From 5ba95754ee27433971f4f247326770019ad475c1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 16 Dec 2025 11:17:11 +0200 Subject: [PATCH 1/8] 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 Date: Tue, 16 Dec 2025 04:45:09 -0500 Subject: [PATCH 2/8] 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 Date: Tue, 16 Dec 2025 04:05:23 -0600 Subject: [PATCH 3/8] 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(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 & 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 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, "\n")) { + if (!inputs.enable_thinking) { + data.prompt += ""; + } else { + data.thinking_forced_open = true; + } + } + + data.preserved_tokens = { + "", + "", + "", + "", + }; + + 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("")) + ("" | 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 = "\n"; + auto tool_close = p.literal("\n"); + auto args = p.sequence(); + auto arg_string = p.rule("xml-arg-string", p.until_one_of({ + "\n", + "\n" + })); + + 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 = "\n"; + auto arg_close = p.literal("\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 + 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", "\n" + tool_choice + "" + 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_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, ""} + }; + } + + 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("") != std::string::npos && src.find("") != 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 & cb, const common_grammar_options & options); std::function _fetch_json; bool _dotall; @@ -729,7 +730,7 @@ private: } public: - SchemaConverter( + common_schema_converter( const std::function & 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( + [](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 visited_refs; + + std::function 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 & 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 #include +#include #include 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 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 add_rule; std::function 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) ~ '' }} + {%- else %} + {{-'\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '' }} + {%- 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" }} + {{- "" }} + {%- for tool in tools %} + {%- if tool.function is defined %} + {%- set tool = tool.function %} + {%- endif %} + {{- "\n\n" ~ tool.name ~ "" }} + {%- if tool.description is defined %} + {{- '\n' ~ (tool.description | trim) ~ '' }} + {%- endif %} + {{- '\n' }} + {%- 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' }} + {{- '\n' ~ param_name ~ '' }} + {%- if param_fields.type is defined %} + {{- '\n' ~ (param_fields.type | string) ~ '' }} + {%- endif %} + {%- if param_fields.description is defined %} + {{- '\n' ~ (param_fields.description | trim) ~ '' }} + {%- endif %} + {%- if param_fields.enum is defined %} + {{- '\n' ~ (param_fields.enum | tojson | safe) ~ '' }} + {%- endif %} + {%- set handled_keys = ['name', 'type', 'description', 'enum'] %} + {{- render_extra_keys(param_fields, handled_keys) }} + {{- '\n' }} + {%- 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' ~ (tool.parameters.required | tojson | safe) ~ '' }} + {%- endif %} + {{- '\n' }} + {%- set handled_keys = ['type', 'name', 'description', 'parameters'] %} + {{- render_extra_keys(tool, handled_keys) }} + {{- '\n' }} + {%- endfor %} + {{- "\n" }} + + {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n\n\n\nvalue_1\n\n\nThis is the value for the second parameter\nthat can span\nmultiple lines\n\n\n\n\n\nReminder:\n- Function calls MUST follow the specified format: an inner block must be nested within 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' }} +{%- 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 = "\n" ~ message.reasoning_content ~ "\n\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 '' not in content and '' not in content -%} + {%- set content = "" ~ 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 '' in c %} + {# Keep only content after the last closing think. Also generation prompt causes this. #} + {%- set c = c.split('')[-1] %} + {%- elif '' in c %} + {# If was opened but never closed, drop the trailing think segment #} + {%- set c = c.split('')[0] %} + {%- endif %} + {%- set c = "" ~ c | trim %} + {%- if c | length > 0 %} + {{- c ~ '\n' -}} + {%- endif %} + {%- endif %} + {%- else %} + {{- "" -}} + {%- endif %} + {%- for tool_call in message.tool_calls %} + {%- if tool_call.function is defined %} + {%- set tool_call = tool_call.function %} + {%- endif %} + {{- '\n\n' -}} + {%- if tool_call.arguments is defined %} + {%- for args_name, args_value in tool_call.arguments|items %} + {{- '\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\n' -}} + {%- endfor %} + {%- endif %} + {{- '\n\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 '' in c and '' in c %} + {%- set c = "" ~ c.split('')[-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 %} + {{- '\n' }} + {{- message.content }} + {{- '\n\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\n' }} + {%- else %} + {{- '<|im_start|>assistant\n' }} + {%- 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\nHello, world!\nWhat's up?"; + t.expect.content = "I'm\nthinking\n\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\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 = + "\n" + "\n" + "\n" + "1\n" + "\n" + "\n" + ""; + 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\n" + "\n" + "\n" + "\n" + "1\n" + "\n" + "\n" + ""; + 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 = + "\n" + "\n" + "\n" + "1\n" + "\n" + "\n" + "\n" + "\n" + "\n" + "\n" + "1\n" + "\n" + "\n" + "2\n" + "\n" + "\n" + ""; + 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 = + "\n" + "\n" + "\n" + "def hello():\n" + " print(\"Hello, world!\")\n" + "\n" + "hello()\n" + "\n" + "\n" + ""; + 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 tag + test_peg_parser(tmpls.get(), [&](auto & t) { + t.input = + "\n" + "\n" + "\n" + "def hello():\n" + " print(\"Hello, world!\")\n" + "\n" + "hello()\n" + "\n" + ""; + 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" + "\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 Date: Tue, 16 Dec 2025 11:17:20 +0100 Subject: [PATCH 4/8] 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 Date: Tue, 16 Dec 2025 11:17:40 +0100 Subject: [PATCH 5/8] 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 Date: Tue, 16 Dec 2025 11:25:26 +0100 Subject: [PATCH 6/8] 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 * process mrope_section on TextModel base class * resolve conflict merge --------- Co-authored-by: Georgi Gerganov --- 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::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 #include +#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_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(ctx, img); } break; + case PROJECTOR_TYPE_GLM4V: + { + builder = std::make_unique(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(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 7/8] 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 Date: Tue, 16 Dec 2025 13:52:23 +0300 Subject: [PATCH 8/8] CLI: fixed adding cli and completion into docker containers, improved docs (#18003) Co-authored-by: Andrew Aladjev --- .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