From 517b7170e1a4d733583c4b07c5b7a49acc05911c Mon Sep 17 00:00:00 2001
From: Max Krasnyansky <maxk@qti.qualcomm.com>
Date: Thu, 30 Oct 2025 09:06:13 -0700
Subject: [PATCH 01/30] cpu: introduce chunking for repack matmuls and enable
 matmul-id chunking on ARM64 (#16833)

Very similar implementation to the flash-attention chunking, with similar benefits.
---
 ggml/src/ggml-cpu/ggml-cpu.c |  5 ---
 ggml/src/ggml-cpu/repack.cpp | 80 ++++++++++++++++++++++++++----------
 2 files changed, 58 insertions(+), 27 deletions(-)

diff --git a/ggml/src/ggml-cpu/ggml-cpu.c b/ggml/src/ggml-cpu/ggml-cpu.c
index 9ec485cfa2..b5466dd703 100644
--- a/ggml/src/ggml-cpu/ggml-cpu.c
+++ b/ggml/src/ggml-cpu/ggml-cpu.c
@@ -1613,13 +1613,8 @@ static void ggml_compute_forward_mul_mat_id(
             chunk_size = 64;
         }
 
-#if defined(__aarch64__)
-        // disable for ARM
-        const bool disable_chunking = true;
-#else
         // disable for NUMA
         const bool disable_chunking = ggml_is_numa();
-#endif // defined(__aarch64__)
 
         int64_t nchunk0 = (nr0 + chunk_size - 1) / chunk_size;
         int64_t nchunk1 = (nr1 + chunk_size - 1) / chunk_size;
diff --git a/ggml/src/ggml-cpu/repack.cpp b/ggml/src/ggml-cpu/repack.cpp
index f531d21e23..8da1e0e924 100644
--- a/ggml/src/ggml-cpu/repack.cpp
+++ b/ggml/src/ggml-cpu/repack.cpp
@@ -1600,6 +1600,32 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PAR
         return false;
     }
 
+    void forward_mul_mat_one_chunk(ggml_compute_params * params, ggml_tensor * op, int64_t src0_start, int64_t src0_end) {
+        const ggml_tensor * src0 = op->src[0];
+        const ggml_tensor * src1 = op->src[1];
+        ggml_tensor *       dst  = op;
+
+        GGML_TENSOR_BINARY_OP_LOCALS
+
+        const void * src1_wdata      = params->wdata;
+        const size_t src1_col_stride = ggml_row_size(PARAM_TYPE, ne10);
+
+        // If there are more than three rows in src1, use gemm; otherwise, use gemv.
+        if (ne11 > 3) {
+            gemm<BLOC_TYPE, INTER_SIZE, NB_COLS, PARAM_TYPE>(ne00,
+                    (float *) ((char *) dst->data) + src0_start, ne01,
+                    (const char *) src0->data + src0_start * nb01,
+                    (const char *) src1_wdata, ne11 - ne11 % 4, src0_end - src0_start);
+        }
+        for (int iter = ne11 - ne11 % 4; iter < ne11; iter++) {
+            gemv<BLOC_TYPE, INTER_SIZE, NB_COLS, PARAM_TYPE>(ne00,
+                    (float *) ((char *) dst->data + (iter * nb1)) + src0_start, ne01,
+                    (const char *) src0->data + src0_start * nb01,
+                    (const char *) src1_wdata + (src1_col_stride * iter), 1,
+                    src0_end - src0_start);
+        }
+    }
+
     void forward_mul_mat(ggml_compute_params * params, ggml_tensor * op) {
         const ggml_tensor * src0 = op->src[0];
         const ggml_tensor * src1 = op->src[1];
@@ -1643,31 +1669,41 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS, ggml_type PAR
             from_float((float *) ((char *) src1->data + i11 * nb11), (void *) (wdata + i11 * nbw1), ne10);
         }
 
+        // disable for NUMA
+        const bool disable_chunking = ggml_is_numa();
+
+        // 4x chunks per thread
+        int64_t nr = ggml_nrows(op->src[0]);
+        int nth_scaled = nth * 4;
+        int64_t chunk_size = (nr + nth_scaled - 1) / nth_scaled;
+        int64_t nchunk     = (nr + chunk_size - 1) / chunk_size;
+
+        if (nth == 1 || nchunk < nth || disable_chunking) {
+            nchunk = nth;
+        }
+
+        if (ith == 0) {
+            // Every thread starts at ith, so the first unprocessed chunk is nth.  This save a bit of coordination right at the start.
+            ggml_threadpool_chunk_set(params->threadpool, nth);
+        }
+
         ggml_barrier(params->threadpool);
 
-        const void * src1_wdata      = params->wdata;
-        const size_t src1_col_stride = ggml_row_size(PARAM_TYPE, ne10);
-        int64_t      src0_start      = (ith * ne01) / nth;
-        int64_t      src0_end        = ((ith + 1) * ne01) / nth;
-        src0_start = (src0_start % NB_COLS) ? src0_start + NB_COLS - (src0_start % NB_COLS) : src0_start;
-        src0_end   = (src0_end   % NB_COLS) ? src0_end   + NB_COLS - (src0_end   % NB_COLS) : src0_end;
-        if (src0_start >= src0_end) {
-            return;
-        }
+        // The first chunk comes from our thread_id, the rest will get auto-assigned.
+        int current_chunk = ith;
 
-        // If there are more than three rows in src1, use gemm; otherwise, use gemv.
-        if (ne11 > 3) {
-            gemm<BLOC_TYPE, INTER_SIZE, NB_COLS, PARAM_TYPE>(ne00,
-                    (float *) ((char *) dst->data) + src0_start, ne01,
-                    (const char *) src0->data + src0_start * nb01,
-                    (const char *) src1_wdata, ne11 - ne11 % 4, src0_end - src0_start);
-        }
-        for (int iter = ne11 - ne11 % 4; iter < ne11; iter++) {
-            gemv<BLOC_TYPE, INTER_SIZE, NB_COLS, PARAM_TYPE>(ne00,
-                    (float *) ((char *) dst->data + (iter * nb1)) + src0_start, ne01,
-                    (const char *) src0->data + src0_start * nb01,
-                    (const char *) src1_wdata + (src1_col_stride * iter), 1,
-                    src0_end - src0_start);
+        while (current_chunk < nchunk) {
+            int64_t src0_start = (current_chunk * ne01) / nchunk;
+            int64_t src0_end   = ((current_chunk + 1) * ne01) / nchunk;
+            src0_start = (src0_start % NB_COLS) ? src0_start + NB_COLS - (src0_start % NB_COLS) : src0_start;
+            src0_end   = (src0_end   % NB_COLS) ? src0_end   + NB_COLS - (src0_end   % NB_COLS) : src0_end;
+            if (src0_start >= src0_end) {
+                break;
+            }
+
+            forward_mul_mat_one_chunk(params, dst, src0_start, src0_end);
+
+            current_chunk = ggml_threadpool_chunk_add(params->threadpool, 1);
         }
     }
 

From b52edd25586fabb70f0c21b274473b307cf14499 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Thu, 30 Oct 2025 18:42:57 +0200
Subject: [PATCH 02/30] server : remove n_past (#16818)

* server : remove n_past

* server : replace slot.n_prompt_tokens() with slot.task->n_tokens()

* server : fixes + clean-up

* cont : fix context shift

* server : add server_tokens::pos_next()

Co-authored-by: Xuan-Son Nguyen <son@huggingface.co>

* server : fix pos_next() usage

Co-authored-by: Xuan-Son Nguyen <son@huggingface.co>

---------

Co-authored-by: Xuan-Son Nguyen <son@huggingface.co>
---
 tools/server/README.md  |   4 +-
 tools/server/server.cpp | 220 ++++++++++++++++++++--------------------
 tools/server/utils.hpp  | 106 +++++++++++--------
 3 files changed, 177 insertions(+), 153 deletions(-)

diff --git a/tools/server/README.md b/tools/server/README.md
index f5ab9236d5..c16d0bd6dc 100644
--- a/tools/server/README.md
+++ b/tools/server/README.md
@@ -587,7 +587,7 @@ These words will not be included in the completion, so make sure to add them to
   - `word`: Stopped due to encountering a stopping word from `stop` JSON array provided
 - `stopping_word`: The stopping word encountered which stopped the generation (or "" if not stopped due to a stopping word)
 - `timings`: Hash of timing information about the completion such as the number of tokens `predicted_per_second`
-- `tokens_cached`: Number of tokens from the prompt which could be re-used from previous completion (`n_past`)
+- `tokens_cached`: Number of tokens from the prompt which could be re-used from previous completion
 - `tokens_evaluated`: Number of tokens evaluated in total from the prompt
 - `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`)
 
@@ -1045,7 +1045,7 @@ Available metrics:
 - `llamacpp:kv_cache_tokens`: KV-cache tokens.
 - `llamacpp:requests_processing`: Number of requests processing.
 - `llamacpp:requests_deferred`: Number of requests deferred.
-- `llamacpp:n_past_max`: High watermark of the context size observed.
+- `llamacpp:n_tokens_max`: High watermark of the context size observed.
 
 ### POST `/slots/{id_slot}?action=save`: Save the prompt cache of the specified slot to a file.
 
diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index cb794ab647..d56468595d 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -292,6 +292,10 @@ struct server_task {
 
     server_task(server_task_type type) : type(type) {}
 
+    int32_t n_tokens() const {
+        return tokens.size();
+    }
+
     static slot_params params_from_json_cmpl(
             const llama_context * ctx,
             const common_params & params_base,
@@ -1308,7 +1312,7 @@ struct server_task_result_metrics : server_task_result {
     uint64_t n_tokens_predicted_total        = 0;
     uint64_t t_tokens_generation_total       = 0;
 
-    uint64_t n_past_max = 0;
+    uint64_t n_tokens_max = 0;
 
     uint64_t n_prompt_tokens_processed = 0;
     uint64_t t_prompt_processing       = 0;
@@ -1335,7 +1339,7 @@ struct server_task_result_metrics : server_task_result {
             { "n_tokens_predicted_total",        n_tokens_predicted_total },
             { "t_prompt_processing_total",       t_prompt_processing_total },
 
-            { "n_past_max",                      n_past_max },
+            { "n_tokens_max",                    n_tokens_max },
 
             { "n_prompt_tokens_processed",       n_prompt_tokens_processed },
             { "t_prompt_processing",             t_prompt_processing },
@@ -1636,7 +1640,6 @@ struct server_slot {
 
     // generation props
     int32_t n_ctx       = 0;  // context size per slot
-    int32_t n_past      = 0;
     int32_t n_keep      = 0;
     int32_t n_decoded   = 0;
     int32_t n_remaining = -1;
@@ -1645,10 +1648,6 @@ struct server_slot {
     int32_t n_prompt_tokens_cache     = 0;
     int32_t n_prompt_tokens_processed = 0;
 
-    int32_t n_prompt_tokens() const {
-        return task->tokens.size();
-    }
-
     size_t last_nl_pos = 0;
 
     std::string  generated_text;
@@ -1733,7 +1732,6 @@ struct server_slot {
         truncated      = false;
         stop           = STOP_TYPE_NONE;
         stopping_word  = "";
-        n_past         = 0;
         n_sent_text    = 0;
         chat_format    = COMMON_CHAT_FORMAT_CONTENT_ONLY;
 
@@ -1818,7 +1816,7 @@ struct server_slot {
         if (is_processing()) {
             GGML_ASSERT(task);
 
-            SLT_INF(*this, "stop processing: n_past = %d, truncated = %d\n", n_past, truncated);
+            SLT_INF(*this, "stop processing: n_tokens = %d, truncated = %d\n", prompt.n_tokens(), truncated);
 
             t_last_used = ggml_time_us();
             t_token_generation = (ggml_time_us() - t_start_generation) / 1e3;
@@ -1970,7 +1968,7 @@ struct server_metrics {
     uint64_t n_tokens_predicted_total        = 0;
     uint64_t t_tokens_generation_total       = 0;
 
-    uint64_t n_past_max = 0;
+    uint64_t n_tokens_max = 0;
 
     uint64_t n_prompt_tokens_processed = 0;
     uint64_t t_prompt_processing       = 0;
@@ -1991,9 +1989,7 @@ struct server_metrics {
         t_prompt_processing             += slot.t_prompt_processing;
         t_prompt_processing_total       += slot.t_prompt_processing;
 
-        if (slot.n_past > 0) {
-            n_past_max = std::max(n_past_max, (uint64_t) slot.n_past);
-        }
+        n_tokens_max = std::max(n_tokens_max, (uint64_t) slot.prompt.n_tokens());
     }
 
     void on_prediction(const server_slot & slot) {
@@ -2009,9 +2005,7 @@ struct server_metrics {
             if (slot.is_processing()) {
                 n_busy_slots_total++;
             }
-            if (slot.n_past > 0) {
-                n_past_max = std::max(n_past_max, (uint64_t) slot.n_past);
-            }
+            n_tokens_max = std::max(n_tokens_max, (uint64_t) slot.prompt.n_tokens());
         }
     }
 
@@ -2865,13 +2859,13 @@ struct server_context {
         }
 
         // if context shifting is disabled, make sure that we don't run out of context
-        if (!params_base.ctx_shift && slot.n_past + 1 >= slot.n_ctx) {
+        if (!params_base.ctx_shift && slot.prompt.n_tokens() + 1 >= slot.n_ctx) {
             slot.truncated      = true;
             slot.stop           = STOP_TYPE_LIMIT;
             slot.has_next_token = false;
 
