ts
+++ b/tools/server/webui/src/lib/utils/index.ts
@@ -40,7 +40,15 @@ export { setConfigValue, getConfigValue, configToParameterRecord } from './confi
 export { createMessageCountMap, getMessageCount } from './conversation-utils';
 
 // Clipboard utilities
-export { copyToClipboard, copyCodeToClipboard } from './copy';
+export {
+	copyToClipboard,
+	copyCodeToClipboard,
+	formatMessageForClipboard,
+	parseClipboardContent,
+	hasClipboardAttachments,
+	type ClipboardTextAttachment,
+	type ParsedClipboardContent
+} from './clipboard';
 
 // File preview utilities
 export { getFileTypeLabel } from './file-preview';
diff --git a/tools/server/webui/tests/server/demo.spec.ts b/tools/server/webui/tests/server/demo.spec.ts
deleted file mode 100644
index e07cbbd725..0000000000
--- a/tools/server/webui/tests/server/demo.spec.ts
+++ /dev/null
@@ -1,7 +0,0 @@
-import { describe, it, expect } from 'vitest';
-
-describe('sum test', () => {
-	it('adds 1 + 2 to equal 3', () => {
-		expect(1 + 2).toBe(3);
-	});
-});
diff --git a/tools/server/webui/tests/unit/clipboard.test.ts b/tools/server/webui/tests/unit/clipboard.test.ts
new file mode 100644
index 0000000000..d8ea4899e2
--- /dev/null
+++ b/tools/server/webui/tests/unit/clipboard.test.ts
@@ -0,0 +1,423 @@
+import { describe, it, expect } from 'vitest';
+import { AttachmentType } from '$lib/enums';
+import {
+	formatMessageForClipboard,
+	parseClipboardContent,
+	hasClipboardAttachments
+} from '$lib/utils/clipboard';
+
+describe('formatMessageForClipboard', () => {
+	it('returns plain content when no extras', () => {
+		const result = formatMessageForClipboard('Hello world', undefined);
+		expect(result).toBe('Hello world');
+	});
+
+	it('returns plain content when extras is empty array', () => {
+		const result = formatMessageForClipboard('Hello world', []);
+		expect(result).toBe('Hello world');
+	});
+
+	it('handles empty string content', () => {
+		const result = formatMessageForClipboard('', undefined);
+		expect(result).toBe('');
+	});
+
+	it('returns plain content when extras has only non-text attachments', () => {
+		const extras = [
+			{
+				type: AttachmentType.IMAGE as const,
+				name: 'image.png',
+				base64Url: 'data:image/png;base64,...'
+			}
+		];
+		const result = formatMessageForClipboard('Hello world', extras);
+		expect(result).toBe('Hello world');
+	});
+
+	it('filters non-text attachments and keeps only text ones', () => {
+		const extras = [
+			{
+				type: AttachmentType.IMAGE as const,
+				name: 'image.png',
+				base64Url: 'data:image/png;base64,...'
+			},
+			{
+				type: AttachmentType.TEXT as const,
+				name: 'file.txt',
+				content: 'Text content'
+			},
+			{
+				type: AttachmentType.PDF as const,
+				name: 'doc.pdf',
+				base64Data: 'data:application/pdf;base64,...',
+				content: 'PDF content',
+				processedAsImages: false
+			}
+		];
+		const result = formatMessageForClipboard('Hello', extras);
+
+		expect(result).toContain('"file.txt"');
+		expect(result).not.toContain('image.png');
+		expect(result).not.toContain('doc.pdf');
+	});
+
+	it('formats message with text attachments', () => {
+		const extras = [
+			{
+				type: AttachmentType.TEXT as const,
+				name: 'file1.txt',
+				content: 'File 1 content'
+			},
+			{
+				type: AttachmentType.TEXT as const,
+				name: 'file2.txt',
+				content: 'File 2 content'
+			}
+		];
+		const result = formatMessageForClipboard('Hello world', extras);
+
+		expect(result).toContain('"Hello world"');
+		expect(result).toContain('"type": "TEXT"');
+		expect(result).toContain('"name": "file1.txt"');
+		expect(result).toContain('"content": "File 1 content"');
+		expect(result).toContain('"name": "file2.txt"');
+	});
+
+	it('handles content with quotes and special characters', () => {
+		const content = 'Hello "world" with\nnewline';
+		const extras = [
+			{
+				type: AttachmentType.TEXT as const,
+				name: 'test.txt',
+				content: 'Test content'
+			}
+		];
+		const result = formatMessageForClipboard(content, extras);
+
+		// Should be valid JSON
+		expect(result.startsWith('"')).toBe(true);
+		// The content should be properly escaped
+		const parsed = JSON.parse(result.split('\n')[0]);
+		expect(parsed).toBe(content);
+	});
+
+	it('converts legacy context type to TEXT type', () => {
+		const extras = [
+			{
+				type: AttachmentType.LEGACY_CONTEXT as const,
+				name: 'legacy.txt',
+				content: 'Legacy content'
+			}
+		];
+		const result = formatMessageForClipboard('Hello', extras);
+
+		expect(result).toContain('"type": "TEXT"');
+		expect(result).not.toContain('"context"');
+	});
+
+	it('handles attachment content with special characters', () => {
+		const extras = [
+			{
+				type: AttachmentType.TEXT as const,
+				name: 'code.js',
+				content: 'const x = "hello\\nworld";\nconst y = `template ${var}`;'
+			}
+		];
+		const formatted = formatMessageForClipboard('Check this code', extras);
+		const parsed = parseClipboardContent(formatted);
+
+		expect(parsed.textAttachments[0].content).toBe(
+			'const x = "hello\\nworld";\nconst y = `template ${var}`;'
+		);
+	});
+
+	it('handles unicode characters in content and attachments', () => {
+		const extras = [
+			{
+				type: AttachmentType.TEXT as const,
+				name: 'unicode.txt',
+				content: '日本語テスト 🎉 émojis'
+			}
+		];
+		const formatted = formatMessageForClipboard('Привет мир 👋', extras);
+		const parsed = parseClipboardContent(formatted);
+
+		expect(parsed.message).toBe('Привет мир 👋');
+		expect(parsed.textAttachments[0].content).toBe('日本語テスト 🎉 émojis');
+	});
+
+	it('formats as plain text when asPlainText is true', () => {
+		const extras = [
+			{
+				type: AttachmentType.TEXT as const,
+				name: 'file1.txt',
+				content: 'File 1 content'
+			},
+			{
+				type: AttachmentType.TEXT as const,
+				name: 'file2.txt',
+				content: 'File 2 content'
+			}
+		];
+		const result = formatMessageForClipboard('Hello world', extras, true);
+
+		expect(result).toBe('Hello world\n\nFile 1 content\n\nFile 2 content');
+	});
+
+	it('returns plain content when asPlainText is true but no attachments', () => {
+		const result = formatMessageForClipboard('Hello world', [], true);
+		expect(result).toBe('Hello world');
+	});
+
+	it('plain text mode does not use JSON format', () => {
+		const extras = [
+			{
+				type: AttachmentType.TEXT as const,
+				name: 'test.txt',
+				content: 'Test content'
+			}
+		];
+		const result = formatMessageForClipboard('Hello', extras, true);
+
+		expect(result).not.toContain('"type"');
+		expect(result).not.toContain('[');
+		expect(result).toBe('Hello\n\nTest content');
+	});
+});
+
+describe('parseClipboardContent', () => {
+	it('returns plain text as message when not in special format', () => {
+		const result = parseClipboardContent('Hello world');
+
+		expect(result.message).toBe('Hello world');
+		expect(result.textAttachments).toHaveLength(0);
+	});
+
+	it('handles empty string input', () => {
+		const result = parseClipboardContent('');
+
+		expect(result.message).toBe('');
+		expect(result.textAttachments).toHaveLength(0);
+	});
+
+	it('handles whitespace-only input', () => {
+		const result = parseClipboardContent('   \n\t  ');
+
+		expect(result.message).toBe('   \n\t  ');
+		expect(result.textAttachments).toHaveLength(0);
+	});
+
+	it('returns plain text as message when starts with quote but invalid format', () => {
+		const result = parseClipboardContent('"Unclosed quote');
+
+		expect(result.message).toBe('"Unclosed quote');
+		expect(result.textAttachments).toHaveLength(0);
+	});
+
+	it('returns original text when JSON array is malformed', () => {
+		const input = '"Hello"\n[invalid json';
+
+		const result = parseClipboardContent(input);
+
+		expect(result.message).toBe('"Hello"\n[invalid json');
+		expect(result.textAttachments).toHaveLength(0);
+	});
+
+	it('parses message with text attachments', () => {
+		const input = `"Hello world"
+[
+  {"type":"TEXT","name":"file1.txt","content":"File 1 content"},
+  {"type":"TEXT","name":"file2.txt","content":"File 2 content"}
+]`;
+
+		const result = parseClipboardContent(input);
+
+		expect(result.message).toBe('Hello world');
+		expect(result.textAttachments).toHaveLength(2);
+		expect(result.textAttachments[0].name).toBe('file1.txt');
+		expect(result.textAttachments[0].content).toBe('File 1 content');
+		expect(result.textAttachments[1].name).toBe('file2.txt');
+		expect(result.textAttachments[1].content).toBe('File 2 content');
+	});
+
+	it('handles escaped quotes in message', () => {
+		const input = `"Hello \\"world\\" with quotes"
+[
+  {"type":"TEXT","name":"file.txt","content":"test"}
+]`;
+
+		const result = parseClipboardContent(input);
+
+		expect(result.message).toBe('Hello "world" with quotes');
+		expect(result.textAttachments).toHaveLength(1);
+	});
+
+	it('handles newlines in message', () => {
+		const input = `"Hello\\nworld"
+[
+  {"type":"TEXT","name":"file.txt","content":"test"}
+]`;
+
+		const result = parseClipboardContent(input);
+
+		expect(result.message).toBe('Hello\nworld');
+		expect(result.textAttachments).toHaveLength(1);
+	});
+
+	it('returns message only when no array follows', () => {
+		const input = '"Just a quoted string"';
+
+		const result = parseClipboardContent(input);
+
+		expect(result.message).toBe('Just a quoted string');
+		expect(result.textAttachments).toHaveLength(0);
+	});
+
+	it('filters out invalid attachment objects', () => {
+		const input = `"Hello"
+[
+  {"type":"TEXT","name":"valid.txt","content":"valid"},
+  {"type":"INVALID","name":"invalid.txt","content":"invalid"},
+  {"name":"missing-type.txt","content":"missing"},
+  {"type":"TEXT","content":"missing name"}
+]`;
+
+		const result = parseClipboardContent(input);
+
+		expect(result.message).toBe('Hello');
+		expect(result.textAttachments).toHaveLength(1);
+		expect(result.textAttachments[0].name).toBe('valid.txt');
+	});
+
+	it('handles empty attachments array', () => {
+		const input = '"Hello"\n[]';
+
+		const result = parseClipboardContent(input);
+
+		expect(result.message).toBe('Hello');
+		expect(result.textAttachments).toHaveLength(0);
+	});
+
+	it('roundtrips correctly with formatMessageForClipboard', () => {
+		const originalContent = 'Hello "world" with\nspecial characters';
+		const originalExtras = [
+			{
+				type: AttachmentType.TEXT as const,
+				name: 'file1.txt',
+				content: 'Content with\nnewlines and "quotes"'
+			},
+			{
+				type: AttachmentType.TEXT as const,
+				name: 'file2.txt',
+				content: 'Another file'
+			}
+		];
+
+		const formatted = formatMessageForClipboard(originalContent, originalExtras);
+		const parsed = parseClipboardContent(formatted);
+
+		expect(parsed.message).toBe(originalContent);
+		expect(parsed.textAttachments).toHaveLength(2);
+		expect(parsed.textAttachments[0].name).toBe('file1.txt');
+		expect(parsed.textAttachments[0].content).toBe('Content with\nnewlines and "quotes"');
+		expect(parsed.textAttachments[1].name).toBe('file2.txt');
+		expect(parsed.textAttachments[1].content).toBe('Another file');
+	});
+});
+
+describe('hasClipboardAttachments', () => {
+	it('returns false for plain text', () => {
+		expect(hasClipboardAttachments('Hello world')).toBe(false);
+	});
+
+	it('returns false for empty string', () => {
+		expect(hasClipboardAttachments('')).toBe(false);
+	});
+
+	it('returns false for quoted string without attachments', () => {
+		expect(hasClipboardAttachments('"Hello world"')).toBe(false);
+	});
+
+	it('returns true for valid format with attachments', () => {
+		const input = `"Hello"
+[{"type":"TEXT","name":"file.txt","content":"test"}]`;
+
+		expect(hasClipboardAttachments(input)).toBe(true);
+	});
+
+	it('returns false for format with empty attachments array', () => {
+		const input = '"Hello"\n[]';
+
+		expect(hasClipboardAttachments(input)).toBe(false);
+	});
+
+	it('returns false for malformed JSON', () => {
+		expect(hasClipboardAttachments('"Hello"\n[broken')).toBe(false);
+	});
+});
+
+describe('roundtrip edge cases', () => {
+	it('preserves empty message with attachments', () => {
+		const extras = [
+			{
+				type: AttachmentType.TEXT as const,
+				name: 'file.txt',
+				content: 'Content only'
+			}
+		];
+		const formatted = formatMessageForClipboard('', extras);
+		const parsed = parseClipboardContent(formatted);
+
+		expect(parsed.message).toBe('');
+		expect(parsed.textAttachments).toHaveLength(1);
+		expect(parsed.textAttachments[0].content).toBe('Content only');
+	});
+
+	it('preserves attachment with empty content', () => {
+		const extras = [
+			{
+				type: AttachmentType.TEXT as const,
+				name: 'empty.txt',
+				content: ''
+			}
+		];
+		const formatted = formatMessageForClipboard('Message', extras);
+		const parsed = parseClipboardContent(formatted);
+
+		expect(parsed.message).toBe('Message');
+		expect(parsed.textAttachments).toHaveLength(1);
+		expect(parsed.textAttachments[0].content).toBe('');
+	});
+
+	it('preserves multiple backslashes', () => {
+		const content = 'Path: C:\\\\Users\\\\test\\\\file.txt';
+		const extras = [
+			{
+				type: AttachmentType.TEXT as const,
+				name: 'path.txt',
+				content: 'D:\\\\Data\\\\file'
+			}
+		];
+		const formatted = formatMessageForClipboard(content, extras);
+		const parsed = parseClipboardContent(formatted);
+
+		expect(parsed.message).toBe(content);
+		expect(parsed.textAttachments[0].content).toBe('D:\\\\Data\\\\file');
+	});
+
+	it('preserves tabs and various whitespace', () => {
+		const content = 'Line1\t\tTabbed\n  Spaced\r\nCRLF';
+		const extras = [
+			{
+				type: AttachmentType.TEXT as const,
+				name: 'whitespace.txt',
+				content: '\t\t\n\n   '
+			}
+		];
+		const formatted = formatMessageForClipboard(content, extras);
+		const parsed = parseClipboardContent(formatted);
+
+		expect(parsed.message).toBe(content);
+		expect(parsed.textAttachments[0].content).toBe('\t\t\n\n   ');
+	});
+});
diff --git a/tools/server/webui/src/lib/utils/latex-protection.test.ts b/tools/server/webui/tests/unit/latex-protection.test.ts
similarity index 99%
rename from tools/server/webui/src/lib/utils/latex-protection.test.ts
rename to tools/server/webui/tests/unit/latex-protection.test.ts
index 40fe1b0db2..84328dbc17 100644
--- a/tools/server/webui/src/lib/utils/latex-protection.test.ts
+++ b/tools/server/webui/tests/unit/latex-protection.test.ts
@@ -1,6 +1,6 @@
 /* eslint-disable no-irregular-whitespace */
 import { describe, it, expect, test } from 'vitest';
-import { maskInlineLaTeX, preprocessLaTeX } from './latex-protection';
+import { maskInlineLaTeX, preprocessLaTeX } from '$lib/utils/latex-protection';
 
 describe('maskInlineLaTeX', () => {
 	it('should protect LaTeX $x + y$ but not money $3.99', () => {
diff --git a/tools/server/webui/src/lib/utils/model-names.test.ts b/tools/server/webui/tests/unit/model-names.test.ts
similarity index 95%
rename from tools/server/webui/src/lib/utils/model-names.test.ts
rename to tools/server/webui/tests/unit/model-names.test.ts
index ca85df3d30..40c5a0e3aa 100644
--- a/tools/server/webui/src/lib/utils/model-names.test.ts
+++ b/tools/server/webui/tests/unit/model-names.test.ts
@@ -1,5 +1,5 @@
 import { describe, expect, it } from 'vitest';
-import { isValidModelName, normalizeModelName } from './model-names';
+import { isValidModelName, normalizeModelName } from '$lib/utils/model-names';
 
 describe('normalizeModelName', () => {
 	it('preserves Hugging Face org/model format (single slash)', () => {
diff --git a/tools/server/webui/vite.config.ts b/tools/server/webui/vite.config.ts
index b41d3511b4..5183c09fca 100644
--- a/tools/server/webui/vite.config.ts
+++ b/tools/server/webui/vite.config.ts
@@ -125,9 +125,9 @@ export default defineConfig({
 			{
 				extends: './vite.config.ts',
 				test: {
-					name: 'server',
+					name: 'unit',
 					environment: 'node',
-					include: ['tests/server/**/*.{test,spec}.{js,ts}']
+					include: ['tests/unit/**/*.{test,spec}.{js,ts}']
 				}
 			},
 			{

From d6742125c317b7daafec038ab54a7c2fb1e2beaf Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Tue, 16 Dec 2025 08:17:26 +0100
Subject: [PATCH 120/135] ci : separate webui from server (#18072)

* separate webui from server

* add public to path
---
 .github/workflows/server-webui.yml | 295 +++++++++++++++++++++++++++++
 .github/workflows/server.yml       | 264 --------------------------
 2 files changed, 295 insertions(+), 264 deletions(-)
 create mode 100644 .github/workflows/server-webui.yml

diff --git a/.github/workflows/server-webui.yml b/.github/workflows/server-webui.yml
new file mode 100644
index 0000000000..f8a261eefa
--- /dev/null
+++ b/.github/workflows/server-webui.yml
@@ -0,0 +1,295 @@
+# Server WebUI build and tests
+name: Server WebUI
+
+on:
+  workflow_dispatch: # allows manual triggering
+    inputs:
+      sha:
+        description: 'Commit SHA1 to build'
+        required: false
+        type: string
+      slow_tests:
+        description: 'Run slow tests'
+        required: true
+        type: boolean
+  push:
+    branches:
+      - master
+    paths: ['.github/workflows/server-webui.yml', 'tools/server/webui/**.*', 'tools/server/tests/**.*', 'tools/server/public/**']
+  pull_request:
+    types: [opened, synchronize, reopened]
+    paths: ['.github/workflows/server-webui.yml', 'tools/server/webui/**.*', 'tools/server/tests/**.*', 'tools/server/public/**']
+
+env:
+  LLAMA_LOG_COLORS: 1
+  LLAMA_LOG_PREFIX: 1
+  LLAMA_LOG_TIMESTAMPS: 1
+  LLAMA_LOG_VERBOSITY: 10
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}
+  cancel-in-progress: true
+
+jobs:
+  webui-setup:
+    name: WebUI Setup
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "22"
+          cache: "npm"
+          cache-dependency-path: "tools/server/webui/package-lock.json"
+
+      - name: Cache node_modules
+        uses: actions/cache@v4
+        id: cache-node-modules
+        with:
+          path: tools/server/webui/node_modules
+          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
+          restore-keys: |
+            ${{ runner.os }}-node-modules-
+
+      - name: Install dependencies
+        if: steps.cache-node-modules.outputs.cache-hit != 'true'
+        run: npm ci
+        working-directory: tools/server/webui
+
+  webui-check:
+    needs: webui-setup
+    name: WebUI Check
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "22"
+
+      - name: Restore node_modules cache
+        uses: actions/cache@v4
+        with:
+          path: tools/server/webui/node_modules
+          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
+          restore-keys: |
+            ${{ runner.os }}-node-modules-
+
+      - name: Run type checking
+        run: npm run check
+        working-directory: tools/server/webui
+
+      - name: Run linting
+        run: npm run lint
+        working-directory: tools/server/webui
+
+  webui-build:
+    needs: webui-check
+    name: WebUI Build
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "22"
+
+      - name: Restore node_modules cache
+        uses: actions/cache@v4
+        with:
+          path: tools/server/webui/node_modules
+          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
+          restore-keys: |
+            ${{ runner.os }}-node-modules-
+
+      - name: Build application
+        run: npm run build
+        working-directory: tools/server/webui
+
+  webui-tests:
+    needs: webui-build
+    name: Run WebUI tests
+    permissions:
+      contents: read
+
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "22"
+
+      - name: Restore node_modules cache
+        uses: actions/cache@v4
+        with:
+          path: tools/server/webui/node_modules
+          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
+          restore-keys: |
+            ${{ runner.os }}-node-modules-
+
+      - name: Install Playwright browsers
+        run: npx playwright install --with-deps
+        working-directory: tools/server/webui
+
+      - name: Build Storybook
+        run: npm run build-storybook
+        working-directory: tools/server/webui
+
+      - name: Run Client tests
+        run: npm run test:client
+        working-directory: tools/server/webui
+
+      - name: Run Server tests
+        run: npm run test:server
+        working-directory: tools/server/webui
+
+      - name: Run UI tests
+        run: npm run test:ui -- --testTimeout=60000
+        working-directory: tools/server/webui
+
+      - name: Run E2E tests
+        run: npm run test:e2e
+        working-directory: tools/server/webui
+
+  server-build:
+    needs: [webui-tests]
+    runs-on: ubuntu-latest
+
+    strategy:
+      matrix:
+        sanitizer: [ADDRESS, UNDEFINED] # THREAD is broken
+        build_type: [RelWithDebInfo]
+        include:
+          - build_type: Release
+            sanitizer: ""
+      fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
+
+    steps:
+      - name: Dependencies
+        id: depends
+        run: |
+          sudo apt-get update
+          sudo apt-get -y install \
+            build-essential \
+            xxd \
+            git \
+            cmake \
+            curl \
+            wget \
+            language-pack-en \
+            libssl-dev
+
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
+
+      - name: Python setup
+        id: setup_python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+
+      - name: Tests dependencies
+        id: test_dependencies
+        run: |
+          pip install -r tools/server/tests/requirements.txt
+
+      - name: Setup Node.js for WebUI
+        uses: actions/setup-node@v4
+        with:
+          node-version: "22"
+          cache: "npm"
+          cache-dependency-path: "tools/server/webui/package-lock.json"
+
+      - name: Install WebUI dependencies
+        run: npm ci
+        working-directory: tools/server/webui
+
+      - name: Build WebUI
+        run: npm run build
+        working-directory: tools/server/webui
+
+      - name: Build (no OpenMP)
+        id: cmake_build_no_openmp
+        if: ${{ matrix.sanitizer == 'THREAD' }}
+        run: |
+          cmake -B build \
+              -DGGML_NATIVE=OFF \
+              -DLLAMA_CURL=OFF \
+              -DLLAMA_OPENSSL=ON \
+              -DLLAMA_BUILD_SERVER=ON \
+              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
+              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
+              -DGGML_OPENMP=OFF ;
+          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
+
+      - name: Build (sanitizers)
+        id: cmake_build_sanitizers
+        if: ${{ matrix.sanitizer != '' && matrix.sanitizer != 'THREAD' }}
+        run: |
+          cmake -B build \
+              -DGGML_NATIVE=OFF \
+              -DLLAMA_CURL=OFF \
+              -DLLAMA_OPENSSL=ON \
+              -DLLAMA_BUILD_SERVER=ON \
+              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
+              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
+          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
+
+      - name: Build (sanitizers)
+        id: cmake_build
+        if: ${{ matrix.sanitizer == '' }}
+        run: |
+          cmake -B build \
+              -DGGML_NATIVE=OFF \
+              -DLLAMA_CURL=OFF \
+              -DLLAMA_OPENSSL=ON \
+              -DLLAMA_BUILD_SERVER=ON \
+              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ;
+          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
+
+      - name: Tests
+        id: server_integration_tests
+        if: ${{ matrix.sanitizer == '' }}
+        env:
+          GITHUB_ACTIONS: "true"
+        run: |
+          cd tools/server/tests
+          ./tests.sh
+
+      - name: Tests (sanitizers)
+        id: server_integration_tests_sanitizers
+        if: ${{ matrix.sanitizer != '' }}
+        run: |
+          cd tools/server/tests
+          LLAMA_SANITIZE=1 ./tests.sh
+
+      - name: Slow tests
+        id: server_integration_tests_slow
+        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
+        run: |
+          cd tools/server/tests
+          SLOW_TESTS=1 ./tests.sh
diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml
index a57d0e8b1c..f9e2a79af7 100644
--- a/.github/workflows/server.yml
+++ b/.github/workflows/server.yml
@@ -76,270 +76,6 @@ jobs:
         run: |
           pip install -r tools/server/tests/requirements.txt
 
-  webui-setup:
-    name: WebUI Setup
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "22"
-          cache: "npm"
-          cache-dependency-path: "tools/server/webui/package-lock.json"
-
-      - name: Cache node_modules
-        uses: actions/cache@v4
-        id: cache-node-modules
-        with:
-          path: tools/server/webui/node_modules
-          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
-          restore-keys: |
-            ${{ runner.os }}-node-modules-
-
-      - name: Install dependencies
-        if: steps.cache-node-modules.outputs.cache-hit != 'true'
-        run: npm ci
-        working-directory: tools/server/webui
-
-  webui-check:
-    needs: webui-setup
-    name: WebUI Check
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "22"
-
-      - name: Restore node_modules cache
-        uses: actions/cache@v4
-        with:
-          path: tools/server/webui/node_modules
-          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
-          restore-keys: |
-            ${{ runner.os }}-node-modules-
-
-      - name: Run type checking
-        run: npm run check
-        working-directory: tools/server/webui
-
-      - name: Run linting
-        run: npm run lint
-        working-directory: tools/server/webui
-
-  webui-build:
-    needs: webui-check
-    name: WebUI Build
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "22"
-
-      - name: Restore node_modules cache
-        uses: actions/cache@v4
-        with:
-          path: tools/server/webui/node_modules
-          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
-          restore-keys: |
-            ${{ runner.os }}-node-modules-
-
-      - name: Build application
-        run: npm run build
-        working-directory: tools/server/webui
-
-  webui-tests:
-    needs: webui-build
-    name: Run WebUI tests
-    permissions:
-      contents: read
-
-    runs-on: ubuntu-latest
-
-    steps:
-      - name: Checkout code
-        uses: actions/checkout@v4
-
-      - name: Setup Node.js
-        uses: actions/setup-node@v4
-        with:
-          node-version: "22"
-
-      - name: Restore node_modules cache
-        uses: actions/cache@v4
-        with:
-          path: tools/server/webui/node_modules
-          key: ${{ runner.os }}-node-modules-${{ hashFiles('tools/server/webui/package-lock.json') }}
-          restore-keys: |
-            ${{ runner.os }}-node-modules-
-
-      - name: Install Playwright browsers
-        run: npx playwright install --with-deps
-        working-directory: tools/server/webui
-
-      - name: Build Storybook
-        run: npm run build-storybook
-        working-directory: tools/server/webui
-
-      - name: Run Client tests
-        run: npm run test:client
-        working-directory: tools/server/webui
-
-      - name: Run Server tests
-        run: npm run test:server
-        working-directory: tools/server/webui
-
-      - name: Run UI tests
-        run: npm run test:ui -- --testTimeout=60000
-        working-directory: tools/server/webui
-
-      - name: Run E2E tests
-        run: npm run test:e2e
-        working-directory: tools/server/webui
-
-  server-build:
-    needs: [webui-tests]
-    runs-on: ubuntu-latest
-
-    strategy:
-      matrix:
-        sanitizer: [ADDRESS, UNDEFINED] # THREAD is broken
-        build_type: [RelWithDebInfo]
-        include:
-          - build_type: Release
-            sanitizer: ""
-      fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
-
-    steps:
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt-get update
-          sudo apt-get -y install \
-            build-essential \
-            xxd \
-            git \
-            cmake \
-            curl \
-            wget \
-            language-pack-en \
-            libssl-dev
-
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Python setup
-        id: setup_python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-
-      - name: Tests dependencies
-        id: test_dependencies
-        run: |
-          pip install -r tools/server/tests/requirements.txt
-
-      - name: Setup Node.js for WebUI
-        uses: actions/setup-node@v4
-        with:
-          node-version: "22"
-          cache: "npm"
-          cache-dependency-path: "tools/server/webui/package-lock.json"
-
-      - name: Install WebUI dependencies
-        run: npm ci
-        working-directory: tools/server/webui
-
-      - name: Build WebUI
-        run: npm run build
-        working-directory: tools/server/webui
-
-      - name: Build (no OpenMP)
-        id: cmake_build_no_openmp
-        if: ${{ matrix.sanitizer == 'THREAD' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_CURL=OFF \
-              -DLLAMA_OPENSSL=ON \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
-              -DGGML_OPENMP=OFF ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Build (sanitizers)
-        id: cmake_build_sanitizers
-        if: ${{ matrix.sanitizer != '' && matrix.sanitizer != 'THREAD' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_CURL=OFF \
-              -DLLAMA_OPENSSL=ON \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Build (sanitizers)
-        id: cmake_build
-        if: ${{ matrix.sanitizer == '' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_CURL=OFF \
-              -DLLAMA_OPENSSL=ON \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Tests
-        id: server_integration_tests
-        if: ${{ matrix.sanitizer == '' }}
-        env:
-          GITHUB_ACTIONS: "true"
-        run: |
-          cd tools/server/tests
-          ./tests.sh
-
-      - name: Tests (sanitizers)
-        id: server_integration_tests_sanitizers
-        if: ${{ matrix.sanitizer != '' }}
-        run: |
-          cd tools/server/tests
-          LLAMA_SANITIZE=1 ./tests.sh
-
-      - name: Slow tests
-        id: server_integration_tests_slow
-        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
-        run: |
-          cd tools/server/tests
-          SLOW_TESTS=1 ./tests.sh
-
-
   server-windows:
     runs-on: windows-2022
 

From c560316440925e03d3ba006eb5710f6678802ce8 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 16 Dec 2025 09:36:21 +0200
Subject: [PATCH 121/135] graph : reuse SSM graphs (#16490)

* graph : reuse hybrid graphs

* graph : reuse recurrent graphs

* graph : fix reuse check for recurrent inputs

* memory : move the recurrent state into the memory context

* Revert "memory : move the recurrent state into the memory context"

This reverts commit 00f115fe810815d4a22a6dee0acc346131e970e1.