-            SLT_DBG(slot, "stopped due to running out of context capacity, n_past = %d, n_prompt_tokens = %d, n_decoded = %d, n_ctx = %d\n",
-                    slot.n_decoded, slot.n_prompt_tokens(), slot.n_past, slot.n_ctx);
+            SLT_DBG(slot, "stopped due to running out of context capacity, prompt.n_tokens() = %d, task.n_tokens = %d, n_decoded = %d, n_ctx = %d\n",
+                    slot.prompt.n_tokens(), slot.task->n_tokens(), slot.n_decoded, slot.n_ctx);
         }
 
         // check the limits
@@ -2998,7 +2992,7 @@ struct server_context {
     }
 
     void send_error(const server_slot & slot, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER) {
-        send_error(slot.task->id, error, type, slot.n_prompt_tokens(), slot.n_ctx);
+        send_error(slot.task->id, error, type, slot.task->n_tokens(), slot.n_ctx);
     }
 
     void send_error(const int id_task, const std::string & error, const enum error_type type = ERROR_TYPE_SERVER, const int32_t n_prompt_tokens = 0, const int32_t n_ctx = 0) {
@@ -3035,7 +3029,7 @@ struct server_context {
 
         if (is_progress) {
             res->is_progress        = true;
-            res->progress.total     = slot.n_prompt_tokens();
+            res->progress.total     = slot.task->n_tokens();
             res->progress.cache     = slot.n_prompt_tokens_cache;
             res->progress.processed = slot.prompt.tokens.size();
             res->progress.time_ms   = (ggml_time_us() - slot.t_start_process_prompt / 1000);
@@ -3047,7 +3041,7 @@ struct server_context {
         }
 
         res->n_decoded           = slot.n_decoded;
-        res->n_prompt_tokens     = slot.n_prompt_tokens();
+        res->n_prompt_tokens     = slot.task->n_tokens();
         res->post_sampling_probs = slot.task->params.post_sampling_probs;
 
         res->verbose           = slot.task->params.verbose;
@@ -3083,8 +3077,8 @@ struct server_context {
 
         res->truncated           = slot.truncated;
         res->n_decoded           = slot.n_decoded;
-        res->n_prompt_tokens     = slot.n_prompt_tokens();
-        res->n_tokens_cached     = slot.n_past;
+        res->n_prompt_tokens     = slot.task->n_tokens();
+        res->n_tokens_cached     = slot.prompt.n_tokens();
         res->has_new_line        = slot.has_new_line;
         res->stopping_word       = slot.stopping_word;
         res->stop                = slot.stop;
@@ -3123,7 +3117,7 @@ struct server_context {
         auto res = std::make_unique<server_task_result_embd>();
         res->id        = slot.task->id;
         res->index     = slot.task->index;
-        res->n_tokens  = slot.n_prompt_tokens();
+        res->n_tokens  = slot.task->n_tokens();
         res->oaicompat = slot.task->params.oaicompat;
 
         const int n_embd = llama_model_n_embd(model);
@@ -3168,7 +3162,7 @@ struct server_context {
         auto res = std::make_unique<server_task_result_rerank>();
         res->id       = slot.task->id;
         res->index    = slot.task->index;
-        res->n_tokens = slot.n_prompt_tokens();
+        res->n_tokens = slot.task->n_tokens();
 
         for (int i = 0; i < batch.n_tokens; ++i) {
             if (!batch.logits[i] || batch.seq_id[i][0] != slot.id) {
@@ -3375,7 +3369,7 @@ struct server_context {
                     res->n_tokens_predicted_total        = metrics.n_tokens_predicted_total;
                     res->t_tokens_generation_total       = metrics.t_tokens_generation_total;
 
-                    res->n_past_max = metrics.n_past_max;
+                    res->n_tokens_max = metrics.n_tokens_max;
 
                     res->n_prompt_tokens_processed = metrics.n_prompt_tokens_processed;
                     res->t_prompt_processing       = metrics.t_prompt_processing;
@@ -3551,7 +3545,7 @@ struct server_context {
         // apply context-shift if needed
         // TODO: simplify and improve
         for (server_slot & slot : slots) {
-            if (slot.is_processing() && slot.n_past + 1 >= slot.n_ctx) {
+            if (slot.is_processing() && slot.prompt.n_tokens() + 1 >= slot.n_ctx) {
                 if (!params_base.ctx_shift) {
                     // this check is redundant (for good)
                     // we should never get here, because generation should already stopped in process_token()
@@ -3567,7 +3561,7 @@ struct server_context {
                 }
 
                 // Shift context
-                int n_keep = slot.task->params.n_keep < 0 ? slot.n_prompt_tokens() : slot.task->params.n_keep;
+                int n_keep = slot.task->params.n_keep < 0 ? slot.task->n_tokens() : slot.task->params.n_keep;
 
                 if (add_bos_token) {
                     n_keep += 1;
@@ -3575,28 +3569,30 @@ struct server_context {
 
                 n_keep = std::min(slot.n_ctx - 4, n_keep);
 
-                const int n_left    = slot.n_past - n_keep;
+                const int n_left    = slot.prompt.n_tokens() - n_keep;
                 const int n_discard = slot.task->params.n_discard ? slot.task->params.n_discard : (n_left / 2);
 
                 SLT_WRN(slot, "slot context shift, n_keep = %d, n_left = %d, n_discard = %d\n", n_keep, n_left, n_discard);
 
                 llama_memory_seq_rm (llama_get_memory(ctx), slot.id, n_keep            , n_keep + n_discard);
-                llama_memory_seq_add(llama_get_memory(ctx), slot.id, n_keep + n_discard, slot.n_past,        -n_discard);
+                llama_memory_seq_add(llama_get_memory(ctx), slot.id, n_keep + n_discard, slot.prompt.n_tokens(), -n_discard);
 
                 // add generated tokens to cache
+                // ref: https://github.com/ggml-org/llama.cpp/pull/16818#discussion_r2473269481
                 {
+                    GGML_ASSERT(!slot.prompt.tokens.has_mtmd);
+
                     llama_tokens new_tokens = slot.prompt.tokens.get_text_tokens(); // copy
                     for (size_t i = n_keep + n_discard; i < new_tokens.size(); i++) {
                         new_tokens[i - n_discard] = new_tokens[i];
                     }
 
                     new_tokens.resize(slot.prompt.tokens.size() - n_discard);
+
                     slot.prompt.tokens.clear();
                     slot.prompt.tokens.insert(new_tokens);
                 }
 
-                slot.n_past -= n_discard;
-
                 slot.truncated = true;
             }
         }
@@ -3627,13 +3623,12 @@ struct server_context {
 
             slot.i_batch = batch.n_tokens;
 
-            common_batch_add(batch, slot.sampled, slot.n_past, { slot.id }, true);
+            common_batch_add(batch, slot.sampled, slot.prompt.tokens.pos_next(), { slot.id }, true);
 
-            slot.n_past += 1;
             slot.prompt.tokens.push_back(slot.sampled);
 
-            SLT_DBG(slot, "slot decode token, n_ctx = %d, n_past = %d, n_cache_tokens = %d, truncated = %d\n",
-                    slot.n_ctx, slot.n_past, (int) slot.prompt.tokens.size(), slot.truncated);
+            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
@@ -3663,11 +3658,10 @@ struct server_context {
                         slot.t_start_process_prompt = ggml_time_us();
                         slot.t_start_generation = 0;
 
-                        slot.n_past = 0;
                         slot.state = SLOT_STATE_PROCESSING_PROMPT;
 
-                        SLT_INF(slot, "new prompt, n_ctx_slot = %d, n_keep = %d, n_prompt_tokens = %d\n",
-                                slot.n_ctx, slot.task->params.n_keep, slot.n_prompt_tokens());
+                        SLT_INF(slot, "new prompt, n_ctx_slot = %d, n_keep = %d, task.n_tokens = %d\n",
+                                slot.n_ctx, slot.task->params.n_keep, slot.task->n_tokens());
 
                         // print prompt tokens (for debugging)
                         /*if (1) {
@@ -3682,6 +3676,9 @@ struct server_context {
                             }
                         }*/
 
+                        // keep track how many tokens we can reuse from the previous state
+                        int n_past = 0;
+
                         // empty prompt passed -> release the slot and send empty response
                         if (input_tokens.empty()) {
                             SLT_WRN(slot, "%s", "empty prompt - releasing slot\n");
@@ -3701,19 +3698,19 @@ struct server_context {
                         }
 
                         if (!slot.can_split()) {
-                            if (slot.n_prompt_tokens() > n_ubatch) {
+                            if (slot.task->n_tokens() > n_ubatch) {
                                 send_error(slot, "input is too large to process. increase the physical batch size", ERROR_TYPE_SERVER);
                                 slot.release();
                                 continue;
                             }
 
-                            if (slot.n_prompt_tokens() > slot.n_ctx) {
+                            if (slot.task->n_tokens() > slot.n_ctx) {
                                 send_error(slot, "input is larger than the max context size. skipping", ERROR_TYPE_EXCEED_CONTEXT_SIZE);
                                 slot.release();
                                 continue;
                             }
                         } else {
-                            if (slot.n_prompt_tokens() >= slot.n_ctx) {
+                            if (slot.task->n_tokens() >= slot.n_ctx) {
                                 send_error(slot, "the request exceeds the available context size, try increasing it", ERROR_TYPE_EXCEED_CONTEXT_SIZE);
                                 slot.release();
                                 continue;
@@ -3721,32 +3718,34 @@ struct server_context {
 
                             if (slot.task->params.cache_prompt) {
                                 // reuse any previously computed tokens that are common with the new prompt
-                                slot.n_past = slot.prompt.tokens.get_common_prefix(input_tokens);
+                                n_past = slot.prompt.tokens.get_common_prefix(input_tokens);
 
                                 // if there is an alora invoked, don't cache after the invocation start
-                                if (slot.alora_invocation_start >= 0) {
-                                    SLT_DBG(slot, "only caching to alora invocation start (n_past=%d, alora_invocation_start=%d)\n", slot.n_past, slot.alora_invocation_start);
-                                    slot.n_past = std::min(slot.n_past, slot.alora_invocation_start - 1);
+                                if (slot.alora_invocation_start > 0) {
+                                    SLT_DBG(slot, "only caching to alora invocation start (n_past = %d, alora_invocation_start = %d)\n", n_past, slot.alora_invocation_start);
+                                    n_past = std::min(n_past, slot.alora_invocation_start - 1);
                                 }
 
                                 // reuse chunks from the cached prompt by shifting their KV cache in the new position
                                 if (params_base.n_cache_reuse > 0) {
-                                    size_t head_c = slot.n_past; // cache
-                                    size_t head_p = slot.n_past; // current prompt
+                                    GGML_ASSERT(!slot.prompt.tokens.has_mtmd);
+
+                                    size_t head_c = n_past; // cache
+                                    size_t head_p = n_past; // current prompt
 
                                     if (mctx) {
                                         // we should never reach this
                                         GGML_ABORT("not supported by multimodal");
                                     }
 
-                                    SLT_DBG(slot, "trying to reuse chunks with size > %d, slot.n_past = %d\n", params_base.n_cache_reuse, slot.n_past);
+                                    SLT_DBG(slot, "trying to reuse chunks with size > %d, n_past = %d\n", params_base.n_cache_reuse, n_past);
 
                                     while (head_c < slot.prompt.tokens.size() &&
                                            head_p < input_tokens.size()) {
 
                                         size_t n_match = 0;
                                         while (head_c + n_match < slot.prompt.tokens.size() &&
-                                               head_p + n_match < input_tokens.size()     &&
+                                               head_p + n_match < input_tokens.size()       &&
                                                slot.prompt.tokens[head_c + n_match] == input_tokens[head_p + n_match]) {
 
                                             n_match++;
@@ -3765,7 +3764,7 @@ struct server_context {
 
                                             for (size_t i = 0; i < n_match; i++) {
                                                 slot.prompt.tokens.set_token(head_p + i, slot.prompt.tokens[head_c + i]);
-                                                slot.n_past++;
+                                                n_past++;
                                             }
 
                                             head_c += n_match;
@@ -3775,31 +3774,31 @@ struct server_context {
                                         }
                                     }
 
-                                    SLT_DBG(slot, "after context reuse, new slot.n_past = %d\n", slot.n_past);
+                                    SLT_DBG(slot, "after context reuse, new n_past = %d\n", n_past);
                                 }
                             } else {
-                                // if we don't cache the prompt, we have to remove the entire KV cache
-                                slot.n_past = 0;
+                                // if we don't cache the prompt, we have to remove all previous tokens
+                                n_past = 0;
                             }
 
                             // note: when n_swa == 0, the model does not use SWA, which is equivalent to a window of 1
                             const auto n_swa = std::max(1, llama_model_n_swa(model));
 
                             // the largest pos_min required for a checkpoint to be useful
-                            const auto pos_min_thold = std::max(0, slot.n_past - n_swa);
+                            const auto pos_min_thold = std::max(0, n_past - n_swa);
 
-                            if (slot.n_past > 0 && slot.n_past < (int) slot.prompt.tokens.size()) {
+                            if (n_past > 0 && n_past < slot.prompt.n_tokens()) {
                                 const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx), slot.id);
                                 if (pos_min == -1) {
-                                    SLT_ERR(slot, "n_past = %d, slot.prompt.tokens.size() = %d, seq_id = %d, pos_min = %d\n", slot.n_past, (int) slot.prompt.tokens.size(), slot.id, pos_min);
+                                    SLT_ERR(slot, "n_past = %d, slot.prompt.tokens.size() = %d, seq_id = %d, pos_min = %d\n", n_past, (int) slot.prompt.tokens.size(), slot.id, pos_min);
                                     GGML_ABORT("pos_min == -1, but n_past > 0 - should not happen: https://github.com/ggml-org/llama.cpp/pull/13833#discussion_r2116181237");
                                 }
 
                                 // when the prompt prefix does not match, print the tokens around the mismatch
                                 // this is useful for debugging prompt caching
                                 {
-                                    const int np0 = std::max<int>(slot.n_past - 4, 0);
-                                    const int np1 = std::min<int>(slot.n_past + 6, std::min(slot.prompt.tokens.size(), slot.task->tokens.size()));
+                                    const int np0 = std::max<int>(n_past - 4, 0);
+                                    const int np1 = std::min<int>(n_past + 6, std::min(slot.prompt.tokens.size(), slot.task->tokens.size()));
 
                                     std::stringstream ss0;
                                     std::stringstream ss1;
@@ -3811,7 +3810,7 @@ struct server_context {
                                     ss1 << "new: ... ";
 
                                     for (int i = np0; i < np1; i++) {
-                                        if (i == slot.n_past) {
+                                        if (i == n_past) {
                                             ss0 << " | ";
                                             ss1 << " | ";
                                         }
@@ -3839,7 +3838,10 @@ struct server_context {
                                 }
 
                                 if (pos_min > pos_min_thold) {
-                                    SLT_WRN(slot, "n_past = %d, slot.prompt.tokens.size() = %d, seq_id = %d, pos_min = %d, n_swa = %d\n", slot.n_past, (int) slot.prompt.tokens.size(), slot.id, pos_min, n_swa);
+                                    // TODO: support can be added in the future when corresponding vision models get released
+                                    GGML_ASSERT(!slot.prompt.tokens.has_mtmd);
+
+                                    SLT_WRN(slot, "n_past = %d, slot.prompt.tokens.size() = %d, seq_id = %d, pos_min = %d, n_swa = %d\n", n_past, (int) slot.prompt.tokens.size(), slot.id, pos_min, n_swa);
 
                                     // search for a context checkpoint
                                     const auto it = std::find_if(
@@ -3863,7 +3865,7 @@ struct server_context {
                                             do_reset = true;
                                             //printf("[DEBUG] `do_reset` was set to `true` after failing to restore a checkpoint");
                                         } else {
-                                            slot.n_past = std::min(slot.n_past, std::max(it->pos_min + 1, it->pos_max));
+                                            n_past = std::min(n_past, std::max(it->pos_min + 1, it->pos_max));
                                             SLT_WRN(slot, "restored context checkpoint (pos_min = %d, pos_max = %d, size = %.3f MiB)\n", it->pos_min, it->pos_max, (float) checkpoint_size / 1024 / 1024);
                                         }
                                     }
@@ -3871,7 +3873,7 @@ struct server_context {
                                     if (do_reset) {
                                         SLT_WRN(slot, "forcing full prompt re-processing due to lack of cache data (likely due to SWA or hybrid/recurrent memory, see %s)\n",
                                                 "https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055");
-                                        slot.n_past = 0;
+                                        n_past = 0;
                                     }
                                 }
                             }
@@ -3891,43 +3893,44 @@ struct server_context {
                         }
 
                         // [TAG_PROMPT_LOGITS]
-                        if (slot.n_past == slot.n_prompt_tokens() && slot.n_past > 0) {
-                            SLT_WRN(slot, "need to evaluate at least 1 token for each active slot (n_past = %d, n_prompt_tokens = %d)\n", slot.n_past, slot.n_prompt_tokens());
-                            slot.n_past--;
-                            SLT_WRN(slot, "n_past was set to %d\n", slot.n_past);
+                        if (n_past == slot.task->n_tokens() && n_past > 0) {
+                            SLT_WRN(slot, "need to evaluate at least 1 token for each active slot (n_past = %d, task.n_tokens() = %d)\n", n_past, slot.task->n_tokens());
+                            n_past--;
+                            SLT_WRN(slot, "n_past was set to %d\n", n_past);
                         }
 
-                        slot.n_prompt_tokens_cache     = slot.n_past;
+                        slot.n_prompt_tokens_cache     = n_past;
                         slot.n_prompt_tokens_processed = 0;
+
+                        slot.prompt.tokens.keep_first(n_past);
                     }
 
                     if (!slot.can_split()) {
                         // cannot fit the prompt in the current batch - will try next iter
-                        if (batch.n_tokens + slot.n_prompt_tokens() > n_batch) {
+                        if (batch.n_tokens + slot.task->n_tokens() > n_batch) {
                             continue;
                         }
                     }
 
                     // truncate any tokens that are beyond n_past for this slot
-                    if (!llama_memory_seq_rm(llama_get_memory(ctx), slot.id, slot.n_past, -1)) {
-                        SLT_WRN(slot, "failed to truncate tokens beyond n_past = %d\n", slot.n_past);
+                    const llama_pos p0 = slot.prompt.tokens.pos_next();
+                    if (!llama_memory_seq_rm(llama_get_memory(ctx), slot.id, p0, -1)) {
+                        SLT_WRN(slot, "failed to truncate tokens with position >= %d\n", p0);
                         llama_memory_seq_rm(llama_get_memory(ctx), slot.id, -1, -1);
 
                         // there is no common part left
-                        slot.n_past                = 0;
                         slot.n_prompt_tokens_cache = 0;
+
+                        slot.prompt.tokens.clear();
                     }
 
-                    SLT_INF(slot, "n_past = %d, memory_seq_rm [%d, end)\n", slot.n_past, slot.n_past);
-
-                    // remove the non-common part from the cache
-                    slot.prompt.tokens.keep_first(slot.n_past);
+                    SLT_INF(slot, "n_tokens = %d, memory_seq_rm [%d, end)\n", slot.prompt.n_tokens(), p0);
 
                     // check if we should process the image
-                    if (slot.n_past < slot.n_prompt_tokens() && input_tokens[slot.n_past] == LLAMA_TOKEN_NULL) {
+                    if (slot.prompt.n_tokens() < slot.task->n_tokens() && input_tokens[slot.prompt.n_tokens()] == LLAMA_TOKEN_NULL) {
                         // process the image
-                        int32_t new_n_past;
-                        int32_t res = input_tokens.process_chunk(ctx, mctx, slot.n_past, slot.id, new_n_past);
+                        size_t n_tokens_out = 0;
+                        int32_t res = input_tokens.process_chunk(ctx, mctx, slot.prompt.n_tokens(), slot.prompt.tokens.pos_next(), slot.id, n_tokens_out);
                         if (res != 0) {
                             SLT_ERR(slot, "failed to process image, res = %d\n", res);
                             send_error(slot, "failed to process image", ERROR_TYPE_SERVER);
@@ -3935,16 +3938,13 @@ struct server_context {
                             continue;
                         }
 
+                        slot.n_prompt_tokens_processed += n_tokens_out;
+
                         // add the image chunk to cache
                         {
-                            const auto & chunk = input_tokens.find_chunk(slot.n_past);
+                            const auto & chunk = input_tokens.find_chunk(slot.prompt.n_tokens());
                             slot.prompt.tokens.push_back(chunk.get()); // copy
                         }
-
-                        const int32_t n_pos = new_n_past - slot.n_past;
-
-                        slot.n_past                    += n_pos;
-                        slot.n_prompt_tokens_processed += n_pos;
                     }
 
                     // If using an alora, there may be uncached tokens that come
@@ -3952,8 +3952,8 @@ struct server_context {
                     // tokens before the invocation sequence need to be
                     // processed without the adpter in a separate batch, then
                     // the adapter needs to be enabled for the remaining tokens.
-                    if (lora_all_alora(slot.lora) && slot.alora_invocation_start - 1 > slot.n_past) {
-                        SLT_DBG(slot, "processing pre-alora tokens without the adapter (n_past = %d, alora_invocation_start = %d)\n", slot.n_past, slot.alora_invocation_start);
+                    if (lora_all_alora(slot.lora) && slot.alora_invocation_start - 1 > slot.prompt.n_tokens()) {
+                        SLT_DBG(slot, "processing pre-alora tokens without the adapter (n_tokens = %d, alora_invocation_start = %d)\n", slot.prompt.n_tokens(), slot.alora_invocation_start);
                         const auto & enabled_loras = lora_get_enabled_ids(slot.lora);
                         GGML_ASSERT(enabled_loras.size() == 1);
                         alora_scale = slot.lora[enabled_loras[0]].scale;
@@ -3979,9 +3979,9 @@ struct server_context {
                             );
 
                     // add prompt tokens for processing in the current batch
-                    while (slot.n_past < slot.n_prompt_tokens() && batch.n_tokens < n_batch) {
+                    while (slot.prompt.n_tokens() < slot.task->n_tokens() && batch.n_tokens < n_batch) {
                         // get next token to process
-                        llama_token cur_tok = input_tokens[slot.n_past];
+                        llama_token cur_tok = input_tokens[slot.prompt.n_tokens()];
                         if (cur_tok == LLAMA_TOKEN_NULL) {
                             break; // end of text chunk
                         }
@@ -3989,30 +3989,33 @@ struct server_context {
                         // if this is an alora request with pre-invocation
                         // tokens that are not cached, we need to stop filling
                         // this batch at those pre-invocation tokens.
-                        if (alora_scale > 0 && slot.n_past == slot.alora_invocation_start - 1) {
-                            SLT_DBG(slot, "stop prompt batch filling at (n_past = %d, alora_invocation_start = %d)\n", slot.n_past, slot.alora_invocation_start);
+                        if (alora_scale > 0 && slot.prompt.n_tokens() == slot.alora_invocation_start - 1) {
+                            SLT_DBG(slot, "stop prompt batch filling at (n_tokens = %d, alora_invocation_start = %d)\n", slot.prompt.n_tokens(), slot.alora_invocation_start);
                             break;
                         }
 
                         // embedding requires all tokens in the batch to be output
-                        common_batch_add(batch, cur_tok, slot.n_past, { slot.id }, slot.need_embd());
+                        common_batch_add(batch,
+                            cur_tok,
+                            slot.prompt.tokens.pos_next(),
+                            { slot.id },
+                            slot.need_embd());
                         slot.prompt.tokens.push_back(cur_tok);
 
                         slot.n_prompt_tokens_processed++;
-                        slot.n_past++;
 
                         // process the last few tokens of the prompt separately in order to allow for a checkpoint to be created.
-                        if (do_checkpoint && slot.n_prompt_tokens() - slot.n_past == 64) {
+                        if (do_checkpoint && slot.task->n_tokens() - slot.prompt.n_tokens() == 64) {
                             break;
                         }
                     }
 
                     // SLT_INF(slot, "new slot.prompt.tokens: %s\n", slot.slot.prompt.tokens.str().c_str());
 
-                    SLT_INF(slot, "prompt processing progress, n_past = %d, n_tokens = %d, progress = %f\n", slot.n_past, batch.n_tokens, (float) slot.n_past / slot.n_prompt_tokens());
+                    SLT_INF(slot, "prompt processing progress, n_tokens = %d, batch.n_tokens = %d, progress = %f\n", slot.prompt.n_tokens(), batch.n_tokens, (float) slot.prompt.n_tokens() / slot.task->n_tokens());
 
                     // entire prompt has been processed
-                    if (slot.n_past == slot.n_prompt_tokens()) {
+                    if (slot.prompt.n_tokens() == slot.task->n_tokens()) {
                         slot.state = SLOT_STATE_DONE_PROMPT;
 
                         GGML_ASSERT(batch.n_tokens > 0);
@@ -4020,7 +4023,7 @@ struct server_context {
                         common_sampler_reset(slot.smpl);
 
                         // Process all prompt tokens through sampler system
-                        for (int i = 0; i < slot.n_prompt_tokens(); ++i) {
+                        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);
@@ -4033,7 +4036,7 @@ struct server_context {
                         slot.n_decoded = 0;
                         slot.i_batch   = batch.n_tokens - 1;
 
-                        SLT_INF(slot, "prompt done, n_past = %d, n_tokens = %d\n", slot.n_past, batch.n_tokens);
+                        SLT_INF(slot, "prompt done, n_tokens = %d, batch.n_tokens = %d\n", slot.prompt.n_tokens(), batch.n_tokens);
 
                         const auto pos_min = llama_memory_seq_pos_min(llama_get_memory(ctx), slot.id);
                         const auto pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx), slot.id);
@@ -4253,9 +4256,9 @@ struct server_context {
                 // determine the max draft that fits the current slot state
                 int n_draft_max = slot.task->params.speculative.n_max;
 
-                // note: n_past is not yet increased for the `id` token sampled above
+                // 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.n_past - 2);
+                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);
@@ -4291,10 +4294,10 @@ struct server_context {
 
                 // construct the speculation batch
                 common_batch_clear(slot.batch_spec);
-                common_batch_add  (slot.batch_spec, id, slot.n_past, { slot.id }, true);
+                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.n_past + 1 + i, { slot.id }, true);
+                    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);
@@ -4304,7 +4307,6 @@ struct server_context {
                 // the accepted tokens from the speculation
                 const auto ids = common_sampler_sample_and_accept_n(slot.smpl, ctx, draft);
 
-                slot.n_past    += ids.size();
                 slot.n_decoded += ids.size();
 
                 // update how many tokens out of those tested were accepted
@@ -4313,7 +4315,7 @@ struct server_context {
                 slot.prompt.tokens.push_back(id);
                 slot.prompt.tokens.insert({ids.begin(), ids.end() - 1});
 
-                llama_memory_seq_rm(llama_get_memory(ctx), slot.id, slot.n_past, -1);
+                llama_memory_seq_rm(llama_get_memory(ctx), slot.id, slot.prompt.n_tokens(), -1);
 
                 for (size_t i = 0; i < ids.size(); ++i) {
                     completion_token_output result;
@@ -4334,7 +4336,7 @@ struct server_context {
                     }
                 }
 
-                SLT_DBG(slot, "accepted %d/%d draft tokens, new n_past = %d\n", (int) ids.size() - 1, (int) draft.size(), slot.n_past);
+                SLT_DBG(slot, "accepted %d/%d draft tokens, new n_tokens = %d\n", (int) ids.size() - 1, (int) draft.size(), slot.prompt.n_tokens());
             }
         }
 