* cont : fix build
---
 src/llama-graph.cpp         | 67 ++++++++++++++++++++++++++++++++++---
 src/llama-graph.h           | 16 +++++++--
 src/llama-memory-hybrid.cpp |  2 +-
 3 files changed, 78 insertions(+), 7 deletions(-)

diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp
index b096b93a9a..1d0d7197e1 100644
--- a/src/llama-graph.cpp
+++ b/src/llama-graph.cpp
@@ -254,6 +254,24 @@ void llm_graph_input_rs::set_input(const llama_ubatch * ubatch) {
     }
 }
 
+bool llm_graph_input_rs::can_reuse(const llm_graph_params & params) {
+    const auto * mctx = static_cast<const llama_memory_recurrent_context *>(params.mctx);
+
+    this->mctx = mctx;
+
+    bool res = true;
+
+    res &= s_copy->ne[0] == mctx->get_n_rs();
+
+    res &= s_copy_main->ne[0]  == params.ubatch.n_seqs;
+    res &= s_copy_extra->ne[0] == mctx->get_n_rs() - params.ubatch.n_seqs;
+
+    res &= head == mctx->get_head();
+    res &= rs_z == mctx->get_rs_z();
+
+    return res;
+}
+
 void llm_graph_input_cross_embd::set_input(const llama_ubatch * ubatch) {
     GGML_UNUSED(ubatch);
 
@@ -461,8 +479,46 @@ void llm_graph_input_attn_cross::set_input(const llama_ubatch * ubatch) {
 }
 
 void llm_graph_input_mem_hybrid::set_input(const llama_ubatch * ubatch) {
-    inp_attn->set_input(ubatch);
-    inp_rs->set_input(ubatch);
+    mctx->get_attn()->set_input_k_idxs(inp_attn->self_k_idxs, ubatch);
+    mctx->get_attn()->set_input_v_idxs(inp_attn->self_v_idxs, ubatch);
+
+    mctx->get_attn()->set_input_kq_mask(inp_attn->self_kq_mask, ubatch, cparams.causal_attn);
+
+    const int64_t n_rs = mctx->get_recr()->get_n_rs();
+
+    if (inp_rs->s_copy) {
+        GGML_ASSERT(ggml_backend_buffer_is_host(inp_rs->s_copy->buffer));
+        int32_t * data = (int32_t *) inp_rs->s_copy->data;
+
+        // assuming copy destinations ALWAYS happen ONLY on the cells between head and head+n
+        for (uint32_t i = 0; i < n_rs; ++i) {
+            data[i] = mctx->get_recr()->s_copy(i);
+        }
+    }
+}
+
+bool llm_graph_input_mem_hybrid::can_reuse(const llm_graph_params & params) {
+    const auto * mctx = static_cast<const llama_memory_hybrid_context *>(params.mctx);
+
+    this->mctx = mctx;
+
+    bool res = true;
+
+    res &= inp_attn->self_k_idxs->ne[0] == params.ubatch.n_tokens;
+  //res &= inp_attn->self_v_idxs->ne[0] == params.ubatch.n_tokens; // TODO: need to move this to the unified cache and check there
+
+    res &= inp_attn->self_kq_mask->ne[0] == mctx->get_attn()->get_n_kv();
+    res &= inp_attn->self_kq_mask->ne[1] == params.ubatch.n_tokens;
+
+    res &= inp_rs->s_copy->ne[0] == mctx->get_recr()->get_n_rs();
+
+    res &= inp_rs->s_copy_main->ne[0]  == params.ubatch.n_seqs;
+    res &= inp_rs->s_copy_extra->ne[0] == mctx->get_recr()->get_n_rs() - params.ubatch.n_seqs;
+
+    res &= inp_rs->head == mctx->get_recr()->get_head();
+    res &= inp_rs->rs_z == mctx->get_recr()->get_rs_z();
+
+    return res;
 }
 
 //
@@ -1850,6 +1906,9 @@ static std::unique_ptr<llm_graph_input_rs> build_rs_inp_impl(
     inp->s_copy_main  = ggml_view_1d(ctx0, inp->s_copy, n_seqs, 0);
     inp->s_copy_extra = ggml_view_1d(ctx0, inp->s_copy, n_rs - n_seqs, n_seqs * inp->s_copy->nb[0]);
 
+    inp->head = mctx_cur->get_head();
+    inp->rs_z = mctx_cur->get_rs_z();
+
     return inp;
 }
 
@@ -1918,10 +1977,10 @@ ggml_tensor * llm_graph_context::build_rwkv_token_shift_store(
 llm_graph_input_mem_hybrid * llm_graph_context::build_inp_mem_hybrid() const {
     const auto * mctx_cur = static_cast<const llama_memory_hybrid_context *>(mctx);
 
-    auto inp_rs   = build_rs_inp_impl(ctx0, ubatch, mctx_cur->get_recr());
+    auto inp_rs   = build_rs_inp_impl     (ctx0, ubatch, mctx_cur->get_recr());
     auto inp_attn = build_attn_inp_kv_impl(ctx0, ubatch, hparams, cparams, mctx_cur->get_attn());
 
-    auto inp = std::make_unique<llm_graph_input_mem_hybrid>(std::move(inp_attn), std::move(inp_rs), mctx_cur);
+    auto inp = std::make_unique<llm_graph_input_mem_hybrid>(cparams, std::move(inp_attn), std::move(inp_rs), mctx_cur);
 
     return (llm_graph_input_mem_hybrid *) res->add_input(std::move(inp));
 }
diff --git a/src/llama-graph.h b/src/llama-graph.h
index e9d387bd7c..81ac329cc3 100644
--- a/src/llama-graph.h
+++ b/src/llama-graph.h
@@ -225,6 +225,8 @@ public:
 
     void set_input(const llama_ubatch * ubatch) override;
 
+    bool can_reuse(const llm_graph_params & params) override;
+
     ggml_tensor * s_copy;  // I32 [n_rs]
 
     // views of s_copy, computed once per graph
@@ -233,6 +235,10 @@ public:
     ggml_tensor * s_copy_extra;  // I32 [n_rs - n_seqs]
 
     const llama_memory_recurrent_context * mctx;
+
+    // used in view offsets, need to match for valid graph reuse
+    uint32_t head;
+    int32_t rs_z;
 };
 
 class llm_graph_input_cross_embd : public llm_graph_input_i {
@@ -365,22 +371,28 @@ public:
 class llm_graph_input_mem_hybrid : public llm_graph_input_i {
 public:
     llm_graph_input_mem_hybrid(
+            const llama_cparams & cparams,
             std::unique_ptr<llm_graph_input_attn_kv> inp_attn,
-            std::unique_ptr<llm_graph_input_rs>              inp_rs,
-            const llama_memory_hybrid_context *              mctx) :
+            std::unique_ptr<llm_graph_input_rs>      inp_rs,
+            const llama_memory_hybrid_context *      mctx) :
         inp_attn(std::move(inp_attn)),
         inp_rs(std::move(inp_rs)),
+        cparams(cparams),
         mctx(mctx) { }
     virtual ~llm_graph_input_mem_hybrid() = default;
 
     void set_input(const llama_ubatch * ubatch) override;
 
+    bool can_reuse(const llm_graph_params & params) override;
+
     std::unique_ptr<llm_graph_input_attn_kv> inp_attn;
     std::unique_ptr<llm_graph_input_rs>      inp_rs;
 
     llm_graph_input_attn_kv * get_attn() const { return inp_attn.get(); }
     llm_graph_input_rs      * get_recr() const { return inp_rs.get(); }
 
+    const llama_cparams cparams;
+
     const llama_memory_hybrid_context * mctx;
 };
 
diff --git a/src/llama-memory-hybrid.cpp b/src/llama-memory-hybrid.cpp
index dfb8439e01..a1b45e4a3c 100644
--- a/src/llama-memory-hybrid.cpp
+++ b/src/llama-memory-hybrid.cpp
@@ -222,7 +222,7 @@ llama_memory_hybrid_context::llama_memory_hybrid_context(
     ubatches(std::move(ubatches)),
     // note: here we copy the ubatches. not sure if this is ideal
     ctx_attn(new llama_kv_cache_context(mem->get_mem_attn(), std::move(sinfos_attn), this->ubatches)),
-    ctx_recr(new llama_memory_recurrent_context(mem->get_mem_recr(),                        this->ubatches)),
+    ctx_recr(new llama_memory_recurrent_context(mem->get_mem_recr(), this->ubatches)),
     status(llama_memory_status_combine(ctx_attn->get_status(), ctx_recr->get_status())) {
 }
 

From 2aa45ef9e31069ea0a7d0fef7ce858facdf25218 Mon Sep 17 00:00:00 2001
From: Chris Peterson <cpeterson@mozilla.com>
Date: Mon, 15 Dec 2025 23:37:55 -0800
Subject: [PATCH 122/135] llama: Include algorithm header needed for C++23
 (#18078)

---
 src/llama-hparams.cpp | 1 +
 1 file changed, 1 insertion(+)

diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp
index 96c9598c24..83d6d6ee3c 100644
--- a/src/llama-hparams.cpp
+++ b/src/llama-hparams.cpp
@@ -2,6 +2,7 @@
 
 #include "ggml.h"
 
+#include <algorithm>
 #include <cassert>
 
 void llama_hparams::set_swa_pattern(uint32_t n_pattern, bool dense_first) {

From 5ba95754ee27433971f4f247326770019ad475c1 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Tue, 16 Dec 2025 11:17:11 +0200
Subject: [PATCH 123/135] security : add collaborator guidance (#18081)

---
 SECURITY.md | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/SECURITY.md b/SECURITY.md
index 9c86ae91b5..ae496f4e3d 100644
--- a/SECURITY.md
+++ b/SECURITY.md
@@ -68,3 +68,6 @@ Please disclose it as a private [security advisory](https://github.com/ggml-org/
 Please note that using AI to identify vulnerabilities and generate reports is permitted. However, you must (1) explicitly disclose how AI was used and (2) conduct a thorough manual review before submitting the report.
 
 A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.
+
+> [!IMPORTANT]
+> For collaborators: if you are interested in helping out with reviewing privting security disclosures, please see: https://github.com/ggml-org/llama.cpp/discussions/18080

From 279cef27c2b297476ecde6e5df729c057691f1de Mon Sep 17 00:00:00 2001
From: Francisco Herrera <ppaanncchhoo507@gmail.com>
Date: Tue, 16 Dec 2025 04:45:09 -0500
Subject: [PATCH 124/135] added note for old Intel hardware pre sycl (#18017)

* added note for old Intel hardware pre sycl

Older hardware used opencl

* typo

* use consistent terms
---
 docs/backend/SYCL.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs/backend/SYCL.md b/docs/backend/SYCL.md
index 02a72a9d51..f44458ed3b 100644
--- a/docs/backend/SYCL.md
+++ b/docs/backend/SYCL.md
@@ -103,6 +103,8 @@ SYCL backend supports Intel GPU Family:
 - Intel Built-in Arc GPU
 - Intel iGPU in Core CPU (11th Generation Core CPU and newer, refer to [oneAPI supported GPU](https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-base-toolkit-system-requirements.html#inpage-nav-1-1)).
 
+On older Intel GPUs, you may try [OpenCL](/docs/backend/OPENCL.md) although the performance is not optimal, and some GPUs may not support OpenCL nor have any GPGPU capabilities.
+
 #### Verified devices
 
 | Intel GPU                     | Status  | Verified Model                        |

From c05aa69f32ee87b2019a1b39979d89302c2d6b7b Mon Sep 17 00:00:00 2001
From: Aldehir Rojas <hello@alde.dev>
Date: Tue, 16 Dec 2025 04:05:23 -0600
Subject: [PATCH 125/135] common : add nemotron 3 parsing (#18077)

* common : expose json-schema functionality to extract type info

* common : fix peg parser negation during needs_more_input

* common : add some defensive measures in constructed peg parser

* common : add nemotron nano 3 support

* common : add nemotron nano 3 tests

* remove debug line
---
 common/chat-peg-parser.cpp                    |  14 +-
 common/chat.cpp                               | 140 ++++++++++++
 common/json-schema-to-grammar.cpp             | 135 +++++++++++-
 common/json-schema-to-grammar.h               |  20 ++
 common/peg-parser.cpp                         |   2 +-
 .../NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.jinja | 204 ++++++++++++++++++
 tests/test-chat.cpp                           | 157 ++++++++++++++
 tests/test-json-schema-to-grammar.cpp         |  75 +++++++
 8 files changed, 741 insertions(+), 6 deletions(-)
 create mode 100644 models/templates/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.jinja

diff --git a/common/chat-peg-parser.cpp b/common/chat-peg-parser.cpp
index 74a7b6a46d..1bcba9cd86 100644
--- a/common/chat-peg-parser.cpp
+++ b/common/chat-peg-parser.cpp
@@ -4,9 +4,14 @@
 
 using json = nlohmann::json;
 
-static std::string_view trim_trailing_space(std::string_view sv) {
+static std::string_view trim_trailing_space(std::string_view sv, int max = -1) {
+    int count = 0;
     while (!sv.empty() && std::isspace(static_cast<unsigned char>(sv.back()))) {
+        if (max != -1 && count <= max) {
+            break;
+        }
         sv.remove_suffix(1);
+        count++;
     }
     return sv;
 }
@@ -93,7 +98,7 @@ void common_chat_peg_constructed_mapper::map(const common_peg_ast_node & node) {
 
     if (is_arg_string && current_tool) {
         // Serialize to JSON, but exclude the end quote
-        std::string dumped = json(node.text).dump();
+        std::string dumped = json(trim_trailing_space(node.text)).dump();
         current_tool->arguments += dumped.substr(0, dumped.size() - 1);
         needs_closing_quote = true;
     }
@@ -101,6 +106,7 @@ void common_chat_peg_constructed_mapper::map(const common_peg_ast_node & node) {
     if (is_arg_close && current_tool) {
         if (needs_closing_quote) {
             current_tool->arguments += "\"";
+            needs_closing_quote = false;
         }
     }
 
@@ -109,6 +115,10 @@ void common_chat_peg_constructed_mapper::map(const common_peg_ast_node & node) {
     }
 
     if (is_tool_close && current_tool) {
+        if (needs_closing_quote) {
+            current_tool->arguments += "\"";
+            needs_closing_quote = false;
+        }
         current_tool->arguments += "}";
     }
 }
diff --git a/common/chat.cpp b/common/chat.cpp
index c371edaa5a..0a426f4478 100644
--- a/common/chat.cpp
+++ b/common/chat.cpp
@@ -711,6 +711,25 @@ static void foreach_function(const json & tools, const std::function<void(const
     }
 }
 
+static void foreach_parameter(const json & function, const std::function<void(const std::string &, const json &, bool)> & fn) {
+    if (!function.contains("parameters") || !function.at("parameters").is_object()) {
+        return;
+    }
+    const auto & params = function.at("parameters");
+    if (!params.contains("properties") || !params.at("properties").is_object()) {
+        return;
+    }
+    const auto & props = params.at("properties");
+    std::set<std::string> required;
+    if (params.contains("required") && params.at("required").is_array()) {
+        params.at("required").get_to(required);
+    }
+    for (const auto & [name, prop] : props.items()) {
+        bool is_required = (required.find(name) != required.end());
+        fn(name, prop, is_required);
+    }
+}
+
 static std::string apply(
     const common_chat_template & tmpl,
     const struct templates_params & inputs,
@@ -1409,6 +1428,123 @@ static common_chat_params common_chat_params_init_nemotron_v2(const common_chat_
     return data;
 }
 
+static common_chat_params common_chat_params_init_nemotron_v3(const common_chat_template & tmpl, const struct templates_params & inputs) {
+    common_chat_params data;
+
+    data.prompt = apply(tmpl, inputs);
+    data.format = COMMON_CHAT_FORMAT_PEG_CONSTRUCTED;
+
+    // Handle thinking tags appropriately based on inputs.enable_thinking
+    if (string_ends_with(data.prompt, "<think>\n")) {
+        if (!inputs.enable_thinking) {
+            data.prompt += "</think>";
+        } else {
+            data.thinking_forced_open = true;
+        }
+    }
+
+    data.preserved_tokens = {
+        "<think>",
+        "</think>",
+        "<tool_call>",
+        "</tool_call>",
+    };
+
+    auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
+    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar = true;
+
+    auto parser = build_chat_peg_constructed_parser([&](auto & p) {
+        auto reasoning = p.eps();
+        if (inputs.enable_thinking && extract_reasoning) {
+            auto reasoning_content = p.reasoning(p.until("</think>")) + ("</think>" | p.end());
+            if (data.thinking_forced_open) {
+                reasoning = reasoning_content;
+            }
+        }
+
+        // Response format parser
+        if (inputs.json_schema.is_object() && !inputs.json_schema.empty()) {
+            return reasoning << p.content(p.schema(p.json(), "response-format", inputs.json_schema));
+        }
+
+        // Tool call parser
+        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
+            auto tool_choice = p.choice();
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                std::string name = function.at("name");
+                auto parameters = function.at("parameters");
+
+                auto schema_info = common_schema_info();
+                schema_info.resolve_refs(parameters);
+
+                auto tool_open = "<function=" + p.tool_name(p.literal(name)) + ">\n";
+                auto tool_close = p.literal("</function>\n");
+                auto args = p.sequence();
+                auto arg_string = p.rule("xml-arg-string", p.until_one_of({
+                    "\n</parameter>",
+                    "\n<parameter=",
+                    "\n</function>"
+                }));
+
+                foreach_parameter(function, [&](const auto & param_name, const json & param_schema, bool is_required) {
+                    auto rule_name = "tool-" + name + "-arg-" + param_name;
+
+                    auto arg_open = "<parameter=" + p.tool_arg_name(p.literal(param_name)) + ">\n";
+                    auto arg_close = p.literal("</parameter>\n");
+                    auto arg_value = p.eps();
+
+                    if (schema_info.resolves_to_string(param_schema)) {
+                        arg_value = p.tool_arg_string_value(arg_string) + "\n";
+                    } else {
+                        arg_value = p.tool_arg_json_value(p.schema(p.json(), rule_name + "-schema", param_schema));
+                    }
+
+                    // Model may or my not close with </parameter>
+                    auto arg_rule = p.rule(rule_name, p.tool_arg_open(arg_open) + arg_value + p.optional(p.tool_arg_close(arg_close)));
+                    args += p.repeat(arg_rule, /* min = */ is_required ? 1 : 0, /* max = */ 1);
+                });
+
+                tool_choice |= p.rule("tool-" + name, p.tool_open(tool_open) + args + p.tool_close(tool_close));
+            });
+
+            auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
+            auto max_calls = inputs.parallel_tool_calls ? -1 : 1;
+            auto tool_call = p.rule("tool-call", "<tool_call>\n" + tool_choice + "</tool_call>" + p.space());
+            auto tool_calls = p.trigger_rule("tool-call-root", p.repeat(tool_call, /* min = */ min_calls, /* max = */ max_calls));
+
+            return reasoning << p.content(p.until("<tool_call>")) << tool_calls;
+        }
+
+        // Content only parser
+        include_grammar = false;
+        return reasoning << p.content(p.rest());
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
+
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto schema = function.at("parameters");
+                builder.resolve_refs(schema);
+            });
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        data.grammar_triggers = {
+            {COMMON_GRAMMAR_TRIGGER_TYPE_WORD, "<tool_call>"}
+        };
+    }
+
+    return data;
+}
+
+
 static common_chat_params common_chat_params_init_apertus(const common_chat_template & tmpl, const struct templates_params & inputs) {
     common_chat_params data;
 
@@ -2534,6 +2670,10 @@ static common_chat_params common_chat_templates_apply_jinja(
         src.find("<function=") != std::string::npos &&
         src.find("<parameters>") != std::string::npos &&
         src.find("<parameter=") != std::string::npos) {
+        // Nemotron 3 Nano 30B A3B
+        if (src.find("<think>") != std::string::npos) {
+            return common_chat_params_init_nemotron_v3(tmpl, params);
+        }
         return common_chat_params_init_qwen3_coder_xml(tmpl, params);
     }
 
diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp
index c3b4e5d9dc..2f67c74d79 100644
--- a/common/json-schema-to-grammar.cpp
+++ b/common/json-schema-to-grammar.cpp
@@ -305,8 +305,9 @@ static std::string format_literal(const std::string & literal) {
 
 std::string gbnf_format_literal(const std::string & literal) { return format_literal(literal); }
 
-class SchemaConverter {
+class common_schema_converter {
 private:
+    friend class common_schema_info;
     friend std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options);
     std::function<json(const std::string &)> _fetch_json;
     bool _dotall;
@@ -729,7 +730,7 @@ private:
     }
 
 public:
-    SchemaConverter(
+    common_schema_converter(
         const std::function<json(const std::string &)> & fetch_json,
         bool dotall)
           : _fetch_json(fetch_json), _dotall(dotall)
@@ -990,6 +991,134 @@ public:
     }
 };
 
+// common_schema_info implementation (pimpl)
+
+common_schema_info::common_schema_info()
+    : impl_(std::make_unique<common_schema_converter>(
+        [](const std::string &) { return json(); },
+        false)) {}
+
+common_schema_info::~common_schema_info() = default;
+
+common_schema_info::common_schema_info(common_schema_info &&) noexcept = default;
+common_schema_info & common_schema_info::operator=(common_schema_info &&) noexcept = default;
+
+void common_schema_info::resolve_refs(nlohmann::ordered_json & schema) {
+    impl_->resolve_refs(schema, "");
+}
+
+// Determines if a JSON schema can resolve to a string type through any path.
+// Some models emit raw string values rather than JSON-encoded strings for string parameters.
+// If any branch of the schema (via oneOf, anyOf, $ref, etc.) permits a string, this returns
+// true, allowing callers to handle the value as a raw string for simplicity.
+bool common_schema_info::resolves_to_string(const nlohmann::ordered_json & schema) {
+    std::unordered_set<std::string> visited_refs;
+
+    std::function<bool(const json &)> check = [&](const json & s) -> bool {
+        if (!s.is_object()) {
+            return false;
+        }
+
+        // Handle $ref
+        if (s.contains("$ref")) {
+            const std::string & ref = s["$ref"];
+            if (visited_refs.find(ref) != visited_refs.end()) {
+                // Circular reference, assume not a string to be safe
+                return false;
+            }
+            visited_refs.insert(ref);
+            auto it = impl_->_refs.find(ref);
+            if (it != impl_->_refs.end()) {
+                return check(it->second);
+            }
+            return false;
+        }
+
+        // Check type field
+        if (s.contains("type")) {
+            const json & schema_type = s["type"];
+            if (schema_type.is_string()) {
+                if (schema_type == "string") {
+                    return true;
+                }
+            } else if (schema_type.is_array()) {
+                // Type can be an array like ["string", "null"]
+                for (const auto & t : schema_type) {
+                    if (t == "string") {
+                        return true;
+                    }
+                }
+            }
+        }
+
+        // Check oneOf/anyOf - if any alternative can be a string
+        if (s.contains("oneOf")) {
+            for (const auto & alt : s["oneOf"]) {
+                if (check(alt)) {
+                    return true;
+                }
+            }
+        }
+        if (s.contains("anyOf")) {
+            for (const auto & alt : s["anyOf"]) {
+                if (check(alt)) {
+                    return true;
+                }
+            }
+        }
+
+        // Check allOf - all components must be compatible with string type
+        if (s.contains("allOf")) {
+            bool all_string = true;
+            for (const auto & component : s["allOf"]) {
+                if (!check(component)) {
+                    all_string = false;
+                    break;
+                }
+            }
+            if (all_string) {
+                return true;
+            }
+        }
+
+        // Check const - if the constant value is a string
+        if (s.contains("const")) {
+            if (s["const"].is_string()) {
+                return true;
+            }
+        }
+
+        // Check enum - if any enum value is a string
+        if (s.contains("enum")) {
+            for (const auto & val : s["enum"]) {
+                if (val.is_string()) {
+                    return true;
+                }
+            }
+        }
+
+        // String-specific keywords imply string type
+        if (s.contains("pattern") || s.contains("minLength") || s.contains("maxLength")) {
+            return true;
+        }
+
+        // Check format - many formats imply string
+        if (s.contains("format")) {
+            const std::string & fmt = s["format"];
+            if (fmt == "date" || fmt == "time" || fmt == "date-time" ||
+                fmt == "uri" || fmt == "email" || fmt == "hostname" ||
+                fmt == "ipv4" || fmt == "ipv6" || fmt == "uuid" ||
+                fmt.find("uuid") == 0) {
+                return true;
+            }
+        }
+
+        return false;
+    };
+
+    return check(schema);
+}
+
 std::string json_schema_to_grammar(const json & schema, bool force_gbnf) {
 #ifdef LLAMA_USE_LLGUIDANCE
     if (!force_gbnf) {
@@ -1006,7 +1135,7 @@ std::string json_schema_to_grammar(const json & schema, bool force_gbnf) {
 }
 
 std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options) {
-    SchemaConverter converter([&](const std::string &) { return json(); }, options.dotall);
+    common_schema_converter converter([&](const std::string &) { return json(); }, options.dotall);
     common_grammar_builder builder {
         /* .add_rule = */ [&](const std::string & name, const std::string & rule) {
             return converter._add_rule(name, rule);
diff --git a/common/json-schema-to-grammar.h b/common/json-schema-to-grammar.h
index c89ab7f997..240d642311 100644
--- a/common/json-schema-to-grammar.h
+++ b/common/json-schema-to-grammar.h
@@ -3,11 +3,31 @@
 #include <nlohmann/json_fwd.hpp>
 
 #include <functional>
+#include <memory>
 #include <string>
 
 std::string json_schema_to_grammar(const nlohmann::ordered_json & schema,
                                    bool force_gbnf = false);
 
+class common_schema_converter;
+
+// Probes a JSON schema to extract information about its structure and type constraints.
+class common_schema_info {
+    std::unique_ptr<common_schema_converter> impl_;
+
+  public:
+    common_schema_info();
+    ~common_schema_info();
+
+    common_schema_info(const common_schema_info &) = delete;
+    common_schema_info & operator=(const common_schema_info &) = delete;
+    common_schema_info(common_schema_info &&) noexcept;
+    common_schema_info & operator=(common_schema_info &&) noexcept;
+
+    void resolve_refs(nlohmann::ordered_json & schema);
+    bool resolves_to_string(const nlohmann::ordered_json & schema);
+};
+
 struct common_grammar_builder {
     std::function<std::string(const std::string &, const std::string &)> add_rule;
     std::function<std::string(const std::string &, const nlohmann::ordered_json &)> add_schema;
diff --git a/common/peg-parser.cpp b/common/peg-parser.cpp
index dec99e1820..f2fc84500f 100644
--- a/common/peg-parser.cpp
+++ b/common/peg-parser.cpp
@@ -425,7 +425,7 @@ struct parser_executor {
 
         if (result.need_more_input()) {
             // Propagate - need to know what child would match before negating
-            return result;
+            return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start_pos);
         }
 
         // Child failed, so negation succeeds
diff --git a/models/templates/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.jinja b/models/templates/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.jinja
new file mode 100644
index 0000000000..a01e0861c6
--- /dev/null
+++ b/models/templates/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.jinja
@@ -0,0 +1,204 @@
+{% macro render_extra_keys(json_dict, handled_keys) %}
+    {%- if json_dict is mapping %}
+        {%- for json_key in json_dict if json_key not in handled_keys %}
+            {%- if json_dict[json_key] is mapping or (json_dict[json_key] is sequence and json_dict[json_key] is not string) %}
+                {{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson | safe) ~ '</' ~ json_key ~ '>' }}
+            {%- else %}
+                {{-'\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '</' ~ json_key ~ '>' }}
+            {%- endif %}
+        {%- endfor %}
+    {%- endif %}
+{% endmacro %}
+{%- set enable_thinking = enable_thinking if enable_thinking is defined else True %}
+{%- set truncate_history_thinking = truncate_history_thinking if truncate_history_thinking is defined else True %}
+
+{%- set ns = namespace(last_user_idx = -1) %}
+{%- set loop_messages = messages %}
+{%- for m in loop_messages %}
+  {%- if m["role"] == "user" %}
+    {%- set ns.last_user_idx = loop.index0 %}
+  {%- endif %}
+{%- endfor %}
+
+{%- if messages[0]["role"] == "system" %}
+    {%- set system_message = messages[0]["content"] %}
+    {%- set loop_messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+    {%- set loop_messages = messages %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = [] %}
+{%- endif %}
+{# Recompute last_user_idx relative to loop_messages after handling system #}
+{%- set ns = namespace(last_user_idx = -1) %}
+{%- for m in loop_messages %}
+  {%- if m["role"] == "user" %}
+    {%- set ns.last_user_idx = loop.index0 %}
+  {%- endif %}
+{%- endfor %}
+{%- if system_message is defined %}
+    {{- "<|im_start|>system\n" + system_message }}
+{%- else %}
+    {%- if tools is iterable and tools | length > 0 %}
+        {{- "<|im_start|>system\n" }}
+    {%- endif %}
+{%- endif %}
+{%- if tools is iterable and tools | length > 0 %}
+    {%- if system_message is defined and system_message | length > 0 %}
+        {{- "\n\n" }}
+    {%- endif %}
+    {{- "# Tools\n\nYou have access to the following functions:\n\n" }}
+    {{- "<tools>" }}
+    {%- for tool in tools %}
+        {%- if tool.function is defined %}
+            {%- set tool = tool.function %}
+        {%- endif %}
+        {{- "\n<function>\n<name>" ~ tool.name ~ "</name>" }}
+        {%- if tool.description is defined %}
+            {{- '\n<description>' ~ (tool.description | trim) ~ '</description>' }}
+        {%- endif %}
+        {{- '\n<parameters>' }}
+        {%- if tool.parameters is defined and tool.parameters is mapping and tool.parameters.properties is defined and tool.parameters.properties is mapping %}
+            {%- for param_name, param_fields in tool.parameters.properties|items %}
+                {{- '\n<parameter>' }}
+                {{- '\n<name>' ~ param_name ~ '</name>' }}
+                {%- if param_fields.type is defined %}
+                    {{- '\n<type>' ~ (param_fields.type | string) ~ '</type>' }}
+                {%- endif %}
+                {%- if param_fields.description is defined %}
+                    {{- '\n<description>' ~ (param_fields.description | trim) ~ '</description>' }}
+                {%- endif %}
+                {%- if param_fields.enum is defined %}
+                    {{- '\n<enum>' ~ (param_fields.enum | tojson | safe) ~ '</enum>' }}
+                {%- endif %}
+                {%- set handled_keys = ['name', 'type', 'description', 'enum'] %}
+                {{- render_extra_keys(param_fields, handled_keys) }}
+                {{- '\n</parameter>' }}
+            {%- endfor %}
+        {%- endif %}
+        {% set handled_keys = ['type', 'properties', 'required'] %}
+        {{- render_extra_keys(tool.parameters, handled_keys) }}
+        {%- if tool.parameters is defined and tool.parameters.required is defined %}
+            {{- '\n<required>' ~ (tool.parameters.required | tojson | safe) ~ '</required>' }}
+        {%- endif %}
+        {{- '\n</parameters>' }}
+        {%- set handled_keys = ['type', 'name', 'description', 'parameters'] %}
+        {{- render_extra_keys(tool, handled_keys) }}
+        {{- '\n</function>' }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+{%- endif %}
+
+
+{%- if system_message is defined %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if tools is iterable and tools | length > 0 %}
+        {{- '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+
+{%- for message in loop_messages %}
+    {%- if message.role == "assistant" %}
+        {# Add reasoning content in to content field for unified processing below. #}
+        {%- if message.reasoning_content is defined and message.reasoning_content is string and message.reasoning_content | trim | length > 0 %}
+            {%- set content = "<think>\n" ~ message.reasoning_content ~ "\n</think>\n" ~ (message.content | default('', true)) %}
+        {%- else %}
+            {%- set content = message.content | default('', true) %}
+            {%- if content is string -%}
+                {# Allow downstream logic to to take care of broken thought, only handle coherent reasoning here. #}
+                {%- if '<think>' not in content and '</think>' not in content -%}
+                    {%- set content = "<think></think>" ~ content -%}
+                {%- endif -%}
+            {%- else -%}
+                {%- set content = content -%}
+            {%- endif -%}
+        {%- endif %}
+        {%- if message.tool_calls is defined and message.tool_calls is iterable and message.tool_calls | length > 0 %}
+            {# Assistant message has tool calls. #}
+            {{- '<|im_start|>assistant\n' }}
+                {%- set include_content = not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %}
+                {%- if content is string and content | trim | length > 0 %}
+                    {%- if include_content %}
+                        {{- (content | trim) ~ '\n' -}}
+                    {%- else %}
+                        {%- set c = (content | string) %}
+                        {%- if '</think>' in c %}
+                            {# Keep only content after the last closing think. Also generation prompt causes this. #}
+                            {%- set c = c.split('</think>')[-1] %}
+                        {%- elif '<think>' in c %}
+                            {# If <think> was opened but never closed, drop the trailing think segment #}
+                            {%- set c = c.split('<think>')[0] %}
+                        {%- endif %}
+                        {%- set c = "<think></think>" ~ c | trim %}
+                        {%- if c | length > 0 %}
+                            {{- c ~ '\n' -}}
+                        {%- endif %}
+                    {%- endif %}
+                {%- else %}
+                    {{- "<think></think>" -}}
+                {%- endif %}
+                {%- for tool_call in message.tool_calls %}
+                    {%- if tool_call.function is defined %}
+                        {%- set tool_call = tool_call.function %}
+                    {%- endif %}
+                    {{- '<tool_call>\n<function=' ~ tool_call.name ~ '>\n' -}}
+                        {%- if tool_call.arguments is defined %}
+                            {%- for args_name, args_value in tool_call.arguments|items %}
+                                {{- '<parameter=' ~ args_name ~ '>\n' -}}
+                                    {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
+                                {{- args_value ~ '\n</parameter>\n' -}}
+                            {%- endfor %}
+                        {%- endif %}
+                    {{- '</function>\n</tool_call>\n' -}}
+                {%- endfor %}
+                {{- '<|im_end|>\n' }}
+        {%- else %}
+            {# Assistant message doesn't have tool calls. #}
+            {%- if not (truncate_history_thinking and loop.index0 < ns.last_user_idx) %}
+                {{- '<|im_start|>assistant\n' ~ (content | default('', true) | string | trim) ~ '<|im_end|>\n' }}
+            {%- else %}
+                {%- set c = (content | default('', true) | string) %}
+                {%- if '<think>' in c and '</think>' in c %}
+                    {%- set c = "<think></think>" ~ c.split('</think>')[-1] %}
+                {%- endif %}
+                {%- set c = c | trim %}
+                {%- if c | length > 0 %}
+                    {{- '<|im_start|>assistant\n' ~ c ~ '<|im_end|>\n' }}
+                {%- else %}
+                    {{- '<|im_start|>assistant\n<|im_end|>\n' }}
+                {%- endif %}
+            {%- endif %}
+        {%- endif %}
+    {%- elif message.role == "user" or message.role == "system" %}
+        {{- '<|im_start|>' + message.role + '\n' }}
+        {%- set content = message.content | string %}
+        {{- content }}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user\n' }}
+        {%- endif %}
+        {{- '<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>\n' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endfor %}
+
+{%- if add_generation_prompt %}
+    {%- if enable_thinking %}
+        {{- '<|im_start|>assistant\n<think>\n' }}
+    {%- else %}
+        {{- '<|im_start|>assistant\n<think></think>' }}
+    {%- endif %}
+{%- endif %}
diff --git a/tests/test-chat.cpp b/tests/test-chat.cpp
index 007929f517..02af5251cc 100644
--- a/tests/test-chat.cpp
+++ b/tests/test-chat.cpp
@@ -3588,6 +3588,163 @@ static void test_template_output_peg_parsers() {
             t.expect.content =R"({"amount": 123.45, "date": "2025-12-03"})";
         });
     }
+
+    {
+        // NVIDIA Nemotron-3 Nano
+        auto tmpls = read_templates("models/templates/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16.jinja");
+
+        // Test basic message
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input = "Hello, world!\nWhat's up?";
+            t.expect = message_assist;
+        });
+
+        // Test basic message and reasoning with reasoning_format = none
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input = "I'm\nthinking\n</think>\nHello, world!\nWhat's up?";
+            t.expect.content = "I'm\nthinking\n</think>\nHello, world!\nWhat's up?";
+        });
+
+        // Test basic message and reasoning with reasoning_format = auto
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input = "I'm\nthinking\n</think>\nHello, world!\nWhat's up?";
+            t.params.enable_thinking = true;
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+
+            t.expect = message_assist_thoughts;
+        });
+
+        // Test tool call
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input =
+                "<tool_call>\n"
+                "<function=special_function>\n"
+                "<parameter=arg1>\n"
+                "1\n"
+                "</parameter>\n"
+                "</function>\n"
+                "</tool_call>";
+            t.params.enable_thinking = false;
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+            t.params.tools = {special_function_tool};
+
+            t.expect = message_assist_call;
+        });
+
+        // Test tool call with reasoning
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input =
+                "I'm\nthinking\n</think>\n"
+                "<tool_call>\n"
+                "<function=special_function>\n"
+                "<parameter=arg1>\n"
+                "1\n"
+                "</parameter>\n"
+                "</function>\n"
+                "</tool_call>";
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+            t.params.tools = {special_function_tool};
+
+            t.expect = message_assist_call_thoughts;
+        });
+
+        // Test parallel tool calls
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input =
+                "<tool_call>\n"
+                "<function=special_function>\n"
+                "<parameter=arg1>\n"
+                "1\n"
+                "</parameter>\n"
+                "</function>\n"
+                "</tool_call>\n"
+                "<tool_call>\n"
+                "<function=special_function_with_opt>\n"
+                "<parameter=arg1>\n"
+                "1\n"
+                "</parameter>\n"
+                "<parameter=arg2>\n"
+                "2\n"
+                "</parameter>\n"
+                "</function>\n"
+                "</tool_call>";
+            t.params.enable_thinking = false;
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+            t.params.parallel_tool_calls = true;
+            t.params.tools = {special_function_tool, special_function_tool_with_optional_param};
+
+            t.expect.tool_calls = {{
+                /* .name = */      "special_function",
+                /* .arguments = */ R"({"arg1": 1})",
+                /* .id = */        {},
+            }, {
+                /* .name = */      "special_function_with_opt",
+                /* .arguments = */ R"({"arg1": 1, "arg2": 2})",
+                /* .id = */        {},
+            }};
+        });
+
+        // Test tool call with string parameter
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input =
+                "<tool_call>\n"
+                "<function=python>\n"
+                "<parameter=code>\n"
+                "def hello():\n"
+                "    print(\"Hello, world!\")\n"
+                "\n"
+                "hello()\n"
+                "</parameter>\n"
+                "</function>\n"
+                "</tool_call>";
+            t.params.enable_thinking = false;
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+            t.params.tools = {python_tool};
+
+            t.expect.tool_calls = {{
+                /* .name = */      "python",
+                /* .arguments = */ "{\"code\": \"def hello():\\n    print(\\\"Hello, world!\\\")\\n\\nhello()\"}",
+                /* .id = */        {},
+            }};
+        });
+
+        // Test tool call with string parameter and no closing </parameter> tag
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input =
+                "<tool_call>\n"
+                "<function=python>\n"
+                "<parameter=code>\n"
+                "def hello():\n"
+                "    print(\"Hello, world!\")\n"
+                "\n"
+                "hello()\n"
+                "</function>\n"
+                "</tool_call>";
+            t.params.enable_thinking = false;
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+            t.params.tools = {python_tool};
+
+            t.expect.tool_calls = {{
+                /* .name = */      "python",
+                /* .arguments = */ "{\"code\": \"def hello():\\n    print(\\\"Hello, world!\\\")\\n\\nhello()\"}",
+                /* .id = */        {},
+            }};
+        });
+
+        // Test response format
+        test_peg_parser(tmpls.get(), [&](auto & t) {
+            t.input =
+              "I need to output the invoice details in JSON\n"
+              "</think>\n"
+              R"({"amount": 123.45, "date": "2025-12-03"})";
+            t.params.reasoning_format = COMMON_REASONING_FORMAT_AUTO;
+            t.params.json_schema = invoice_schema;
+
+            t.expect.reasoning_content = "I need to output the invoice details in JSON";
+            t.expect.content = R"({"amount": 123.45, "date": "2025-12-03"})";
+        });
+    }
+
 }
 
 static void test_msg_diffs_compute() {
diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp
index 6a4bd8fb4d..a8e9ff33a4 100755
--- a/tests/test-json-schema-to-grammar.cpp
+++ b/tests/test-json-schema-to-grammar.cpp
@@ -1367,10 +1367,85 @@ static void test_all(const std::string & lang, std::function<void(const TestCase
     });
 }
 