@@ -4662,9 +4664,9 @@ int main(int argc, char ** argv) {
                     {"help",  "Total number of llama_decode() calls"},
                     {"value",  res_task->n_decode_total}
             }, {
-                    {"name",  "n_past_max"},
-                    {"help",  "Largest observed n_past."},
-                    {"value",  res_task->n_past_max}
+                    {"name",  "n_tokens_max"},
+                    {"help",  "Largest observed n_tokens."},
+                    {"value",  res_task->n_tokens_max}
             }, {
                     {"name",  "n_busy_slots_per_decode"},
                     {"help",  "Average number of busy slots per llama_decode() call"},
diff --git a/tools/server/utils.hpp b/tools/server/utils.hpp
index cc48f5a9d0..fb95361b28 100644
--- a/tools/server/utils.hpp
+++ b/tools/server/utils.hpp
@@ -1080,19 +1080,22 @@ struct server_tokens {
 
 private: // disallow accessing these members directly, risking out-of-sync
 
-    // map a **start** position in tokens to the image chunk
-    std::unordered_map<llama_pos, mtmd::input_chunk_ptr> map_pos_to_media;
+    // map a **start** index in tokens to the image chunk
+    // note: the order need to be in-sync with tokens
+    std::map<size_t, mtmd::input_chunk_ptr> map_idx_to_media;
 
     // list of tokens
-    // it can include LLAMA_TOKEN_NULL, which is used to indicate a token that is not a text token
-    // a mtmd_input_chunk can occupy multiple tokens, one llama_token per **position**
-    // important: for models using mrope, an image can contain multiple tokens but will use only one **position**
+    //   if the token is LLAMA_TOKEN_NULL, it indicates that this position is occupied by media chunk
+    //   otherwise, it is a normal text token
+    // note: a non-text chunk can occupy multiple tokens (aka memory cells) in the token list
+    // note(2): for M-RoPE, an image can occupy different number of pos; do not assume 1-to-1 mapping tokens <-> pos
     llama_tokens tokens;
 
-    // for ex. with input of 5 text tokens and 2 images:
-    //      [0] [1] [2] [3] [4] [img0] [img0] [img0] [img1] [img1]
-    // pos  0   1   2   3   4   5      6      7      8      9
-    // map_pos_to_media will contain: {5, img0}, {8, img1}
+    // for ex. with input of 5 text tokens and 2 images (each image occupies 3 tokens and 2 pos):
+    //      [0] [1] [2] [3] [4] [img0] [img0] [img0] [img1] [img1] [img1]
+    // idx  0   1   2   3   4   5      6      7      8      9      10
+    // pos  0   1   2   3   4   5      5      5      7      7      7
+    // map_idx_to_media will contain: {5, img0}, {8, img1}
 
 public:
     server_tokens() = default;
@@ -1117,13 +1120,31 @@ public:
         }
     }
 
-    server_tokens(const llama_tokens & tokens, bool has_mtmd) : has_mtmd(has_mtmd), tokens(tokens) {}
+    server_tokens(const llama_tokens & tokens, bool has_mtmd) : has_mtmd(has_mtmd), tokens(tokens) {
+    }
+
+    llama_pos pos_next() const {
+        if (!has_mtmd) {
+            return tokens.size();
+        }
+
+        llama_pos res = tokens.size();
+
+        for (auto it = map_idx_to_media.begin(); it != map_idx_to_media.end(); ++it) {
+            const auto & chunk = it->second;
+            res += mtmd_input_chunk_get_n_pos(chunk.get()) - mtmd_input_chunk_get_n_tokens(chunk.get());
+        }
+
+        return res;
+    }
 
     // for debugging
     std::string str() const {
         std::ostringstream oss;
         oss << "tokens: ";
-        for (const auto & t : tokens) {
+        for (size_t idx = 0; idx < tokens.size(); ++idx) {
+            llama_token t = tokens[idx];
+            oss << "idx:" << idx << " ";
             if (t == LLAMA_TOKEN_NULL) {
                 oss << "<embd> ";
             } else {
@@ -1131,16 +1152,16 @@ public:
             }
         }
         oss << "\n";
-        oss << "image pos: ";
-        for (const auto & it : map_pos_to_media) {
+        oss << "image idx: ";
+        for (const auto & it : map_idx_to_media) {
             oss << it.first << ", ";
         }
         return oss.str();
     }
 
-    const mtmd::input_chunk_ptr & find_chunk(llama_pos pos) const {
-        auto it = map_pos_to_media.find(pos);
-        if (it != map_pos_to_media.end()) {
+    const mtmd::input_chunk_ptr & find_chunk(size_t idx) const {
+        auto it = map_idx_to_media.find(idx);
+        if (it != map_idx_to_media.end()) {
             return it->second;
         }
         throw std::runtime_error("Chunk not found");
@@ -1158,13 +1179,13 @@ public:
         auto type = mtmd_input_chunk_get_type(chunk);
         if (type == MTMD_INPUT_CHUNK_TYPE_IMAGE || type == MTMD_INPUT_CHUNK_TYPE_AUDIO) {
             GGML_ASSERT(has_mtmd);
-            const int n_pos = mtmd_input_chunk_get_n_pos(chunk);
-            llama_pos start_pos = tokens.size();
-            for (int i = 0; i < n_pos; ++i) {
+            const size_t n_tokens = mtmd_input_chunk_get_n_tokens(chunk);
+            size_t start_idx = tokens.size();
+            for (size_t i = 0; i < n_tokens; ++i) {
                 tokens.emplace_back(LLAMA_TOKEN_NULL);
             }
             mtmd::input_chunk_ptr new_chunk(mtmd_input_chunk_copy(chunk));
-            map_pos_to_media[start_pos] = std::move(new_chunk);
+            map_idx_to_media[start_idx] = std::move(new_chunk);
         } else if (type == MTMD_INPUT_CHUNK_TYPE_TEXT) {
             size_t n_tokens;
             const auto * text_tokens = mtmd_input_chunk_get_tokens_text(chunk, &n_tokens);
@@ -1178,7 +1199,7 @@ public:
 
     // appends server tokens, updates the media map. copies media chunks.
     void push_back(server_tokens & tokens) {
-        size_t start_pos = size();
+        size_t start_idx = size();
         for (size_t i = 0; i < tokens.size(); i++) {
             push_back(tokens[i]);
         }
@@ -1186,10 +1207,10 @@ public:
             // Assert if we are copying MTMD chunks to a server_tokens that does not have mtmd.
             // We could also just check, but this will prevent silently dropping MTMD data.
             GGML_ASSERT(has_mtmd);
-            for (auto it = tokens.map_pos_to_media.begin(); it != tokens.map_pos_to_media.end(); ) {
-                auto * chunk = tokens.map_pos_to_media[it->first].get();
+            for (auto it = tokens.map_idx_to_media.begin(); it != tokens.map_idx_to_media.end(); ) {
+                auto * chunk = tokens.map_idx_to_media[it->first].get();
                 mtmd::input_chunk_ptr new_chunk(mtmd_input_chunk_copy(chunk));
-                map_pos_to_media[start_pos+it->first] = std::move(new_chunk);
+                map_idx_to_media[start_idx+it->first] = std::move(new_chunk);
             }
         }
     }
@@ -1245,10 +1266,10 @@ public:
                 }
             }
             // remove all image chunks that are not used anymore
-            for (auto it = map_pos_to_media.begin(); it != map_pos_to_media.end(); ) {
-                llama_pos pos = it->first;
-                if (pos >= (llama_pos)n) {
-                    it = map_pos_to_media.erase(it);
+            for (auto it = map_idx_to_media.begin(); it != map_idx_to_media.end(); ) {
+                size_t idx = it->first;
+                if (idx >= n) {
+                    it = map_idx_to_media.erase(it);
                 } else {
                     ++it;
                 }
@@ -1296,12 +1317,12 @@ public:
                 const std::string id_ai = mtmd_input_chunk_get_id(a_chunk.get());
                 const std::string id_bi = mtmd_input_chunk_get_id(b_chunk.get());
 
-                const size_t pos_a = mtmd_input_chunk_get_n_pos(a_chunk.get());
-                const size_t pos_b = mtmd_input_chunk_get_n_pos(b_chunk.get());
+                const size_t n_tok_a = mtmd_input_chunk_get_n_tokens(a_chunk.get());
+                const size_t n_tok_b = mtmd_input_chunk_get_n_tokens(b_chunk.get());
 
-                if (id_ai == id_bi && pos_a == pos_b) {
-                    GGML_ASSERT(pos_a > 0 && "Invalid media chunk"); // should never happen
-                    i += pos_a - 1; // will be +1 by the for loop
+                if (id_ai == id_bi && n_tok_a == n_tok_b) {
+                    GGML_ASSERT(n_tok_a > 0 && "Invalid media chunk"); // should never happen
+                    i += n_tok_a - 1; // will be +1 by the for loop
                     continue;
                 }
 
@@ -1329,8 +1350,8 @@ public:
             if (t == LLAMA_TOKEN_NULL) {
                 try {
                     const auto & chunk = find_chunk(i);
-                    size_t n_pos = mtmd_input_chunk_get_n_pos(chunk.get());
-                    i += n_pos - 1; // will be +1 by the for loop
+                    size_t n_tokens = mtmd_input_chunk_get_n_tokens(chunk.get());
+                    i += n_tokens - 1; // will be +1 by the for loop
                 } catch (const std::exception & e) {
                     return false;
                 }
@@ -1345,19 +1366,20 @@ public:
     int32_t process_chunk(
                 llama_context * ctx,
                 mtmd_context * mctx,
-                llama_pos n_past,
+                size_t idx,
+                llama_pos pos,
                 int32_t seq_id,
-                llama_pos & n_pos_out) const {
-        const auto & chunk = find_chunk(n_past);
+                size_t & n_tokens_out) const {
+        const auto & chunk = find_chunk(idx);
         const char * name = mtmd_input_chunk_get_type(chunk.get()) == MTMD_INPUT_CHUNK_TYPE_IMAGE
                             ? "image" : "audio";
         SRV_INF("processing %s...\n", name);
         int32_t n_batch = llama_n_batch(ctx);
         int64_t t0 = ggml_time_ms();
-        llama_pos new_n_past = n_past;
+        llama_pos new_n_past; // unused for now
         int32_t result = mtmd_helper_eval_chunk_single(mctx, ctx,
             chunk.get(),
-            n_past,
+            pos,
             seq_id,
             n_batch,
             true, // logits last
@@ -1365,10 +1387,10 @@ public:
         SRV_INF("%s processed in %" PRId64 " ms\n", name, ggml_time_ms() - t0);
         if (result != 0) {
             LOG_ERR("mtmd_helper_eval failed with status %d", result);
-            n_pos_out = n_past;
+            n_tokens_out = 0;
             return result;
         }
-        n_pos_out = new_n_past;
+        n_tokens_out = mtmd_input_chunk_get_n_tokens(chunk.get());
         return 0;
     }
 };

From 16724b5b6836a2d4b8936a5824d2ff27c52b4517 Mon Sep 17 00:00:00 2001
From: chansikpark <chansik.park@gmail.com>
Date: Thu, 30 Oct 2025 14:22:23 -0400
Subject: [PATCH 03/30] server : bump request URI max length to 32768 (#16862)

---
 tools/server/utils.hpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/tools/server/utils.hpp b/tools/server/utils.hpp
index fb95361b28..b6198edfc4 100644
--- a/tools/server/utils.hpp
+++ b/tools/server/utils.hpp
@@ -13,6 +13,8 @@
 #define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576
 // increase backlog size to avoid connection resets for >> 1 slots
 #define CPPHTTPLIB_LISTEN_BACKLOG 512
+// increase max URI length to handle longer prompts in query string
+#define CPPHTTPLIB_REQUEST_URI_MAX_LENGTH 32768
 // disable Nagle's algorithm
 #define CPPHTTPLIB_TCP_NODELAY true
 #include <cpp-httplib/httplib.h>

From ce18efeaf1234bd08f25bc08f88ef95cf5d9e51f Mon Sep 17 00:00:00 2001
From: RodriMora <bullerwins@gmail.com>
Date: Thu, 30 Oct 2025 23:15:03 +0100
Subject: [PATCH 04/30] convert : update transformers requirements (#16866)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Update requirements-convert_legacy_llama.txt

Updated requirements to support Qwen3-VL in transformers 4.57.1 version

* Update requirements/requirements-convert_legacy_llama.txt

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

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---
 requirements/requirements-convert_legacy_llama.txt | 9 +--------
 1 file changed, 1 insertion(+), 8 deletions(-)

diff --git a/requirements/requirements-convert_legacy_llama.txt b/requirements/requirements-convert_legacy_llama.txt
index f6076142ce..dbab3b9508 100644
--- a/requirements/requirements-convert_legacy_llama.txt
+++ b/requirements/requirements-convert_legacy_llama.txt
@@ -1,14 +1,7 @@
 numpy~=1.26.4
 sentencepiece~=0.2.0
 
-# Embedding Gemma is currently a preview release:
-# https://github.com/huggingface/transformers/releases/tag/v4.56.0-Embedding-Gemma-preview
-
-# The version is needed to be able to convert Embedding Gemma models to GGUF format:
-git+https://github.com/huggingface/transformers@v4.56.0-Embedding-Gemma-preview
-
-# Once Embedding Gemma is officially released, we can switch to:
-#transformers>=4.57.1,<5.0.0
+transformers>=4.57.1,<5.0.0
 
 gguf>=0.1.0
 protobuf>=4.21.0,<5.0.0

From 9984cbb61d12491d604484fb38b678fa15064061 Mon Sep 17 00:00:00 2001
From: lhez <lih@qti.qualcomm.com>
Date: Thu, 30 Oct 2025 16:00:20 -0700
Subject: [PATCH 05/30] opencl: fix boundary handling for mul_mm (#16875)

---
 ggml/src/ggml-opencl/kernels/mul_mm_f16_f32_l4_lm.cl  | 6 +++---
 ggml/src/ggml-opencl/kernels/mul_mm_f32_f32_l4_lm.cl  | 4 ++--
 ggml/src/ggml-opencl/kernels/mul_mm_q8_0_f32_l4_lm.cl | 4 ++--
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/ggml/src/ggml-opencl/kernels/mul_mm_f16_f32_l4_lm.cl b/ggml/src/ggml-opencl/kernels/mul_mm_f16_f32_l4_lm.cl
index 1a1bfe144f..6982f8f514 100644
--- a/ggml/src/ggml-opencl/kernels/mul_mm_f16_f32_l4_lm.cl
+++ b/ggml/src/ggml-opencl/kernels/mul_mm_f16_f32_l4_lm.cl
@@ -79,8 +79,8 @@ kernel void kernel_mul_mm_f16_f32_l4_lm(
 
     for (int block = 0; block < ne00; block += BK) {
         for (int l = 0; l < BM; l += loadstride_a) {
-            if (loadc_a + l < ne01) {
-            const int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a;
+            if (ir*BM + loadc_a + l < ne01) {
+                const int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a;
                 buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = src0[idx].s0;
                 buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = src0[idx].s1;
                 buf_a[(loadr_a * LOAD_VEC_A + 2) * BM + loadc_a + l] = src0[idx].s2;
@@ -94,7 +94,7 @@ kernel void kernel_mul_mm_f16_f32_l4_lm(
         }
 
         for (int l = 0; l < BN; l += loadstride_b) {
-            if (loadc_b + l < ne11) {
+            if (ic*BN + loadc_b + l < ne11) {
                 const int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b;
                 buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0;
                 buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1;
diff --git a/ggml/src/ggml-opencl/kernels/mul_mm_f32_f32_l4_lm.cl b/ggml/src/ggml-opencl/kernels/mul_mm_f32_f32_l4_lm.cl
index 39a5d4868f..d7d5ba647e 100644
--- a/ggml/src/ggml-opencl/kernels/mul_mm_f32_f32_l4_lm.cl
+++ b/ggml/src/ggml-opencl/kernels/mul_mm_f32_f32_l4_lm.cl
@@ -79,7 +79,7 @@ kernel void kernel_mul_mm_f32_f32_l4_lm(
 
     for (int block = 0; block < ne00; block += BK) {
         for (int l = 0; l < BM; l += loadstride_a) {
-            if (loadc_a + l < ne01) {
+            if (ir*BM + loadc_a + l < ne01) {
                 const int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a;
                 buf_a[(loadr_a * LOAD_VEC_A + 0) * BM + loadc_a + l] = src0[idx].s0;
                 buf_a[(loadr_a * LOAD_VEC_A + 1) * BM + loadc_a + l] = src0[idx].s1;
@@ -94,7 +94,7 @@ kernel void kernel_mul_mm_f32_f32_l4_lm(
         }
 
         for (int l = 0; l < BN; l += loadstride_b) {
-            if (loadc_b + l < ne11) {
+            if (ic*BN + loadc_b + l < ne11) {
                 const int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b;
                 buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0;
                 buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1;
diff --git a/ggml/src/ggml-opencl/kernels/mul_mm_q8_0_f32_l4_lm.cl b/ggml/src/ggml-opencl/kernels/mul_mm_q8_0_f32_l4_lm.cl
index fd47e8a89d..147b66f669 100644
--- a/ggml/src/ggml-opencl/kernels/mul_mm_q8_0_f32_l4_lm.cl
+++ b/ggml/src/ggml-opencl/kernels/mul_mm_q8_0_f32_l4_lm.cl
@@ -78,7 +78,7 @@ kernel void kernel_mul_mm_q8_0_f32_l4_lm(
 
     for (int block = 0; block < ne00; block += BK) {
         for (int l = 0; l < BM; l += loadstride_a) {
-            if (loadc_a + l < ne01) {
+            if (ir*BM + loadc_a + l < ne01) {
                 int idx = pos_a + (loadc_a + l) * stride_a / LOAD_VEC_A + loadr_a;
                 int ib  = idx / 8;
                 int iqs = idx % 8;
@@ -101,7 +101,7 @@ kernel void kernel_mul_mm_q8_0_f32_l4_lm(
         }
 
         for (int l = 0; l < BN; l += loadstride_b) {
-            if (loadc_b + l < ne11) {
+            if (ic*BN + loadc_b + l < ne11) {
                 int idx = pos_b + (loadc_b + l) * stride_b / LOAD_VEC_B + loadr_b;
                 buf_b[(loadr_b * LOAD_VEC_B + 0) * BN + loadc_b + l] = src1[idx].s0;
                 buf_b[(loadr_b * LOAD_VEC_B + 1) * BN + loadc_b + l] = src1[idx].s1;

From 6eb208d17ea29bb60295d9a2b5e7122dfb8f4b55 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Fri, 31 Oct 2025 00:34:27 +0100
Subject: [PATCH 06/30] ci : enable free-disk-space on cuda docker build
 (#16877)

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

diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml
index f73a2bc9f4..7ca11b1dff 100644
--- a/.github/workflows/docker.yml
+++ b/.github/workflows/docker.yml
@@ -40,7 +40,7 @@ jobs:
           # https://github.com/ggml-org/llama.cpp/issues/11888
           #- { tag: "cpu", dockerfile: ".devops/cpu.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, free_disk_space: false }
           - { tag: "cpu",    dockerfile: ".devops/cpu.Dockerfile",    platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04" }
-          - { tag: "cuda",   dockerfile: ".devops/cuda.Dockerfile",   platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04" }
+          - { tag: "cuda",   dockerfile: ".devops/cuda.Dockerfile",   platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04" }
           - { tag: "musa",   dockerfile: ".devops/musa.Dockerfile",   platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04" }
           - { tag: "intel",  dockerfile: ".devops/intel.Dockerfile",  platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true,  runs_on: "ubuntu-22.04" }
           - { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false, runs_on: "ubuntu-22.04" }

From 13002a08960e51a76c4d696165b5d7638d2f2b99 Mon Sep 17 00:00:00 2001
From: l3utterfly <gc.pthzfoldr@gmail.com>
Date: Fri, 31 Oct 2025 12:46:31 +0800
Subject: [PATCH 07/30] ggml-hexagon: respect input size when getting/setting
 tensor data (#16836)

* respect input size when getting/setting tensor data

allows partial repacking/copying when get tensor size is smaller than the actual tensor

* Removed duplicate repack_mxfp4_mxfp4x4x2 function
---
 ggml/src/ggml-hexagon/ggml-hexagon.cpp | 180 +++++++++++++++++++++++--
 1 file changed, 168 insertions(+), 12 deletions(-)

diff --git a/ggml/src/ggml-hexagon/ggml-hexagon.cpp b/ggml/src/ggml-hexagon/ggml-hexagon.cpp
index 2d376a6025..945652263d 100644
--- a/ggml/src/ggml-hexagon/ggml-hexagon.cpp
+++ b/ggml/src/ggml-hexagon/ggml-hexagon.cpp
@@ -676,6 +676,15 @@ static void repack_q4_0_q4x4x2(ggml_tensor * t, const void * data, size_t size)
     size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q4_0x4x2));  // extra elements for the pad
     size_t row_size_rp = row_size * 2;  // extra space for tmp pad (if any)
 
+    // Ensure we don't try to read more data than is available in the source buffer 'data'
+    // or write more than the tensor can hold.
+    const size_t total_tensor_size = (size_t)nrows * row_size;
+    const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
+
+    // Calculate how many full rows and how many remaining bytes we need to process.
+    const int64_t n_full_rows = n_bytes_to_copy / row_size;
+    const size_t  n_rem_bytes = n_bytes_to_copy % row_size;
+
     void * buf_pd = ggml_aligned_malloc(row_size_pd);
     GGML_ASSERT(buf_pd != NULL);
 
@@ -687,7 +696,8 @@ static void repack_q4_0_q4x4x2(ggml_tensor * t, const void * data, size_t size)
 
     init_row_q4x4x2((block_q4_0 *) buf_pd, t->ne[0]);  // init padded buffer to make sure the tail is all zeros
 
-    for (int64_t i = 0; i < nrows; i++) {
+    // 1. Process all the full rows
+    for (int64_t i = 0; i < n_full_rows; i++) {
         const uint8_t * src = (const uint8_t *) data + (i * row_size);
         uint8_t *       dst = (uint8_t *) t->data + (i * row_size);
 
@@ -696,6 +706,25 @@ static void repack_q4_0_q4x4x2(ggml_tensor * t, const void * data, size_t size)
         memcpy(dst, buf_rp, row_size);
     }
 
+    // 2. Process the final, potentially partial, row
+    if (n_rem_bytes > 0) {
+        const int64_t i = n_full_rows;
+        const uint8_t * src = (const uint8_t *) data + (i * row_size);
+        uint8_t *       dst = (uint8_t *) t->data + (i * row_size);
+
+        // re-init the row because we are potentially copying a partial row
+        init_row_q4x4x2((block_q4_0 *) buf_pd, t->ne[0]);
+
+        // Copy only the remaining bytes from the source.
+        memcpy(buf_pd, src, n_rem_bytes);
+
+        // Repack the entire buffer
+        repack_row_q4x4x2((uint8_t *) buf_rp, (const block_q4_0 *) buf_pd, t->ne[0]);
+
+        // Write only the corresponding remaining bytes to the destination tensor.
+        memcpy(dst, buf_rp, n_rem_bytes);
+    }
+
     ggml_aligned_free(buf_pd, row_size_pd);
     ggml_aligned_free(buf_rp, row_size_rp);
 }
@@ -708,6 +737,14 @@ static void repack_q4x4x2_q4_0(void * data, const ggml_tensor * t, size_t size)
     size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q4_0x4x2));  // extra elements for the pad
     size_t row_size_rp = row_size * 2;  // extra space for tmp pad (if any)
 
+    // Ensure we don't try to copy more data than the tensor actually contains.
+    const size_t total_tensor_size = (size_t)nrows * row_size;
+    const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
+
+    // Calculate how many full rows and how many remaining bytes we need to process.
+    const int64_t n_full_rows = n_bytes_to_copy / row_size;
+    const size_t  n_rem_bytes = n_bytes_to_copy % row_size;
+
     void * buf_pd = ggml_aligned_malloc(row_size_pd);
     GGML_ASSERT(buf_pd != NULL);
 
@@ -719,7 +756,8 @@ static void repack_q4x4x2_q4_0(void * data, const ggml_tensor * t, size_t size)
 
     memset(buf_pd, 0, row_size_pd);  // clear-out padded buffer to make sure the tail is all zeros
 
-    for (int64_t i = 0; i < nrows; i++) {
+    // 1. Process all the full rows
+    for (int64_t i = 0; i < n_full_rows; i++) {
         const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
         uint8_t *       dst = (uint8_t *) data + (i * row_size);
 
@@ -728,6 +766,20 @@ static void repack_q4x4x2_q4_0(void * data, const ggml_tensor * t, size_t size)
         memcpy(dst, buf_rp, row_size);
     }
 
+    // 2. Process the final, potentially partial, row
+    if (n_rem_bytes > 0) {
+        const int64_t i = n_full_rows;
+        const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
+        uint8_t *       dst = (uint8_t *) data + (i * row_size);
+
+        // We still need to read and unpack the entire source row because quantization is block-based.
+        memcpy(buf_pd, src, row_size);
+        unpack_row_q4x4x2((block_q4_0 *) buf_rp, (const uint8_t *) buf_pd, t->ne[0]);
+
+        // But we only copy the remaining number of bytes to the destination.
+        memcpy(dst, buf_rp, n_rem_bytes);
+    }
+
     ggml_aligned_free(buf_pd, row_size_pd);
     ggml_aligned_free(buf_rp, row_size_rp);
 }
@@ -950,6 +1002,15 @@ static void repack_q8_0_q8x4x2(ggml_tensor * t, const void * data, size_t size)
     size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q8_0x4x2));  // extra elements for the pad
     size_t row_size_rp = row_size * 2;  // extra space for tmp pad (if any)
 
+    // Ensure we don't try to read more data than is available in the source buffer 'data'
+    // or write more than the tensor can hold.
+    const size_t total_tensor_size = (size_t)nrows * row_size;
+    const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
+
+    // Calculate how many full rows and how many remaining bytes we need to process.
+    const int64_t n_full_rows = n_bytes_to_copy / row_size;
+    const size_t  n_rem_bytes = n_bytes_to_copy % row_size;
+
     void * buf_pd = ggml_aligned_malloc(row_size_pd);
     GGML_ASSERT(buf_pd != NULL);
 
@@ -961,7 +1022,8 @@ static void repack_q8_0_q8x4x2(ggml_tensor * t, const void * data, size_t size)
 
     init_row_q8x4x2((block_q8_0 *) buf_pd, t->ne[0]);  // init padded buffer to make sure the tail is all zeros
 
-    for (int64_t i = 0; i < nrows; i++) {
+    // 1. Process all the full rows
+    for (int64_t i = 0; i < n_full_rows; i++) {
         const uint8_t * src = (const uint8_t *) data + (i * row_size);
         uint8_t *       dst = (uint8_t *) t->data + (i * row_size);
 
@@ -970,6 +1032,25 @@ static void repack_q8_0_q8x4x2(ggml_tensor * t, const void * data, size_t size)
         memcpy(dst, buf_rp, row_size);
     }
 