+static void test_resolves_to_string() {
+    fprintf(stderr, "#\n# Testing resolves_to_string\n#\n");
+
+    auto test = [](const std::string & name, const std::string & schema_str, bool expected) {
+        fprintf(stderr, "- %s\n", name.c_str());
+        common_schema_info info;
+        auto schema = nlohmann::ordered_json::parse(schema_str);
+        info.resolve_refs(schema);
+        bool result = info.resolves_to_string(schema);
+        if (result != expected) {
+            fprintf(stderr, "#\n# Test '%s' failed.\n#\n", name.c_str());
+            fprintf(stderr, "Schema: %s\n", schema_str.c_str());
+            fprintf(stderr, "Expected: %s, Got: %s\n", expected ? "true" : "false", result ? "true" : "false");
+            assert(false);
+        }
+    };
+
+    // Basic type checks
+    test("type string", R"({"type": "string"})", true);
+    test("type integer", R"({"type": "integer"})", false);
+    test("type number", R"({"type": "number"})", false);
+    test("type boolean", R"({"type": "boolean"})", false);
+    test("type object", R"({"type": "object"})", false);
+    test("type array", R"({"type": "array"})", false);
+
+    // Type array (nullable string)
+    test("type array with string", R"({"type": ["string", "null"]})", true);
+    test("type array without string", R"({"type": ["integer", "null"]})", false);
+
+    // String-specific keywords
+    test("minLength implies string", R"({"minLength": 1})", true);
+    test("maxLength implies string", R"({"maxLength": 10})", true);
+    test("pattern implies string", R"({"pattern": "^[a-z]+$"})", true);
+
+    // Format
+    test("format date", R"({"format": "date"})", true);
+    test("format uuid", R"({"format": "uuid"})", true);
+    test("format email", R"({"format": "email"})", true);
+
+    // Const
+    test("const string", R"({"const": "hello"})", true);
+    test("const number", R"({"const": 123})", false);
+
+    // Enum
+    test("enum with strings", R"({"enum": ["a", "b", "c"]})", true);
+    test("enum with numbers", R"({"enum": [1, 2, 3]})", false);
+    test("enum mixed with string", R"({"enum": [1, "a", null]})", true);
+
+    // anyOf
+    test("anyOf with string", R"({"anyOf": [{"type": "string"}, {"type": "integer"}]})", true);
+    test("anyOf without string", R"({"anyOf": [{"type": "integer"}, {"type": "boolean"}]})", false);
+
+    // oneOf
+    test("oneOf with string", R"({"oneOf": [{"type": "string"}, {"type": "number"}]})", true);
+    test("oneOf without string", R"({"oneOf": [{"type": "object"}, {"type": "array"}]})", false);
+
+    // allOf - all must be strings
+    test("allOf all strings", R"({"allOf": [{"type": "string"}, {"minLength": 1}]})", true);
+    test("allOf mixed types", R"({"allOf": [{"type": "string"}, {"type": "integer"}]})", false);
+
+    // $ref
+    test("$ref to string",
+        R"({"$ref": "#/$defs/str", "$defs": {"str": {"type": "string"}}})", true);
+    test("$ref to integer",
+        R"({"$ref": "#/$defs/num", "$defs": {"num": {"type": "integer"}}})", false);
+
+    // Nested
+    test("nested anyOf with string",
+        R"({"anyOf": [{"anyOf": [{"type": "integer"}, {"type": "string"}]}, {"type": "boolean"}]})", true);
+
+    fprintf(stderr, "All resolves_to_string tests passed!\n");
+}
+
 int main() {
     fprintf(stderr, "LLAMA_NODE_AVAILABLE = %s\n", getenv("LLAMA_NODE_AVAILABLE") ? "true" : "false");
     fprintf(stderr, "LLAMA_PYTHON_AVAILABLE = %s\n", getenv("LLAMA_PYTHON_AVAILABLE") ? "true" : "false");
 
+    test_resolves_to_string();
+
     test_all("C++", [](const TestCase & tc) {
         try {
             tc.verify(json_schema_to_grammar(nlohmann::ordered_json::parse(tc.schema), true));

From db81d5ec4b0a9cb19e98c4533731c9554eb025db Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Tue, 16 Dec 2025 11:17:20 +0100
Subject: [PATCH 126/135] model-conversion : use CONVERTED_EMBEDDING_MODEL for
 embedding_verify_logits (#18079)

This commit updates the embedding model verification script to use the
CONVERTED_EMBEDDING_MODEL environment variable instead of using the
EMBEDDING_MODEL_PATH (the original embedding model path) as the basis
for the converted model file name.

The motivation for this that currently if the converted embedding model
file name differs from the original embedding model directory/name the
verification script will look for the wrong .bin files that were
generating when running the models.
---
 .../scripts/embedding/compare-embeddings-logits.sh           | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/examples/model-conversion/scripts/embedding/compare-embeddings-logits.sh b/examples/model-conversion/scripts/embedding/compare-embeddings-logits.sh
index c48af3075c..984d03e95d 100755
--- a/examples/model-conversion/scripts/embedding/compare-embeddings-logits.sh
+++ b/examples/model-conversion/scripts/embedding/compare-embeddings-logits.sh
@@ -34,8 +34,11 @@ done
 MODEL_PATH="${MODEL_PATH:-"$EMBEDDING_MODEL_PATH"}"
 MODEL_NAME="${MODEL_NAME:-$(basename "$MODEL_PATH")}"
 
+CONVERTED_MODEL_PATH="${CONVERTED_EMBEDDING_PATH:-"$CONVERTED_EMBEDDING_MODEL"}"
+CONVERTED_MODEL_NAME="${CONVERTED_MODEL_NAME:-$(basename "$CONVERTED_MODEL_PATH" .gguf)}"
+
 if [ -t 0 ]; then
-    CPP_EMBEDDINGS="data/llamacpp-${MODEL_NAME}-embeddings.bin"
+    CPP_EMBEDDINGS="data/llamacpp-${CONVERTED_MODEL_NAME}-embeddings.bin"
 else
     # Process piped JSON data and convert to binary (matching logits.cpp format)
     TEMP_FILE=$(mktemp /tmp/tmp.XXXXXX.binn)

From 9963b81f6392da8066958c177db77ad4b4a8f284 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Tue, 16 Dec 2025 11:17:40 +0100
Subject: [PATCH 127/135] model-conversion : add note about verifying previous
 models (#18082)

This commit adds a note to the README in the model-conversion
examples, advising developers to verify that previous versions of models
pass logits verification before adding new models from the same family.
---
 examples/model-conversion/README.md | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/examples/model-conversion/README.md b/examples/model-conversion/README.md
index 05d95d588b..8163b306b4 100644
--- a/examples/model-conversion/README.md
+++ b/examples/model-conversion/README.md
@@ -10,6 +10,13 @@ and in some cases perplexity checked of the quantized model. And finally the
 model/models need to the ggml-org on Hugging Face. This tool/example tries to
 help with this process.
 
+> 📝 **Note:** When adding a new model from an existing family, verify the
+> previous version passes logits verification first. Existing models can have
+> subtle numerical differences that don't affect generation quality but cause
+> logits mismatches. Identifying these upfront whether they exist in llama.cpp,
+> the conversion script, or in an upstream implementation, can save significant
+> debugging time.
+
 ### Overview
 The idea is that the makefile targets and scripts here can be used in the
 development/conversion process assisting with things like:

From 3d86c6c2b546ae5ce9cdee3ca0ac4a8181e0a073 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Tue, 16 Dec 2025 11:25:26 +0100
Subject: [PATCH 128/135] model: support GLM4V vision encoder (#18042)

* convert ok

* no deepstack

* less new tensors

* cgraph ok

* add mrope for text model

* faster patch merger

* add GGML_ROPE_TYPE_MRNORM

* add support for metal

* move glm4v do dedicated graph

* convert: add norm_embd

* clip: add debugging fn

* working correctly

* fix style

* use bicubic

* fix mrope metal

* improve cpu

* convert to neox ordering on conversion

* revert backend changes

* force stop if using old weight

* support moe variant

* fix conversion

* fix convert (2)

* Update tools/mtmd/clip-graph.h

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* process mrope_section on TextModel base class

* resolve conflict merge

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 convert_hf_to_gguf.py          | 111 ++++++++++++++++++++----------
 gguf-py/gguf/constants.py      |   7 ++
 gguf-py/gguf/tensor_mapping.py |  15 +++++
 src/llama-hparams.cpp          |   4 ++
 src/llama-hparams.h            |   2 +
 src/llama-model.cpp            |  15 +++--
 src/models/glm4-moe.cpp        |  37 +++++++---
 src/models/glm4.cpp            |  31 +++++++--
 tools/mtmd/CMakeLists.txt      |   1 +
 tools/mtmd/clip-graph.h        |   4 +-
 tools/mtmd/clip-impl.h         |  11 ++-
 tools/mtmd/clip-model.h        |  13 +++-
 tools/mtmd/clip.cpp            | 107 +++++++++++++++++++++++------
 tools/mtmd/clip.h              |   2 +-
 tools/mtmd/models/glm4v.cpp    | 120 +++++++++++++++++++++++++++++++++
 tools/mtmd/models/models.h     |   5 ++
 tools/mtmd/mtmd.cpp            |   6 +-
 17 files changed, 412 insertions(+), 79 deletions(-)
 create mode 100644 tools/mtmd/models/glm4v.cpp

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 01b15e4b9a..bd16ba312f 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -862,6 +862,14 @@ class TextModel(ModelBase):
                 logger.warning(f"Unknown RoPE type: {rope_type}")
             logger.info(f"gguf: rope scaling type = {rope_gguf_type.name}")
 
+        if "mrope_section" in self.rope_parameters:
+            mrope_section = self.rope_parameters["mrope_section"]
+            # Pad to 4 dimensions [time, height, width, extra]
+            while len(mrope_section) < 4:
+                mrope_section.append(0)
+            self.gguf_writer.add_rope_dimension_sections(mrope_section[:4])
+            logger.info(f"gguf: mrope sections: {mrope_section[:4]}")
+
         if (rope_theta := rope_params.get("rope_theta")) is not None:
             self.gguf_writer.add_rope_freq_base(rope_theta)
             logger.info(f"gguf: rope theta = {rope_theta}")
@@ -3739,9 +3747,6 @@ class Qwen2VLModel(TextModel):
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
-        mrope_section = self.hparams["rope_scaling"]["mrope_section"]
-        mrope_section += [0] * max(0, 4 - len(mrope_section))
-        self.gguf_writer.add_rope_dimension_sections(mrope_section)
 
     def set_vocab(self):
         try:
@@ -4377,6 +4382,30 @@ class Qwen3VLVisionModel(MmprojModel):
         return super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("Glm4vForConditionalGeneration", "Glm4vMoeForConditionalGeneration")
+class Glm4VVisionModel(Qwen3VLVisionModel):
+    def set_gguf_parameters(self):
+        MmprojModel.set_gguf_parameters(self) # skip Qwen3VLVisionModel parameters
+        assert self.hparams_vision is not None
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GLM4V)
+
+        hidden_act = str(self.hparams_vision.get("hidden_act", "")).lower()
+        if hidden_act == "gelu":
+            self.gguf_writer.add_vision_use_gelu(True)
+        elif hidden_act == "silu":
+            self.gguf_writer.add_vision_use_silu(True)
+
+        rms_norm_eps = self.hparams_vision.get("rms_norm_eps", 1e-5)
+        self.gguf_writer.add_vision_attention_layernorm_eps(rms_norm_eps)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name.startswith("model.visual."):
+            name = name.replace("model.visual.", "visual.")
+        if name.startswith("visual.merger."):
+            return [(self.map_tensor_name(name), data_torch)]
+        return super().modify_tensors(data_torch, name, bid)
+
+
 @ModelBase.register("Qwen3VLForConditionalGeneration")
 class Qwen3VLTextModel(Qwen3Model):
     model_arch = gguf.MODEL_ARCH.QWEN3VL
@@ -4385,20 +4414,6 @@ class Qwen3VLTextModel(Qwen3Model):
         super().set_gguf_parameters()
 
         # Handle MRoPE (Multi-axis Rotary Position Embedding) for Qwen3-VL
-        text_config = self.hparams.get("text_config", {})
-        # rope_scaling is deprecated in V5, use rope_parameters instead
-        rope_scaling = text_config.get("rope_scaling") or text_config.get("rope_parameters") or {}
-
-        if rope_scaling.get("mrope_section"):
-            # mrope_section contains [time, height, width] dimensions
-            mrope_section = rope_scaling["mrope_section"]
-            # Pad to 4 dimensions [time, height, width, extra]
-            while len(mrope_section) < 4:
-                mrope_section.append(0)
-            self.gguf_writer.add_rope_dimension_sections(mrope_section[:4])
-
-            logger.info(f"MRoPE sections: {mrope_section[:4]}")
-
         vision_config = self.hparams.get("vision_config", {})
         deepstack_layer_num = len(vision_config.get("deepstack_visual_indexes", []))
         self.gguf_writer.add_num_deepstack_layers(deepstack_layer_num)
@@ -4417,22 +4432,6 @@ class Qwen3VLMoeTextModel(Qwen3MoeModel):
 
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
-
-        # Handle MRoPE (Multi-axis Rotary Position Embedding) for Qwen3-VL
-        text_config = self.hparams.get("text_config", {})
-        # rope_scaling is deprecated in V5, use rope_parameters instead
-        rope_scaling = text_config.get("rope_scaling") or text_config.get("rope_parameters") or {}
-
-        if rope_scaling.get("mrope_section"):
-            # mrope_section contains [time, height, width] dimensions
-            mrope_section = rope_scaling["mrope_section"]
-            # Pad to 4 dimensions [time, height, width, extra]
-            while len(mrope_section) < 4:
-                mrope_section.append(0)
-            self.gguf_writer.add_rope_dimension_sections(mrope_section[:4])
-
-            logger.info(f"MRoPE sections: {mrope_section[:4]}")
-
         vision_config = self.hparams.get("vision_config", {})
         deepstack_layer_num = len(vision_config.get("deepstack_visual_indexes", []))
         self.gguf_writer.add_num_deepstack_layers(deepstack_layer_num)
@@ -7795,6 +7794,15 @@ class JaisModel(TextModel):
 @ModelBase.register("Glm4ForCausalLM", "Glm4vForConditionalGeneration")
 class Glm4Model(TextModel):
     model_arch = gguf.MODEL_ARCH.GLM4
+    use_mrope = False
+    partial_rotary_factor = 0.5
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.partial_rotary_factor = self.rope_parameters.get("partial_rotary_factor", 0.5)
+        if "mrope_section" in self.rope_parameters:
+            self.use_mrope = True
+            logger.info("Q/K weight will need to be permuted for M-RoPE")
 
     def set_vocab(self):
         from transformers import AutoTokenizer
@@ -7816,17 +7824,49 @@ class Glm4Model(TextModel):
         super().set_gguf_parameters()
         if (rope_dim := self.hparams.get("head_dim")) is None:
             rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
-        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5)))
+        self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.partial_rotary_factor))
+
+    @staticmethod
+    def normal_to_neox(weights: Tensor, n_head: int, n_head_kv: int, head_dim: int, partial_rotary_factor: float) -> Tensor:
+        orig_shape = weights.shape
+        if len(orig_shape) == 1:
+            weights = weights.unsqueeze(1)  # [out_dim, 1]
+        if len(weights.shape) != 2:
+            raise ValueError("Only 1D and 2D tensors are supported.")
+        n_effective_heads = weights.shape[0] // head_dim
+        if n_head_kv is not None and n_effective_heads != n_head:
+            if n_effective_heads != n_head_kv:
+                raise AssertionError(f"Mismatch in effective heads: computed {n_effective_heads}, expected {n_head} or {n_head_kv}")
+        rotary_dim = int(head_dim * partial_rotary_factor)
+        if rotary_dim % 2 != 0:
+            raise ValueError("rotary_dim must be even.")
+        reshaped = weights.reshape(n_effective_heads, head_dim, -1)
+        rot_part = reshaped[:, :rotary_dim, :]
+        non_rot_part = reshaped[:, rotary_dim:, :]
+        permuted_rot = torch.cat((rot_part[:, ::2, :], rot_part[:, 1::2, :]), dim=1)
+        combined = torch.cat((permuted_rot, non_rot_part), dim=1)
+        result = combined.reshape(weights.shape)
+        return result if len(orig_shape) != 1 else result.squeeze(1)
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         if name.startswith("model.visual."): # ignore visual part of Glm4v
             return []
         elif name.startswith("model.language_model."):
             name = name.replace("language_model.", "") # for Glm4v
+        if self.use_mrope:
+            n_head = self.hparams["num_attention_heads"]
+            n_kv_head = self.hparams["num_key_value_heads"]
+            n_embd = self.hparams["hidden_size"]
+            head_dim = n_embd // n_head
+            # because llama.cpp M-RoPE kernel only supports Neox ordering, we have to permute the weights here
+            if name.endswith(("q_proj.weight", "q_proj.bias")):
+                data_torch = Glm4Model.normal_to_neox(data_torch, n_head, n_head, head_dim, self.partial_rotary_factor)
+            if name.endswith(("k_proj.weight", "k_proj.bias")):
+                data_torch = Glm4Model.normal_to_neox(data_torch, n_head, n_kv_head, head_dim, self.partial_rotary_factor)
         return super().modify_tensors(data_torch, name, bid)
 
 
-@ModelBase.register("Glm4MoeForCausalLM")
+@ModelBase.register("Glm4MoeForCausalLM", "Glm4vMoeForConditionalGeneration")
 class Glm4MoeModel(TextModel):
     model_arch = gguf.MODEL_ARCH.GLM4_MOE
 
@@ -7893,6 +7933,7 @@ class Glm4MoeModel(TextModel):
 
     _experts: list[dict[str, Tensor]] | None = None
 
+    # note: unlike GLM4V non-MoE, we don't need to permute Q/K here since GLM4V_MOE uses Neox ordering already
     def modify_tensors(
         self, data_torch: Tensor, name: str, bid: int | None
     ) -> Iterable[tuple[str, Tensor]]:
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 5ca4efd043..fed0e94dcf 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -643,6 +643,7 @@ class MODEL_TENSOR(IntEnum):
     V_MMPROJ_PEG         = auto()
     V_ENC_EMBD_CLS       = auto()
     V_ENC_EMBD_PATCH     = auto()
+    V_ENC_EMBD_NORM      = auto()
     V_ENC_EMBD_POS       = auto()
     V_ENC_INPUT_NORM     = auto()
     V_ENC_ATTN_QKV       = auto()
@@ -661,6 +662,7 @@ class MODEL_TENSOR(IntEnum):
     V_LAYER_SCALE_2      = auto()
     V_PRE_NORM           = auto()
     V_POST_NORM          = auto()
+    V_MM_POST_NORM       = auto()
     V_MM_INP_NORM        = auto()
     V_MM_INP_PROJ        = auto() # gemma3
     V_MM_SOFT_EMB_NORM   = auto() # gemma3
@@ -1016,6 +1018,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.V_MMPROJ_PEG:              "mm.model.peg.{bid}",
     MODEL_TENSOR.V_ENC_EMBD_CLS:            "v.class_embd",
     MODEL_TENSOR.V_ENC_EMBD_PATCH:          "v.patch_embd",
+    MODEL_TENSOR.V_ENC_EMBD_NORM:           "v.norm_embd",
     MODEL_TENSOR.V_ENC_EMBD_POS:            "v.position_embd",
     MODEL_TENSOR.V_ENC_ATTN_QKV:            "v.blk.{bid}.attn_qkv",
     MODEL_TENSOR.V_ENC_ATTN_Q:              "v.blk.{bid}.attn_q",
@@ -1034,6 +1037,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.V_LAYER_SCALE_2:           "v.blk.{bid}.ls2",
     MODEL_TENSOR.V_PRE_NORM:                "v.pre_ln",
     MODEL_TENSOR.V_POST_NORM:               "v.post_ln",
+    MODEL_TENSOR.V_MM_POST_NORM:            "mm.post_norm",
     MODEL_TENSOR.V_MM_INP_PROJ:             "mm.input_projection",
     MODEL_TENSOR.V_MM_INP_NORM:             "mm.input_norm",
     MODEL_TENSOR.V_MM_SOFT_EMB_NORM:        "mm.soft_emb_norm",
@@ -1094,6 +1098,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.V_MMPROJ_PEG,
         MODEL_TENSOR.V_ENC_EMBD_CLS,
         MODEL_TENSOR.V_ENC_EMBD_PATCH,
+        MODEL_TENSOR.V_ENC_EMBD_NORM,
         MODEL_TENSOR.V_ENC_EMBD_POS,
         MODEL_TENSOR.V_ENC_INPUT_NORM,
         MODEL_TENSOR.V_ENC_ATTN_QKV,
@@ -1112,6 +1117,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.V_LAYER_SCALE_2,
         MODEL_TENSOR.V_PRE_NORM,
         MODEL_TENSOR.V_POST_NORM,
+        MODEL_TENSOR.V_MM_POST_NORM,
         MODEL_TENSOR.V_MM_INP_PROJ,
         MODEL_TENSOR.V_MM_INP_NORM,
         MODEL_TENSOR.V_MM_SOFT_EMB_NORM,
@@ -3357,6 +3363,7 @@ class VisionProjectorType:
     LIGHTONOCR = "lightonocr"
     COGVLM = "cogvlm"
     JANUS_PRO = "janus_pro"
+    GLM4V = "glm4v"
 
 
 # Items here are (block size, type size)
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 610227231f..e04ff11164 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -1212,6 +1212,7 @@ class TensorNameMap:
         MODEL_TENSOR.V_MMPROJ_FC: (
             "model.connector.modality_projection.proj", # SmolVLM
             "model.vision.linear_proj.linear_proj", # cogvlm
+            "visual.merger.proj", # glm4v
         ),
 
         MODEL_TENSOR.V_MMPROJ_MLP: (
@@ -1245,6 +1246,10 @@ class TensorNameMap:
             "model.vision.patch_embedding.proj", # cogvlm
         ),
 
+        MODEL_TENSOR.V_ENC_EMBD_NORM: (
+            "visual.post_conv_layernorm", # glm4v
+        ),
+
         MODEL_TENSOR.V_ENC_EMBD_POS: (
             "vision_tower.vision_model.embeddings.position_embedding",
             "model.vision_tower.embeddings.position_embeddings", # Intern-S1
@@ -1254,6 +1259,7 @@ class TensorNameMap:
             "vision_tower.patch_embed.pos_emb", # kimi-vl
             "visual.pos_embed", # qwen3vl
             "model.vision.patch_embedding.position_embedding", # cogvlm
+            "visual.embeddings.position_embedding", # glm4v
         ),
 
         MODEL_TENSOR.V_ENC_ATTN_QKV: (
@@ -1409,6 +1415,11 @@ class TensorNameMap:
             "vision_model.layernorm_post", # llama4
             "visual.merger.ln_q", # qwen2vl
             "vision_tower.encoder.final_layernorm", # kimi-vl
+            "visual.post_layernorm", # glm4v
+        ),
+
+        MODEL_TENSOR.V_MM_POST_NORM: (
+            "visual.merger.post_projection_norm", # glm4v
         ),
 
         MODEL_TENSOR.V_MM_INP_PROJ: (
@@ -1478,6 +1489,7 @@ class TensorNameMap:
         MODEL_TENSOR.V_MM_PATCH_MERGER: (
             "multi_modal_projector.patch_merger.merging_layer", # mistral small 3.1 - hf
             "patch_merger.merging_layer", # mistral
+            "visual.downsample", # glm4v
         ),
 
         MODEL_TENSOR.V_DS_NORM: (
@@ -1498,14 +1510,17 @@ class TensorNameMap:
 
         MODEL_TENSOR.V_MM_UP: (
             "model.vision.linear_proj.dense_h_to_4h", # cogvlm
+            "visual.merger.up_proj", # glm4v
         ),
 
         MODEL_TENSOR.V_MM_DOWN: (
             "model.vision.linear_proj.dense_4h_to_h", # cogvlm
+            "visual.merger.down_proj", # glm4v
         ),
 
         MODEL_TENSOR.V_MM_GATE: (
             "model.vision.linear_proj.gate_proj", # cogvlm
+            "visual.merger.gate_proj", # glm4v
         ),
 
         MODEL_TENSOR.V_TOK_BOI: (
diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp
index 83d6d6ee3c..fe1fa4341d 100644
--- a/src/llama-hparams.cpp
+++ b/src/llama-hparams.cpp
@@ -231,3 +231,7 @@ bool llama_hparams::is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama
 
     return false;
 }
+
+bool llama_hparams::use_mrope() const {
+    return rope_sections[0] > 0 && rope_sections[1] > 0;
+}
diff --git a/src/llama-hparams.h b/src/llama-hparams.h
index cecb476e91..f6e95b5d2a 100644
--- a/src/llama-hparams.h
+++ b/src/llama-hparams.h
@@ -270,6 +270,8 @@ struct llama_hparams {
     // TODO: think of a better place for this function
     // TODO: pack the SWA params in a struct?
     static bool is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama_pos p0, llama_pos p1);
+
+    bool use_mrope() const;
 };
 
 static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 15547403b4..ae8207ee1a 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1689,7 +1689,8 @@ void llama_model::load_hparams(llama_model_loader & ml) {
             } break;
         case LLM_ARCH_GLM4:
             {
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,    hparams.f_norm_rms_eps);
+                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
                 switch (hparams.n_layer) {
                     case 40: type = LLM_TYPE_9B; break;
                     case 61: type = LLM_TYPE_32B; break;
@@ -1698,8 +1699,9 @@ void llama_model::load_hparams(llama_model_loader & ml) {
             } break;
         case LLM_ARCH_GLM4_MOE:
             {
-                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,  hparams.n_ff_exp);
-                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH,     hparams.n_ff_exp);
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,    hparams.f_norm_rms_eps);
+                ml.get_key_or_arr(LLM_KV_ROPE_DIMENSION_SECTIONS, hparams.rope_sections, 4, false);
 
                 // MoE parameters
                 ml.get_key(LLM_KV_EXPERT_COUNT,                hparams.n_expert);
@@ -7792,7 +7794,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_DEEPSEEK2:
         case LLM_ARCH_PLM:
         case LLM_ARCH_CHATGLM:
-        case LLM_ARCH_GLM4:
         case LLM_ARCH_GRANITE:
         case LLM_ARCH_GRANITE_MOE:
         case LLM_ARCH_GRANITE_HYBRID:
@@ -7854,7 +7855,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_LFM2:
         case LLM_ARCH_LFM2MOE:
         case LLM_ARCH_SMALLTHINKER:
-        case LLM_ARCH_GLM4_MOE:
         case LLM_ARCH_SEED_OSS:
         case LLM_ARCH_GROVEMOE:
         case LLM_ARCH_APERTUS:
@@ -7871,6 +7871,11 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_QWEN3VLMOE:
             return LLAMA_ROPE_TYPE_IMROPE;
 
+        case LLM_ARCH_GLM4:
+            return model->hparams.use_mrope() ? LLAMA_ROPE_TYPE_MROPE : LLAMA_ROPE_TYPE_NORM;
+        case LLM_ARCH_GLM4_MOE:
+            return model->hparams.use_mrope() ? LLAMA_ROPE_TYPE_MROPE : LLAMA_ROPE_TYPE_NEOX;
+
         // all model arches should be listed explicitly here
         case LLM_ARCH_UNKNOWN:
             GGML_ABORT("unknown architecture");
diff --git a/src/models/glm4-moe.cpp b/src/models/glm4-moe.cpp
index 33ee707046..003f70f739 100644
--- a/src/models/glm4-moe.cpp
+++ b/src/models/glm4-moe.cpp
@@ -5,11 +5,20 @@ llm_build_glm4_moe::llm_build_glm4_moe(const llama_model & model, const llm_grap
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
+    int sections[4];
+    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
     ggml_tensor * cur;
     ggml_tensor * inpL;
 
     inpL = build_inp_embd(model.tok_embd);
 
+    bool use_mrope = hparams.use_mrope();
+    if (ubatch.embd && !use_mrope) {
+        // unfortunately, we need to forcefully stop here, to avoid users complaining about wrong results
+        GGML_ABORT("This GGUF does not support multimodal. Please reconvert it.");
+    }
+
     // inp_pos - contains the positions
     ggml_tensor * inp_pos = build_inp_pos();
 
@@ -60,17 +69,25 @@ llm_build_glm4_moe::llm_build_glm4_moe(const llama_model & model, const llm_grap
                 Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
                 cb(Kcur, "Kcur_normed", il);
             }
-            Qcur = ggml_rope_ext(
-                    ctx0, Qcur, inp_pos, nullptr,
-                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                    ext_factor, attn_factor, beta_fast, beta_slow
-                    );
 
-            Kcur = ggml_rope_ext(
-                    ctx0, Kcur, inp_pos, nullptr,
-                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                    ext_factor, attn_factor, beta_fast, beta_slow
-                    );
+            if (use_mrope) {
+                Qcur = ggml_rope_multi(ctx0, Qcur, inp_pos, nullptr,
+                            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow);
+
+                Kcur = ggml_rope_multi(ctx0, Kcur, inp_pos, nullptr,
+                            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow);
+            } else {
+                // Normal RoPE
+                Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot,
+                                    rope_type, n_ctx_orig, freq_base, freq_scale,
+                                    ext_factor, attn_factor, beta_fast, beta_slow);
+
+                Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot,
+                                    rope_type, n_ctx_orig, freq_base, freq_scale,
+                                    ext_factor, attn_factor, beta_fast, beta_slow);
+            }
 
             cb(Qcur, "Qcur", il);
             cb(Kcur, "Kcur", il);
diff --git a/src/models/glm4.cpp b/src/models/glm4.cpp
index f789b28248..204aa3932a 100644
--- a/src/models/glm4.cpp
+++ b/src/models/glm4.cpp
@@ -8,11 +8,20 @@ llm_build_glm4::llm_build_glm4(const llama_model & model, const llm_graph_params
 
     GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
 
+    int sections[4];
+    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
     ggml_tensor * cur;
     ggml_tensor * inpL;
 
     inpL = build_inp_embd(model.tok_embd);
 
+    bool use_mrope = hparams.use_mrope();
+    if (ubatch.embd && !use_mrope) {
+        // unfortunately, we need to forcefully stop here, to avoid users complaining about wrong results
+        GGML_ABORT("This GGUF does not support multimodal. Please reconvert it.");
+    }
+
     // inp_pos - contains the positions
     ggml_tensor * inp_pos = build_inp_pos();
 
@@ -63,11 +72,25 @@ llm_build_glm4::llm_build_glm4(const llama_model & model, const llm_graph_params
                 Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),
                                     cur->nb[1], 1 * sizeof(float) * (n_embd + n_embd_gqa));
             }
-            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                                 ext_factor, attn_factor, beta_fast, beta_slow);
 
-            Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                                 ext_factor, attn_factor, beta_fast, beta_slow);
+            if (use_mrope) {
+                Qcur = ggml_rope_multi(ctx0, Qcur, inp_pos, nullptr,
+                            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow);
+
+                Kcur = ggml_rope_multi(ctx0, Kcur, inp_pos, nullptr,
+                            n_rot, sections, rope_type, n_ctx_orig, freq_base, freq_scale,
+                            ext_factor, attn_factor, beta_fast, beta_slow);
+            } else {
+                // Normal RoPE
+                Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot,
+                                    rope_type, n_ctx_orig, freq_base, freq_scale,
+                                    ext_factor, attn_factor, beta_fast, beta_slow);
+
+                Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot,
+                                    rope_type, n_ctx_orig, freq_base, freq_scale,
+                                    ext_factor, attn_factor, beta_fast, beta_slow);
+            }
 
             cb(Qcur, "Qcur", il);
             cb(Kcur, "Kcur", il);
diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt
index 3ee42036fd..e7f3067a16 100644
--- a/tools/mtmd/CMakeLists.txt
+++ b/tools/mtmd/CMakeLists.txt
@@ -15,6 +15,7 @@ add_library(mtmd
             clip-graph.h
             models/models.h
             models/cogvlm.cpp
+            models/glm4v.cpp
             models/internvl.cpp
             models/kimivl.cpp
             models/llama4.cpp
diff --git a/tools/mtmd/clip-graph.h b/tools/mtmd/clip-graph.h
index 17f90e8aa8..2b1915779f 100644
--- a/tools/mtmd/clip-graph.h
+++ b/tools/mtmd/clip-graph.h
@@ -9,6 +9,8 @@
 #include <vector>
 #include <functional>
 
+#define DEFAULT_INTERPOLATION_MODE (GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS)
+
 struct clip_graph {
     const clip_model & model;
     const clip_hparams & hparams;
@@ -49,7 +51,7 @@ struct clip_graph {
     void cb(ggml_tensor * cur0, const char * name, int il) const;
 
     // siglip2 naflex
-    ggml_tensor * resize_position_embeddings();
+    ggml_tensor * resize_position_embeddings(uint32_t interpolation_mode = DEFAULT_INTERPOLATION_MODE);
 