+    // 2. Process the final, potentially partial, row
+    if (n_rem_bytes > 0) {
+        const int64_t i = n_full_rows;
+        const uint8_t * src = (const uint8_t *) data + (i * row_size);
+        uint8_t *       dst = (uint8_t *) t->data + (i * row_size);
+
+        // re-init the row because we are potentially copying a partial row
+        init_row_q8x4x2((block_q8_0 *) buf_pd, t->ne[0]);
+
+        // Copy only the remaining bytes from the source.
+        memcpy(buf_pd, src, n_rem_bytes);
+
+        // Repack the entire buffer
+        repack_row_q8x4x2((uint8_t *) buf_rp, (const block_q8_0 *) buf_pd, t->ne[0]);
+
+        // Write only the corresponding remaining bytes to the destination tensor.
+        memcpy(dst, buf_rp, n_rem_bytes);
+    }
+
     ggml_aligned_free(buf_pd, row_size_pd);
     ggml_aligned_free(buf_rp, row_size_rp);
 }
@@ -982,6 +1063,14 @@ static void repack_q8x4x2_q8_0(void * data, const ggml_tensor * t, size_t size)
     size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_Q8_0x4x2));  // extra elements for the pad
     size_t row_size_rp = row_size * 2;  // extra space for tmp pad (if any)
 
+    // Ensure we don't try to copy more data than the tensor actually contains.
+    const size_t total_tensor_size = (size_t)nrows * row_size;
+    const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
+
+    // Calculate how many full rows and how many remaining bytes we need to process.
+    const int64_t n_full_rows = n_bytes_to_copy / row_size;
+    const size_t  n_rem_bytes = n_bytes_to_copy % row_size;
+
     void * buf_pd = ggml_aligned_malloc(row_size_pd);
     GGML_ASSERT(buf_pd != NULL);
 
@@ -993,7 +1082,8 @@ static void repack_q8x4x2_q8_0(void * data, const ggml_tensor * t, size_t size)
 
     memset(buf_pd, 0, row_size_pd);  // clear-out padded buffer to make sure the tail is all zeros
 
-    for (int64_t i = 0; i < nrows; i++) {
+    // 1. Process all the full rows
+    for (int64_t i = 0; i < n_full_rows; i++) {
         const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
         uint8_t *       dst = (uint8_t *) data + (i * row_size);
 
@@ -1002,6 +1092,20 @@ static void repack_q8x4x2_q8_0(void * data, const ggml_tensor * t, size_t size)
         memcpy(dst, buf_rp, row_size);
     }
 
+    // 2. Process the final, potentially partial, row
+    if (n_rem_bytes > 0) {
+        const int64_t i = n_full_rows;
+        const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
+        uint8_t *       dst = (uint8_t *) data + (i * row_size);
+
+        // We still need to read and unpack the entire source row because quantization is block-based.
+        memcpy(buf_pd, src, row_size);
+        unpack_row_q8x4x2((block_q8_0 *) buf_rp, (const uint8_t *) buf_pd, t->ne[0]);
+
+        // But we only copy the remaining number of bytes to the destination.
+        memcpy(dst, buf_rp, n_rem_bytes);
+    }
+
     ggml_aligned_free(buf_pd, row_size_pd);
     ggml_aligned_free(buf_rp, row_size_rp);
 }
@@ -1249,6 +1353,15 @@ static void repack_mxfp4_mxfp4x4x2(ggml_tensor * t, const void * data, size_t si
     size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_MXFP4x4x2));  // extra elements for the pad
     size_t row_size_rp = row_size * 2;  // extra space for tmp pad (if any)
 
+    // Ensure we don't try to read more data than is available in the source buffer 'data'
+    // or write more than the tensor can hold.
+    const size_t total_tensor_size = (size_t)nrows * row_size;
+    const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
+
+    // Calculate how many full rows and how many remaining bytes we need to process.
+    const int64_t n_full_rows = n_bytes_to_copy / row_size;
+    const size_t  n_rem_bytes = n_bytes_to_copy % row_size;
+
     void * buf_pd = ggml_aligned_malloc(row_size_pd);
     GGML_ASSERT(buf_pd != NULL);
 
@@ -1260,7 +1373,8 @@ static void repack_mxfp4_mxfp4x4x2(ggml_tensor * t, const void * data, size_t si
 
     init_row_mxfp4x4x2((block_mxfp4 *) buf_pd, t->ne[0]);  // init padded buffer to make sure the tail is all zeros
 
-    for (int64_t i = 0; i < nrows; i++) {
+    // 1. Process all the full rows
+    for (int64_t i = 0; i < n_full_rows; i++) {
         const uint8_t * src = (const uint8_t *) data + (i * row_size);
         uint8_t *       dst = (uint8_t *) t->data + (i * row_size);
 
@@ -1269,6 +1383,25 @@ static void repack_mxfp4_mxfp4x4x2(ggml_tensor * t, const void * data, size_t si
         memcpy(dst, buf_rp, row_size);
     }
 
+    // 2. Process the final, potentially partial, row
+    if (n_rem_bytes > 0) {
+        const int64_t i = n_full_rows;
+        const uint8_t * src = (const uint8_t *) data + (i * row_size);
+        uint8_t *       dst = (uint8_t *) t->data + (i * row_size);
+
+        // re-init the row because we are potentially copying a partial row
+        init_row_mxfp4x4x2((block_mxfp4 *) buf_pd, t->ne[0]);
+
+        // Copy only the remaining bytes from the source.
+        memcpy(buf_pd, src, n_rem_bytes);
+
+        // Repack the entire buffer (partial data + zero padding).
+        repack_row_mxfp4x4x2((uint8_t *) buf_rp, (const block_mxfp4 *) buf_pd, t->ne[0]);
+
+        // Write only the corresponding remaining bytes to the destination tensor.
+        memcpy(dst, buf_rp, n_rem_bytes);
+    }
+
     ggml_aligned_free(buf_pd, row_size_pd);
     ggml_aligned_free(buf_rp, row_size_rp);
 }
@@ -1281,6 +1414,14 @@ static void repack_mxfp4x4x2_mxfp4(void * data, const ggml_tensor * t, size_t si
     size_t row_size_pd = ggml_row_size(t->type, hex_round_up(t->ne[0], QK_MXFP4x4x2));  // extra elements for the pad
     size_t row_size_rp = row_size * 2;  // extra space for tmp pad (if any)
 
+    // Ensure we don't try to copy more data than the tensor actually contains.
+    const size_t total_tensor_size = (size_t)nrows * row_size;
+    const size_t n_bytes_to_copy = size < total_tensor_size ? size : total_tensor_size;
+
+    // Calculate how many full rows and how many remaining bytes we need to process.
+    const int64_t n_full_rows = n_bytes_to_copy / row_size;
+    const size_t  n_rem_bytes = n_bytes_to_copy % row_size;
+
     void * buf_pd = ggml_aligned_malloc(row_size_pd);
     GGML_ASSERT(buf_pd != NULL);
 
@@ -1292,7 +1433,8 @@ static void repack_mxfp4x4x2_mxfp4(void * data, const ggml_tensor * t, size_t si
 
     memset(buf_pd, 0, row_size_pd);  // clear-out padded buffer to make sure the tail is all zeros
 
-    for (int64_t i = 0; i < nrows; i++) {
+    // 1. Process all the full rows
+    for (int64_t i = 0; i < n_full_rows; i++) {
         const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
         uint8_t *       dst = (uint8_t *) data + (i * row_size);
 
@@ -1301,6 +1443,20 @@ static void repack_mxfp4x4x2_mxfp4(void * data, const ggml_tensor * t, size_t si
         memcpy(dst, buf_rp, row_size);
     }
 
+    // 2. Process the final, potentially partial, row
+    if (n_rem_bytes > 0) {
+        const int64_t i = n_full_rows;
+        const uint8_t * src = (const uint8_t *) t->data + (i * row_size);
+        uint8_t *       dst = (uint8_t *) data + (i * row_size);
+
+        // We still need to read and unpack the entire source row because the format is block-based.
+        memcpy(buf_pd, src, row_size);
+        unpack_row_mxfp4x4x2((block_mxfp4 *) buf_rp, (const uint8_t *) buf_pd, t->ne[0]);
+
+        // But we only copy the remaining number of bytes to the destination to respect the size limit.
+        memcpy(dst, buf_rp, n_rem_bytes);
+    }
+
     ggml_aligned_free(buf_pd, row_size_pd);
     ggml_aligned_free(buf_rp, row_size_rp);
 }
@@ -1319,19 +1475,19 @@ static void ggml_backend_hexagon_buffer_set_tensor(ggml_backend_buffer_t buffer,
     switch (tensor->type) {
         case GGML_TYPE_Q4_0:
             GGML_ASSERT(offset == 0);
-            GGML_ASSERT(size == ggml_nbytes(tensor));
+            GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
             repack_q4_0_q4x4x2(tensor, data, size);
             break;
 
         case GGML_TYPE_Q8_0:
             GGML_ASSERT(offset == 0);
-            GGML_ASSERT(size == ggml_nbytes(tensor));
+            GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
             repack_q8_0_q8x4x2(tensor, data, size);
             break;
 
         case GGML_TYPE_MXFP4:
             GGML_ASSERT(offset == 0);
-            GGML_ASSERT(size == ggml_nbytes(tensor));
+            GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
             repack_mxfp4_mxfp4x4x2(tensor, data, size);
             break;
 
@@ -1355,19 +1511,19 @@ static void ggml_backend_hexagon_buffer_get_tensor(ggml_backend_buffer_t buffer,
     switch (tensor->type) {
         case GGML_TYPE_Q4_0:
             GGML_ASSERT(offset == 0);
-            GGML_ASSERT(size == ggml_nbytes(tensor));
+            GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
             repack_q4x4x2_q4_0(data, tensor, size);
             break;
 
         case GGML_TYPE_Q8_0:
             GGML_ASSERT(offset == 0);
-            GGML_ASSERT(size == ggml_nbytes(tensor));
+            GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
             repack_q8x4x2_q8_0(data, tensor, size);
             break;
 
         case GGML_TYPE_MXFP4:
             GGML_ASSERT(offset == 0);
-            GGML_ASSERT(size == ggml_nbytes(tensor));
+            GGML_ASSERT(offset + size <= ggml_nbytes(tensor));
             repack_mxfp4x4x2_mxfp4(data, tensor, size);
             break;
 

From d2a2673dd1c86a01ab010e18b13b8bb959968c48 Mon Sep 17 00:00:00 2001
From: Ruben Ortlam <picard12@live.de>
Date: Fri, 31 Oct 2025 08:14:49 +0100
Subject: [PATCH 08/30] vulkan: fix shmem overrun in mmq id shader (#16873)

* vulkan: fix shmem overrun in mmq id shader

* metal : fix mul_mm_id

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---
 ggml/src/ggml-metal/ggml-metal-device.cpp                    | 2 +-
 ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq.comp             | 4 ++++
 ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl | 2 +-
 tests/test-backend-ops.cpp                                   | 3 +++
 4 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/ggml/src/ggml-metal/ggml-metal-device.cpp b/ggml/src/ggml-metal/ggml-metal-device.cpp
index 1a3c7873b7..5607deaf41 100644
--- a/ggml/src/ggml-metal/ggml-metal-device.cpp
+++ b/ggml/src/ggml-metal/ggml-metal-device.cpp
@@ -677,7 +677,7 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_mul_mm_id_map0(ggml_metal_
     char name[256];
 
     snprintf(base, 256, "kernel_mul_mm_id_map0_ne20_%d", ne20);
-    snprintf(name, 256, "%s", base);
+    snprintf(name, 256, "%s_ne02=%d", base, ne02);
 
     ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name);
     if (res) {
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq.comp
index 8b238ac4bc..d955b4fc7a 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq.comp
@@ -82,9 +82,13 @@ layout (constant_id = 10) const uint WARP = 32;
 
 #include "mul_mmq_shmem_types.glsl"
 
+#ifdef MUL_MAT_ID
+#define BK_STEP 1
+#else
 #ifndef BK_STEP
 #define BK_STEP 4
 #endif
+#endif
 
 // Shared memory cache
 shared block_a_cache buf_a[BM * BK_STEP];
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl
index 72fec44049..1c0f5306f3 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mmq_shmem_types.glsl
@@ -27,7 +27,7 @@ struct block_a_cache {
 #elif defined(DATA_A_Q8_0)
 #define QUANT_R_MMQ 1
 // AMD likes 4, Intel likes 1 and Nvidia likes 2
-#define BK_STEP 1
+// #define BK_STEP 1
 struct block_a_cache {
     int32_t qs[32/4];
     FLOAT_TYPE dm;
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index 92361d6f0f..fa12c06ccd 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -6880,6 +6880,9 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 1, 1, false, 8, 16, 1));
     test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_F16, GGML_TYPE_F32, 16, 16, false, 32, 32, 32, 3));
 
+    // gpt-oss issue with Vulkan mmq_id
+    test_cases.emplace_back(new test_mul_mat_id(GGML_TYPE_MXFP4, GGML_TYPE_F32, 32, 2, false, 2880, 32, 2880));
+
     for (ggml_type type_a : base_types) {
         for (ggml_type type_b : {GGML_TYPE_F32 /*, GGML_TYPE_F16 */}) {
             for (int n_mats : {4, 8}) {

From 2976b0374d36609b0429dd6ce48807e2ad39a7c2 Mon Sep 17 00:00:00 2001
From: Masato Nakasaka <masato.nakasaka@intel.com>
Date: Fri, 31 Oct 2025 16:18:59 +0900
Subject: [PATCH 09/30] vulkan: Fix crash when FP16 mul_mat accumulation is not
 supported (#16796)