     // build vision transformer (ViT) cgraph
     // this function should cover most of the models
diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h
index 9ef0d301e4..d75233cc0a 100644
--- a/tools/mtmd/clip-impl.h
+++ b/tools/mtmd/clip-impl.h
@@ -68,6 +68,7 @@
 #define TN_PATCH_EMBD      "v.patch_embd.weight"  // not rename tensor with ".0" postfix for backwrad compat
 #define TN_PATCH_EMBD_1    "v.patch_embd.weight.1"
 #define TN_PATCH_BIAS      "v.patch_embd.bias"
+#define TN_NORM_EMBD       "v.norm_embd.%s"
 #define TN_ATTN_QKV        "%s.blk.%d.attn_qkv.%s"
 #define TN_ATTN_K          "%s.blk.%d.attn_k.%s"
 #define TN_ATTN_Q          "%s.blk.%d.attn_q.%s"
@@ -86,6 +87,10 @@
 #define TN_LN_PRE          "%s.pre_ln.%s"
 #define TN_LN_POST         "%s.post_ln.%s"
 #define TN_LLAVA_PROJ      "mm.%d.%s"
+#define TN_MM_UP           "mm.up.%s"
+#define TN_MM_GATE         "mm.gate.%s"
+#define TN_MM_DOWN         "mm.down.%s"
+#define TN_MM_POST_NORM    "mm.post_norm.%s"
 #define TN_MVLM_PROJ_MLP   "mm.model.mlp.%d.%s"
 #define TN_MVLM_PROJ_BLOCK "mm.model.mb_block.%d.block.%d.%s"
 #define TN_MVLM_PROJ_PEG   "mm.model.peg.%d.%s"
@@ -95,7 +100,7 @@
 #define TN_MM_INP_PROJ     "mm.input_projection.weight" // gemma3
 #define TN_MM_SOFT_EMB_N   "mm.soft_emb_norm.weight"    // gemma3
 #define TN_MM_PROJECTOR    "mm.model.fc.weight"         // idefics3
-#define TN_MM_PATCH_MERGER "mm.patch_merger.weight"     // mistral small 3.1
+#define TN_MM_PATCH_MERGER "mm.patch_merger.%s"         // mistral small 3.1, glm4v
 #define TN_TOK_IMG_BREAK   "v.token_embd.img_break"     // pixtral
 #define TN_TOK_GLM_BOI     "adapter.boi"                // glm-edge (these embeddings are not in text model)
 #define TN_TOK_GLM_EOI     "adapter.eoi"                // glm-edge (these embeddings are not in text model)
@@ -165,6 +170,7 @@ enum projector_type {
     PROJECTOR_TYPE_LIGHTONOCR,
     PROJECTOR_TYPE_COGVLM,
     PROJECTOR_TYPE_JANUS_PRO,
+    PROJECTOR_TYPE_GLM4V,
     PROJECTOR_TYPE_UNKNOWN,
 };
 
@@ -192,6 +198,7 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
     { PROJECTOR_TYPE_LIGHTONOCR,"lightonocr"},
     { PROJECTOR_TYPE_COGVLM,    "cogvlm"},
     { PROJECTOR_TYPE_JANUS_PRO, "janus_pro"},
+    { PROJECTOR_TYPE_GLM4V,     "glm4v"},
 };
 
 static projector_type clip_projector_type_from_string(const std::string & str) {
@@ -495,6 +502,8 @@ static void print_tensor_data(ggml_tensor * t, uint8_t * data, int64_t n) {
     }
 }
 
+void clip_debug_encode(clip_ctx * ctx, int h, int w, float fill_value);
+
 //
 // API used internally with mtmd
 //
diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h
index 2f7dbb458e..f5c41ff138 100644
--- a/tools/mtmd/clip-model.h
+++ b/tools/mtmd/clip-model.h
@@ -158,6 +158,8 @@ struct clip_model {
     ggml_tensor * patch_embeddings_1 = nullptr;  // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL)
     ggml_tensor * patch_bias = nullptr;
     ggml_tensor * position_embeddings = nullptr;
+    ggml_tensor * norm_embd_w = nullptr;
+    ggml_tensor * norm_embd_b = nullptr;
 
     ggml_tensor * pre_ln_w = nullptr;
     ggml_tensor * pre_ln_b = nullptr;
@@ -172,6 +174,14 @@ struct clip_model {
     ggml_tensor * projection; // TODO: rename it to fc (fully connected layer)
     ggml_tensor * mm_fc_w;
     ggml_tensor * mm_fc_b;
+    ggml_tensor * mm_ffn_up_w = nullptr;
+    ggml_tensor * mm_ffn_up_b = nullptr;
+    ggml_tensor * mm_ffn_gate_w = nullptr;
+    ggml_tensor * mm_ffn_gate_b = nullptr;
+    ggml_tensor * mm_ffn_down_w = nullptr;
+    ggml_tensor * mm_ffn_down_b = nullptr;
+    ggml_tensor * mm_post_norm_w = nullptr;
+    ggml_tensor * mm_post_norm_b = nullptr;
 
     // LLaVA projection
     ggml_tensor * mm_input_norm_w = nullptr;
@@ -253,9 +263,10 @@ struct clip_model {
     ggml_tensor * mm_input_proj_w = nullptr;
     ggml_tensor * mm_soft_emb_norm_w = nullptr;
 
-    // pixtral
+    // pixtral, glm4v
     ggml_tensor * token_embd_img_break = nullptr;
     ggml_tensor * mm_patch_merger_w = nullptr;
+    ggml_tensor * mm_patch_merger_b = nullptr;
 
     // ultravox / whisper encoder
     ggml_tensor * conv1d_1_w = nullptr;
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index fee49e465c..35e3aef0ab 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -264,11 +264,11 @@ void clip_graph::cb(ggml_tensor * cur0, const char * name, int il) const {
 }
 
 // siglip2 naflex
-ggml_tensor * clip_graph::resize_position_embeddings() {
+ggml_tensor * clip_graph::resize_position_embeddings(uint32_t interpolation_mode) {
     ggml_tensor * pos_embd = model.position_embeddings;
     const int height       = img.ny / patch_size;
     const int width        = img.nx / patch_size;
-    const uint32_t mode    = GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS;
+    const uint32_t mode    = interpolation_mode;
     const int n_per_side   = (int)std::sqrt(pos_embd->ne[1]);
 
     GGML_ASSERT(pos_embd);
@@ -485,19 +485,14 @@ ggml_tensor * clip_graph::build_norm(
         ? ggml_rms_norm(ctx0, cur, norm_eps)
         : ggml_norm(ctx0, cur, norm_eps);
 
-    if (mw || mb) {
-        cb(cur, "norm", il);
-    }
-
     if (mw) {
         cur = ggml_mul(ctx0, cur, mw);
-        if (mb) {
-            cb(cur, "norm_w", il);
-        }
+        cb(cur, "norm_w", il);
     }
 
     if (mb) {
         cur = ggml_add(ctx0, cur, mb);
+        cb(cur, "norm_b", il);
     }
 
     return cur;
@@ -842,6 +837,10 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
             {
                 builder = std::make_unique<clip_graph_llava>(ctx, img);
             } break;
+        case PROJECTOR_TYPE_GLM4V:
+            {
+                builder = std::make_unique<clip_graph_glm4v>(ctx, img);
+            } break;
         default:
             GGML_ABORT("missing cgraph builder");
     }
@@ -1155,6 +1154,14 @@ struct clip_model_loader {
                             LOG_WRN("%s: more info: https://github.com/ggml-org/llama.cpp/issues/16842\n\n", __func__);
                         }
                     } break;
+                case PROJECTOR_TYPE_GLM4V:
+                    {
+                        hparams.rope_theta = 10000.0f;
+                        hparams.n_merge = 2; // default value for GLM4-V
+                        get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false);
+                        hparams.set_limit_image_tokens(8, 4096);
+                        hparams.set_warmup_n_tokens(46*46); // avoid OOM on warmup
+                    } break;
                 case PROJECTOR_TYPE_LLAMA4:
                     {
                         hparams.rope_theta = 10000.0f;
@@ -1282,6 +1289,9 @@ struct clip_model_loader {
         model.patch_embeddings_0 = get_tensor(TN_PATCH_EMBD,   false);
         model.patch_embeddings_1 = get_tensor(TN_PATCH_EMBD_1, false);
 
+        model.norm_embd_w = get_tensor(string_format(TN_NORM_EMBD, "weight"), false);
+        model.norm_embd_b = get_tensor(string_format(TN_NORM_EMBD, "bias"),   false);
+
         model.position_embeddings = get_tensor(string_format(TN_POS_EMBD, prefix), false);
 
         // layers
@@ -1470,6 +1480,20 @@ struct clip_model_loader {
                     model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));
                     model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"));
                 } break;
+            case PROJECTOR_TYPE_GLM4V:
+                {
+                    model.projection     = get_tensor(TN_MM_PROJECTOR);
+                    model.mm_ffn_up_w    = get_tensor(string_format(TN_MM_UP,        "weight"));
+                    model.mm_ffn_up_b    = get_tensor(string_format(TN_MM_UP,        "bias"), false);
+                    model.mm_ffn_gate_w  = get_tensor(string_format(TN_MM_GATE,      "weight"));
+                    model.mm_ffn_gate_b  = get_tensor(string_format(TN_MM_GATE,      "bias"), false);
+                    model.mm_ffn_down_w  = get_tensor(string_format(TN_MM_DOWN,      "weight"));
+                    model.mm_ffn_down_b  = get_tensor(string_format(TN_MM_DOWN,      "bias"), false);
+                    model.mm_post_norm_w = get_tensor(string_format(TN_MM_POST_NORM, "weight"));
+                    model.mm_post_norm_b = get_tensor(string_format(TN_MM_POST_NORM, "bias"), false);
+                    model.mm_patch_merger_w = get_tensor(string_format(TN_MM_PATCH_MERGER, "weight"));
+                    model.mm_patch_merger_b = get_tensor(string_format(TN_MM_PATCH_MERGER, "bias"));
+                } break;
             case PROJECTOR_TYPE_GEMMA3:
                 {
                     model.mm_input_proj_w = get_tensor(TN_MM_INP_PROJ);
@@ -1498,8 +1522,8 @@ struct clip_model_loader {
                     // [IMG_BREAK] token embedding
                     model.token_embd_img_break = get_tensor(TN_TOK_IMG_BREAK);
                     // for mistral small 3.1
-                    model.mm_input_norm_w   = get_tensor(TN_MM_INP_NORM,     false);
-                    model.mm_patch_merger_w = get_tensor(TN_MM_PATCH_MERGER, false);
+                    model.mm_input_norm_w   = get_tensor(TN_MM_INP_NORM, false);
+                    model.mm_patch_merger_w = get_tensor(string_format(TN_MM_PATCH_MERGER, "weight"), false);
                 } break;
             case PROJECTOR_TYPE_LIGHTONOCR:
                 {
@@ -1507,8 +1531,8 @@ struct clip_model_loader {
                     model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 1, "bias"), false);
                     model.mm_2_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));
                     model.mm_2_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"), false);
-                    model.mm_input_norm_w   = get_tensor(TN_MM_INP_NORM,     false);
-                    model.mm_patch_merger_w = get_tensor(TN_MM_PATCH_MERGER, false);
+                    model.mm_input_norm_w   = get_tensor(TN_MM_INP_NORM, false);
+                    model.mm_patch_merger_w = get_tensor(string_format(TN_MM_PATCH_MERGER, "weight"), false);
                 } break;
             case PROJECTOR_TYPE_ULTRAVOX:
                 {
@@ -1873,6 +1897,8 @@ struct clip_init_result clip_init(const char * fname, struct clip_context_params
             if (ctx_params.warmup) {
                 loader.warmup(*ctx_vision);
             }
+
+            // clip_debug_encode(ctx_vision, 24*14, 24*14, 0.5f);
         }
 
         if (loader.has_audio) {
@@ -2582,6 +2608,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
         case PROJECTOR_TYPE_QWEN2VL:
         case PROJECTOR_TYPE_QWEN25VL:
         case PROJECTOR_TYPE_QWEN3VL:
+        case PROJECTOR_TYPE_GLM4V:
             {
                 GGML_ASSERT(params.image_min_pixels > 0 && params.image_max_pixels > 0);
                 clip_image_u8 resized;
@@ -2824,16 +2851,30 @@ const char * clip_patch_merge_type(const struct clip_ctx * ctx) {
 int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
     const auto & params = ctx->model.hparams;
     const int n_total = clip_n_output_tokens(ctx, img);
-    if (ctx->proj_type() == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type() == PROJECTOR_TYPE_QWEN25VL || ctx->proj_type() == PROJECTOR_TYPE_QWEN3VL) {
-        return img->nx / (params.patch_size * 2);
+    const auto & proj = ctx->proj_type();
+    switch (proj) {
+        case PROJECTOR_TYPE_QWEN2VL:
+        case PROJECTOR_TYPE_QWEN25VL:
+        case PROJECTOR_TYPE_QWEN3VL:
+        case PROJECTOR_TYPE_GLM4V:
+            return (img->nx / params.patch_size) / 2;
+        default:
+            break;
     }
     return n_total;
 }
 
 int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
     const auto & params = ctx->model.hparams;
-    if (ctx->proj_type() == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type() == PROJECTOR_TYPE_QWEN25VL || ctx->proj_type() == PROJECTOR_TYPE_QWEN3VL) {
-        return img->ny / (params.patch_size * 2);
+    const auto & proj = ctx->proj_type();
+    switch (proj) {
+        case PROJECTOR_TYPE_QWEN2VL:
+        case PROJECTOR_TYPE_QWEN25VL:
+        case PROJECTOR_TYPE_QWEN3VL:
+        case PROJECTOR_TYPE_GLM4V:
+            return (img->ny / params.patch_size) / 2;
+        default:
+            break;
     }
     return 1;
 }
@@ -2890,6 +2931,7 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
         case PROJECTOR_TYPE_QWEN2VL:
         case PROJECTOR_TYPE_QWEN25VL:
         case PROJECTOR_TYPE_QWEN3VL:
+        case PROJECTOR_TYPE_GLM4V:
             {
                 // dynamic size (2 conv, so double patch size)
                 int x_patch = img->nx / (params.patch_size * 2);
@@ -3137,6 +3179,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
             } break;
         case PROJECTOR_TYPE_QWEN2VL:
         case PROJECTOR_TYPE_QWEN3VL:
+        case PROJECTOR_TYPE_GLM4V:
             {
                 const int merge_ratio = hparams.n_merge;
                 const int pw = image_size_width  / patch_size;
@@ -3363,7 +3406,9 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
     }
 
     // copy the embeddings to the location passed by the user
-    ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
+    if (vec != nullptr) {
+        ggml_backend_tensor_get(embeddings, vec, 0, ggml_nbytes(embeddings));
+    }
 
     return true;
 }
@@ -3411,6 +3456,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
             return ctx->model.mm_2_w->ne[1];
         case PROJECTOR_TYPE_COGVLM:
             return ctx->model.mm_4h_to_h_w->ne[1];
+        case PROJECTOR_TYPE_GLM4V:
+            return ctx->model.mm_ffn_down_w->ne[1];
         default:
             GGML_ABORT("Unknown projector type");
     }
@@ -3427,10 +3474,11 @@ bool clip_is_glm(const struct clip_ctx * ctx) {
     return ctx->proj_type() == PROJECTOR_TYPE_GLM_EDGE;
 }
 
-bool clip_is_qwen2vl(const struct clip_ctx * ctx) {
+bool clip_is_mrope(const struct clip_ctx * ctx) {
     return ctx->proj_type() == PROJECTOR_TYPE_QWEN2VL
         || ctx->proj_type() == PROJECTOR_TYPE_QWEN25VL
-        || ctx->proj_type() == PROJECTOR_TYPE_QWEN3VL;
+        || ctx->proj_type() == PROJECTOR_TYPE_QWEN3VL
+        || ctx->proj_type() == PROJECTOR_TYPE_GLM4V;
 }
 
 bool clip_is_llava(const struct clip_ctx * ctx) {
@@ -3491,3 +3539,22 @@ void clip_image_f32_batch_add_mel(struct clip_image_f32_batch * batch, int n_mel
 const clip_hparams * clip_get_hparams(const struct clip_ctx * ctx) {
     return &ctx->model.hparams;
 }
+
+//
+// API for debugging
+//
+
+void clip_debug_encode(clip_ctx * ctx, int h, int w, float fill_value) {
+    clip_image_f32 img;
+    img.nx = w;
+    img.ny = h;
+    img.buf.resize(h * w * 3);
+    for (int i = 0; i < h * w * 3; i++) {
+        img.buf[i] = static_cast<float>(fill_value);
+    }
+    bool cur_debug_graph = ctx->debug_graph;
+    ctx->debug_graph = true;
+    clip_image_encode(ctx, 1, &img, nullptr);
+    ctx->debug_graph = cur_debug_graph;
+    GGML_ASSERT(img.buf.empty() && "expected, always stop here");
+}
diff --git a/tools/mtmd/clip.h b/tools/mtmd/clip.h
index 5aae27a700..68a0d6e857 100644
--- a/tools/mtmd/clip.h
+++ b/tools/mtmd/clip.h
@@ -104,7 +104,7 @@ bool clip_image_batch_encode(struct clip_ctx * ctx, int n_threads, const struct
 
 int clip_is_minicpmv(const struct clip_ctx * ctx);
 bool clip_is_glm(const struct clip_ctx * ctx);
-bool clip_is_qwen2vl(const struct clip_ctx * ctx);
+bool clip_is_mrope(const struct clip_ctx * ctx);
 bool clip_is_llava(const struct clip_ctx * ctx);
 bool clip_is_gemma3(const struct clip_ctx * ctx);
 
diff --git a/tools/mtmd/models/glm4v.cpp b/tools/mtmd/models/glm4v.cpp
new file mode 100644
index 0000000000..f39b6922eb
--- /dev/null
+++ b/tools/mtmd/models/glm4v.cpp
@@ -0,0 +1,120 @@
+#include "models.h"
+
+ggml_cgraph * clip_graph_glm4v::build() {
+    GGML_ASSERT(model.patch_bias != nullptr);
+    GGML_ASSERT(model.position_embeddings != nullptr);
+    GGML_ASSERT(model.class_embedding == nullptr);
+
+    const int batch_size = 1;
+
+    norm_type norm_t = NORM_TYPE_RMS;
+
+    ggml_tensor * inp_raw = build_inp_raw();
+    ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+
+    int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
+    ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches * 4);
+    ggml_set_name(positions, "positions");
+    ggml_set_input(positions);
+
+    GGML_ASSERT(img.nx % (patch_size * 2) == 0);
+    GGML_ASSERT(img.ny % (patch_size * 2) == 0);
+
+    // second conv dimension
+    {
+        auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1);
+        inp = ggml_add(ctx0, inp, inp_1);
+
+        inp = ggml_permute(ctx0, inp, 1, 2, 0, 3);  // [w, h, c, b] -> [c, w, h, b]
+        inp = ggml_cont_4d(
+            ctx0, inp,
+            n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
+        inp = ggml_reshape_4d(
+            ctx0, inp,
+            n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
+        inp = ggml_permute(ctx0, inp, 0, 2, 1, 3);
+        inp = ggml_cont_3d(
+            ctx0, inp,
+            n_embd, n_patches_x * n_patches_y, batch_size);
+    }
+
+    // add patch bias
+    inp = ggml_add(ctx0, inp, model.patch_bias);
+    cb(inp, "patch_bias", -1);
+
+    // pos-conv norm
+    inp = build_norm(inp, model.norm_embd_w, model.norm_embd_b, norm_t, eps, -1);
+
+    // calculate absolute position embedding and apply
+    ggml_tensor * learned_pos_embd = resize_position_embeddings(GGML_SCALE_MODE_BICUBIC);
+    learned_pos_embd = ggml_cont_4d(
+        ctx0, learned_pos_embd,
+        n_embd * 2, n_patches_x / 2, n_patches_y, batch_size);
+    learned_pos_embd = ggml_reshape_4d(
+        ctx0, learned_pos_embd,
+        n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2));
+    learned_pos_embd = ggml_permute(ctx0, learned_pos_embd, 0, 2, 1, 3);
+    learned_pos_embd = ggml_cont_3d(
+        ctx0, learned_pos_embd,
+        n_embd, n_patches_x * n_patches_y, batch_size);
+    cb(learned_pos_embd, "learned_pos_embd", -1);
+
+    auto add_pos = [&](ggml_tensor * cur, const clip_layer &) {
+        return ggml_rope_multi(
+                    ctx0, cur, positions, nullptr,
+                    d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION,
+                    32768, hparams.rope_theta, 1, 0, 1, 32, 1);
+    };
+
+    ggml_tensor * cur = build_vit(
+                            inp, n_patches,
+                            norm_t,
+                            hparams.ffn_op,
+                            learned_pos_embd,
+                            add_pos);
+
+    cb(cur, "vit_out", -1);
+    // cb(ggml_sum(ctx0, cur), "vit_out_sum", -1);
+
+    // GLM4V projector
+    // ref: https://github.com/huggingface/transformers/blob/40dc11cd3eb4126652aa41ef8272525affd4a636/src/transformers/models/glm4v/modeling_glm4v.py#L116-L130
+
+    // patch merger (downsample)
+    {
+        int n_merge = hparams.n_merge;
+        GGML_ASSERT(n_merge > 0);
+
+        int n_token_out = n_patches / n_merge / n_merge;
+        cur = ggml_reshape_4d(ctx0, cur, n_embd, n_merge, n_merge, n_token_out);
+        cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 2, 0, 1, 3)); // [n_merge, n_merge, n_embd, n_token_out]
+        cur = ggml_conv_2d(ctx0, model.mm_patch_merger_w, cur, n_merge, n_merge, 0, 0, 1, 1);
+        cur = ggml_reshape_2d(ctx0, cur, cur->ne[2], n_token_out); // [n_embd_out, n_token_out]
+
+        cur = ggml_add(ctx0, cur, model.mm_patch_merger_b);
+    }
+
+    // FC projector
+    {
+        cur = ggml_mul_mat(ctx0, model.projection, cur);
+        // default LayerNorm (post_projection_norm)
+        cur = build_norm(cur, model.mm_post_norm_w, model.mm_post_norm_b, NORM_TYPE_NORMAL, 1e-5, -1);
+        cur = ggml_gelu_erf(ctx0, cur);
+        cb(cur, "after_fc_proj", -1);
+    }
+
+    // FFN projector
+    {
+        cur = build_ffn(cur,
+            model.mm_ffn_up_w, model.mm_ffn_up_b,
+            model.mm_ffn_gate_w, model.mm_ffn_gate_b,
+            model.mm_ffn_down_w, model.mm_ffn_down_b,
+            hparams.ffn_op, -1);
+        cb(cur, "after_ffn_proj", -1);
+        // cb(ggml_sum(ctx0, cur), "merged_sum", -1);
+    }
+
+    // build the graph
+    ggml_build_forward_expand(gf, cur);
+
+    return gf;
+}
diff --git a/tools/mtmd/models/models.h b/tools/mtmd/models/models.h
index 4b35da259c..0496d6b22f 100644
--- a/tools/mtmd/models/models.h
+++ b/tools/mtmd/models/models.h
@@ -56,3 +56,8 @@ struct clip_graph_whisper_enc : clip_graph {
     clip_graph_whisper_enc(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
     ggml_cgraph * build() override;
 };
+
+struct clip_graph_glm4v : clip_graph {
+    clip_graph_glm4v(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
+    ggml_cgraph * build() override;
+};
diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp
index c63f299cd9..2638fe4fc5 100644
--- a/tools/mtmd/mtmd.cpp
+++ b/tools/mtmd/mtmd.cpp
@@ -217,7 +217,7 @@ struct mtmd_context {
 
     void init_vision() {
         GGML_ASSERT(ctx_v != nullptr);
-        use_mrope = clip_is_qwen2vl(ctx_v);
+        use_mrope = clip_is_mrope(ctx_v);
 
         projector_type proj = clip_get_projector_type(ctx_v);
         int minicpmv_version = clip_is_minicpmv(ctx_v);
@@ -309,6 +309,10 @@ struct mtmd_context {
             img_beg = "<|image_start|>";
             img_end = "<|image_end|>";
 
+        } else if (proj == PROJECTOR_TYPE_GLM4V) {
+            img_beg = "<|begin_of_image|>";
+            img_end = "<|end_of_image|>";
+
         }
     }
 

From 5f5f9b46376ac14d7f95b0d968c182f522602880 Mon Sep 17 00:00:00 2001
From: 2114L3 <2114L3@users.noreply.github.com>
Date: Tue, 16 Dec 2025 20:50:43 +1000
Subject: [PATCH 129/135] server: Update README.md incorrect argument (#18073)

n-gpu-layer is incorrect
argument is n-gpu-layers with the 's'
---
 tools/server/README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/server/README.md b/tools/server/README.md
index 073bcd2ccd..ef4990faf1 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -1430,7 +1430,7 @@ Model presets allow advanced users to define custom configurations using an `.in
 llama-server --models-preset ./my-models.ini
 ```
 
-Each section in the file defines a new preset. Keys within a section correspond to command-line arguments (without leading dashes). For example, the argument `--n-gpu-layer 123` is written as `n-gpu-layer = 123`.
+Each section in the file defines a new preset. Keys within a section correspond to command-line arguments (without leading dashes). For example, the argument `--n-gpu-layers 123` is written as `n-gpu-layers = 123`.
 
 Short argument forms (e.g., `c`, `ngl`) and environment variable names (e.g., `LLAMA_ARG_N_GPU_LAYERS`) are also supported as keys.
 
@@ -1445,7 +1445,7 @@ version = 1
 ; string value
 chat-template = chatml
 ; numeric value
-n-gpu-layer = 123
+n-gpu-layers = 123
 ; flag value (for certain flags, you need to use the "no-" prefix for negation)
 jinja = true
 ; shorthand argument (for example, context size)

From fb644247de14c616b10deb5e6b17e6f4230f0601 Mon Sep 17 00:00:00 2001
From: Andrew Aladjev <aladjev.andrew@gmail.com>
Date: Tue, 16 Dec 2025 13:52:23 +0300
Subject: [PATCH 130/135] CLI: fixed adding cli and completion into docker
 containers, improved docs (#18003)

Co-authored-by: Andrew Aladjev <andrew.aladjev@gmail.com>
---
 .devops/cann.Dockerfile           |  2 +-
 .devops/llama-cli-cann.Dockerfile |  5 +++--
 .devops/llama-cpp-cuda.srpm.spec  |  2 ++
 .devops/llama-cpp.srpm.spec       |  2 ++
 docs/docker.md                    | 26 +++++++++++++++-----------
 5 files changed, 23 insertions(+), 14 deletions(-)

diff --git a/.devops/cann.Dockerfile b/.devops/cann.Dockerfile
index 83182c9700..db221b0b81 100644
--- a/.devops/cann.Dockerfile
+++ b/.devops/cann.Dockerfile
@@ -107,7 +107,7 @@ ENTRYPOINT ["/app/tools.sh"]
 # ENTRYPOINT ["/app/llama-server"]
 
 ### Target: light
-# Lightweight image containing only llama-cli
+# Lightweight image containing only llama-cli and llama-completion
 # ==============================================================================
 FROM base AS light
 
diff --git a/.devops/llama-cli-cann.Dockerfile b/.devops/llama-cli-cann.Dockerfile
index ef43d78cd2..6581187f32 100644
--- a/.devops/llama-cli-cann.Dockerfile
+++ b/.devops/llama-cli-cann.Dockerfile
@@ -23,11 +23,12 @@ ENV LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/runtime/lib64/stub:$LD_LIBRARY_PATH
 RUN echo "Building with static libs" && \
     source /usr/local/Ascend/ascend-toolkit/set_env.sh --force && \
     cmake -B build -DGGML_NATIVE=OFF -DGGML_CANN=ON -DBUILD_SHARED_LIBS=OFF -DLLAMA_BUILD_TESTS=OFF  && \
-    cmake --build build --config Release --target llama-cli
+    cmake --build build --config Release --target llama-cli && \
+    cmake --build build --config Release --target llama-completion
 
 # TODO: use image with NNRT
 FROM ascendai/cann:$ASCEND_VERSION AS runtime
-COPY --from=build /app/build/bin/llama-cli /llama-cli
+COPY --from=build /app/build/bin/llama-cli /app/build/bin/llama-completion /
 
 ENV LC_ALL=C.utf8
 
diff --git a/.devops/llama-cpp-cuda.srpm.spec b/.devops/llama-cpp-cuda.srpm.spec
index 3bbf4a4def..4d42a906b1 100644
--- a/.devops/llama-cpp-cuda.srpm.spec
+++ b/.devops/llama-cpp-cuda.srpm.spec
@@ -37,6 +37,7 @@ make -j GGML_CUDA=1
 %install
 mkdir -p %{buildroot}%{_bindir}/
 cp -p llama-cli %{buildroot}%{_bindir}/llama-cuda-cli
+cp -p llama-completion %{buildroot}%{_bindir}/llama-cuda-completion
 cp -p llama-server %{buildroot}%{_bindir}/llama-cuda-server
 cp -p llama-simple %{buildroot}%{_bindir}/llama-cuda-simple
 
@@ -68,6 +69,7 @@ rm -rf %{_builddir}/*
 
 %files
 %{_bindir}/llama-cuda-cli
+%{_bindir}/llama-cuda-completion
 %{_bindir}/llama-cuda-server
 %{_bindir}/llama-cuda-simple
 /usr/lib/systemd/system/llamacuda.service
diff --git a/.devops/llama-cpp.srpm.spec b/.devops/llama-cpp.srpm.spec
index 45902dcf89..0a4f43058d 100644
--- a/.devops/llama-cpp.srpm.spec
+++ b/.devops/llama-cpp.srpm.spec
@@ -39,6 +39,7 @@ make -j
 %install
 mkdir -p %{buildroot}%{_bindir}/
 cp -p llama-cli %{buildroot}%{_bindir}/llama-cli
+cp -p llama-completion %{buildroot}%{_bindir}/llama-completion
 cp -p llama-server %{buildroot}%{_bindir}/llama-server
 cp -p llama-simple %{buildroot}%{_bindir}/llama-simple
 
@@ -70,6 +71,7 @@ rm -rf %{_builddir}/*
 
 %files
 %{_bindir}/llama-cli
+%{_bindir}/llama-completion
 %{_bindir}/llama-server
 %{_bindir}/llama-simple
 /usr/lib/systemd/system/llama.service
diff --git a/docs/docker.md b/docs/docker.md
index b9e5015396..a3b263497c 100644
--- a/docs/docker.md
+++ b/docs/docker.md
@@ -7,9 +7,9 @@
 ## Images
 We have three Docker images available for this project:
 
-1. `ghcr.io/ggml-org/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)
-2. `ghcr.io/ggml-org/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)
-3. `ghcr.io/ggml-org/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)
+1. `ghcr.io/ggml-org/llama.cpp:full`: This image includes both the `llama-cli` and `llama-completion` executables and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)
+2. `ghcr.io/ggml-org/llama.cpp:light`: This image only includes the `llama-cli` and `llama-completion` executables. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)
+3. `ghcr.io/ggml-org/llama.cpp:server`: This image only includes the `llama-server` executable. (platforms: `linux/amd64`, `linux/arm64`, `linux/s390x`)
 
 Additionally, there the following images, similar to the above:
 
@@ -44,13 +44,15 @@ docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:full --all-in-o
 On completion, you are ready to play!
 
 ```bash
-docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
+docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf
+docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:full --run-legacy -m /models/32B/ggml-model-q8_0.gguf -no-cnv -p "Building a mobile app can be done in 15 steps:" -n 512
 ```
 
 or with a light image:
 
 ```bash
-docker run -v /path/to/models:/models ghcr.io/ggml-org/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
+docker run -v /path/to/models:/models --entrypoint /app/llama-cli ghcr.io/ggml-org/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf
+docker run -v /path/to/models:/models --entrypoint /app/llama-completion ghcr.io/ggml-org/llama.cpp:light -m /models/32B/ggml-model-q8_0.gguf -no-cnv -p "Building a mobile app can be done in 15 steps:" -n 512
 ```
 
 or with a server image:
@@ -59,6 +61,8 @@ or with a server image:
 docker run -v /path/to/models:/models -p 8080:8080 ghcr.io/ggml-org/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8080 --host 0.0.0.0 -n 512
 ```
 
+In the above examples, `--entrypoint /app/llama-cli` is specified for clarity, but you can safely omit it since it's the default entrypoint in the container.
+
 ## Docker With CUDA
 
 Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container.
@@ -80,9 +84,9 @@ The defaults are:
 
 The resulting images, are essentially the same as the non-CUDA images:
 
-1. `local/llama.cpp:full-cuda`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
-2. `local/llama.cpp:light-cuda`: This image only includes the main executable file.
-3. `local/llama.cpp:server-cuda`: This image only includes the server executable file.
+1. `local/llama.cpp:full-cuda`: This image includes both the `llama-cli` and `llama-completion` executables and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
+2. `local/llama.cpp:light-cuda`: This image only includes the `llama-cli` and `llama-completion` executables.
+3. `local/llama.cpp:server-cuda`: This image only includes the `llama-server` executable.
 
 ## Usage
 
@@ -114,9 +118,9 @@ The defaults are:
 
 The resulting images, are essentially the same as the non-MUSA images:
 
-1. `local/llama.cpp:full-musa`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
-2. `local/llama.cpp:light-musa`: This image only includes the main executable file.
-3. `local/llama.cpp:server-musa`: This image only includes the server executable file.
+1. `local/llama.cpp:full-musa`: This image includes both the `llama-cli` and `llama-completion` executables and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
+2. `local/llama.cpp:light-musa`: This image only includes the `llama-cli` and `llama-completion` executables.
+3. `local/llama.cpp:server-musa`: This image only includes the `llama-server` executable.
 
 ## Usage
 

From a5251ca11d2317d93a7b6da4217483f4e83beb3d Mon Sep 17 00:00:00 2001
From: "Piotr Wilkin (ilintar)" <piotr.wilkin@syndatis.com>
Date: Tue, 16 Dec 2025 11:59:53 +0100
Subject: [PATCH 131/135] Optimization: Qwen3 next autoregressive pass (#17996)

* It's Qwen3 Next, the lean mean token generation machine!

* Apply patches from thread

* Remove recurrent version, only keep chunked and autoregressive

* Remove unnecessary conts and asserts

* Remove more extra conts and asserts

* Cleanup masking
---
 src/models/models.h      |  22 +--
 src/models/qwen3next.cpp | 333 +++++++++------------------------------
 2 files changed, 85 insertions(+), 270 deletions(-)

diff --git a/src/models/models.h b/src/models/models.h
index 6494f54501..ffb36acc61 100644
--- a/src/models/models.h
+++ b/src/models/models.h
@@ -441,23 +441,13 @@ private:
                 ggml_tensor * cur,
                 ggml_tensor * causal_mask,
                 ggml_tensor * identity,
+                ggml_tensor * diag_mask,
                         int   il);
 
     ggml_tensor * build_layer_ffn(
                 ggml_tensor * cur,
                         int   il);
 
-    ggml_tensor * build_delta_net_recurrent(
-                ggml_tensor * q,
-                ggml_tensor * k,
-                ggml_tensor * v,
-                ggml_tensor * g,
-                ggml_tensor * beta,
-                ggml_tensor * state,
-                ggml_tensor * causal_mask,
-                ggml_tensor * identity,
-                        int   il);
-
     ggml_tensor * build_delta_net_chunking(
                 ggml_tensor * q,
                 ggml_tensor * k,
@@ -467,8 +457,18 @@ private:
                 ggml_tensor * state,
                 ggml_tensor * causal_mask,
                 ggml_tensor * identity,
+                ggml_tensor * diag_mask,
                         int   il);
 
+    ggml_tensor * build_delta_net_autoregressive(
+                ggml_tensor * q,
+                ggml_tensor * k,
+                ggml_tensor * v,
+                ggml_tensor * g,
+                ggml_tensor * beta,
+                ggml_tensor * state,
+                int           il);
+
     ggml_tensor * build_norm_gated(
                 ggml_tensor * input,
                 ggml_tensor * weights,
diff --git a/src/models/qwen3next.cpp b/src/models/qwen3next.cpp
index c8f1b5ec90..775b3135d3 100644
--- a/src/models/qwen3next.cpp
+++ b/src/models/qwen3next.cpp
@@ -17,13 +17,15 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
     ggml_tensor * causal_mask =
-        ggml_tri(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, ubatch.n_seq_tokens, ubatch.n_seq_tokens), 1.0f),
+        ggml_tri(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, CHUNK_SIZE, CHUNK_SIZE), 1.0f),
                     GGML_TRI_TYPE_LOWER);
 
-    ggml_tensor * identity = ggml_diag(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, ubatch.n_seq_tokens), 1.0f));
+    ggml_tensor * identity = ggml_diag(ctx0, ggml_fill_inplace(ctx0, ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, CHUNK_SIZE), 1.0f));
+    ggml_tensor * diag_mask = ggml_add(ctx0, causal_mask, identity);
 
     ggml_build_forward_expand(gf, causal_mask);
     ggml_build_forward_expand(gf, identity);
+    ggml_build_forward_expand(gf, diag_mask);
 
     for (int il = 0; il < n_layer; ++il) {
         ggml_tensor * inpSA = inpL;
@@ -34,7 +36,7 @@ llm_build_qwen3next::llm_build_qwen3next(const llama_model & model, const llm_gr
         // Determine layer type and build appropriate attention mechanism
         if (hparams.is_recurrent(il)) {
             // Linear attention layer (gated delta net)
-            cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, il);
+            cur = build_layer_attn_linear(inp->get_recr(), cur, causal_mask, identity, diag_mask, il);
         } else {
             // Full attention layer
             cur = build_layer_attn(inp->get_attn(), cur, inp_pos, il);
@@ -93,14 +95,8 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
         ggml_tensor * state,
         ggml_tensor * causal_mask,
         ggml_tensor * identity,
+        ggml_tensor * diag_mask,
         int           il) {
-    GGML_ASSERT(ggml_is_contiguous(q));
-    GGML_ASSERT(ggml_is_contiguous(k));
-    GGML_ASSERT(ggml_is_contiguous(v));
-    GGML_ASSERT(ggml_is_contiguous(g));
-    GGML_ASSERT(ggml_is_contiguous(beta));
-    GGML_ASSERT(ggml_is_contiguous(state));
-
     const int64_t S_k      = q->ne[0];
     const int64_t H_k      = q->ne[1];
     const int64_t n_tokens = q->ne[2];
@@ -120,15 +116,10 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
 
     GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
 
-    // TODO: can this ever be false?
-    const bool use_qk_l2norm = true;
+    const float eps_norm = hparams.f_norm_rms_eps;
 
-    if (use_qk_l2norm) {
-        const float eps_norm = hparams.f_norm_rms_eps;
-
-        q = ggml_l2_norm(ctx0, q, eps_norm);
-        k = ggml_l2_norm(ctx0, k, eps_norm);
-    }
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
 
     const float scale = 1.0f / sqrtf(S_v);
 
@@ -136,8 +127,6 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
 
     beta = ggml_sigmoid(ctx0, beta);
 
-    ggml_tensor * causal_diag_mask = ggml_add(ctx0, causal_mask, identity);
-
     cb(q, "q_in", il);
     cb(k, "k_in", il);
     cb(v, "v_in", il);
@@ -188,36 +177,21 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
     cb(v_beta, "v_beta", il);
     cb(k_beta, "k_beta", il);
 
-    ggml_tensor * chunked_mask =
-        ggml_view_4d(ctx0, causal_mask, chunk_size,
-                chunk_size,         causal_mask->ne[2], causal_mask->ne[3],
-                causal_mask->nb[1], causal_mask->nb[2], causal_mask->nb[3], 0);
+    q      = ggml_reshape_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k      = ggml_reshape_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
+    k_beta = ggml_reshape_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
+    v      = ggml_reshape_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
+    v_beta = ggml_reshape_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
 
-    ggml_tensor * chunked_diag_mask =
-        ggml_view_4d(ctx0, causal_diag_mask, chunk_size,
-                chunk_size,              causal_diag_mask->ne[2], causal_diag_mask->ne[3],
-                causal_diag_mask->nb[1], causal_diag_mask->nb[2], causal_diag_mask->nb[3], 0);
-
-    ggml_tensor * chunked_identity =
-        ggml_view_4d(ctx0, identity, chunk_size,
-            chunk_size,      identity->ne[2], identity->ne[3],
-            identity->nb[1], identity->nb[2], identity->nb[3], 0);
-
-    q      = ggml_cont_4d(ctx0, q,      S_k, chunk_size, n_chunks, H_k * n_seqs);
-    k      = ggml_cont_4d(ctx0, k,      S_k, chunk_size, n_chunks, H_k * n_seqs);
-    k_beta = ggml_cont_4d(ctx0, k_beta, S_k, chunk_size, n_chunks, H_k * n_seqs);
-    v      = ggml_cont_4d(ctx0, v,      S_v, chunk_size, n_chunks, H_v * n_seqs);
-    v_beta = ggml_cont_4d(ctx0, v_beta, S_v, chunk_size, n_chunks, H_v * n_seqs);
-
-    g    = ggml_cont_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
-    beta = ggml_cont_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
+    g    = ggml_reshape_4d(ctx0, g, chunk_size, 1, n_chunks, H_k * n_seqs);
+    beta = ggml_reshape_4d(ctx0, beta, 1, chunk_size, n_chunks, H_k * n_seqs);
 
     ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
 
     cb(g_cumsum, "g_cumsum", il);
 
-    ggml_tensor * gcs_i = ggml_cont_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
-    ggml_tensor * gcs_j = ggml_cont_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
+    ggml_tensor * gcs_i = ggml_reshape_4d(ctx0, g_cumsum, chunk_size, 1, n_chunks, H_v * n_seqs);
+    ggml_tensor * gcs_j = ggml_reshape_4d(ctx0, g_cumsum, 1, chunk_size, n_chunks, H_v * n_seqs);
 
     ggml_tensor * gcs_j_broadcast =
         ggml_repeat_4d(ctx0, gcs_j, chunk_size, chunk_size, n_chunks, H_v * n_seqs);
@@ -226,23 +200,23 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
 
     cb(decay_mask, "decay_mask", il);
 
-    decay_mask = ggml_mul(ctx0, decay_mask, chunked_diag_mask);
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
     decay_mask = ggml_exp(ctx0, decay_mask);
-    decay_mask = ggml_mul(ctx0, decay_mask, chunked_diag_mask);
+    decay_mask = ggml_mul(ctx0, decay_mask, diag_mask);
 
     ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
 
     ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
-    ggml_tensor * attn    = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, chunked_mask));
+    ggml_tensor * attn    = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
 
     cb(attn, "attn_pre_solve", il);
 
-    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, chunked_mask);
-    ggml_tensor * lhs        = ggml_sub(ctx0, ggml_repeat(ctx0, chunked_identity, attn_lower), attn_lower);
+    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
+    ggml_tensor * lhs        = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
 
     ggml_tensor * lin_solve  = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
-    attn                     = ggml_mul(ctx0, lin_solve, chunked_mask);
-    attn                     = ggml_add(ctx0, attn, chunked_identity);
+    attn                     = ggml_mul(ctx0, lin_solve, causal_mask);
+    attn                     = ggml_add(ctx0, attn, identity);
 
     cb(attn, "attn_solved", il);
 
@@ -291,7 +265,7 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
         // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
         attn = ggml_mul_mat(ctx0, k_chunk, q_chunk);
         attn = ggml_mul(ctx0, attn, decay_mask_chunk);
-        attn = ggml_mul(ctx0, attn, ggml_add(ctx0, chunked_identity, chunked_mask));
+        attn = ggml_mul(ctx0, attn, diag_mask);
 
         ggml_tensor * state_t = ggml_cont_4d(ctx0, ggml_permute(ctx0, new_state, 1, 0, 2, 3), S_v, S_v, 1, H_v * n_seqs);
 
@@ -361,23 +335,14 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_chunking(
     return ggml_concat(ctx0, flat_output, flat_state, 0);
 }
 
-ggml_tensor * llm_build_qwen3next::build_delta_net_recurrent(
+ggml_tensor * llm_build_qwen3next::build_delta_net_autoregressive(
         ggml_tensor * q,
         ggml_tensor * k,
         ggml_tensor * v,
         ggml_tensor * g,
         ggml_tensor * beta,
         ggml_tensor * state,
-        ggml_tensor * causal_mask,
-        ggml_tensor * identity,
         int           il) {
-    GGML_ASSERT(ggml_is_contiguous(q));
-    GGML_ASSERT(ggml_is_contiguous(k));
-    GGML_ASSERT(ggml_is_contiguous(v));
-    GGML_ASSERT(ggml_is_contiguous(g));
-    GGML_ASSERT(ggml_is_contiguous(beta));
-    GGML_ASSERT(ggml_is_contiguous(state));
-
     const int64_t S_k      = q->ne[0];
     const int64_t H_k      = q->ne[1];
     const int64_t n_tokens = q->ne[2];
@@ -386,6 +351,7 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_recurrent(
     const int64_t S_v = v->ne[0];
     const int64_t H_v = v->ne[1];
 
+    GGML_ASSERT(n_tokens == 1);  // This function is optimized for single token processing
     GGML_ASSERT(v->ne[2] == n_tokens);
     GGML_ASSERT(k->ne[2] == n_tokens);
     GGML_ASSERT(g->ne[0] == H_v && g->ne[1] == n_tokens && g->ne[2] == n_seqs);
@@ -397,215 +363,65 @@ ggml_tensor * llm_build_qwen3next::build_delta_net_recurrent(
 
     GGML_ASSERT(H_k == H_v);  // we did a repeat to make sure this is the case
 
-    // TODO: can this ever be false?
-    const bool use_qk_l2norm = true;
+    const float eps_norm = hparams.f_norm_rms_eps;
 
-    if (use_qk_l2norm) {
-        const float eps_norm = hparams.f_norm_rms_eps;
-
-        q = ggml_l2_norm(ctx0, q, eps_norm);
-        k = ggml_l2_norm(ctx0, k, eps_norm);
-    }
+    q = ggml_l2_norm(ctx0, q, eps_norm);
+    k = ggml_l2_norm(ctx0, k, eps_norm);
 
     const float scale = 1.0f / sqrtf(S_v);
 
-    q = ggml_scale(ctx0, q, scale);
-
+    q    = ggml_scale(ctx0, q, scale);
     beta = ggml_sigmoid(ctx0, beta);
 
-    ggml_tensor * causal_diag_mask = ggml_add(ctx0, causal_mask, identity);
-
     cb(q, "q_in", il);
     cb(k, "k_in", il);
     cb(v, "v_in", il);
     cb(beta, "beta_in", il);
     cb(g, "g_in", il);
 
-    q = ggml_cont_4d(ctx0, ggml_permute(ctx0, q, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
-    k = ggml_cont_4d(ctx0, ggml_permute(ctx0, k, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
-    v = ggml_cont_4d(ctx0, ggml_permute(ctx0, v, 0, 2, 1, 3), S_v, n_tokens, H_v, n_seqs);
-    g = ggml_cont_4d(ctx0, ggml_permute(ctx0, g, 2, 0, 3, 1), n_tokens, 1, H_k, n_seqs);
-
-    beta  = ggml_cont(ctx0, ggml_permute(ctx0, beta, 2, 0, 1, 3));
     state = ggml_reshape_4d(ctx0, state, S_v, S_v, H_v, n_seqs);
 
-    cb(q, "q_perm", il);
-    cb(k, "k_perm", il);
-    cb(v, "v_perm", il);
-    cb(beta, "beta_perm", il);
-    cb(g, "g_perm", il);
-    cb(state, "state_in", il);
+    ggml_tensor * g_t    = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, g), 1, 1, H_k, n_seqs);
+    ggml_tensor * beta_t = ggml_reshape_4d(ctx0, ggml_transpose(ctx0, beta), 1, 1, H_k, n_seqs);
 
-    GGML_ASSERT(q->ne[1] == n_tokens && q->ne[0] == S_k && q->ne[2] == H_k && q->ne[3] == n_seqs);
-    GGML_ASSERT(k->ne[1] == n_tokens && k->ne[0] == S_k && k->ne[2] == H_k && k->ne[3] == n_seqs);
-    GGML_ASSERT(v->ne[1] == n_tokens && v->ne[0] == S_v && v->ne[2] == H_k && v->ne[3] == n_seqs);
-    GGML_ASSERT(beta->ne[1] == n_tokens && beta->ne[2] == H_k && beta->ne[0] == 1 && beta->ne[3] == n_seqs);
+    // Apply exponential to g_t
+    g_t = ggml_exp(ctx0, g_t);
 
-    ggml_tensor * v_beta = ggml_mul(ctx0, v, beta);
-    ggml_tensor * k_beta = ggml_mul(ctx0, k, beta);
+    // Apply the gated delta rule for the single timestep
+    // last_recurrent_state = last_recurrent_state * g_t
+    state = ggml_mul(ctx0, state, g_t);
 
-    ggml_tensor * g_cumsum = ggml_cumsum(ctx0, g);
+    // kv_mem = (last_recurrent_state * k_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * k_t_unsqueezed = ggml_reshape_4d(ctx0, k, 1, S_v, H_v, n_seqs);
+    ggml_tensor * kv_mem         = ggml_mul(ctx0, state, k_t_unsqueezed);
+    // we need to sum over dim=-2, so we transpose, sum, then transpose again
+    kv_mem = ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, kv_mem))));
 
-    cb(k_beta, "k_beta", il);
-    cb(v_beta, "v_beta", il);
-    cb(g_cumsum, "g_cumsum", il);
+    // v_t = v.unsqueeze(2) (we insert the singleton dimension after n_seqs and H_v)
+    ggml_tensor * v_t    = ggml_reshape_4d(ctx0, v, S_v, 1, H_v, n_seqs);
+    // delta = (v_t - kv_mem) * beta_t
+    ggml_tensor * v_diff = ggml_sub(ctx0, v_t, kv_mem);  // both should be [S_v, 1, H_v, n_seqs]
+    ggml_tensor * delta  = ggml_mul(ctx0, v_diff, beta_t);
 
-    ggml_tensor * gcs_i = ggml_cont_4d(ctx0, g_cumsum, n_tokens, 1, H_v, n_seqs);  // [chunk_size, 1, n_tokens, n_seqs]
-    ggml_tensor * gcs_j = ggml_cont_4d(ctx0, g_cumsum, 1, n_tokens, H_v, n_seqs);  // [1, chunk_size, n_tokens, n_seqs]
+    // last_recurrent_state = last_recurrent_state + k_t.unsqueeze(-1) * delta
+    ggml_tensor * k_t_delta = ggml_mul(ctx0, ggml_repeat_4d(ctx0, k_t_unsqueezed, S_v, S_v, H_v, n_seqs), delta);
+    state                   = ggml_add(ctx0, state, k_t_delta);
 
-    // Broadcast both tensors to [chunk_size, chunk_size, H_v, n_seqs]
-    // ggml_tensor * gcs_i_broadcast =
-    //     ggml_repeat_4d(ctx0, gcs_i, GGML_DELTA_NET_CHUNK, GGML_DELTA_NET_CHUNK, num_chunks * H_v,
-    //                     n_seqs);  // [chunk_size, 1, H_v, n_seqs] -> [chunk_size, chunk_size, H_v, n_seqs]
-    // Don't need this, this one will get auto-broadcast
-    ggml_tensor * gcs_j_broadcast =
-        ggml_repeat_4d(ctx0, gcs_j, n_tokens, n_tokens, H_v, n_seqs);  // [1, chunk_size, H_v, n_seqs] -> [chunk_size, chunk_size, H_v, n_seqs]
-
-    ggml_tensor * decay_mask = ggml_sub(ctx0, gcs_j_broadcast, gcs_i);
-
-    // Apply lower triangular mask to ensure attention is causal (only past tokens influence current)
-    decay_mask = ggml_mul(ctx0, decay_mask, causal_diag_mask);
-    // Apply exponential to get the decay mask values
-    decay_mask = ggml_exp(ctx0, decay_mask);
-    // Apply lower triangular mask again to ensure only lower triangular values remain
-    decay_mask = ggml_mul(ctx0, decay_mask, causal_diag_mask);
-
-    cb(decay_mask, "decay_mask", il);
-
-    // attn = -((k_beta @ key.transpose(-1, -2)) * decay_mask).masked_fill(mask, 0)
-    ggml_tensor * kmulkbeta = ggml_mul_mat(ctx0, k, k_beta);
-
-    cb(kmulkbeta, "kmulkbeta", il);
-
-    ggml_tensor * k_decay = ggml_mul(ctx0, kmulkbeta, decay_mask);
-    ggml_tensor * attn    = ggml_neg(ctx0, ggml_mul(ctx0, k_decay, causal_mask));
-
-    cb(attn, "attn_pre_rec", il);
-
-    // for i in range(1, chunk_size):
-    //          row = attn[..., i, :i].clone()
-    //          sub = attn[..., :i, :i].clone()
-    //          attn[..., i, :i] = row + (row.unsqueeze(-1) * sub).sum(-2)
-    // attn = attn + torch.eye(chunk_size, dtype=attn.dtype, device=attn.device)
-    //
-    // We reduce this to a linear triangular solve: AX = B, where B = attn, A = I - tril(A)
-    ggml_tensor * attn_lower = ggml_mul(ctx0, attn, causal_mask);
-    ggml_tensor * lhs        = ggml_sub(ctx0, ggml_repeat(ctx0, identity, attn_lower), attn_lower);
-
-    ggml_tensor * lin_solve  = ggml_solve_tri(ctx0, lhs, attn, true, true, false);
-    attn                     = ggml_mul(ctx0, lin_solve, causal_mask);
-    attn                     = ggml_add(ctx0, attn, identity);
-
-    // value = attn @ v_beta
-    v = ggml_mul_mat(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, v_beta)), attn);
-
-    cb(v, "value_beta", il);
-
-    // k_cumdecay = attn @ (k_beta * g.exp().unsqueeze(-1))
-    ggml_tensor * g_cumsum_t = ggml_cont(ctx0, ggml_transpose(ctx0, g_cumsum));
-    ggml_tensor * gexp       = ggml_exp(ctx0, g_cumsum_t);
-
-    cb(gexp, "g_cum_exp", il);
-
-    ggml_tensor * kbeta_gexp = ggml_mul(ctx0, k_beta, gexp);
-
-    cb(kbeta_gexp, "kbeta_gexp", il);
-
-    ggml_tensor * k_cumdecay =
-        ggml_cont(ctx0, ggml_transpose(ctx0, ggml_mul_mat(ctx0, attn, ggml_cont(ctx0, ggml_transpose(ctx0, kbeta_gexp)))));
-
-    cb(k_cumdecay, "k_cumdecay", il);
-
-    // attn = (q_i @ k_i.transpose(-1, -2) * decay_mask[:, :, i]).masked_fill_(mask, 0)
-    attn = ggml_mul_mat(ctx0, k, q);
-    attn = ggml_mul(ctx0, attn, decay_mask);
-    attn = ggml_mul(ctx0, attn, ggml_add(ctx0, identity, causal_mask));
-
-    cb(attn, "attn_decay_key", il);
-
-    ggml_tensor * state_t = ggml_cont(ctx0, ggml_transpose(ctx0, state));
-
-    // v_prime = (k_cumdecay[:, :, i]) @ last_recurrent_state
-    ggml_tensor * v_prime = ggml_mul_mat(ctx0, state_t, k_cumdecay);
-
-    cb(v_prime, "v_prime", il);
-
-    // v_new = v_i - v_prime
-    ggml_tensor * v_new = ggml_sub(ctx0, ggml_repeat(ctx0, v, v_prime), v_prime);
-
-    ggml_tensor * v_new_t = ggml_cont(ctx0, ggml_transpose(ctx0, v_new));
-
-    cb(v_new, "v_new", il);
-
-    // attn_inter = (q_i * g[:, :, i, :, None].exp()) @ last_recurrent_state
-    ggml_tensor * q_g_exp    = ggml_mul(ctx0, q, gexp);
-    ggml_tensor * attn_inter = ggml_mul_mat(ctx0, state_t, q_g_exp);
-
-    cb(attn_inter, "attn_inter", il);
-
-    // core_attn_out[:, :, i] = attn_inter + attn @ v_new
-    ggml_tensor * v_attn = ggml_mul_mat(ctx0, v_new_t, attn);
-
-    cb(v_attn, "v_attn", il);
-
-    ggml_tensor * core_attn_out = ggml_add(ctx0, attn_inter, v_attn);
-
-    cb(core_attn_out, "core_attn_out", il);
-
-    // g_last = torch.clamp(g_cum[:, :, -1], max=50.0).exp().unsqueeze(-1).unsqueeze(-1)
-    // g_diff = torch.clamp(g_cum[:, :, -1:] - g_cum, max=50.0).exp()
-    // key_gdiff = key * g_diff.unsqueeze(-1)
-    // kgdmulvnew = (key_gdiff).transpose(-1, -2) @ v_new
-    // last_recurrent_state = last_recurrent_state * g_last + kgdmulvnew
-
-    ggml_tensor * g_cum_last =
-        ggml_cont(ctx0, ggml_view_4d(ctx0, g_cumsum_t, g_cumsum_t->ne[0], 1, g_cumsum_t->ne[2], g_cumsum_t->ne[3],
-                                    g_cumsum_t->nb[1], g_cumsum_t->nb[2], g_cumsum_t->nb[3],
-                                    g_cumsum_t->nb[0] * (g_cumsum_t->ne[1] - 1)));
-
-    cb(g_cum_last, "g_cum_last", il);
-
-    ggml_tensor * gexp_last =
-        ggml_reshape_4d(ctx0, ggml_exp(ctx0, g_cum_last), 1, 1, g_cum_last->ne[0] * g_cum_last->ne[2], g_cum_last->ne[3]);
-
-    cb(gexp_last, "gexp_last", il);
-
-    ggml_tensor * g_cum_last_3d =
-        ggml_reshape_3d(ctx0, g_cum_last, g_cum_last->ne[0], g_cum_last->ne[2], g_cum_last->ne[3]);
-
-    cb(g_cum_last_3d, "g_cum_last_3d", il);
-
-    ggml_tensor * g_cumsum_3d = ggml_reshape_3d(ctx0, g_cumsum, g_cumsum->ne[0], g_cumsum->ne[2], g_cumsum->ne[3]);
-
-    cb(g_cumsum_3d, "g_cumsum_3d", il);
-
-    ggml_tensor * g_diff = ggml_neg(ctx0, ggml_sub(ctx0, g_cumsum_3d, g_cum_last_3d));
-
-    cb(g_diff, "g_diff", il);
-
-    ggml_tensor * g_diff_exp = ggml_exp(ctx0, g_diff);
-
-    cb(g_diff_exp, "g_diff_exp", il);
-
-    ggml_tensor * key_gdiff = ggml_mul(ctx0, k,
-                                    ggml_reshape_4d(ctx0, g_diff_exp, 1, g_diff_exp->ne[0], g_diff_exp->ne[1],
-                                                    g_diff_exp->ne[2] * g_diff_exp->ne[3]));
-
-    cb(key_gdiff, "key_gdiff", il);
-
-    ggml_tensor * kgdmulvnew = ggml_mul_mat(ctx0, v_new_t, ggml_cont(ctx0, ggml_transpose(ctx0, key_gdiff)));
-
-    cb(kgdmulvnew, "kgdmulvnew", il);
-
-    state = ggml_add(ctx0, ggml_mul(ctx0, state, gexp_last), kgdmulvnew);
+    // Compute the attention output
+    // core_attn_out = (last_recurrent_state * q_t.unsqueeze(-1)).sum(dim=-2)
+    ggml_tensor * q_t_unsqueezed = ggml_reshape_4d(ctx0, q, 1, S_v, H_v, n_seqs);  // unsqueeze q_t
+    ggml_tensor * state_q        = ggml_mul(ctx0, state, q_t_unsqueezed);
+    // again, since it's over dim = -2, transpose, sum, transpose back
+    ggml_tensor * core_attn_out =
+        ggml_transpose(ctx0, ggml_sum_rows(ctx0, ggml_cont(ctx0, ggml_transpose(ctx0, state_q))));
 
+    // core_attn_out should be [S_v, 1, H_v, n_seqs] after this
+    cb(core_attn_out, "output_tokens", il);
     cb(state, "new_state", il);
 
-    // flatten output
-    ggml_tensor * flat_output =
-        ggml_cont_1d(ctx0, ggml_permute(ctx0, core_attn_out, 0, 2, 1, 3), S_v * H_v * n_tokens * n_seqs);
-
-    ggml_tensor * flat_state = ggml_cont_1d(ctx0, state, S_v * S_v * H_v * n_seqs);
+    // flatten output, no need to permute since n_tokens is 1 so [S_v, 1, H_v, n_seqs] and [S_v, H_v, 1, n_seqs] are equivalent memory-layout wise
+    ggml_tensor * flat_output = ggml_reshape_1d(ctx0, core_attn_out, S_v * H_v * n_tokens * n_seqs);
+    ggml_tensor * flat_state  = ggml_reshape_1d(ctx0, state, S_v * S_v * H_v * n_seqs);
 
     return ggml_concat(ctx0, flat_output, flat_state, 0);
 }
@@ -712,6 +528,7 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
         ggml_tensor *        cur,
         ggml_tensor *        causal_mask,
         ggml_tensor *        identity,
+        ggml_tensor *        diag_mask,
         int                  il) {
     const auto * mctx_cur = inp->mctx;
 
@@ -737,11 +554,11 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     cb(mixed_ba, "linear_attn_mixed_ba", il);
 
     int64_t       qkvz_new_dim        = 2 * head_k_dim + 2 * head_v_dim * (num_v_heads / num_k_heads);
-    ggml_tensor * mixed_qkvz_reshaped = ggml_cont_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);
+    ggml_tensor * mixed_qkvz_reshaped = ggml_reshape_4d(ctx0, mixed_qkvz, qkvz_new_dim, num_k_heads, n_seq_tokens, n_seqs);
 
     // Reshape mixed_ba: [batch, seq_len, hidden_size] -> [batch, seq_len, num_k_heads, 2*num_v_heads/num_k_heads]
     int64_t       ba_new_dim        = 2 * num_v_heads / num_k_heads;
-    ggml_tensor * mixed_ba_reshaped = ggml_cont_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_seq_tokens, n_seqs);
+    ggml_tensor * mixed_ba_reshaped = ggml_reshape_4d(ctx0, mixed_ba, ba_new_dim, num_k_heads, n_seq_tokens, n_seqs);
 
     // Split mixed_ba into b and a (beta and alpha parameters)
     int64_t split_sizes_ba[2] = {
@@ -762,8 +579,6 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     ggml_tensor * beta  = ggml_cont_3d(ctx0, b, num_v_heads, n_seq_tokens, n_seqs);
     ggml_tensor * alpha = ggml_cont_3d(ctx0, a, num_v_heads, n_seq_tokens, n_seqs);
 
-    GGML_ASSERT(ggml_nelements(beta) + ggml_nelements(alpha) == ggml_nelements(mixed_ba));
-
     ggml_tensor * alpha_biased   = ggml_add(ctx0, alpha, model.layers[il].ssm_dt);
     ggml_tensor * alpha_softplus = ggml_softplus(ctx0, alpha_biased);
     cb(alpha_softplus, "a_softplus", il);
@@ -799,9 +614,6 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
                                    (split_sizes_qkvz[0] + split_sizes_qkvz[1] + split_sizes_qkvz[2]) * sizeof(float));
     cb(z, "z", il);
 
-    GGML_ASSERT(ggml_nelements(query) + ggml_nelements(key) + ggml_nelements(value) + ggml_nelements(z) ==
-                ggml_nelements(mixed_qkvz));
-
     // After creating query, key, and value_reshaped, reshape each to flatten the head dimensions
     // query: [head_k_dim, num_k_heads, n_tokens, n_seqs] -> [head_k_dim * num_k_heads, n_tokens, n_seqs]
     ggml_tensor * query_flat = ggml_cont_3d(ctx0, query, head_k_dim * num_k_heads, n_seq_tokens, n_seqs);
@@ -925,10 +737,13 @@ ggml_tensor * llm_build_qwen3next::build_layer_attn_linear(
     cb(k_conv, "k_conv_predelta", il);
     cb(v_conv, "v_conv_predelta", il);
 
-    // Choose between build_delta_net_chunking and build_delta_net_recurrent based on n_tokens
-    ggml_tensor * attn_out = n_seq_tokens > CHUNK_SIZE ?
-        build_delta_net_chunking (q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, il) :
-        build_delta_net_recurrent(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, il);
+    // Choose between build_delta_net_chunking, build_delta_net_recurrent, and build_delta_net_autoregressive based on n_tokens
+    ggml_tensor * attn_out;
+    if (n_seq_tokens == 1) {
+        attn_out = build_delta_net_autoregressive(q_conv, k_conv, v_conv, gate, beta, state, il);
+    } else {
+        attn_out = build_delta_net_chunking(q_conv, k_conv, v_conv, gate, beta, state, causal_mask, identity, diag_mask, il);
+    }
     cb(attn_out, "attn_out", il);
 
     // The tensors were concatenated 1d, so we need to extract them 1d as well

From 7b1db3d3b770d0affbf3aadee033e1614280085f Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Tue, 16 Dec 2025 12:01:27 +0100
Subject: [PATCH 132/135] arg: clarify auto kvu/np being set on server (#17997)

* arg: clarify auto kvu/np being set on server

* improve docs

* use invalid_argument
---
 common/arg.cpp                  | 50 +++++++++++++++++++++++++--------
 examples/gen-docs/gen-docs.cpp  |  9 +++---
 tools/completion/completion.cpp |  3 --
 tools/mtmd/mtmd-cli.cpp         |  2 --
 tools/server/README.md          | 13 ++++-----
 tools/server/server.cpp         |  9 ++----
 6 files changed, 51 insertions(+), 35 deletions(-)

diff --git a/common/arg.cpp b/common/arg.cpp
index acf4c8f8a8..f2aec895ba 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -835,6 +835,19 @@ bool common_arg_utils::is_autoy(const std::string & value) {
 }
 
 common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **)) {
+    // per-example default params
+    // we define here to make sure it's included in llama-gen-docs
+    if (ex == LLAMA_EXAMPLE_COMPLETION) {
+        params.use_jinja = false;   // disable jinja by default
+
+    } else if (ex == LLAMA_EXAMPLE_MTMD) {
+        params.use_jinja = false;   // disable jinja by default
+        params.sampling.temp = 0.2; // lower temp by default for better quality
+
+    } else if (ex == LLAMA_EXAMPLE_SERVER) {
+        params.n_parallel = -1;     // auto by default
+    }
+
     params.use_color = tty_can_use_colors();
 
     // load dynamic backends
@@ -1107,7 +1120,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_ARG_SWA_FULL"));
     add_opt(common_arg(
         {"--ctx-checkpoints", "--swa-checkpoints"}, "N",
-        string_format("max number of context checkpoints to create per slot (default: %d)\n"
+        string_format("max number of context checkpoints to create per slot (default: %d)"
             "[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)", params.n_ctx_checkpoints),
         [](common_params & params, int value) {
             params.n_ctx_checkpoints = value;
@@ -1115,7 +1128,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_ARG_CTX_CHECKPOINTS").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
         {"--cache-ram", "-cram"}, "N",
-        string_format("set the maximum cache size in MiB (default: %d, -1 - no limit, 0 - disable)\n"
+        string_format("set the maximum cache size in MiB (default: %d, -1 - no limit, 0 - disable)"
             "[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)", params.cache_ram_mib),
         [](common_params & params, int value) {
             params.cache_ram_mib = value;
@@ -1123,12 +1136,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_ARG_CACHE_RAM").set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}));
     add_opt(common_arg(
         {"--kv-unified", "-kvu"},
-        string_format("use single unified KV buffer for the KV cache of all sequences (default: %s)\n"
-            "[(more info)](https://github.com/ggml-org/llama.cpp/pull/14363)", params.kv_unified ? "true" : "false"),
+        "use single unified KV buffer shared across all sequences (default: enabled if number of slots is auto)",
         [](common_params & params) {
             params.kv_unified = true;
         }
-    ).set_env("LLAMA_ARG_KV_UNIFIED"));
+    ).set_env("LLAMA_ARG_KV_UNIFIED").set_examples({LLAMA_EXAMPLE_SERVER}));
     add_opt(common_arg(
         {"--context-shift"},
         {"--no-context-shift"},
@@ -1888,13 +1900,27 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             LOG_WRN("DEPRECATED: --defrag-thold is deprecated and no longer necessary to specify\n");
         }
     ).set_env("LLAMA_ARG_DEFRAG_THOLD"));
-    add_opt(common_arg(
-        {"-np", "--parallel"}, "N",
-        string_format("number of parallel sequences to decode (default: %d)", params.n_parallel),
-        [](common_params & params, int value) {
-            params.n_parallel = value;
-        }
-    ).set_env("LLAMA_ARG_N_PARALLEL"));
+    if (ex == LLAMA_EXAMPLE_SERVER) {
+        // this is to make sure this option appears in the server-specific section of the help message
+        add_opt(common_arg(
+            {"-np", "--parallel"}, "N",
+            string_format("number of server slots (default: %d, -1 = auto)", params.n_parallel),
+            [](common_params & params, int value) {
+                if (value == 0) {
+                    throw std::invalid_argument("error: invalid value for n_parallel\n");
+                }
+                params.n_parallel = value;
+            }
+        ).set_env("LLAMA_ARG_N_PARALLEL").set_examples({LLAMA_EXAMPLE_SERVER}));
+    } else {
+        add_opt(common_arg(
+            {"-np", "--parallel"}, "N",
+            string_format("number of parallel sequences to decode (default: %d)", params.n_parallel),
+            [](common_params & params, int value) {
+                params.n_parallel = value;
+            }
+        ).set_env("LLAMA_ARG_N_PARALLEL"));
+    }
     add_opt(common_arg(
         {"-ns", "--sequences"}, "N",
         string_format("number of sequences to decode (default: %d)", params.n_sequences),
diff --git a/examples/gen-docs/gen-docs.cpp b/examples/gen-docs/gen-docs.cpp
index e9f7bf9313..dc76c4cf53 100644
--- a/examples/gen-docs/gen-docs.cpp
+++ b/examples/gen-docs/gen-docs.cpp
@@ -48,7 +48,7 @@ static void write_table(std::ofstream & file, std::vector<common_arg *> & opts)
     }
 }
 
-static void export_md(std::string fname, llama_example ex) {
+static void export_md(std::string fname, llama_example ex, std::string name) {
     std::ofstream file(fname, std::ofstream::out | std::ofstream::trunc);
 
     common_params params;
@@ -72,13 +72,14 @@ static void export_md(std::string fname, llama_example ex) {
     write_table(file, common_options);
     file << "\n\n**Sampling params**\n\n";
     write_table(file, sparam_options);
-    file << "\n\n**Example-specific params**\n\n";
+    file << "\n\n**" << name << "-specific params**\n\n";
     write_table(file, specific_options);
 }
 
 int main(int, char **) {
-    export_md("autogen-main.md", LLAMA_EXAMPLE_COMPLETION);
-    export_md("autogen-server.md", LLAMA_EXAMPLE_SERVER);
+    // TODO: add CLI
+    export_md("autogen-completion.md", LLAMA_EXAMPLE_COMPLETION, "Tool");
+    export_md("autogen-server.md", LLAMA_EXAMPLE_SERVER, "Server");
 
     return 0;
 }
diff --git a/tools/completion/completion.cpp b/tools/completion/completion.cpp
index 85480f3369..29770515f5 100644
--- a/tools/completion/completion.cpp
+++ b/tools/completion/completion.cpp
@@ -87,9 +87,6 @@ int main(int argc, char ** argv) {
     common_params params;
     g_params = &params;
 