* Experimenting crash fix

* added assert for aborting and fixed comment

* changed to check if a pipeline is empty or not

* Moved function in class definition

* replaced with is_empty

* Modified is_empty to check only unaligned pipelines
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp | 20 +++++++++++++-------
 1 file changed, 13 insertions(+), 7 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index b61879aa5d..c3e5a9eccc 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -145,8 +145,13 @@ static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline);
 struct vk_matmul_pipeline_struct {
     vk_pipeline l, m, s;
     vk_pipeline a_l, a_m, a_s;
+    // Returns true when all unaligned pipelines are null.
+    // We only check for unaligned variants since one of the unaligned pipelines must exist
+    // while aligned pipelines are optional
+    bool is_empty() const {
+        return l == nullptr && m == nullptr && s == nullptr;
+    }
 };
-
 typedef std::shared_ptr<vk_matmul_pipeline_struct> vk_matmul_pipeline;
 
 struct vk_matmul_pipeline2 {
@@ -5079,7 +5084,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte
     if (src1_type == GGML_TYPE_Q8_1) {
         vk_matmul_pipeline pipelines = ctx->device->pipeline_dequant_mul_mat_mat_q8_1[src0_type].f32acc;
 
-        if (pipelines->s == nullptr && pipelines->m == nullptr && pipelines->l == nullptr) {
+        if (pipelines->is_empty()) {
             return nullptr;
         }
 
@@ -5228,7 +5233,7 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co
     if (src1_type == GGML_TYPE_Q8_1) {
         vk_matmul_pipeline pipelines = ctx->device->pipeline_dequant_mul_mat_mat_id_q8_1[src0_type].f32acc;
 
-        if (pipelines->s == nullptr && pipelines->m == nullptr && pipelines->l == nullptr) {
+        if (pipelines->is_empty()) {
             return nullptr;
         }
 
@@ -5263,16 +5268,17 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co
             return nullptr;
     }
 
+    vk_matmul_pipeline2& mmp = ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type];
     // XXX TODO 'prec' is not actually allowed in mul_mat_id.
     bool prefer_fp16acc = ctx->device->fp16 /*&& prec == GGML_PREC_DEFAULT*/;
-    bool support_fp16acc = ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type].f16acc != nullptr;
-    bool support_fp32acc = ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type].f32acc != nullptr;
+    bool support_fp16acc = !mmp.f16acc->is_empty();
+    bool support_fp32acc = !mmp.f32acc->is_empty();
 
     if (support_fp16acc && (prefer_fp16acc || !support_fp32acc)) {
-        return ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type].f16acc;
+        return mmp.f16acc;
     } else {
         GGML_ASSERT(support_fp32acc);
-        return ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type].f32acc;
+        return mmp.f32acc;
     }
 }
 

From d2d931f173b8a736b08999436e9259aafddec718 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Fri, 31 Oct 2025 02:34:47 -0500
Subject: [PATCH 10/30] vulkan: disable spirv-opt for rope shaders (#16872)

---
 ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

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 e6ec589fb8..bd178875d5 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -317,7 +317,8 @@ void string_to_spv_func(std::string name, std::string in_path, std::string out_p
 
     // disable spirv-opt for coopmat shaders for https://github.com/ggerganov/llama.cpp/issues/10734
     // disable spirv-opt for bf16 shaders for https://github.com/ggml-org/llama.cpp/issues/15344
-    std::string opt_level = (coopmat || name.find("bf16") != std::string::npos) ? "" : "-O";
+    // disable spirv-opt for rope shaders for https://github.com/ggml-org/llama.cpp/issues/16860
+    std::string opt_level = (coopmat || name.find("bf16") != std::string::npos || name.find("rope") != std::string::npos) ? "" : "-O";
 
     #ifdef _WIN32
         std::vector<std::string> cmd = {GLSLC, "-fshader-stage=compute", target_env, opt_level, "\"" + in_path + "\"", "-o", "\"" + out_path + "\""};

From 0f715b4e759acceccb9f437cfd2a988fff85514a Mon Sep 17 00:00:00 2001
From: Daniel Bevenius <daniel.bevenius@gmail.com>
Date: Fri, 31 Oct 2025 09:51:26 +0100
Subject: [PATCH 11/30] server : fix typos in server.cpp comments [no ci]
 (#16883)

---
 tools/server/server.cpp | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index d56468595d..cb49254a1c 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -3608,7 +3608,7 @@ struct server_context {
                 slot.task->params.sampling.preserved_tokens.find(token) != slot.task->params.sampling.preserved_tokens.end();
         };
 
-        // frist, add sampled tokens from any ongoing sequences
+        // first, add sampled tokens from any ongoing sequences
         for (auto & slot : slots) {
             if (slot.state != SLOT_STATE_GENERATING) {
                 continue;
@@ -3950,7 +3950,7 @@ struct server_context {
                     // If using an alora, there may be uncached tokens that come
                     // before the invocation sequence. When this happens, the
                     // tokens before the invocation sequence need to be
-                    // processed without the adpter in a separate batch, then
+                    // processed without the adapter in a separate batch, then
                     // the adapter needs to be enabled for the remaining tokens.
                     if (lora_all_alora(slot.lora) && slot.alora_invocation_start - 1 > slot.prompt.n_tokens()) {
                         SLT_DBG(slot, "processing pre-alora tokens without the adapter (n_tokens = %d, alora_invocation_start = %d)\n", slot.prompt.n_tokens(), slot.alora_invocation_start);

From c22473b580807929fd9e3a3344a48e8cfbe6c88f Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 31 Oct 2025 10:54:19 +0200
Subject: [PATCH 12/30] server : don't print user inputs to console (#16871)

---
 tools/server/server.cpp | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/tools/server/server.cpp b/tools/server/server.cpp
index cb49254a1c..92d30664e4 100644
--- a/tools/server/server.cpp
+++ b/tools/server/server.cpp
@@ -3796,7 +3796,7 @@ struct server_context {
 
                                 // when the prompt prefix does not match, print the tokens around the mismatch
                                 // this is useful for debugging prompt caching
-                                {
+                                if (slots_debug) {
                                     const int np0 = std::max<int>(n_past - 4, 0);
                                     const int np1 = std::min<int>(n_past + 6, std::min(slot.prompt.tokens.size(), slot.task->tokens.size()));
 

From 8da3c0e200a586f768ada6f38745acb01380174c Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 31 Oct 2025 13:50:33 +0200
Subject: [PATCH 13/30] batch : fix consistency checks for the input positions
 (#16890)

---
 src/llama-batch.cpp | 30 ++++++++++++++++++++++--------
 1 file changed, 22 insertions(+), 8 deletions(-)

diff --git a/src/llama-batch.cpp b/src/llama-batch.cpp
index 0d4939fdb0..86a1a4ba18 100644
--- a/src/llama-batch.cpp
+++ b/src/llama-batch.cpp
@@ -261,15 +261,29 @@ bool llama_batch_allocr::init(
 
             const llama_pos p0 = memory ? memory->seq_pos_max(s) : -1;
 
-            if (p0 >= 0 && p0 >= seq_pos_min(s)) {
-                LLAMA_LOG_ERROR(
-                        "%s: the tokens of sequence %d in the input batch have inconsistent sequence positions:\n"
-                        " - the last position stored in the memory module of the context (i.e. the KV cache) for sequence %d is X = %d\n"
-                        " - the tokens for sequence %d in the input batch have a starting position of Y = %d\n"
-                        " for M-RoPE, it is required that the position satisfies: X < Y\n",
-                        __func__, s, s, p0, s, seq_pos_min(s));
+            if (batch.token) {
+                if (p0 >= 0 && p0 >= seq_pos_min(s)) {
+                    LLAMA_LOG_ERROR(
+                            "%s: the tokens of sequence %d in the input batch have inconsistent sequence positions:\n"
+                            " - the last position stored in the memory module of the context (i.e. the KV cache) for sequence %d is X = %d\n"
+                            " - the tokens for sequence %d in the input batch have a starting position of Y = %d\n"
+                            " for M-RoPE, it is required that the position satisfies: X < Y\n",
+                            __func__, s, s, p0, s, seq_pos_min(s));
 
-                return false;
+                    return false;
+                }
+            } else {
+                // embedding inputs can have overlapping positions
+                if (p0 >= 0 && p0 > seq_pos_min(s)) {
+                    LLAMA_LOG_ERROR(
+                            "%s: the tokens of sequence %d in the input batch have inconsistent sequence positions:\n"
+                            " - the last position stored in the memory module of the context (i.e. the KV cache) for sequence %d is X = %d\n"
+                            " - the tokens for sequence %d in the input batch have a starting position of Y = %d\n"
+                            " for M-RoPE, it is required that the position satisfies: X <= Y\n",
+                            __func__, s, s, p0, s, seq_pos_min(s));
+
+                    return false;
+                }
             }
         }
     } else {

From 4146d6a1a6228711a487a1e3e9ddd120f8d027d7 Mon Sep 17 00:00:00 2001
From: Aman Gupta <amangupta052@gmail.com>
Date: Fri, 31 Oct 2025 20:05:07 +0800
Subject: [PATCH 14/30] CUDA: add expert reduce kernel (#16857)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* CUDA: add expert reduce kernel

* contigous checks, better formatting, use std::vector instead of array

* use vector empty instead of size

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

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---
 ggml/src/ggml-cuda/ggml-cuda.cu          |  26 ++++
 ggml/src/ggml-cuda/moe-expert-reduce.cu  | 168 +++++++++++++++++++++++
 ggml/src/ggml-cuda/moe-expert-reduce.cuh |  11 ++
 tests/test-backend-ops.cpp               |  58 ++++++++
 4 files changed, 263 insertions(+)
 create mode 100644 ggml/src/ggml-cuda/moe-expert-reduce.cu
 create mode 100644 ggml/src/ggml-cuda/moe-expert-reduce.cuh

diff --git a/ggml/src/ggml-cuda/ggml-cuda.cu b/ggml/src/ggml-cuda/ggml-cuda.cu
index fcff5d7cdc..61a8f1df87 100644
--- a/ggml/src/ggml-cuda/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda/ggml-cuda.cu
@@ -27,6 +27,7 @@
 #include "ggml-cuda/mmq.cuh"
 #include "ggml-cuda/mmvf.cuh"
 #include "ggml-cuda/mmvq.cuh"
+#include "ggml-cuda/moe-expert-reduce.cuh"
 #include "ggml-cuda/norm.cuh"
 #include "ggml-cuda/opt-step-adamw.cuh"
 #include "ggml-cuda/opt-step-sgd.cuh"
@@ -3169,6 +3170,31 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
                         continue;
                     }
 