-    // disable jinja by default
-    params.use_jinja = false;
-
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMPLETION, print_usage)) {
         return 1;
     }
diff --git a/tools/mtmd/mtmd-cli.cpp b/tools/mtmd/mtmd-cli.cpp
index 332d2049e5..3ee1c2eccf 100644
--- a/tools/mtmd/mtmd-cli.cpp
+++ b/tools/mtmd/mtmd-cli.cpp
@@ -270,8 +270,6 @@ int main(int argc, char ** argv) {
     ggml_time_init();
 
     common_params params;
-    params.use_jinja = false;   // disable jinja by default
-    params.sampling.temp = 0.2; // lower temp by default for better quality
 
     if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_MTMD, show_additional_info)) {
         return 1;
diff --git a/tools/server/README.md b/tools/server/README.md
index ef4990faf1..9a2b9b1f36 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -52,7 +52,6 @@ For the ful list of features, please refer to [server's changelog](https://githu
 | `-ub, --ubatch-size N` | physical maximum batch size (default: 512)<br/>(env: LLAMA_ARG_UBATCH) |
 | `--keep N` | number of tokens to keep from the initial prompt (default: 0, -1 = all) |
 | `--swa-full` | use full-size SWA cache (default: false)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)<br/>(env: LLAMA_ARG_SWA_FULL) |
-| `--kv-unified, -kvu` | use single unified KV buffer for the KV cache of all sequences (default: false)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/14363)<br/>(env: LLAMA_ARG_KV_UNIFIED) |
 | `-fa, --flash-attn [on\|off\|auto]` | set Flash Attention use ('on', 'off', or 'auto', default: 'auto')<br/>(env: LLAMA_ARG_FLASH_ATTN) |
 | `--perf, --no-perf` | whether to enable internal libllama performance timings (default: false)<br/>(env: LLAMA_ARG_PERF) |
 | `-e, --escape, --no-escape` | whether to process escapes sequences (\n, \r, \t, \', \", \\) (default: true) |
@@ -67,11 +66,10 @@ For the ful list of features, please refer to [server's changelog](https://githu
 | `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: -1.0)<br/>(env: LLAMA_ARG_YARN_BETA_FAST) |
 | `-kvo, --kv-offload, -nkvo, --no-kv-offload` | whether to enable KV cache offloading (default: enabled)<br/>(env: LLAMA_ARG_KV_OFFLOAD) |
 | `--repack, -nr, --no-repack` | whether to enable weight repacking (default: enabled)<br/>(env: LLAMA_ARG_REPACK) |
-| `--no-host` | bypass host buffer allowing extra buffers to be used<br/>(env: LLAMA_ARG_HOST) |
+| `--no-host` | bypass host buffer allowing extra buffers to be used<br/>(env: LLAMA_ARG_NO_HOST) |
 | `-ctk, --cache-type-k TYPE` | KV cache data type for K<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_K) |
 | `-ctv, --cache-type-v TYPE` | KV cache data type for V<br/>allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1<br/>(default: f16)<br/>(env: LLAMA_ARG_CACHE_TYPE_V) |
 | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)<br/>(env: LLAMA_ARG_DEFRAG_THOLD) |
-| `-np, --parallel N` | number of parallel sequences to decode (default: 1)<br/>(env: LLAMA_ARG_N_PARALLEL) |
 | `--mlock` | force system to keep model in RAM rather than swapping or compressing<br/>(env: LLAMA_ARG_MLOCK) |
 | `--mmap, --no-mmap` | whether to memory-map model (if disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)<br/>(env: LLAMA_ARG_MMAP) |
 | `--numa TYPE` | attempt optimizations that help on some NUMA systems<br/>- distribute: spread execution evenly over all nodes<br/>- isolate: only spawn threads on CPUs on the node that execution started on<br/>- numactl: use the CPU map provided by numactl<br/>if run without this previously, it is recommended to drop the system page cache before using this<br/>see https://github.com/ggml-org/llama.cpp/issues/1437<br/>(env: LLAMA_ARG_NUMA) |
@@ -150,19 +148,20 @@ For the ful list of features, please refer to [server's changelog](https://githu
 | `-jf, --json-schema-file FILE` | File containing a JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object<br/>For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead |
 
 
-**Example-specific params**
+**Server-specific params**
 
 | Argument | Explanation |
 | -------- | ----------- |
-| `--ctx-checkpoints, --swa-checkpoints N` | max number of context checkpoints to create per slot (default: 8)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)<br/>(env: LLAMA_ARG_CTX_CHECKPOINTS) |
-| `--cache-ram, -cram N` | set the maximum cache size in MiB (default: 8192, -1 - no limit, 0 - disable)<br/>[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)<br/>(env: LLAMA_ARG_CACHE_RAM) |
+| `--ctx-checkpoints, --swa-checkpoints N` | max number of context checkpoints to create per slot (default: 8)[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)<br/>(env: LLAMA_ARG_CTX_CHECKPOINTS) |
+| `--cache-ram, -cram N` | set the maximum cache size in MiB (default: 8192, -1 - no limit, 0 - disable)[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)<br/>(env: LLAMA_ARG_CACHE_RAM) |
+| `--kv-unified, -kvu` | use single unified KV buffer shared across all sequences (default: enabled if number of slots is auto)<br/>(env: LLAMA_ARG_KV_UNIFIED) |
 | `--context-shift, --no-context-shift` | whether to use context shift on infinite text generation (default: disabled)<br/>(env: LLAMA_ARG_CONTEXT_SHIFT) |
 | `-r, --reverse-prompt PROMPT` | halt generation at PROMPT, return control in interactive mode<br/> |
 | `-sp, --special` | special tokens output enabled (default: false) |
 | `--warmup, --no-warmup` | whether to perform warmup with an empty run (default: enabled) |
 | `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) |
 | `--pooling {none,mean,cls,last,rank}` | pooling type for embeddings, use model default if unspecified<br/>(env: LLAMA_ARG_POOLING) |
-| `-cb, --cont-batching, -nocb, --no-cont-batching` | whether to enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
+| `-np, --parallel N` | number of server slots (default: -1, -1 = auto)<br/>(env: LLAMA_ARG_N_PARALLEL) |
 | `-cb, --cont-batching, -nocb, --no-cont-batching` | whether to enable continuous batching (a.k.a dynamic batching) (default: enabled)<br/>(env: LLAMA_ARG_CONT_BATCHING) |
 | `-mm, --mmproj FILE` | path to a multimodal projector file. see tools/mtmd/README.md<br/>note: if -hf is used, this argument can be omitted<br/>(env: LLAMA_ARG_MMPROJ) |
 | `-mmu, --mmproj-url URL` | URL to a multimodal projector file. see tools/mtmd/README.md<br/>(env: LLAMA_ARG_MMPROJ_URL) |
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index d5bef3df44..235ae4e8c0 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -73,13 +73,8 @@ int main(int argc, char ** argv, char ** envp) {
         return 1;
     }
 
-    // TODO: should we have a separate n_parallel parameter for the server?
-    //       https://github.com/ggml-org/llama.cpp/pull/16736#discussion_r2483763177
-    // TODO: this is a common configuration that is suitable for most local use cases
-    //       however, overriding the parameters is a bit confusing - figure out something more intuitive
-    if (params.n_parallel == 1 && params.kv_unified == false && !params.has_speculative()) {
-        LOG_WRN("%s: setting n_parallel = 4 and kv_unified = true (add -kvu to disable this)\n", __func__);
-
+    if (params.n_parallel < 0) {
+        LOG_INF("%s: n_parallel is set to auto, using n_parallel = 4 and kv_unified = true\n", __func__);
         params.n_parallel = 4;
         params.kv_unified = true;
     }

From 7f2b2f3c778f430edc57d5728641317e9ac5a505 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Tue, 16 Dec 2025 13:22:30 +0100
Subject: [PATCH 133/135] arch: refactor LLM_TENSOR_NAMES (#18051)

* arch: refactor LLM_TENSOR_NAMES

* update docs

* typo

* fix LLM_ARCH_NEMOTRON_H_MOE

* show more meaningful error message on missing tensor

* fix and tested LLM_ARCH_NEMOTRON_H_MOE
---
 docs/development/HOWTO-add-model.md |    2 +-
 src/llama-arch.cpp                  | 4169 ++++++++++++---------------
 src/llama-arch.h                    |   10 +-
 3 files changed, 1897 insertions(+), 2284 deletions(-)

diff --git a/docs/development/HOWTO-add-model.md b/docs/development/HOWTO-add-model.md
index 9d1452e3f0..b6870f6e25 100644
--- a/docs/development/HOWTO-add-model.md
+++ b/docs/development/HOWTO-add-model.md
@@ -97,7 +97,7 @@ The model params and tensors layout must be defined in `llama.cpp` source files:
 1. Define a new `llm_arch` enum value in `src/llama-arch.h`.
 2. In `src/llama-arch.cpp`:
     - Add the architecture name to the `LLM_ARCH_NAMES` map.
-    - Add the tensor mappings to the `LLM_TENSOR_NAMES` map.
+    - Add the list of model tensors to `llm_get_tensor_names` (you may also need to update `LLM_TENSOR_NAMES`)
 3. Add any non-standard metadata loading in the `llama_model_loader` constructor in `src/llama-model-loader.cpp`.
 4. If the model has a RoPE operation, add a case for the architecture in `llama_model_rope_type` function in `src/llama-model.cpp`.
 
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index 05b12a6072..8caf80afcf 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -3,6 +3,7 @@
 #include "llama-impl.h"
 
 #include <map>
+#include <set>
 
 static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_CLIP,             "clip"             }, // dummy, only used by llama-quantize
@@ -302,2286 +303,1884 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_TOKENIZER_MIDDLE_ID, "tokenizer.ggml.middle_token_id" },
 };
 
-static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_NAMES = {
-    {
-        LLM_ARCH_CLIP,
-        {},
-    },
-    {
-        LLM_ARCH_LLAMA,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
-            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
-            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_ARCEE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_AFMOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_GATE,       "blk.%d.attn_gate" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_GATE_SHEXP,  "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,    "blk.%d.ffn_up_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP,  "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_EXP_PROBS_B, "blk.%d.exp_probs_b" },
-        },
-    },
-    {
-        LLM_ARCH_LLAMA4,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
-            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
-            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_GATE_SHEXP,  "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP,  "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,    "blk.%d.ffn_up_shexp" },
-        },
-    },
-    {
-        LLM_ARCH_DECI,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
-            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
-            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_BAICHUAN,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_FALCON,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_NORM_2,     "blk.%d.attn_norm_2" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_GROK,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
-            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
-            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
-            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
-            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
-        },
-    },
-    {
-        LLM_ARCH_GPT2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_POS_EMBD,        "position_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-        },
-    },
-    {
-        LLM_ARCH_GPTJ,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-        },
-    },
-    {
-        LLM_ARCH_GPTNEOX,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_MPT,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output"},
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_ACT,         "blk.%d.ffn.act" },
-            { LLM_TENSOR_POS_EMBD,        "position_embd" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm"},
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm"},
-        },
-    },
-    {
-        LLM_ARCH_STARCODER,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_POS_EMBD,        "position_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-        },
-    },
-    {
-        LLM_ARCH_REFACT,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_BERT,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
-            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
-            { LLM_TENSOR_POS_EMBD,        "position_embd" },
-            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_CLS,             "cls" },
-            { LLM_TENSOR_CLS_OUT,         "cls.output" },
-        },
-    },
-    {
-        LLM_ARCH_NOMIC_BERT,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
-            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
-            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_NOMIC_BERT_MOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
-            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
-            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_NEO_BERT,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_ENC_OUTPUT_NORM, "enc.output_norm" },
-            { LLM_TENSOR_CLS,             "cls" },
-            { LLM_TENSOR_CLS_OUT,         "cls.output" },
-        },
-    },
-    {
-        LLM_ARCH_JINA_BERT_V2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
-            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
-            { LLM_TENSOR_ATTN_NORM_2,     "blk.%d.attn_norm_2" },
-            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_CLS,             "cls" },
-        },
-    },
-    {
-        LLM_ARCH_JINA_BERT_V3,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
-            { LLM_TENSOR_TOKEN_TYPES,     "token_types" },
-            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_LAYER_OUT_NORM,  "blk.%d.layer_output_norm" },
-        },
-    },
-    {
-        LLM_ARCH_BLOOM,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-        },
-    },
-    {
-        LLM_ARCH_STABLELM,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-        },
-    },
-    {
-        LLM_ARCH_QWEN,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_QWEN2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_QWEN2VL,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_QWEN2MOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
-            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
-        },
-    },
-    {
-        LLM_ARCH_QWEN3,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_CLS_OUT,         "cls.output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_QWEN3MOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_NORM,        "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_K_NORM,        "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_QWEN3NEXT,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_POST_NORM,     "blk.%d.post_attention_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_NORM,        "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_K_NORM,        "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
-            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
-            { LLM_TENSOR_SSM_A_NOSCAN,       "blk.%d.ssm_a" },
-            { LLM_TENSOR_SSM_CONV1D,         "blk.%d.ssm_conv1d" },
-            { LLM_TENSOR_SSM_DT,             "blk.%d.ssm_dt" },
-            { LLM_TENSOR_SSM_BETA_ALPHA,     "blk.%d.ssm_ba" },
-            { LLM_TENSOR_SSM_IN,             "blk.%d.ssm_in" },
-            { LLM_TENSOR_SSM_NORM,           "blk.%d.ssm_norm" },
-            { LLM_TENSOR_SSM_OUT,            "blk.%d.ssm_out" },
-        },
-    },
-    {
-        LLM_ARCH_QWEN3VL,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_QWEN3VLMOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_NORM,        "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_K_NORM,        "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_PHI2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_PHI3,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ROPE_FACTORS_LONG,  "rope_factors_long" },
-            { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,           "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_PHIMOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ROPE_FACTORS_LONG,  "rope_factors_long" },
-            { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,           "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_PLAMO,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_PLAMO2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_SSM_IN,          "blk.%d.ssm_in" },
-            { LLM_TENSOR_SSM_CONV1D,      "blk.%d.ssm_conv1d" },
-            { LLM_TENSOR_SSM_X,           "blk.%d.ssm_x" },
-            { LLM_TENSOR_SSM_DT,          "blk.%d.ssm_dt" },
-            { LLM_TENSOR_SSM_A,           "blk.%d.ssm_a" },
-            { LLM_TENSOR_SSM_D,           "blk.%d.ssm_d" },
-            { LLM_TENSOR_SSM_OUT,         "blk.%d.ssm_out" },
-            { LLM_TENSOR_SSM_DT_NORM,     "blk.%d.ssm_dt_norm" },
-            { LLM_TENSOR_SSM_B_NORM,      "blk.%d.ssm_b_norm" },
-            { LLM_TENSOR_SSM_C_NORM,      "blk.%d.ssm_c_norm" },
-            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
-            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
-        },
-    },
-    {
-        LLM_ARCH_CODESHELL,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_ORION,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_INTERNLM2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_MINICPM,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ROPE_FACTORS_LONG,  "rope_factors_long" },
-            { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
-            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
-            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
-        },
-    },
-    {
-        LLM_ARCH_MINICPM3,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ROPE_FACTORS_LONG,  "rope_factors_long" },
-            { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q_A_NORM,      "blk.%d.attn_q_a_norm" },
-            { LLM_TENSOR_ATTN_KV_A_NORM,     "blk.%d.attn_kv_a_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_A,           "blk.%d.attn_q_a" },
-            { LLM_TENSOR_ATTN_Q_B,           "blk.%d.attn_q_b" },
-            { LLM_TENSOR_ATTN_KV_A_MQA,      "blk.%d.attn_kv_a_mqa" },
-            { LLM_TENSOR_ATTN_KV_B,          "blk.%d.attn_kv_b" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-        },
-    },
-    {
-        LLM_ARCH_GEMMA,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_GEMMA2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
-        },
-    },
-    {
-        LLM_ARCH_GEMMA3,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
-        },
-    },
-    {
-        LLM_ARCH_GEMMA3N,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,           "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,          "output_norm" },
-            { LLM_TENSOR_ATTN_NORM,            "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,               "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_NORM,          "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K,               "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_K_NORM,          "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_V,               "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,             "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_POST_NORM,       "blk.%d.post_attention_norm" },
-            { LLM_TENSOR_FFN_NORM,             "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,             "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,             "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,               "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_POST_NORM,        "blk.%d.post_ffw_norm" },
-            { LLM_TENSOR_PER_LAYER_TOKEN_EMBD, "per_layer_token_embd" },
-            { LLM_TENSOR_PER_LAYER_MODEL_PROJ, "per_layer_model_proj" },
-            { LLM_TENSOR_PER_LAYER_PROJ_NORM,  "per_layer_proj_norm" },
-            { LLM_TENSOR_ALTUP_UNEMBD_PROJ,    "altup_unembd_proj" },
-            { LLM_TENSOR_ALTUP_PROJ,           "altup_proj" },
-            { LLM_TENSOR_PER_LAYER_INP_GATE,   "blk.%d.inp_gate" },
-            { LLM_TENSOR_PER_LAYER_PROJ,       "blk.%d.proj" },
-            { LLM_TENSOR_PER_LAYER_POST_NORM,  "blk.%d.post_norm" },
-            { LLM_TENSOR_ALTUP_CORRECT_COEF,   "blk.%d.altup_correct_coef" },
-            { LLM_TENSOR_ALTUP_CORRECT_SCALE,  "blk.%d.altup_correct_scale" },
-            { LLM_TENSOR_ALTUP_PREDICT_COEF,   "blk.%d.altup_predict_coef" },
-            { LLM_TENSOR_ALTUP_ROUTER,         "blk.%d.altup_router" },
-            { LLM_TENSOR_ALTUP_ROUTER_NORM,    "blk.%d.altup_router_norm" },
-            { LLM_TENSOR_LAUREL_L,             "blk.%d.laurel_l" },
-            { LLM_TENSOR_LAUREL_R,             "blk.%d.laurel_r" },
-            { LLM_TENSOR_LAUREL_POST_NORM,     "blk.%d.laurel_post_norm" },
-        },
-    },
-    {
-        LLM_ARCH_GEMMA_EMBEDDING,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_DENSE_2_OUT,     "dense_2" },
-            { LLM_TENSOR_DENSE_3_OUT,     "dense_3" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
-        },
-    },
-    {
-        LLM_ARCH_STARCODER2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_MAMBA,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_SSM_IN,          "blk.%d.ssm_in" },
-            { LLM_TENSOR_SSM_CONV1D,      "blk.%d.ssm_conv1d" },
-            { LLM_TENSOR_SSM_X,           "blk.%d.ssm_x" },
-            { LLM_TENSOR_SSM_DT,          "blk.%d.ssm_dt" },
-            { LLM_TENSOR_SSM_A,           "blk.%d.ssm_a" },
-            { LLM_TENSOR_SSM_D,           "blk.%d.ssm_d" },
-            { LLM_TENSOR_SSM_OUT,         "blk.%d.ssm_out" },
-        },
-    },
-    {
-        LLM_ARCH_MAMBA2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_SSM_IN,          "blk.%d.ssm_in" },
-            { LLM_TENSOR_SSM_CONV1D,      "blk.%d.ssm_conv1d" },
-            { LLM_TENSOR_SSM_DT,          "blk.%d.ssm_dt" },
-            { LLM_TENSOR_SSM_A,           "blk.%d.ssm_a" },
-            { LLM_TENSOR_SSM_D,           "blk.%d.ssm_d" },
-            { LLM_TENSOR_SSM_NORM,        "blk.%d.ssm_norm" },
-            { LLM_TENSOR_SSM_OUT,         "blk.%d.ssm_out" },
-        },
-    },
-    {
-        LLM_ARCH_JAMBA,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_SSM_IN,          "blk.%d.ssm_in" },
-            { LLM_TENSOR_SSM_CONV1D,      "blk.%d.ssm_conv1d" },
-            { LLM_TENSOR_SSM_X,           "blk.%d.ssm_x" },
-            { LLM_TENSOR_SSM_DT,          "blk.%d.ssm_dt" },
-            { LLM_TENSOR_SSM_DT_NORM,     "blk.%d.ssm_dt_norm" },
-            { LLM_TENSOR_SSM_A,           "blk.%d.ssm_a" },
-            { LLM_TENSOR_SSM_B_NORM,      "blk.%d.ssm_b_norm" },
-            { LLM_TENSOR_SSM_C_NORM,      "blk.%d.ssm_c_norm" },
-            { LLM_TENSOR_SSM_D,           "blk.%d.ssm_d" },
-            { LLM_TENSOR_SSM_OUT,         "blk.%d.ssm_out" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_FALCON_H1,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_SSM_IN,          "blk.%d.ssm_in" },
-            { LLM_TENSOR_SSM_CONV1D,      "blk.%d.ssm_conv1d" },
-            { LLM_TENSOR_SSM_DT,          "blk.%d.ssm_dt" },
-            { LLM_TENSOR_SSM_A,           "blk.%d.ssm_a" },
-            { LLM_TENSOR_SSM_D,           "blk.%d.ssm_d" },
-            { LLM_TENSOR_SSM_NORM,        "blk.%d.ssm_norm" },
-            { LLM_TENSOR_SSM_OUT,         "blk.%d.ssm_out" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_XVERSE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_COMMAND_R,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-        },
-    },
-    {
-        LLM_ARCH_COHERE2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_DBRX,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_OUT_NORM,   "blk.%d.attn_output_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_OLMO,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_OLMO2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_OLMOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_Q_NORM,        "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,        "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_OPENELM,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_ARCTIC,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_NORM_EXPS,   "blk.%d.ffn_norm_exps" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_DEEPSEEK,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ROPE_FREQS,         "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,      "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
-            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
-        },
-    },
-    {
-        LLM_ARCH_DEEPSEEK2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q_A_NORM,      "blk.%d.attn_q_a_norm" },
-            { LLM_TENSOR_ATTN_KV_A_NORM,     "blk.%d.attn_kv_a_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_A,           "blk.%d.attn_q_a" },
-            { LLM_TENSOR_ATTN_Q_B,           "blk.%d.attn_q_b" },
-            { LLM_TENSOR_ATTN_KV_A_MQA,      "blk.%d.attn_kv_a_mqa" },
-            { LLM_TENSOR_ATTN_KV_B,          "blk.%d.attn_kv_b" },
-            { LLM_TENSOR_ATTN_K_B,           "blk.%d.attn_k_b" },
-            { LLM_TENSOR_ATTN_V_B,           "blk.%d.attn_v_b" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
-            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
-            { LLM_TENSOR_FFN_EXP_PROBS_B,    "blk.%d.exp_probs_b" },
-        },
-    },
-    {
-        LLM_ARCH_PLM,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_KV_A_MQA,      "blk.%d.attn_kv_a_mqa" },
-            { LLM_TENSOR_ATTN_KV_A_NORM,     "blk.%d.attn_kv_a_norm" },
-            { LLM_TENSOR_ATTN_KV_B,          "blk.%d.attn_kv_b" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_CHATGLM,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-        },
-    },
-    {
-        LLM_ARCH_GLM4,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
-            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
-        },
-    },
-    {
-        LLM_ARCH_GLM4_MOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_POST_NORM,     "blk.%d.post_attention_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_Q_NORM,        "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,        "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
-            { LLM_TENSOR_FFN_EXP_PROBS_B,    "blk.%d.exp_probs_b" },
-            // NextN/MTP tensors - preserved but unused (in final layer, dynamic layer number)
-            { LLM_TENSOR_NEXTN_EH_PROJ,      "blk.%d.nextn.eh_proj" },
-            { LLM_TENSOR_NEXTN_EMBED_TOKENS, "blk.%d.nextn.embed_tokens" },
-            { LLM_TENSOR_NEXTN_ENORM,        "blk.%d.nextn.enorm" },
-            { LLM_TENSOR_NEXTN_HNORM,        "blk.%d.nextn.hnorm" },
-            { LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "blk.%d.nextn.shared_head_head" },
-            { LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "blk.%d.nextn.shared_head_norm" },
-        },
-    },
-    {
-        LLM_ARCH_BITNET,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_SUB_NORM,      "blk.%d.attn_sub_norm" },
-            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_SUB_NORM,       "blk.%d.ffn_sub_norm" },
-        },
-    },
-    {
-        LLM_ARCH_T5,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,           "token_embd" },
-            { LLM_TENSOR_OUTPUT,               "output" },
-            { LLM_TENSOR_DEC_OUTPUT_NORM,      "dec.output_norm" },
-            { LLM_TENSOR_DEC_ATTN_NORM,        "dec.blk.%d.attn_norm" },
-            { LLM_TENSOR_DEC_ATTN_Q,           "dec.blk.%d.attn_q" },
-            { LLM_TENSOR_DEC_ATTN_K,           "dec.blk.%d.attn_k" },
-            { LLM_TENSOR_DEC_ATTN_V,           "dec.blk.%d.attn_v" },
-            { LLM_TENSOR_DEC_ATTN_OUT,         "dec.blk.%d.attn_o" },
-            { LLM_TENSOR_DEC_ATTN_REL_B,       "dec.blk.%d.attn_rel_b" },
-            { LLM_TENSOR_DEC_CROSS_ATTN_NORM,  "dec.blk.%d.cross_attn_norm" },
-            { LLM_TENSOR_DEC_CROSS_ATTN_Q,     "dec.blk.%d.cross_attn_q" },
-            { LLM_TENSOR_DEC_CROSS_ATTN_K,     "dec.blk.%d.cross_attn_k" },
-            { LLM_TENSOR_DEC_CROSS_ATTN_V,     "dec.blk.%d.cross_attn_v" },
-            { LLM_TENSOR_DEC_CROSS_ATTN_OUT,   "dec.blk.%d.cross_attn_o" },
-            { LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "dec.blk.%d.cross_attn_rel_b" },
-            { LLM_TENSOR_DEC_FFN_NORM,         "dec.blk.%d.ffn_norm" },
-            { LLM_TENSOR_DEC_FFN_GATE,         "dec.blk.%d.ffn_gate" },
-            { LLM_TENSOR_DEC_FFN_DOWN,         "dec.blk.%d.ffn_down" },
-            { LLM_TENSOR_DEC_FFN_UP,           "dec.blk.%d.ffn_up" },
-            { LLM_TENSOR_ENC_OUTPUT_NORM,      "enc.output_norm" },
-            { LLM_TENSOR_ENC_ATTN_NORM,        "enc.blk.%d.attn_norm" },
-            { LLM_TENSOR_ENC_ATTN_Q,           "enc.blk.%d.attn_q" },
-            { LLM_TENSOR_ENC_ATTN_K,           "enc.blk.%d.attn_k" },
-            { LLM_TENSOR_ENC_ATTN_V,           "enc.blk.%d.attn_v" },
-            { LLM_TENSOR_ENC_ATTN_OUT,         "enc.blk.%d.attn_o" },
-            { LLM_TENSOR_ENC_ATTN_REL_B,       "enc.blk.%d.attn_rel_b" },
-            { LLM_TENSOR_ENC_FFN_NORM,         "enc.blk.%d.ffn_norm" },
-            { LLM_TENSOR_ENC_FFN_GATE,         "enc.blk.%d.ffn_gate" },
-            { LLM_TENSOR_ENC_FFN_DOWN,         "enc.blk.%d.ffn_down" },
-            { LLM_TENSOR_ENC_FFN_UP,           "enc.blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_T5ENCODER,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,           "token_embd" },
-            { LLM_TENSOR_OUTPUT,               "output" },
-            { LLM_TENSOR_ENC_OUTPUT_NORM,      "enc.output_norm" },
-            { LLM_TENSOR_ENC_ATTN_NORM,        "enc.blk.%d.attn_norm" },
-            { LLM_TENSOR_ENC_ATTN_Q,           "enc.blk.%d.attn_q" },
-            { LLM_TENSOR_ENC_ATTN_K,           "enc.blk.%d.attn_k" },
-            { LLM_TENSOR_ENC_ATTN_V,           "enc.blk.%d.attn_v" },
-            { LLM_TENSOR_ENC_ATTN_OUT,         "enc.blk.%d.attn_o" },
-            { LLM_TENSOR_ENC_ATTN_REL_B,       "enc.blk.%d.attn_rel_b" },
-            { LLM_TENSOR_ENC_FFN_NORM,         "enc.blk.%d.ffn_norm" },
-            { LLM_TENSOR_ENC_FFN_GATE,         "enc.blk.%d.ffn_gate" },
-            { LLM_TENSOR_ENC_FFN_DOWN,         "enc.blk.%d.ffn_down" },
-            { LLM_TENSOR_ENC_FFN_UP,           "enc.blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_JAIS,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-        },
-    },
-    {
-        LLM_ARCH_NEMOTRON,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_NEMOTRON_H,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,     "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,    "output_norm" },
-            { LLM_TENSOR_OUTPUT,         "output" },
-            { LLM_TENSOR_ATTN_NORM,      "blk.%d.attn_norm" },
-            // mamba(2) ssm layers
-            { LLM_TENSOR_SSM_IN,         "blk.%d.ssm_in" },
-            { LLM_TENSOR_SSM_CONV1D,     "blk.%d.ssm_conv1d" },
-            { LLM_TENSOR_SSM_DT,         "blk.%d.ssm_dt" },
-            { LLM_TENSOR_SSM_A,          "blk.%d.ssm_a" },
-            { LLM_TENSOR_SSM_D,          "blk.%d.ssm_d" },
-            { LLM_TENSOR_SSM_NORM,       "blk.%d.ssm_norm" },
-            { LLM_TENSOR_SSM_OUT,        "blk.%d.ssm_out" },
-            // attention layers
-            { LLM_TENSOR_ATTN_Q,         "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,         "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,         "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,       "blk.%d.attn_output" },
-            // dense FFN
-            { LLM_TENSOR_FFN_DOWN,       "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,         "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_NEMOTRON_H_MOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,     "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,    "output_norm" },
-            { LLM_TENSOR_OUTPUT,         "output" },
-            { LLM_TENSOR_ATTN_NORM,      "blk.%d.attn_norm" },
-            // mamba(2) ssm layers
-            { LLM_TENSOR_SSM_IN,         "blk.%d.ssm_in" },
-            { LLM_TENSOR_SSM_CONV1D,     "blk.%d.ssm_conv1d" },
-            { LLM_TENSOR_SSM_DT,         "blk.%d.ssm_dt" },
-            { LLM_TENSOR_SSM_A,          "blk.%d.ssm_a" },
-            { LLM_TENSOR_SSM_D,          "blk.%d.ssm_d" },
-            { LLM_TENSOR_SSM_NORM,       "blk.%d.ssm_norm" },
-            { LLM_TENSOR_SSM_OUT,        "blk.%d.ssm_out" },
-            // attention layers
-            { LLM_TENSOR_ATTN_Q,         "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,         "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,         "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,       "blk.%d.attn_output" },
-            // dense FFN
-            { LLM_TENSOR_FFN_DOWN,       "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,         "blk.%d.ffn_up" },
-            // MoE FFN (for MoE layers)
-            { LLM_TENSOR_FFN_GATE_INP,   "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_UP_EXPS,    "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,  "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_EXP_PROBS_B,"blk.%d.exp_probs_b" },
-            // MoE shared expert layer
-            { LLM_TENSOR_FFN_DOWN_SHEXP,  "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,    "blk.%d.ffn_up_shexp" },
-        },
-    },
-    {
-        LLM_ARCH_EXAONE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_EXAONE4,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_POST_NORM,   "blk.%d.post_ffw_norm" },
-        }
-    },
-    {
-        LLM_ARCH_RWKV6,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,                "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM,           "token_embd_norm" },
-            { LLM_TENSOR_OUTPUT_NORM,               "output_norm" },
-            { LLM_TENSOR_OUTPUT,                    "output" },
-            { LLM_TENSOR_ATTN_NORM,                 "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_NORM_2,               "blk.%d.attn_norm_2" },
-            { LLM_TENSOR_TIME_MIX_W1,               "blk.%d.time_mix_w1" },
-            { LLM_TENSOR_TIME_MIX_W2,               "blk.%d.time_mix_w2" },
-            { LLM_TENSOR_TIME_MIX_LERP_X,           "blk.%d.time_mix_lerp_x" },
-            { LLM_TENSOR_TIME_MIX_LERP_W,           "blk.%d.time_mix_lerp_w" },
-            { LLM_TENSOR_TIME_MIX_LERP_K,           "blk.%d.time_mix_lerp_k" },
-            { LLM_TENSOR_TIME_MIX_LERP_V,           "blk.%d.time_mix_lerp_v" },
-            { LLM_TENSOR_TIME_MIX_LERP_R,           "blk.%d.time_mix_lerp_r" },
-            { LLM_TENSOR_TIME_MIX_LERP_G,           "blk.%d.time_mix_lerp_g" },
-            { LLM_TENSOR_TIME_MIX_LERP_FUSED,       "blk.%d.time_mix_lerp_fused" },
-            { LLM_TENSOR_TIME_MIX_FIRST,            "blk.%d.time_mix_first" },
-            { LLM_TENSOR_TIME_MIX_DECAY,            "blk.%d.time_mix_decay" },
-            { LLM_TENSOR_TIME_MIX_DECAY_W1,         "blk.%d.time_mix_decay_w1" },
-            { LLM_TENSOR_TIME_MIX_DECAY_W2,         "blk.%d.time_mix_decay_w2" },
-            { LLM_TENSOR_TIME_MIX_KEY,              "blk.%d.time_mix_key" },
-            { LLM_TENSOR_TIME_MIX_VALUE,            "blk.%d.time_mix_value" },
-            { LLM_TENSOR_TIME_MIX_RECEPTANCE,       "blk.%d.time_mix_receptance" },
-            { LLM_TENSOR_TIME_MIX_GATE,             "blk.%d.time_mix_gate" },
-            { LLM_TENSOR_TIME_MIX_LN,               "blk.%d.time_mix_ln" },
-            { LLM_TENSOR_TIME_MIX_OUTPUT,           "blk.%d.time_mix_output" },
-            { LLM_TENSOR_CHANNEL_MIX_LERP_K,        "blk.%d.channel_mix_lerp_k" },
-            { LLM_TENSOR_CHANNEL_MIX_LERP_R,        "blk.%d.channel_mix_lerp_r" },
-            { LLM_TENSOR_CHANNEL_MIX_KEY,           "blk.%d.channel_mix_key" },
-            { LLM_TENSOR_CHANNEL_MIX_VALUE,         "blk.%d.channel_mix_value" },
-            { LLM_TENSOR_CHANNEL_MIX_RECEPTANCE,    "blk.%d.channel_mix_receptance" },
-        },
-    },
-    {
-        LLM_ARCH_RWKV6QWEN2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,                "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,               "output_norm" },
-            { LLM_TENSOR_OUTPUT,                    "output" },
-            { LLM_TENSOR_ATTN_NORM,                 "blk.%d.attn_norm" },
-            { LLM_TENSOR_TIME_MIX_W1,               "blk.%d.time_mix_w1" },
-            { LLM_TENSOR_TIME_MIX_W2,               "blk.%d.time_mix_w2" },
-            { LLM_TENSOR_TIME_MIX_LERP_X,           "blk.%d.time_mix_lerp_x" },
-            { LLM_TENSOR_TIME_MIX_LERP_FUSED,       "blk.%d.time_mix_lerp_fused" },
-            { LLM_TENSOR_TIME_MIX_FIRST,            "blk.%d.time_mix_first" },
-            { LLM_TENSOR_TIME_MIX_DECAY,            "blk.%d.time_mix_decay" },
-            { LLM_TENSOR_TIME_MIX_DECAY_W1,         "blk.%d.time_mix_decay_w1" },
-            { LLM_TENSOR_TIME_MIX_DECAY_W2,         "blk.%d.time_mix_decay_w2" },
-            { LLM_TENSOR_TIME_MIX_KEY,              "blk.%d.time_mix_key" },
-            { LLM_TENSOR_TIME_MIX_VALUE,            "blk.%d.time_mix_value" },
-            { LLM_TENSOR_TIME_MIX_RECEPTANCE,       "blk.%d.time_mix_receptance" },
-            { LLM_TENSOR_TIME_MIX_GATE,             "blk.%d.time_mix_gate" },
-            { LLM_TENSOR_TIME_MIX_OUTPUT,           "blk.%d.time_mix_output" },
-            { LLM_TENSOR_FFN_NORM,                  "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,                  "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,                  "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,                    "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_RWKV7,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,                "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM,           "token_embd_norm" },
-            { LLM_TENSOR_OUTPUT_NORM,               "output_norm" },
-            { LLM_TENSOR_OUTPUT,                    "output" },
-            { LLM_TENSOR_ATTN_NORM,                 "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_NORM_2,               "blk.%d.attn_norm_2" },
-            { LLM_TENSOR_TIME_MIX_W0,               "blk.%d.time_mix_w0" },
-            { LLM_TENSOR_TIME_MIX_W1,               "blk.%d.time_mix_w1" },
-            { LLM_TENSOR_TIME_MIX_W2,               "blk.%d.time_mix_w2" },
-            { LLM_TENSOR_TIME_MIX_A0,               "blk.%d.time_mix_a0" },
-            { LLM_TENSOR_TIME_MIX_A1,               "blk.%d.time_mix_a1" },
-            { LLM_TENSOR_TIME_MIX_A2,               "blk.%d.time_mix_a2" },
-            { LLM_TENSOR_TIME_MIX_V0,               "blk.%d.time_mix_v0" },
-            { LLM_TENSOR_TIME_MIX_V1,               "blk.%d.time_mix_v1" },
-            { LLM_TENSOR_TIME_MIX_V2,               "blk.%d.time_mix_v2" },
-            { LLM_TENSOR_TIME_MIX_G1,               "blk.%d.time_mix_g1" },
-            { LLM_TENSOR_TIME_MIX_G2,               "blk.%d.time_mix_g2" },
-            { LLM_TENSOR_TIME_MIX_K_K,              "blk.%d.time_mix_k_k" },
-            { LLM_TENSOR_TIME_MIX_K_A,              "blk.%d.time_mix_k_a" },
-            { LLM_TENSOR_TIME_MIX_R_K,              "blk.%d.time_mix_r_k" },
-            { LLM_TENSOR_TIME_MIX_LERP_FUSED,       "blk.%d.time_mix_lerp_fused" },
-            { LLM_TENSOR_TIME_MIX_KEY,              "blk.%d.time_mix_key" },
-            { LLM_TENSOR_TIME_MIX_VALUE,            "blk.%d.time_mix_value" },
-            { LLM_TENSOR_TIME_MIX_RECEPTANCE,       "blk.%d.time_mix_receptance" },
-            { LLM_TENSOR_TIME_MIX_LN,               "blk.%d.time_mix_ln" },
-            { LLM_TENSOR_TIME_MIX_OUTPUT,           "blk.%d.time_mix_output" },
-            { LLM_TENSOR_CHANNEL_MIX_LERP_K,        "blk.%d.channel_mix_lerp_k" },
-            { LLM_TENSOR_CHANNEL_MIX_KEY,           "blk.%d.channel_mix_key" },
-            { LLM_TENSOR_CHANNEL_MIX_VALUE,         "blk.%d.channel_mix_value" },
-        },
-    },
-    {
-        LLM_ARCH_ARWKV7,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,                "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM,           "token_embd_norm" },
-            { LLM_TENSOR_OUTPUT_NORM,               "output_norm" },
-            { LLM_TENSOR_OUTPUT,                    "output" },
-            { LLM_TENSOR_ATTN_NORM,                 "blk.%d.attn_norm" },
-            { LLM_TENSOR_TIME_MIX_W0,               "blk.%d.time_mix_w0" },
-            { LLM_TENSOR_TIME_MIX_W1,               "blk.%d.time_mix_w1" },
-            { LLM_TENSOR_TIME_MIX_W2,               "blk.%d.time_mix_w2" },
-            { LLM_TENSOR_TIME_MIX_A0,               "blk.%d.time_mix_a0" },
-            { LLM_TENSOR_TIME_MIX_A1,               "blk.%d.time_mix_a1" },
-            { LLM_TENSOR_TIME_MIX_A2,               "blk.%d.time_mix_a2" },
-            { LLM_TENSOR_TIME_MIX_V0,               "blk.%d.time_mix_v0" },
-            { LLM_TENSOR_TIME_MIX_V1,               "blk.%d.time_mix_v1" },
-            { LLM_TENSOR_TIME_MIX_V2,               "blk.%d.time_mix_v2" },
-            { LLM_TENSOR_TIME_MIX_G1,               "blk.%d.time_mix_g1" },
-            { LLM_TENSOR_TIME_MIX_G2,               "blk.%d.time_mix_g2" },
-            { LLM_TENSOR_TIME_MIX_K_K,              "blk.%d.time_mix_k_k" },
-            { LLM_TENSOR_TIME_MIX_K_A,              "blk.%d.time_mix_k_a" },
-            { LLM_TENSOR_TIME_MIX_R_K,              "blk.%d.time_mix_r_k" },
-            { LLM_TENSOR_TIME_MIX_LERP_FUSED,       "blk.%d.time_mix_lerp_fused" },
-            { LLM_TENSOR_TIME_MIX_KEY,              "blk.%d.time_mix_key" },
-            { LLM_TENSOR_TIME_MIX_VALUE,            "blk.%d.time_mix_value" },
-            { LLM_TENSOR_TIME_MIX_RECEPTANCE,       "blk.%d.time_mix_receptance" },
-            { LLM_TENSOR_TIME_MIX_LN,               "blk.%d.time_mix_ln" },
-            { LLM_TENSOR_TIME_MIX_OUTPUT,           "blk.%d.time_mix_output" },
-            { LLM_TENSOR_FFN_NORM,                  "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,                  "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,                  "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,                    "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_GRANITE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_GRANITE_MOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_GATE_SHEXP,  "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP,  "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,    "blk.%d.ffn_up_shexp" },
-        },
-    },
-    {
-        LLM_ARCH_GRANITE_HYBRID,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,     "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,    "output_norm" },
-            { LLM_TENSOR_OUTPUT,         "output" },
-            { LLM_TENSOR_ATTN_NORM,      "blk.%d.attn_norm" },
-            // mamba(2) ssm layers
-            { LLM_TENSOR_SSM_IN,         "blk.%d.ssm_in" },
-            { LLM_TENSOR_SSM_CONV1D,     "blk.%d.ssm_conv1d" },
-            { LLM_TENSOR_SSM_DT,         "blk.%d.ssm_dt" },
-            { LLM_TENSOR_SSM_A,          "blk.%d.ssm_a" },
-            { LLM_TENSOR_SSM_D,          "blk.%d.ssm_d" },
-            { LLM_TENSOR_SSM_NORM,       "blk.%d.ssm_norm" },
-            { LLM_TENSOR_SSM_OUT,        "blk.%d.ssm_out" },
-            // attention layers
-            { LLM_TENSOR_ATTN_Q,         "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,         "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,         "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,       "blk.%d.attn_output" },
-            // dense FFN
-            { LLM_TENSOR_FFN_NORM,       "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,       "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,       "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,         "blk.%d.ffn_up" },
-            // moe FFN
-            { LLM_TENSOR_FFN_NORM,       "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,   "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,  "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,  "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,    "blk.%d.ffn_up_exps" },
-            // shared expert
-            { LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,   "blk.%d.ffn_up_shexp" },
-        },
-    },
-    {
-        LLM_ARCH_CHAMELEON,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-        },
-    },
-    {
-        LLM_ARCH_WAVTOKENIZER_DEC,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,        "token_embd" },
-            { LLM_TENSOR_TOKEN_EMBD_NORM,   "token_embd_norm" },
-            { LLM_TENSOR_CONV1D,            "conv1d" },
-            { LLM_TENSOR_CONVNEXT_DW,       "convnext.%d.dw" },
-            { LLM_TENSOR_CONVNEXT_NORM,     "convnext.%d.norm" },
-            { LLM_TENSOR_CONVNEXT_PW1,      "convnext.%d.pw1" },
-            { LLM_TENSOR_CONVNEXT_PW2,      "convnext.%d.pw2" },
-            { LLM_TENSOR_CONVNEXT_GAMMA,    "convnext.%d.gamma" },
-            { LLM_TENSOR_OUTPUT_NORM,       "output_norm" },
-            { LLM_TENSOR_OUTPUT,            "output" },
-            { LLM_TENSOR_POS_NET_CONV1,     "posnet.%d.conv1" },
-            { LLM_TENSOR_POS_NET_CONV2,     "posnet.%d.conv2" },
-            { LLM_TENSOR_POS_NET_NORM,      "posnet.%d.norm" },
-            { LLM_TENSOR_POS_NET_NORM1,     "posnet.%d.norm1" },
-            { LLM_TENSOR_POS_NET_NORM2,     "posnet.%d.norm2" },
-            { LLM_TENSOR_POS_NET_ATTN_NORM, "posnet.%d.attn_norm" },
-            { LLM_TENSOR_POS_NET_ATTN_Q,    "posnet.%d.attn_q" },
-            { LLM_TENSOR_POS_NET_ATTN_K,    "posnet.%d.attn_k" },
-            { LLM_TENSOR_POS_NET_ATTN_V,    "posnet.%d.attn_v" },
-            { LLM_TENSOR_POS_NET_ATTN_OUT,  "posnet.%d.attn_output" },
-        },
-    },
-    {
-        LLM_ARCH_BAILINGMOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ROPE_FREQS,         "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
-            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
-        },
-    },
-    {
-        LLM_ARCH_BAILINGMOE2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q_NORM,        "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,        "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_QKV,           "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_EXP_PROBS_B,    "blk.%d.exp_probs_b" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
-            { LLM_TENSOR_NEXTN_EH_PROJ,      "blk.%d.nextn.eh_proj" },
-            { LLM_TENSOR_NEXTN_EMBED_TOKENS, "blk.%d.nextn.embed_tokens" },
-            { LLM_TENSOR_NEXTN_ENORM,        "blk.%d.nextn.enorm" },
-            { LLM_TENSOR_NEXTN_HNORM,        "blk.%d.nextn.hnorm" },
-            { LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "blk.%d.nextn.shared_head_head" },
-            { LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "blk.%d.nextn.shared_head_norm" },
-            { LLM_TENSOR_LAYER_OUT_NORM,     "blk.%d.layer_output_norm" },
-        },
-    },
-    {
-        LLM_ARCH_DOTS1,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_NORM,        "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_K_NORM,        "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" },
-            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
-            { LLM_TENSOR_FFN_EXP_PROBS_B,    "blk.%d.exp_probs_b" },
-        }
-    },
-    {
-        LLM_ARCH_ERNIE4_5,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_ERNIE4_5_MOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_SHEXP,     "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP,     "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,       "blk.%d.ffn_up_shexp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_EXP_PROBS_B,    "blk.%d.exp_probs_b" },
-        },
-    },
-    {
-        LLM_ARCH_HUNYUAN_MOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_SHEXP,  "blk.%d.ffn_gate_shexp" },
-            { LLM_TENSOR_FFN_DOWN_SHEXP,  "blk.%d.ffn_down_shexp" },
-            { LLM_TENSOR_FFN_UP_SHEXP,    "blk.%d.ffn_up_shexp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_HUNYUAN_DENSE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-
-        },
-    },
-    {
-        LLM_ARCH_SMOLLM3,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,     "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,    "output_norm" },
-            { LLM_TENSOR_OUTPUT,         "output" },
-            { LLM_TENSOR_ATTN_NORM,      "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,         "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,         "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,         "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,       "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,       "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,       "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,       "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,         "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_OPENAI_MOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_POST_NORM,     "blk.%d.post_attention_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_SINKS,         "blk.%d.attn_sinks" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_LFM2,
-        {
-            { LLM_TENSOR_ATTN_NORM,         "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,            "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,            "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,            "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,          "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_K_NORM,       "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_Q_NORM,       "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_FFN_DOWN,          "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_GATE,          "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_NORM,          "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_UP,            "blk.%d.ffn_up" },
-            { LLM_TENSOR_SHORTCONV_CONV,    "blk.%d.shortconv.conv" },
-            { LLM_TENSOR_SHORTCONV_INPROJ,  "blk.%d.shortconv.in_proj" },
-            { LLM_TENSOR_SHORTCONV_OUTPROJ, "blk.%d.shortconv.out_proj" },
-            { LLM_TENSOR_TOKEN_EMBD,        "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,       "token_embd_norm" }, // note: wrong tensor name
-            { LLM_TENSOR_OUTPUT,            "output" },
-        }
-    },
-    {
-        LLM_ARCH_LFM2MOE,
-        {
-            { LLM_TENSOR_ATTN_NORM,         "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,            "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,            "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,            "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,          "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_K_NORM,       "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_Q_NORM,       "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_FFN_DOWN,          "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_GATE,          "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_NORM,          "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_UP,            "blk.%d.ffn_up" },
-            { LLM_TENSOR_SHORTCONV_CONV,    "blk.%d.shortconv.conv" },
-            { LLM_TENSOR_SHORTCONV_INPROJ,  "blk.%d.shortconv.in_proj" },
-            { LLM_TENSOR_SHORTCONV_OUTPROJ, "blk.%d.shortconv.out_proj" },
-            { LLM_TENSOR_TOKEN_EMBD,        "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,       "token_embd_norm" }, // note: wrong tensor name
-            { LLM_TENSOR_FFN_GATE_INP,      "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,     "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,     "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,       "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_EXP_PROBS_B,   "blk.%d.exp_probs_b" },
-        }
-    },
-    {
-        LLM_ARCH_SMALLTHINKER,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,           "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,           "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,             "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" }
-        },
-    },
-    {
-        LLM_ARCH_APERTUS,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_DREAM,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_LLADA,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_LLADA_MOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_NORM,        "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_K_NORM,        "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_SEED_OSS,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_POST_NORM,  "blk.%d.post_attention_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_GROVEMOE,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_NORM,        "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_K_NORM,        "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_GATE_CHEXPS,    "blk.%d.ffn_gate_chexps" },
-            { LLM_TENSOR_FFN_DOWN_CHEXPS,    "blk.%d.ffn_down_chexps" },
-            { LLM_TENSOR_FFN_UP_CHEXPS,      "blk.%d.ffn_up_chexps" },
-        },
-    },
-    {
-        LLM_ARCH_MINIMAX_M2,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_Q_NORM,        "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K_NORM,        "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-            { LLM_TENSOR_FFN_EXP_PROBS_B,    "blk.%d.exp_probs_b" },
-        },
-    },
-    {
-        LLM_ARCH_PANGU_EMBED,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-        },
-    },
-    {
-        LLM_ARCH_COGVLM,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_VISEXP_ATTN_QKV, "blk.%d.vis_attn_qkv" },
-            { LLM_TENSOR_VISEXP_ATTN_OUT, "blk.%d.vis_attn_output" },
-            { LLM_TENSOR_VISEXP_FFN_GATE, "blk.%d.vis_gate" },
-            { LLM_TENSOR_VISEXP_FFN_DOWN, "blk.%d.vis_down" },
-            { LLM_TENSOR_VISEXP_FFN_UP,   "blk.%d.vis_up" },
-        },
-    },
-    {
-        LLM_ARCH_RND1,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,         "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,        "output_norm" },
-            { LLM_TENSOR_OUTPUT,             "output" },
-            { LLM_TENSOR_ATTN_NORM,          "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,             "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_Q_NORM,        "blk.%d.attn_q_norm" },
-            { LLM_TENSOR_ATTN_K,             "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_K_NORM,        "blk.%d.attn_k_norm" },
-            { LLM_TENSOR_ATTN_V,             "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,           "blk.%d.attn_output" },
-            { LLM_TENSOR_FFN_NORM,           "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE_INP,       "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_GATE_EXPS,      "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,      "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,        "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_MISTRAL3,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
-            { LLM_TENSOR_OUTPUT,          "output" },
-            { LLM_TENSOR_ROPE_FREQS,      "rope_freqs" },
-            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
-            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
-            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
-            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
-            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
-            { LLM_TENSOR_ATTN_ROT_EMBD,   "blk.%d.attn_rot_embd" },
-            { LLM_TENSOR_FFN_GATE_INP,    "blk.%d.ffn_gate_inp" },
-            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
-            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
-            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
-            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
-            { LLM_TENSOR_FFN_GATE_EXP,    "blk.%d.ffn_gate.%d" },
-            { LLM_TENSOR_FFN_DOWN_EXP,    "blk.%d.ffn_down.%d" },
-            { LLM_TENSOR_FFN_UP_EXP,      "blk.%d.ffn_up.%d" },
-            { LLM_TENSOR_FFN_GATE_EXPS,   "blk.%d.ffn_gate_exps" },
-            { LLM_TENSOR_FFN_DOWN_EXPS,   "blk.%d.ffn_down_exps" },
-            { LLM_TENSOR_FFN_UP_EXPS,     "blk.%d.ffn_up_exps" },
-        },
-    },
-    {
-        LLM_ARCH_UNKNOWN,
-        {
-            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
-        },
-    },
+static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
+    { LLM_TENSOR_TOKEN_EMBD,                             "token_embd" },
+    { LLM_TENSOR_OUTPUT_NORM,                            "output_norm" },
+    { LLM_TENSOR_OUTPUT_NORM_LFM2,                       "token_embd_norm" }, // fix for wrong tensor name
+    { LLM_TENSOR_OUTPUT,                                 "output" },
+    { LLM_TENSOR_ROPE_FREQS,                             "rope_freqs" },
+    { LLM_TENSOR_ATTN_NORM,                              "blk.%d.attn_norm" },
+    { LLM_TENSOR_ATTN_Q,                                 "blk.%d.attn_q" },
+    { LLM_TENSOR_ATTN_K,                                 "blk.%d.attn_k" },
+    { LLM_TENSOR_ATTN_V,                                 "blk.%d.attn_v" },
+    { LLM_TENSOR_ATTN_OUT,                               "blk.%d.attn_output" },
+    { LLM_TENSOR_ATTN_ROT_EMBD,                          "blk.%d.attn_rot_embd" },
+    { LLM_TENSOR_FFN_GATE_INP,                           "blk.%d.ffn_gate_inp" },
+    { LLM_TENSOR_FFN_NORM,                               "blk.%d.ffn_norm" },
+    { LLM_TENSOR_FFN_GATE,                               "blk.%d.ffn_gate" },
+    { LLM_TENSOR_FFN_DOWN,                               "blk.%d.ffn_down" },
+    { LLM_TENSOR_FFN_UP,                                 "blk.%d.ffn_up" },
+    { LLM_TENSOR_FFN_GATE_EXP,                           "blk.%d.ffn_gate.%d" },
+    { LLM_TENSOR_FFN_DOWN_EXP,                           "blk.%d.ffn_down.%d" },
+    { LLM_TENSOR_FFN_UP_EXP,                             "blk.%d.ffn_up.%d" },
+    { LLM_TENSOR_FFN_GATE_EXPS,                          "blk.%d.ffn_gate_exps" },
+    { LLM_TENSOR_FFN_DOWN_EXPS,                          "blk.%d.ffn_down_exps" },
+    { LLM_TENSOR_FFN_UP_EXPS,                            "blk.%d.ffn_up_exps" },
+    { LLM_TENSOR_ATTN_POST_NORM,                         "blk.%d.post_attention_norm" },
+    { LLM_TENSOR_ATTN_Q_NORM,                            "blk.%d.attn_q_norm" },
+    { LLM_TENSOR_ATTN_K_NORM,                            "blk.%d.attn_k_norm" },
+    { LLM_TENSOR_ATTN_GATE,                              "blk.%d.attn_gate" },
+    { LLM_TENSOR_FFN_POST_NORM,                          "blk.%d.post_ffw_norm" },
+    { LLM_TENSOR_FFN_GATE_SHEXP,                         "blk.%d.ffn_gate_shexp" },
+    { LLM_TENSOR_FFN_UP_SHEXP,                           "blk.%d.ffn_up_shexp" },
+    { LLM_TENSOR_FFN_DOWN_SHEXP,                         "blk.%d.ffn_down_shexp" },
+    { LLM_TENSOR_FFN_EXP_PROBS_B,                        "blk.%d.exp_probs_b" },
+    { LLM_TENSOR_ATTN_NORM_2,                            "blk.%d.attn_norm_2" },
+    { LLM_TENSOR_ATTN_QKV,                               "blk.%d.attn_qkv" },
+    { LLM_TENSOR_LAYER_OUT_NORM,                         "blk.%d.layer_output_norm" },
+    { LLM_TENSOR_ATTN_OUT_NORM,                          "blk.%d.attn_output_norm" },
+    { LLM_TENSOR_POS_EMBD,                               "position_embd" },
+    { LLM_TENSOR_FFN_ACT,                                "blk.%d.ffn.act" },
+    { LLM_TENSOR_TOKEN_EMBD_NORM,                        "token_embd_norm" },
+    { LLM_TENSOR_TOKEN_TYPES,                            "token_types" },
+    { LLM_TENSOR_CLS,                                    "cls" },
+    { LLM_TENSOR_CLS_OUT,                                "cls.output" },
+    { LLM_TENSOR_ENC_OUTPUT_NORM,                        "enc.output_norm" },
+    { LLM_TENSOR_FFN_GATE_INP_SHEXP,                     "blk.%d.ffn_gate_inp_shexp" },
+    { LLM_TENSOR_SSM_A_NOSCAN,                           "blk.%d.ssm_a" },
+    { LLM_TENSOR_SSM_CONV1D,                             "blk.%d.ssm_conv1d" },
+    { LLM_TENSOR_SSM_DT,                                 "blk.%d.ssm_dt" },
+    { LLM_TENSOR_SSM_BETA_ALPHA,                         "blk.%d.ssm_ba" },
+    { LLM_TENSOR_SSM_IN,                                 "blk.%d.ssm_in" },
+    { LLM_TENSOR_SSM_NORM,                               "blk.%d.ssm_norm" },
+    { LLM_TENSOR_SSM_OUT,                                "blk.%d.ssm_out" },
+    { LLM_TENSOR_ROPE_FACTORS_LONG,                      "rope_factors_long" },
+    { LLM_TENSOR_ROPE_FACTORS_SHORT,                     "rope_factors_short" },
+    { LLM_TENSOR_SSM_X,                                  "blk.%d.ssm_x" },
+    { LLM_TENSOR_SSM_A,                                  "blk.%d.ssm_a" },
+    { LLM_TENSOR_SSM_D,                                  "blk.%d.ssm_d" },
+    { LLM_TENSOR_SSM_DT_NORM,                            "blk.%d.ssm_dt_norm" },
+    { LLM_TENSOR_SSM_B_NORM,                             "blk.%d.ssm_b_norm" },
+    { LLM_TENSOR_SSM_C_NORM,                             "blk.%d.ssm_c_norm" },
+    { LLM_TENSOR_ATTN_Q_A_NORM,                          "blk.%d.attn_q_a_norm" },
+    { LLM_TENSOR_ATTN_KV_A_NORM,                         "blk.%d.attn_kv_a_norm" },
+    { LLM_TENSOR_ATTN_Q_A,                               "blk.%d.attn_q_a" },
+    { LLM_TENSOR_ATTN_Q_B,                               "blk.%d.attn_q_b" },
+    { LLM_TENSOR_ATTN_KV_A_MQA,                          "blk.%d.attn_kv_a_mqa" },
+    { LLM_TENSOR_ATTN_KV_B,                              "blk.%d.attn_kv_b" },
+    { LLM_TENSOR_PER_LAYER_TOKEN_EMBD,                   "per_layer_token_embd" },
+    { LLM_TENSOR_PER_LAYER_MODEL_PROJ,                   "per_layer_model_proj" },
+    { LLM_TENSOR_PER_LAYER_PROJ_NORM,                    "per_layer_proj_norm" },
+    { LLM_TENSOR_ALTUP_UNEMBD_PROJ,                      "altup_unembd_proj" },
+    { LLM_TENSOR_ALTUP_PROJ,                             "altup_proj" },
+    { LLM_TENSOR_PER_LAYER_INP_GATE,                     "blk.%d.inp_gate" },
+    { LLM_TENSOR_PER_LAYER_PROJ,                         "blk.%d.proj" },
+    { LLM_TENSOR_PER_LAYER_POST_NORM,                    "blk.%d.post_norm" },
+    { LLM_TENSOR_ALTUP_CORRECT_COEF,                     "blk.%d.altup_correct_coef" },
+    { LLM_TENSOR_ALTUP_CORRECT_SCALE,                    "blk.%d.altup_correct_scale" },
+    { LLM_TENSOR_ALTUP_PREDICT_COEF,                     "blk.%d.altup_predict_coef" },
+    { LLM_TENSOR_ALTUP_ROUTER,                           "blk.%d.altup_router" },
+    { LLM_TENSOR_ALTUP_ROUTER_NORM,                      "blk.%d.altup_router_norm" },
+    { LLM_TENSOR_LAUREL_L,                               "blk.%d.laurel_l" },
+    { LLM_TENSOR_LAUREL_R,                               "blk.%d.laurel_r" },
+    { LLM_TENSOR_LAUREL_POST_NORM,                       "blk.%d.laurel_post_norm" },
+    { LLM_TENSOR_DENSE_2_OUT,                            "dense_2" },
+    { LLM_TENSOR_DENSE_3_OUT,                            "dense_3" },
+    { LLM_TENSOR_FFN_NORM_EXPS,                          "blk.%d.ffn_norm_exps" },
+    { LLM_TENSOR_ATTN_K_B,                               "blk.%d.attn_k_b" },
+    { LLM_TENSOR_ATTN_V_B,                               "blk.%d.attn_v_b" },
+    { LLM_TENSOR_NEXTN_EH_PROJ,                          "blk.%d.nextn.eh_proj" },
+    { LLM_TENSOR_NEXTN_EMBED_TOKENS,                     "blk.%d.nextn.embed_tokens" },
+    { LLM_TENSOR_NEXTN_ENORM,                            "blk.%d.nextn.enorm" },
+    { LLM_TENSOR_NEXTN_HNORM,                            "blk.%d.nextn.hnorm" },
+    { LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,                 "blk.%d.nextn.shared_head_head" },
+    { LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,                 "blk.%d.nextn.shared_head_norm" },
+    { LLM_TENSOR_ATTN_SUB_NORM,                          "blk.%d.attn_sub_norm" },
+    { LLM_TENSOR_FFN_SUB_NORM,                           "blk.%d.ffn_sub_norm" },
+    { LLM_TENSOR_DEC_OUTPUT_NORM,                        "dec.output_norm" },
+    { LLM_TENSOR_DEC_ATTN_NORM,                          "dec.blk.%d.attn_norm" },
+    { LLM_TENSOR_DEC_ATTN_Q,                             "dec.blk.%d.attn_q" },
+    { LLM_TENSOR_DEC_ATTN_K,                             "dec.blk.%d.attn_k" },
+    { LLM_TENSOR_DEC_ATTN_V,                             "dec.blk.%d.attn_v" },
+    { LLM_TENSOR_DEC_ATTN_OUT,                           "dec.blk.%d.attn_o" },
+    { LLM_TENSOR_DEC_ATTN_REL_B,                         "dec.blk.%d.attn_rel_b" },
+    { LLM_TENSOR_DEC_CROSS_ATTN_NORM,                    "dec.blk.%d.cross_attn_norm" },
+    { LLM_TENSOR_DEC_CROSS_ATTN_Q,                       "dec.blk.%d.cross_attn_q" },
+    { LLM_TENSOR_DEC_CROSS_ATTN_K,                       "dec.blk.%d.cross_attn_k" },
+    { LLM_TENSOR_DEC_CROSS_ATTN_V,                       "dec.blk.%d.cross_attn_v" },
+    { LLM_TENSOR_DEC_CROSS_ATTN_OUT,                     "dec.blk.%d.cross_attn_o" },
+    { LLM_TENSOR_DEC_CROSS_ATTN_REL_B,                   "dec.blk.%d.cross_attn_rel_b" },
+    { LLM_TENSOR_DEC_FFN_NORM,                           "dec.blk.%d.ffn_norm" },
+    { LLM_TENSOR_DEC_FFN_GATE,                           "dec.blk.%d.ffn_gate" },
+    { LLM_TENSOR_DEC_FFN_DOWN,                           "dec.blk.%d.ffn_down" },
+    { LLM_TENSOR_DEC_FFN_UP,                             "dec.blk.%d.ffn_up" },
+    { LLM_TENSOR_ENC_ATTN_NORM,                          "enc.blk.%d.attn_norm" },
+    { LLM_TENSOR_ENC_ATTN_Q,                             "enc.blk.%d.attn_q" },
+    { LLM_TENSOR_ENC_ATTN_K,                             "enc.blk.%d.attn_k" },
+    { LLM_TENSOR_ENC_ATTN_V,                             "enc.blk.%d.attn_v" },
+    { LLM_TENSOR_ENC_ATTN_OUT,                           "enc.blk.%d.attn_o" },
+    { LLM_TENSOR_ENC_ATTN_REL_B,                         "enc.blk.%d.attn_rel_b" },
+    { LLM_TENSOR_ENC_FFN_NORM,                           "enc.blk.%d.ffn_norm" },
+    { LLM_TENSOR_ENC_FFN_GATE,                           "enc.blk.%d.ffn_gate" },
+    { LLM_TENSOR_ENC_FFN_DOWN,                           "enc.blk.%d.ffn_down" },
+    { LLM_TENSOR_ENC_FFN_UP,                             "enc.blk.%d.ffn_up" },
+    { LLM_TENSOR_TIME_MIX_W1,                            "blk.%d.time_mix_w1" },
+    { LLM_TENSOR_TIME_MIX_W2,                            "blk.%d.time_mix_w2" },
+    { LLM_TENSOR_TIME_MIX_LERP_X,                        "blk.%d.time_mix_lerp_x" },
+    { LLM_TENSOR_TIME_MIX_LERP_W,                        "blk.%d.time_mix_lerp_w" },
+    { LLM_TENSOR_TIME_MIX_LERP_K,                        "blk.%d.time_mix_lerp_k" },
+    { LLM_TENSOR_TIME_MIX_LERP_V,                        "blk.%d.time_mix_lerp_v" },
+    { LLM_TENSOR_TIME_MIX_LERP_R,                        "blk.%d.time_mix_lerp_r" },
+    { LLM_TENSOR_TIME_MIX_LERP_G,                        "blk.%d.time_mix_lerp_g" },
+    { LLM_TENSOR_TIME_MIX_LERP_FUSED,                    "blk.%d.time_mix_lerp_fused" },
+    { LLM_TENSOR_TIME_MIX_FIRST,                         "blk.%d.time_mix_first" },
+    { LLM_TENSOR_TIME_MIX_DECAY,                         "blk.%d.time_mix_decay" },
+    { LLM_TENSOR_TIME_MIX_DECAY_W1,                      "blk.%d.time_mix_decay_w1" },
+    { LLM_TENSOR_TIME_MIX_DECAY_W2,                      "blk.%d.time_mix_decay_w2" },
+    { LLM_TENSOR_TIME_MIX_KEY,                           "blk.%d.time_mix_key" },
+    { LLM_TENSOR_TIME_MIX_VALUE,                         "blk.%d.time_mix_value" },
+    { LLM_TENSOR_TIME_MIX_RECEPTANCE,                    "blk.%d.time_mix_receptance" },
+    { LLM_TENSOR_TIME_MIX_GATE,                          "blk.%d.time_mix_gate" },
+    { LLM_TENSOR_TIME_MIX_LN,                            "blk.%d.time_mix_ln" },
+    { LLM_TENSOR_TIME_MIX_OUTPUT,                        "blk.%d.time_mix_output" },
+    { LLM_TENSOR_CHANNEL_MIX_LERP_K,                     "blk.%d.channel_mix_lerp_k" },
+    { LLM_TENSOR_CHANNEL_MIX_LERP_R,                     "blk.%d.channel_mix_lerp_r" },
+    { LLM_TENSOR_CHANNEL_MIX_KEY,                        "blk.%d.channel_mix_key" },
+    { LLM_TENSOR_CHANNEL_MIX_VALUE,                      "blk.%d.channel_mix_value" },
+    { LLM_TENSOR_CHANNEL_MIX_RECEPTANCE,                 "blk.%d.channel_mix_receptance" },
+    { LLM_TENSOR_TIME_MIX_W0,                            "blk.%d.time_mix_w0" },
+    { LLM_TENSOR_TIME_MIX_A0,                            "blk.%d.time_mix_a0" },
+    { LLM_TENSOR_TIME_MIX_A1,                            "blk.%d.time_mix_a1" },
+    { LLM_TENSOR_TIME_MIX_A2,                            "blk.%d.time_mix_a2" },
+    { LLM_TENSOR_TIME_MIX_V0,                            "blk.%d.time_mix_v0" },
+    { LLM_TENSOR_TIME_MIX_V1,                            "blk.%d.time_mix_v1" },
+    { LLM_TENSOR_TIME_MIX_V2,                            "blk.%d.time_mix_v2" },
+    { LLM_TENSOR_TIME_MIX_G1,                            "blk.%d.time_mix_g1" },
+    { LLM_TENSOR_TIME_MIX_G2,                            "blk.%d.time_mix_g2" },
+    { LLM_TENSOR_TIME_MIX_K_K,                           "blk.%d.time_mix_k_k" },
+    { LLM_TENSOR_TIME_MIX_K_A,                           "blk.%d.time_mix_k_a" },
+    { LLM_TENSOR_TIME_MIX_R_K,                           "blk.%d.time_mix_r_k" },
+    { LLM_TENSOR_CONV1D,                                 "conv1d" },
+    { LLM_TENSOR_CONVNEXT_DW,                            "convnext.%d.dw" },
+    { LLM_TENSOR_CONVNEXT_NORM,                          "convnext.%d.norm" },
+    { LLM_TENSOR_CONVNEXT_PW1,                           "convnext.%d.pw1" },
+    { LLM_TENSOR_CONVNEXT_PW2,                           "convnext.%d.pw2" },
+    { LLM_TENSOR_CONVNEXT_GAMMA,                         "convnext.%d.gamma" },
+    { LLM_TENSOR_POS_NET_CONV1,                          "posnet.%d.conv1" },
+    { LLM_TENSOR_POS_NET_CONV2,                          "posnet.%d.conv2" },
+    { LLM_TENSOR_POS_NET_NORM,                           "posnet.%d.norm" },
+    { LLM_TENSOR_POS_NET_NORM1,                          "posnet.%d.norm1" },
+    { LLM_TENSOR_POS_NET_NORM2,                          "posnet.%d.norm2" },
+    { LLM_TENSOR_POS_NET_ATTN_NORM,                      "posnet.%d.attn_norm" },
+    { LLM_TENSOR_POS_NET_ATTN_Q,                         "posnet.%d.attn_q" },
+    { LLM_TENSOR_POS_NET_ATTN_K,                         "posnet.%d.attn_k" },
+    { LLM_TENSOR_POS_NET_ATTN_V,                         "posnet.%d.attn_v" },
+    { LLM_TENSOR_POS_NET_ATTN_OUT,                       "posnet.%d.attn_output" },
+    { LLM_TENSOR_ATTN_SINKS,                             "blk.%d.attn_sinks" },
+    { LLM_TENSOR_SHORTCONV_CONV,                         "blk.%d.shortconv.conv" },
+    { LLM_TENSOR_SHORTCONV_INPROJ,                       "blk.%d.shortconv.in_proj" },
+    { LLM_TENSOR_SHORTCONV_OUTPROJ,                      "blk.%d.shortconv.out_proj" },
+    { LLM_TENSOR_FFN_GATE_CHEXPS,                        "blk.%d.ffn_gate_chexps" },
+    { LLM_TENSOR_FFN_DOWN_CHEXPS,                        "blk.%d.ffn_down_chexps" },
+    { LLM_TENSOR_FFN_UP_CHEXPS,                          "blk.%d.ffn_up_chexps" },
+    { LLM_TENSOR_VISEXP_ATTN_QKV,                        "blk.%d.vis_attn_qkv" },
+    { LLM_TENSOR_VISEXP_ATTN_OUT,                        "blk.%d.vis_attn_output" },
+    { LLM_TENSOR_VISEXP_FFN_GATE,                        "blk.%d.vis_gate" },
+    { LLM_TENSOR_VISEXP_FFN_DOWN,                        "blk.%d.vis_down" },
+    { LLM_TENSOR_VISEXP_FFN_UP,                          "blk.%d.vis_up" },
 };
 
+static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
+    switch (arch) {
+        case LLM_ARCH_CLIP:
+            return {};
+        case LLM_ARCH_LLAMA:
+        case LLM_ARCH_DECI:
+        case LLM_ARCH_MISTRAL3:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_ROT_EMBD,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE_EXP,
+                LLM_TENSOR_FFN_DOWN_EXP,
+                LLM_TENSOR_FFN_UP_EXP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+            };
+        case LLM_ARCH_ARCEE:
+        case LLM_ARCH_STARCODER2:
+        case LLM_ARCH_NEMOTRON:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_ROT_EMBD,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_AFMOE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_GATE,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_POST_NORM,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_EXP_PROBS_B,
+            };
+        case LLM_ARCH_LLAMA4:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_ROT_EMBD,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE_EXP,
+                LLM_TENSOR_FFN_DOWN_EXP,
+                LLM_TENSOR_FFN_UP_EXP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+            };
+        case LLM_ARCH_BAICHUAN:
+        case LLM_ARCH_ORION:
+        case LLM_ARCH_XVERSE:
+        case LLM_ARCH_EXAONE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_ROT_EMBD,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_FALCON:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_NORM_2,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_GROK:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_ROT_EMBD,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE_EXP,
+                LLM_TENSOR_FFN_DOWN_EXP,
+                LLM_TENSOR_FFN_UP_EXP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_POST_NORM,
+                LLM_TENSOR_LAYER_OUT_NORM,
+                LLM_TENSOR_ATTN_OUT_NORM,
+            };
+        case LLM_ARCH_GPT2:
+        case LLM_ARCH_STARCODER:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_POS_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_DOWN,
+            };
+        case LLM_ARCH_GPTNEOX:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_MPT:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_ACT,
+                LLM_TENSOR_POS_EMBD,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K_NORM,
+            };
+        case LLM_ARCH_REFACT:
+        case LLM_ARCH_QWEN2:
+        case LLM_ARCH_QWEN2VL:
+        case LLM_ARCH_INTERNLM2:
+        case LLM_ARCH_GRANITE:
+        case LLM_ARCH_ERNIE4_5:
+        case LLM_ARCH_SMOLLM3:
+        case LLM_ARCH_DREAM:
+        case LLM_ARCH_LLADA:
+        case LLM_ARCH_PANGU_EMBED:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_BERT:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_TOKEN_EMBD_NORM,
+                LLM_TENSOR_TOKEN_TYPES,
+                LLM_TENSOR_POS_EMBD,
+                LLM_TENSOR_ATTN_OUT_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_LAYER_OUT_NORM,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_CLS,
+                LLM_TENSOR_CLS_OUT,
+            };
+        case LLM_ARCH_NOMIC_BERT:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_TOKEN_EMBD_NORM,
+                LLM_TENSOR_TOKEN_TYPES,
+                LLM_TENSOR_ATTN_OUT_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_LAYER_OUT_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_NOMIC_BERT_MOE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_TOKEN_EMBD_NORM,
+                LLM_TENSOR_TOKEN_TYPES,
+                LLM_TENSOR_ATTN_OUT_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_LAYER_OUT_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+            };
+        case LLM_ARCH_NEO_BERT:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_ENC_OUTPUT_NORM,
+                LLM_TENSOR_CLS,
+                LLM_TENSOR_CLS_OUT,
+            };
+        case LLM_ARCH_JINA_BERT_V2:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_TOKEN_EMBD_NORM,
+                LLM_TENSOR_TOKEN_TYPES,
+                LLM_TENSOR_ATTN_NORM_2,
+                LLM_TENSOR_ATTN_OUT_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_LAYER_OUT_NORM,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_CLS,
+            };
+        case LLM_ARCH_JINA_BERT_V3:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_TOKEN_EMBD_NORM,
+                LLM_TENSOR_TOKEN_TYPES,
+                LLM_TENSOR_ATTN_OUT_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_LAYER_OUT_NORM,
+            };
+        case LLM_ARCH_BLOOM:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_TOKEN_EMBD_NORM,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_DOWN,
+            };
+        case LLM_ARCH_STABLELM:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K_NORM,
+            };
+        case LLM_ARCH_QWEN:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_QWEN2MOE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_INP_SHEXP,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+            };
+        case LLM_ARCH_QWEN3:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_CLS_OUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_QWEN3MOE:
+        case LLM_ARCH_QWEN3VLMOE:
+        case LLM_ARCH_OLMOE:
+        case LLM_ARCH_LLADA_MOE:
+        case LLM_ARCH_RND1:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+            };
+        case LLM_ARCH_QWEN3NEXT:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_INP_SHEXP,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+                LLM_TENSOR_SSM_A_NOSCAN,
+                LLM_TENSOR_SSM_CONV1D,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_BETA_ALPHA,
+                LLM_TENSOR_SSM_IN,
+                LLM_TENSOR_SSM_NORM,
+                LLM_TENSOR_SSM_OUT,
+            };
+        case LLM_ARCH_QWEN3VL:
+        case LLM_ARCH_CHAMELEON:
+        case LLM_ARCH_HUNYUAN_DENSE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_PHI2:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_PHI3:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FACTORS_LONG,
+                LLM_TENSOR_ROPE_FACTORS_SHORT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_PHIMOE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FACTORS_LONG,
+                LLM_TENSOR_ROPE_FACTORS_SHORT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+            };
+        case LLM_ARCH_PLAMO:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_ROT_EMBD,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_PLAMO2:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_ROT_EMBD,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_SSM_IN,
+                LLM_TENSOR_SSM_CONV1D,
+                LLM_TENSOR_SSM_X,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_A,
+                LLM_TENSOR_SSM_D,
+                LLM_TENSOR_SSM_OUT,
+                LLM_TENSOR_SSM_DT_NORM,
+                LLM_TENSOR_SSM_B_NORM,
+                LLM_TENSOR_SSM_C_NORM,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_FFN_POST_NORM,
+            };
+        case LLM_ARCH_CODESHELL:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_ROT_EMBD,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_MINICPM:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ROPE_FACTORS_LONG,
+                LLM_TENSOR_ROPE_FACTORS_SHORT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_ROT_EMBD,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE_EXP,
+                LLM_TENSOR_FFN_DOWN_EXP,
+                LLM_TENSOR_FFN_UP_EXP,
+            };
+        case LLM_ARCH_MINICPM3:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FACTORS_LONG,
+                LLM_TENSOR_ROPE_FACTORS_SHORT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q_A_NORM,
+                LLM_TENSOR_ATTN_KV_A_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_A,
+                LLM_TENSOR_ATTN_Q_B,
+                LLM_TENSOR_ATTN_KV_A_MQA,
+                LLM_TENSOR_ATTN_KV_B,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_DOWN,
+            };
+        case LLM_ARCH_GEMMA:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_GEMMA2:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_POST_NORM,
+            };
+        case LLM_ARCH_GEMMA3:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_POST_NORM,
+            };
+        case LLM_ARCH_GEMMA3N:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_POST_NORM,
+                LLM_TENSOR_PER_LAYER_TOKEN_EMBD,
+                LLM_TENSOR_PER_LAYER_MODEL_PROJ,
+                LLM_TENSOR_PER_LAYER_PROJ_NORM,
+                LLM_TENSOR_ALTUP_UNEMBD_PROJ,
+                LLM_TENSOR_ALTUP_PROJ,
+                LLM_TENSOR_PER_LAYER_INP_GATE,
+                LLM_TENSOR_PER_LAYER_PROJ,
+                LLM_TENSOR_PER_LAYER_POST_NORM,
+                LLM_TENSOR_ALTUP_CORRECT_COEF,
+                LLM_TENSOR_ALTUP_CORRECT_SCALE,
+                LLM_TENSOR_ALTUP_PREDICT_COEF,
+                LLM_TENSOR_ALTUP_ROUTER,
+                LLM_TENSOR_ALTUP_ROUTER_NORM,
+                LLM_TENSOR_LAUREL_L,
+                LLM_TENSOR_LAUREL_R,
+                LLM_TENSOR_LAUREL_POST_NORM,
+            };
+        case LLM_ARCH_GEMMA_EMBEDDING:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_DENSE_2_OUT,
+                LLM_TENSOR_DENSE_3_OUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_POST_NORM,
+            };
+        case LLM_ARCH_MAMBA:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_SSM_IN,
+                LLM_TENSOR_SSM_CONV1D,
+                LLM_TENSOR_SSM_X,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_A,
+                LLM_TENSOR_SSM_D,
+                LLM_TENSOR_SSM_OUT,
+            };
+        case LLM_ARCH_MAMBA2:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_SSM_IN,
+                LLM_TENSOR_SSM_CONV1D,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_A,
+                LLM_TENSOR_SSM_D,
+                LLM_TENSOR_SSM_NORM,
+                LLM_TENSOR_SSM_OUT,
+            };
+        case LLM_ARCH_JAMBA:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_SSM_IN,
+                LLM_TENSOR_SSM_CONV1D,
+                LLM_TENSOR_SSM_X,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_DT_NORM,
+                LLM_TENSOR_SSM_A,
+                LLM_TENSOR_SSM_B_NORM,
+                LLM_TENSOR_SSM_C_NORM,
+                LLM_TENSOR_SSM_D,
+                LLM_TENSOR_SSM_OUT,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+            };
+        case LLM_ARCH_FALCON_H1:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_SSM_IN,
+                LLM_TENSOR_SSM_CONV1D,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_A,
+                LLM_TENSOR_SSM_D,
+                LLM_TENSOR_SSM_NORM,
+                LLM_TENSOR_SSM_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_COMMAND_R:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K_NORM,
+            };
+        case LLM_ARCH_COHERE2:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_DBRX:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_OUT_NORM,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+            };
+        case LLM_ARCH_OLMO:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_OLMO2:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_FFN_POST_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_OPENELM:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_ARCTIC:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_NORM_EXPS,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+            };
+        case LLM_ARCH_DEEPSEEK:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_ROT_EMBD,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_INP_SHEXP,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+            };
+        case LLM_ARCH_DEEPSEEK2:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q_A_NORM,
+                LLM_TENSOR_ATTN_KV_A_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_A,
+                LLM_TENSOR_ATTN_Q_B,
+                LLM_TENSOR_ATTN_KV_A_MQA,
+                LLM_TENSOR_ATTN_KV_B,
+                LLM_TENSOR_ATTN_K_B,
+                LLM_TENSOR_ATTN_V_B,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_INP_SHEXP,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+                LLM_TENSOR_FFN_EXP_PROBS_B,
+            };
+        case LLM_ARCH_PLM:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_KV_A_MQA,
+                LLM_TENSOR_ATTN_KV_A_NORM,
+                LLM_TENSOR_ATTN_KV_B,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_CHATGLM:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_DOWN,
+            };
+        case LLM_ARCH_GLM4:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_FFN_POST_NORM,
+            };
+        case LLM_ARCH_GLM4_MOE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+                LLM_TENSOR_FFN_EXP_PROBS_B,
+                LLM_TENSOR_NEXTN_EH_PROJ,
+                LLM_TENSOR_NEXTN_EMBED_TOKENS,
+                LLM_TENSOR_NEXTN_ENORM,
+                LLM_TENSOR_NEXTN_HNORM,
+                LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,
+                LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,
+            };
+        case LLM_ARCH_BITNET:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_SUB_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_SUB_NORM,
+            };
+        case LLM_ARCH_T5:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_DEC_OUTPUT_NORM,
+                LLM_TENSOR_DEC_ATTN_NORM,
+                LLM_TENSOR_DEC_ATTN_Q,
+                LLM_TENSOR_DEC_ATTN_K,
+                LLM_TENSOR_DEC_ATTN_V,
+                LLM_TENSOR_DEC_ATTN_OUT,
+                LLM_TENSOR_DEC_ATTN_REL_B,
+                LLM_TENSOR_DEC_CROSS_ATTN_NORM,
+                LLM_TENSOR_DEC_CROSS_ATTN_Q,
+                LLM_TENSOR_DEC_CROSS_ATTN_K,
+                LLM_TENSOR_DEC_CROSS_ATTN_V,
+                LLM_TENSOR_DEC_CROSS_ATTN_OUT,
+                LLM_TENSOR_DEC_CROSS_ATTN_REL_B,
+                LLM_TENSOR_DEC_FFN_NORM,
+                LLM_TENSOR_DEC_FFN_GATE,
+                LLM_TENSOR_DEC_FFN_DOWN,
+                LLM_TENSOR_DEC_FFN_UP,
+                LLM_TENSOR_ENC_OUTPUT_NORM,
+                LLM_TENSOR_ENC_ATTN_NORM,
+                LLM_TENSOR_ENC_ATTN_Q,
+                LLM_TENSOR_ENC_ATTN_K,
+                LLM_TENSOR_ENC_ATTN_V,
+                LLM_TENSOR_ENC_ATTN_OUT,
+                LLM_TENSOR_ENC_ATTN_REL_B,
+                LLM_TENSOR_ENC_FFN_NORM,
+                LLM_TENSOR_ENC_FFN_GATE,
+                LLM_TENSOR_ENC_FFN_DOWN,
+                LLM_TENSOR_ENC_FFN_UP,
+            };
+        case LLM_ARCH_T5ENCODER:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ENC_OUTPUT_NORM,
+                LLM_TENSOR_ENC_ATTN_NORM,
+                LLM_TENSOR_ENC_ATTN_Q,
+                LLM_TENSOR_ENC_ATTN_K,
+                LLM_TENSOR_ENC_ATTN_V,
+                LLM_TENSOR_ENC_ATTN_OUT,
+                LLM_TENSOR_ENC_ATTN_REL_B,
+                LLM_TENSOR_ENC_FFN_NORM,
+                LLM_TENSOR_ENC_FFN_GATE,
+                LLM_TENSOR_ENC_FFN_DOWN,
+                LLM_TENSOR_ENC_FFN_UP,
+            };
+        case LLM_ARCH_JAIS:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+            };
+        case LLM_ARCH_NEMOTRON_H:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_SSM_IN,
+                LLM_TENSOR_SSM_CONV1D,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_A,
+                LLM_TENSOR_SSM_D,
+                LLM_TENSOR_SSM_NORM,
+                LLM_TENSOR_SSM_OUT,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_NEMOTRON_H_MOE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                // mamba(2) ssm layers
+                LLM_TENSOR_SSM_IN,
+                LLM_TENSOR_SSM_CONV1D,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_A,
+                LLM_TENSOR_SSM_D,
+                LLM_TENSOR_SSM_NORM,
+                LLM_TENSOR_SSM_OUT,
+                // attention layers
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                // dense FFN
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                // MoE FFN (for MoE layers)
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_EXP_PROBS_B,
+                // MoE shared expert layer
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+            };
+        case LLM_ARCH_EXAONE4:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_POST_NORM,
+            };
+        case LLM_ARCH_RWKV6:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_TOKEN_EMBD_NORM,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_NORM_2,
+                LLM_TENSOR_TIME_MIX_W1,
+                LLM_TENSOR_TIME_MIX_W2,
+                LLM_TENSOR_TIME_MIX_LERP_X,
+                LLM_TENSOR_TIME_MIX_LERP_W,
+                LLM_TENSOR_TIME_MIX_LERP_K,
+                LLM_TENSOR_TIME_MIX_LERP_V,
+                LLM_TENSOR_TIME_MIX_LERP_R,
+                LLM_TENSOR_TIME_MIX_LERP_G,
+                LLM_TENSOR_TIME_MIX_LERP_FUSED,
+                LLM_TENSOR_TIME_MIX_FIRST,
+                LLM_TENSOR_TIME_MIX_DECAY,
+                LLM_TENSOR_TIME_MIX_DECAY_W1,
+                LLM_TENSOR_TIME_MIX_DECAY_W2,
+                LLM_TENSOR_TIME_MIX_KEY,
+                LLM_TENSOR_TIME_MIX_VALUE,
+                LLM_TENSOR_TIME_MIX_RECEPTANCE,
+                LLM_TENSOR_TIME_MIX_GATE,
+                LLM_TENSOR_TIME_MIX_LN,
+                LLM_TENSOR_TIME_MIX_OUTPUT,
+                LLM_TENSOR_CHANNEL_MIX_LERP_K,
+                LLM_TENSOR_CHANNEL_MIX_LERP_R,
+                LLM_TENSOR_CHANNEL_MIX_KEY,
+                LLM_TENSOR_CHANNEL_MIX_VALUE,
+                LLM_TENSOR_CHANNEL_MIX_RECEPTANCE,
+            };
+        case LLM_ARCH_RWKV6QWEN2:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_TIME_MIX_W1,
+                LLM_TENSOR_TIME_MIX_W2,
+                LLM_TENSOR_TIME_MIX_LERP_X,
+                LLM_TENSOR_TIME_MIX_LERP_FUSED,
+                LLM_TENSOR_TIME_MIX_FIRST,
+                LLM_TENSOR_TIME_MIX_DECAY,
+                LLM_TENSOR_TIME_MIX_DECAY_W1,
+                LLM_TENSOR_TIME_MIX_DECAY_W2,
+                LLM_TENSOR_TIME_MIX_KEY,
+                LLM_TENSOR_TIME_MIX_VALUE,
+                LLM_TENSOR_TIME_MIX_RECEPTANCE,
+                LLM_TENSOR_TIME_MIX_GATE,
+                LLM_TENSOR_TIME_MIX_OUTPUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_RWKV7:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_TOKEN_EMBD_NORM,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_NORM_2,
+                LLM_TENSOR_TIME_MIX_W0,
+                LLM_TENSOR_TIME_MIX_W1,
+                LLM_TENSOR_TIME_MIX_W2,
+                LLM_TENSOR_TIME_MIX_A0,
+                LLM_TENSOR_TIME_MIX_A1,
+                LLM_TENSOR_TIME_MIX_A2,
+                LLM_TENSOR_TIME_MIX_V0,
+                LLM_TENSOR_TIME_MIX_V1,
+                LLM_TENSOR_TIME_MIX_V2,
+                LLM_TENSOR_TIME_MIX_G1,
+                LLM_TENSOR_TIME_MIX_G2,
+                LLM_TENSOR_TIME_MIX_K_K,
+                LLM_TENSOR_TIME_MIX_K_A,
+                LLM_TENSOR_TIME_MIX_R_K,
+                LLM_TENSOR_TIME_MIX_LERP_FUSED,
+                LLM_TENSOR_TIME_MIX_KEY,
+                LLM_TENSOR_TIME_MIX_VALUE,
+                LLM_TENSOR_TIME_MIX_RECEPTANCE,
+                LLM_TENSOR_TIME_MIX_LN,
+                LLM_TENSOR_TIME_MIX_OUTPUT,
+                LLM_TENSOR_CHANNEL_MIX_LERP_K,
+                LLM_TENSOR_CHANNEL_MIX_KEY,
+                LLM_TENSOR_CHANNEL_MIX_VALUE,
+            };
+        case LLM_ARCH_ARWKV7:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_TOKEN_EMBD_NORM,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_TIME_MIX_W0,
+                LLM_TENSOR_TIME_MIX_W1,
+                LLM_TENSOR_TIME_MIX_W2,
+                LLM_TENSOR_TIME_MIX_A0,
+                LLM_TENSOR_TIME_MIX_A1,
+                LLM_TENSOR_TIME_MIX_A2,
+                LLM_TENSOR_TIME_MIX_V0,
+                LLM_TENSOR_TIME_MIX_V1,
+                LLM_TENSOR_TIME_MIX_V2,
+                LLM_TENSOR_TIME_MIX_G1,
+                LLM_TENSOR_TIME_MIX_G2,
+                LLM_TENSOR_TIME_MIX_K_K,
+                LLM_TENSOR_TIME_MIX_K_A,
+                LLM_TENSOR_TIME_MIX_R_K,
+                LLM_TENSOR_TIME_MIX_LERP_FUSED,
+                LLM_TENSOR_TIME_MIX_KEY,
+                LLM_TENSOR_TIME_MIX_VALUE,
+                LLM_TENSOR_TIME_MIX_RECEPTANCE,
+                LLM_TENSOR_TIME_MIX_LN,
+                LLM_TENSOR_TIME_MIX_OUTPUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_GRANITE_MOE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+            };
+        case LLM_ARCH_GRANITE_HYBRID:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_SSM_IN,
+                LLM_TENSOR_SSM_CONV1D,
+                LLM_TENSOR_SSM_DT,
+                LLM_TENSOR_SSM_A,
+                LLM_TENSOR_SSM_D,
+                LLM_TENSOR_SSM_NORM,
+                LLM_TENSOR_SSM_OUT,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+            };
+        case LLM_ARCH_WAVTOKENIZER_DEC:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_TOKEN_EMBD_NORM,
+                LLM_TENSOR_CONV1D,
+                LLM_TENSOR_CONVNEXT_DW,
+                LLM_TENSOR_CONVNEXT_NORM,
+                LLM_TENSOR_CONVNEXT_PW1,
+                LLM_TENSOR_CONVNEXT_PW2,
+                LLM_TENSOR_CONVNEXT_GAMMA,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_POS_NET_CONV1,
+                LLM_TENSOR_POS_NET_CONV2,
+                LLM_TENSOR_POS_NET_NORM,
+                LLM_TENSOR_POS_NET_NORM1,
+                LLM_TENSOR_POS_NET_NORM2,
+                LLM_TENSOR_POS_NET_ATTN_NORM,
+                LLM_TENSOR_POS_NET_ATTN_Q,
+                LLM_TENSOR_POS_NET_ATTN_K,
+                LLM_TENSOR_POS_NET_ATTN_V,
+                LLM_TENSOR_POS_NET_ATTN_OUT,
+            };
+        case LLM_ARCH_BAILINGMOE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_INP_SHEXP,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+            };
+        case LLM_ARCH_BAILINGMOE2:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_EXP_PROBS_B,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+                LLM_TENSOR_NEXTN_EH_PROJ,
+                LLM_TENSOR_NEXTN_EMBED_TOKENS,
+                LLM_TENSOR_NEXTN_ENORM,
+                LLM_TENSOR_NEXTN_HNORM,
+                LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD,
+                LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,
+                LLM_TENSOR_LAYER_OUT_NORM,
+            };
+        case LLM_ARCH_DOTS1:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_INP_SHEXP,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+                LLM_TENSOR_FFN_EXP_PROBS_B,
+            };
+        case LLM_ARCH_ERNIE4_5_MOE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_EXP_PROBS_B,
+            };
+        case LLM_ARCH_HUNYUAN_MOE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE_SHEXP,
+                LLM_TENSOR_FFN_DOWN_SHEXP,
+                LLM_TENSOR_FFN_UP_SHEXP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+            };
+        case LLM_ARCH_OPENAI_MOE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_SINKS,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+            };
+        case LLM_ARCH_LFM2:
+            return {
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_SHORTCONV_CONV,
+                LLM_TENSOR_SHORTCONV_INPROJ,
+                LLM_TENSOR_SHORTCONV_OUTPROJ,
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM_LFM2,
+                LLM_TENSOR_OUTPUT,
+            };
+        case LLM_ARCH_LFM2MOE:
+            return {
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_SHORTCONV_CONV,
+                LLM_TENSOR_SHORTCONV_INPROJ,
+                LLM_TENSOR_SHORTCONV_OUTPROJ,
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_EXP_PROBS_B,
+            };
+        case LLM_ARCH_SMALLTHINKER:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+            };
+        case LLM_ARCH_APERTUS:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ROPE_FREQS,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_SEED_OSS:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_POST_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+            };
+        case LLM_ARCH_GROVEMOE:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_GATE_CHEXPS,
+                LLM_TENSOR_FFN_DOWN_CHEXPS,
+                LLM_TENSOR_FFN_UP_CHEXPS,
+            };
+        case LLM_ARCH_MINIMAX_M2:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_Q,
+                LLM_TENSOR_ATTN_K,
+                LLM_TENSOR_ATTN_V,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_ATTN_Q_NORM,
+                LLM_TENSOR_ATTN_K_NORM,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE_INP,
+                LLM_TENSOR_FFN_GATE_EXPS,
+                LLM_TENSOR_FFN_DOWN_EXPS,
+                LLM_TENSOR_FFN_UP_EXPS,
+                LLM_TENSOR_FFN_EXP_PROBS_B,
+            };
+        case LLM_ARCH_COGVLM:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+                LLM_TENSOR_OUTPUT_NORM,
+                LLM_TENSOR_OUTPUT,
+                LLM_TENSOR_ATTN_NORM,
+                LLM_TENSOR_ATTN_QKV,
+                LLM_TENSOR_ATTN_OUT,
+                LLM_TENSOR_FFN_NORM,
+                LLM_TENSOR_FFN_GATE,
+                LLM_TENSOR_FFN_DOWN,
+                LLM_TENSOR_FFN_UP,
+                LLM_TENSOR_VISEXP_ATTN_QKV,
+                LLM_TENSOR_VISEXP_ATTN_OUT,
+                LLM_TENSOR_VISEXP_FFN_GATE,
+                LLM_TENSOR_VISEXP_FFN_DOWN,
+                LLM_TENSOR_VISEXP_FFN_UP,
+            };
+        case LLM_ARCH_GPTJ:
+        case LLM_ARCH_UNKNOWN:
+            return {
+                LLM_TENSOR_TOKEN_EMBD,
+            };
+        default:
+            GGML_ABORT("unknown architecture for tensor mapping");
+    }
+}
+
 // declare information about the model weight tensors:
 // - the layer in which the tensor is going to be used. this is needed in order to assign the correct buffer type for the weight
 // - the operator which is going to use the weight. this is needed to determine if the respective backend supports the operator
@@ -2603,6 +2202,7 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_DENSE_2_OUT,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, // Dense layer output
     {LLM_TENSOR_DENSE_3_OUT,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}}, // Dense layer output
     {LLM_TENSOR_OUTPUT_NORM,                {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
+    {LLM_TENSOR_OUTPUT_NORM_LFM2,           {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
     {LLM_TENSOR_DEC_OUTPUT_NORM,            {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
     {LLM_TENSOR_ENC_OUTPUT_NORM,            {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL}},
     {LLM_TENSOR_ROPE_FREQS,                 {LLM_TENSOR_LAYER_REPEATING, GGML_OP_ROPE}},
@@ -2791,13 +2391,20 @@ std::string LLM_KV::operator()(llm_kv kv) const {
     return name;
 }
 
+LLM_TN_IMPL::LLM_TN_IMPL(llm_arch arch, llm_tensor tensor, const char * suffix, int bid, int xid)
+    : arch(arch), tensor(tensor), suffix(suffix), bid(bid), xid(xid),
+      model_tensors(llm_get_tensor_names(arch)) {}
+
 std::string LLM_TN_IMPL::str() const {
-    if (LLM_TENSOR_NAMES.at(arch).find(tensor) == LLM_TENSOR_NAMES.at(arch).end()) {
-        return "__missing__";
+    if (LLM_TENSOR_NAMES.find(tensor) == LLM_TENSOR_NAMES.end()) {
+        GGML_ABORT("unknown tensor name for tensor id %d", static_cast<int>(tensor));
     }
 
-    std::string name = ::format(LLM_TENSOR_NAMES.at(arch).at(tensor), bid, xid);
+    if (model_tensors.find(tensor) == model_tensors.end()) {
+        return LLM_TENSOR_NAMES.at(tensor);
+    }
 
+    std::string name = ::format(LLM_TENSOR_NAMES.at(tensor), bid, xid);
     if (suffix != nullptr) {
         name += ".";
         name += suffix;
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 455658f5dc..6cbf9b1f89 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -3,6 +3,7 @@
 #include "ggml.h" // ggml_op
 
 #include <string>
+#include <set>
 
 //
 // gguf constants (sync with gguf.py)
@@ -316,6 +317,7 @@ enum llm_tensor {
     LLM_TENSOR_DENSE_3_OUT,
     LLM_TENSOR_OUTPUT,
     LLM_TENSOR_OUTPUT_NORM,
+    LLM_TENSOR_OUTPUT_NORM_LFM2, // fix for wrong tensor name
     LLM_TENSOR_ROPE_FREQS,
     LLM_TENSOR_ROPE_FACTORS_LONG,
     LLM_TENSOR_ROPE_FACTORS_SHORT,
@@ -526,6 +528,10 @@ struct LLM_TN_IMPL {
     const int bid;
     const int xid;
 
+    const std::set<llm_tensor> model_tensors;
+
+    LLM_TN_IMPL(llm_arch arch, llm_tensor tensor, const char * suffix, int bid, int xid);
+
     std::string str() const;
 
     operator std::string() const {
@@ -547,11 +553,11 @@ struct LLM_TN {
     llm_arch arch;
 
     LLM_TN_IMPL operator()(llm_tensor tensor, const char * suffix, int bid = -1, int xid = -1) const {
-        return { arch, tensor, suffix, bid, xid };
+        return LLM_TN_IMPL(arch, tensor, suffix, bid, xid);
     }
 
     LLM_TN_IMPL operator()(llm_tensor tensor, int bid = -1, int xid = -1) const {
-        return { arch, tensor, nullptr, bid, xid };
+        return LLM_TN_IMPL(arch, tensor, nullptr, bid, xid);
     }
 };
 

From 79dbae034afdfaa8e17989ea1b9b20094c8d0a36 Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Tue, 16 Dec 2025 13:25:09 +0100
Subject: [PATCH 134/135] model-conversion : remove -fa option in model card
 template [no ci] (#18088)

This commit updates the causal model card template and removes the
-fa option as it is no longer required (fa is auto detected).
---
 examples/model-conversion/scripts/causal/modelcard.template | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/model-conversion/scripts/causal/modelcard.template b/examples/model-conversion/scripts/causal/modelcard.template
index 87800a1b93..cfa8e6b433 100644
--- a/examples/model-conversion/scripts/causal/modelcard.template
+++ b/examples/model-conversion/scripts/causal/modelcard.template
@@ -7,7 +7,7 @@ base_model:
 Recommended way to run this model:
 
 ```sh
-llama-server -hf {namespace}/{model_name}-GGUF -c 0 -fa
+llama-server -hf {namespace}/{model_name}-GGUF -c 0
 ```
 
 Then, access http://localhost:8080

From 59977eba7b0a3603d0017717d3beec7bde018f3c Mon Sep 17 00:00:00 2001
From: yifant-code <tian.yifan123@gmail.com>
Date: Tue, 16 Dec 2025 07:27:36 -0500
Subject: [PATCH 135/135] server: fix crash when batch > ubatch with embeddings
 (#17912)

* server: fix crash when batch > ubatch with embeddings (#12836)

Fixes #12836 where the server crashes with GGML_ASSERT failure when
running with embeddings enabled and n_batch > n_ubatch.

Root cause: Embeddings use non-causal attention which requires all
tokens to be processed within a single ubatch. When n_batch > n_ubatch,
the server attempts to split processing, causing assertion failure.

Solution:
- Add parameter validation in main() after common_params_parse()
- When embeddings enabled and n_batch > n_ubatch:
  * Log warnings explaining the issue
  * Automatically set n_batch = n_ubatch
  * Prevent server crash

This follows the approach suggested by @ggerganov in issue #12836.

Note: This supersedes stalled PR #12940 which attempted a runtime fix
in the old examples/server/server.cpp location. This implementation
validates at startup in tools/server/server.cpp (current location).

Testing:
- Build: Compiles successfully
- Validation triggers: Warns when -b > -ub with --embedding
- Auto-correction works: Adjusts n_batch = n_ubatch
- No false positives: Valid params don't trigger warnings
- Verified on macOS M3 Pro with embedding model

* Update tools/server/server.cpp

---------

Co-authored-by: ytian218 <ytian218@bloomberg.net>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 tools/server/server.cpp | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index 235ae4e8c0..8538427f73 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -73,8 +73,18 @@ int main(int argc, char ** argv, char ** envp) {
         return 1;
     }
 
+    // validate batch size for embeddings
+    // embeddings require all tokens to be processed in a single ubatch
+    // see https://github.com/ggml-org/llama.cpp/issues/12836
+    if (params.embedding && params.n_batch > params.n_ubatch) {
+        LOG_WRN("%s: embeddings enabled with n_batch (%d) > n_ubatch (%d)\n", __func__, params.n_batch, params.n_ubatch);
+        LOG_WRN("%s: setting n_batch = n_ubatch = %d to avoid assertion failure\n", __func__, params.n_ubatch);
+        params.n_batch = params.n_ubatch;
+    }
+
     if (params.n_parallel < 0) {
         LOG_INF("%s: n_parallel is set to auto, using n_parallel = 4 and kv_unified = true\n", __func__);
+
         params.n_parallel = 4;
         params.kv_unified = true;
     }