+                    if (node->op == GGML_OP_MUL) {
+                        int current_node = i + 1;
+                        int num_views    = 0;
+                        int num_adds     = 0;
+                        while (current_node < cgraph->n_nodes && cgraph->nodes[current_node]->op == GGML_OP_VIEW) {
+                            num_views++;
+                            current_node++;
+                        }
+
+                        while (current_node < cgraph->n_nodes && cgraph->nodes[current_node]->op == GGML_OP_ADD &&
+                                num_adds < num_views - 1) {
+                            num_adds++;
+                            current_node++;
+                        }
+
+                        if (num_adds == num_views - 1 && num_views > 0) {
+                            ggml_tensor * dst_node = cgraph->nodes[current_node - 1];
+                            if (ggml_cuda_should_use_moe_expert_reduce(cgraph, i, current_node)) {
+                                ggml_cuda_op_moe_expert_reduce(*cuda_ctx, node->src[0], node->src[1], dst_node);
+                                i += num_views + num_adds;
+                                continue;
+                            }
+                        }
+                    }
+
                     if (node->op == GGML_OP_ADD) {
                         int n_fuse = 0;
                         ggml_op ops[8];
diff --git a/ggml/src/ggml-cuda/moe-expert-reduce.cu b/ggml/src/ggml-cuda/moe-expert-reduce.cu
new file mode 100644
index 0000000000..a97c5d573b
--- /dev/null
+++ b/ggml/src/ggml-cuda/moe-expert-reduce.cu
@@ -0,0 +1,168 @@
+#include "moe-expert-reduce.cuh"
+
+// This kernel is a fusion of the expert weight reduce, common in MoE models
+
+template <int n_expert_used_template>
+__global__ void moe_expert_reduce_cuda(const float * __restrict__ experts,
+                                       const float * __restrict__ weights,
+                                       float * __restrict__ dst,
+                                       const int n_expert_used,
+                                       const int n_cols) {
+    const int row = blockIdx.x;
+    const int col = blockIdx.y * blockDim.x + threadIdx.x;
+    if (col >= n_cols) {
+        return;
+    }
+
+    experts += row * n_cols * n_expert_used;
+    weights += row * n_expert_used;
+    dst += row * n_cols;
+
+    float acc = 0.f;
+    if constexpr (n_expert_used_template == 0) {
+        for (int expert = 0; expert < n_expert_used; ++expert) {
+            ggml_cuda_mad(acc, experts[col], weights[expert]);
+            experts += n_cols;
+        }
+        dst[col] = acc;
+    } else {
+#pragma unroll
+        for (int i = 0; i < n_expert_used_template; ++i) {
+            ggml_cuda_mad(acc, experts[col], weights[i]);
+            experts += n_cols;
+        }
+        dst[col] = acc;
+    }
+}
+
+static void launch_moe_expert_reduce(ggml_backend_cuda_context & ctx,
+                                     const float *               experts,
+                                     const float *               weights,
+                                     float *                     dst,
+                                     const int                   n_expert_used,
+                                     const int                   n_cols,
+                                     const int                   n_rows) {
+    const int block_size = 32;
+
+    const int n_blocks_x = n_rows;
+    const int n_blocks_y = (n_cols + block_size - 1) / block_size;
+
+    dim3 block_dims(block_size);
+    dim3 grid_dims(n_blocks_x, n_blocks_y);
+
+    cudaStream_t stream = ctx.stream();
+    switch (n_expert_used) {
+        case 1:
+            moe_expert_reduce_cuda<1>
+                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
+            break;
+        case 2:
+            moe_expert_reduce_cuda<2>
+                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
+            break;
+        case 4:
+            moe_expert_reduce_cuda<4>
+                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
+            break;
+        case 6:
+            moe_expert_reduce_cuda<6>
+                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
+            break;
+        case 8:
+            moe_expert_reduce_cuda<8>
+                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
+            break;
+        case 16:
+            moe_expert_reduce_cuda<16>
+                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
+            break;
+        case 32:
+            moe_expert_reduce_cuda<32>
+                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
+            break;
+        case 64:
+            moe_expert_reduce_cuda<64>
+                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
+            break;
+        case 128:
+            moe_expert_reduce_cuda<128>
+                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
+            break;
+        default:
+            moe_expert_reduce_cuda<0>
+                <<<grid_dims, block_dims, 0, stream>>>(experts, weights, dst, n_expert_used, n_cols);
+            break;
+    }
+}
+
+bool ggml_cuda_should_use_moe_expert_reduce(const ggml_cgraph * cgraph, int start_index, int end_index) {
+    const ggml_tensor * mul = cgraph->nodes[start_index];
+
+    if (mul->op != GGML_OP_MUL || !ggml_is_contiguous(mul->src[0]) || !ggml_is_contiguous(mul->src[1])) {
+        return false;
+    }
+
+    int    current_node   = start_index + 1;
+    size_t current_offset = 0;
+
+    std::vector<const ggml_tensor *> view_nodes;
+    //check if all are views of the expert in increasing order
+    while (current_node < end_index && cgraph->nodes[current_node]->op == GGML_OP_VIEW) {
+        const ggml_tensor * node = cgraph->nodes[current_node];
+        if (node->view_src != mul) {
+            return false;
+        }
+        if (node->view_offs < current_offset) {
+            return false;
+        }
+        current_offset = node->view_offs;
+        current_node++;
+        view_nodes.push_back(node);
+    }
+
+    //check if all the adds are in increasing order
+    const ggml_tensor * prev_add_src = view_nodes.empty() ? nullptr : view_nodes[0];
+    int                 num_adds     = 0;
+    int                 num_views    = view_nodes.size();
+    while (current_node < end_index && cgraph->nodes[current_node]->op == GGML_OP_ADD) {
+        const ggml_tensor * add_node = cgraph->nodes[current_node];
+
+        bool is_first_op_ok  = num_views > num_adds ? add_node->src[0] == prev_add_src : false;
+        bool is_second_op_ok = num_views > num_adds ? add_node->src[1] == view_nodes[num_adds + 1] : false;
+
+        if (!is_first_op_ok || !is_second_op_ok) {
+            return false;
+        }
+        prev_add_src = add_node;
+
+        num_adds++;
+        current_node++;
+    }
+
+    if (num_views != num_adds + 1) {
+        return false;
+    }
+
+    return true;
+}
+
+void ggml_cuda_op_moe_expert_reduce(ggml_backend_cuda_context & ctx,
+                                    const ggml_tensor *         experts,
+                                    const ggml_tensor *         weights,
+                                    ggml_tensor *               dst) {
+    const int n_rows        = experts->ne[2];
+    const int n_expert_used = experts->ne[1];
+    const int n_cols        = experts->ne[0];
+
+    GGML_ASSERT(experts->type == GGML_TYPE_F32);
+    GGML_ASSERT(weights->type == GGML_TYPE_F32);
+    GGML_ASSERT(ggml_is_contiguous(experts));
+    GGML_ASSERT(ggml_is_contiguous(weights));
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    const float * experts_d = (const float *) experts->data;
+    const float * weights_d = (const float *) weights->data;
+    float *       dst_d     = (float *) dst->data;
+
+    launch_moe_expert_reduce(ctx, experts_d, weights_d, dst_d, n_expert_used, n_cols, n_rows);
+}
diff --git a/ggml/src/ggml-cuda/moe-expert-reduce.cuh b/ggml/src/ggml-cuda/moe-expert-reduce.cuh
new file mode 100644
index 0000000000..cafc50e104
--- /dev/null
+++ b/ggml/src/ggml-cuda/moe-expert-reduce.cuh
@@ -0,0 +1,11 @@
+#include "common.cuh"
+#include "ggml.h"
+
+#include <initializer_list>
+
+void ggml_cuda_op_moe_expert_reduce(ggml_backend_cuda_context & ctx,
+                                    const ggml_tensor *         experts,
+                                    const ggml_tensor *         weights,
+                                    ggml_tensor *               dst);
+
+bool ggml_cuda_should_use_moe_expert_reduce(const ggml_cgraph * cgraph, int start_index, int end_index);
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index fa12c06ccd..04fa1b62d3 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -4807,6 +4807,60 @@ struct test_topk_moe: public test_case {
     }
 };
 
+struct test_moe_expert_reduce : public test_case {
+    const int64_t n_embd;
+    const int64_t n_tokens;
+    const int64_t n_expert_used;
+
+    test_moe_expert_reduce(int64_t n_embd = 64, int64_t n_tokens = 5, int64_t n_expert_used = 4)
+        : n_embd(n_embd), n_tokens(n_tokens), n_expert_used(n_expert_used) {
+        GGML_ASSERT(n_expert_used > 1);
+    }
+
+    std::string vars() override {
+        return VARS_TO_STR3(n_embd, n_tokens, n_expert_used);
+    }
+
+    std::string op_desc(ggml_tensor * t) override {
+        GGML_UNUSED(t);
+        return "MOE_EXPERT_REDUCE";
+    }
+
+    bool run_whole_graph() override { return true; }
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * experts = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, n_embd, n_expert_used, n_tokens);
+        ggml_set_name(experts, "experts");
+
+        ggml_tensor * weights = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, 1, n_expert_used, n_tokens);
+        ggml_set_name(weights, "weights");
+
+        ggml_tensor * weighted = ggml_mul(ctx, experts, weights);
+        ggml_set_name(weighted, "weighted_experts");
+
+        std::vector<ggml_tensor *> expert_views(n_expert_used);
+        for (int64_t i = 0; i < n_expert_used; ++i) {
+            expert_views[i] = ggml_view_2d(ctx, weighted, n_embd, n_tokens, weighted->nb[2], i * weighted->nb[1]);
+
+            std::string name = "expert_view_" + std::to_string(i);
+            ggml_set_name(expert_views[i], name.c_str());
+            ggml_build_forward_expand(gf, expert_views[i]);
+        }
+
+        ggml_tensor * moe_out = expert_views[0];
+        for (int64_t i = 1; i < n_expert_used; ++i) {
+            moe_out = ggml_add(ctx, moe_out, expert_views[i]);
+
+            std::string name = "expert_add_" + std::to_string(i - 1);
+            ggml_set_name(moe_out, name.c_str());
+        }
+
+        ggml_set_name(moe_out, "moe_out");
+
+        return moe_out;
+    }
+};
+
 struct test_mul_mat_vec_fusion : public test_case {
     const ggml_type type;
     const ggml_glu_op glu_op;
@@ -7260,6 +7314,10 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
     test_cases.emplace_back(new test_topk_moe({ 8, 22, 1, 1 }, 4, /*with_norm*/ false, /*delayed_softmax*/ true));
     test_cases.emplace_back(new test_topk_moe({ 32, 22, 1, 1 }, 8, /*with_norm*/ false, /*delayed_softmax*/ true));
 
+    test_cases.emplace_back(new test_moe_expert_reduce(1024, 5, 4));
+    test_cases.emplace_back(new test_moe_expert_reduce(80, 3, 6));
+    test_cases.emplace_back(new test_moe_expert_reduce(80, 3, 7));
+
 #if 0
     // these tests are disabled to save execution time, sbut they can be handy for debugging
     test_cases.emplace_back(new test_llama(2, true));

From 6d39015a744b47bb7643ea73f412e6d64677f305 Mon Sep 17 00:00:00 2001
From: Georgi Gerganov <ggerganov@gmail.com>
Date: Fri, 31 Oct 2025 16:25:50 +0200
Subject: [PATCH 15/30] 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 5e09de499e..64a544d911 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-72632094336524a9c809e129e8b1c52154543a5a
+e02fb860ccbba8967905bceff23b677e88105280

From 31c511a968348281e11d590446bb815048a1e912 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de>
Date: Fri, 31 Oct 2025 15:57:19 +0100
Subject: [PATCH 16/30] CUDA: Volta tensor core support for MMF (#16843)

* CUDA: Volta tensor core support for MMF

* more generic checks for hardware support

* Update ggml/src/ggml-cuda/mmf.cuh

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

---------

Co-authored-by: Aman Gupta <amangupta052@gmail.com>
---
 ggml/src/ggml-cuda/common.cuh |  10 +-
 ggml/src/ggml-cuda/mma.cuh    | 237 ++++++++++++++++++++++++++++++----
 ggml/src/ggml-cuda/mmf.cu     |   2 +-
 ggml/src/ggml-cuda/mmf.cuh    |  41 ++++--
 4 files changed, 254 insertions(+), 36 deletions(-)

diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 6a472be7fb..ca876459d4 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -224,6 +224,11 @@ static const char * cu_get_error_str(CUresult err) {
 #define AMD_MFMA_AVAILABLE
 #endif // defined(GGML_USE_HIP) && defined(CDNA) && !defined(GGML_HIP_NO_MMQ_MFMA)
 
+// The Volta instructions are in principle available on Turing or newer but they are effectively unusable:
+#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
+#define VOLTA_MMA_AVAILABLE
+#endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
+
 #if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
 #define TURING_MMA_AVAILABLE
 #endif // !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
@@ -278,7 +283,10 @@ static bool amd_mfma_available(const int cc) {
 #endif //!defined(GGML_HIP_NO_MMQ_MFMA)
 }
 
-// Volta technically had FP16 tensor cores but they work very differently compared to Turing and later.
+static bool volta_mma_available(const int cc) {
+    return GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) == GGML_CUDA_CC_VOLTA;
+}
+
 static bool turing_mma_available(const int cc) {
     return GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_TURING;
 }
diff --git a/ggml/src/ggml-cuda/mma.cuh b/ggml/src/ggml-cuda/mma.cuh
index c1f24243fe..a7a28fd1ae 100644
--- a/ggml/src/ggml-cuda/mma.cuh
+++ b/ggml/src/ggml-cuda/mma.cuh
@@ -18,6 +18,10 @@
 
 #include "common.cuh"
 
+// On Volta each warp is doing 4 8x8 mma operations in parallel.
+// The basic memory layout for a 32x8 output tile is to stack 4 input tiles in I direction and to mirror the B tile.
+// However, the i indices in this file are by default permuted to simplify the index calculations.
+// #define GGML_CUDA_MMA_NO_VOLTA_PERM
 
 #if CUDART_VERSION >= 11080
 
@@ -73,6 +77,15 @@ namespace ggml_cuda_mma {
         static constexpr int ne = I * J / 64;
         T x[ne] = {0};
 
+        static constexpr __device__ bool supported() {
+            if (I == 64 && J ==  2) return true;
+            if (I == 16 && J ==  8) return true;
+            if (I == 32 && J ==  4) return true;
+            if (I == 16 && J == 16) return true;
+            if (I == 32 && J == 32) return true;
+            return false;
+        }
+
         static __device__ __forceinline__ int get_i(const int l) {
             if constexpr (I == 64 && J == 2) { // Special tile size to load <16, 4> as <16, 8>
                 return threadIdx.x % 16;
@@ -85,7 +98,8 @@ namespace ggml_cuda_mma {
             } else if constexpr (I == 32 && J == 32) {
                 return 4 * (threadIdx.x / 32) + 8 * (l / 4) + (l % 4);
             } else {
-                static_assert(I == -1 && J == -1, "template specialization not implemented");
+                NO_DEVICE_CODE;
+                return -1;
             }
         }
 
@@ -101,22 +115,67 @@ namespace ggml_cuda_mma {
             } else if constexpr (I == 32 && J == 32) {
                 return threadIdx.x % 32;
             } else {
-                static_assert(I == -1 && J == -1, "template specialization not implemented");
+                NO_DEVICE_CODE;
+                return -1;
+            }
+        }
+#elif __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
+        static constexpr int ne = I * J / 32;
+        T x[ne] = {0};
+
+        static constexpr __device__ bool supported() {
+            if (I == 32 && J ==  8) return true;
+            return false;
+        }
+
+        static __device__ __forceinline__ int get_i(const int l) {
+            if constexpr (I == 32 && J == 8) {
+#ifdef GGML_CUDA_MMA_NO_VOLTA_PERM
+                return (((threadIdx.x % 16) / 4) * 8) | ((threadIdx.x / 16) * 4) | (l & 2) | (threadIdx.x % 2);
+#else
+                return (l & 2) | (threadIdx.x & ~2);
+#endif // GGML_CUDA_MMA_NO_VOLTA_PERM
+            } else {
+                NO_DEVICE_CODE;
+                return -1;
+            }
+        }
+
+        static __device__ __forceinline__ int get_j(const int l) {
+            if constexpr (I == 32 && J == 8) {
+                return (threadIdx.x & 2) | (l & (4 + 1));
+            } else {
+                NO_DEVICE_CODE;
+                return -1;
             }
         }
 #else
         static constexpr int ne = I * J / 32;
         T x[ne] = {0};
 
+        static constexpr __device__ bool supported() {
+            if (I ==  8 && J ==  4) return true;
+            if (I ==  8 && J ==  8) return true;
+            if (I == 16 && J ==  8) return true;
+            if (I == 16 && J == 16) return true;
+            if (I == 32 && J ==  8) return true;
+            return false;
+        }
+
         static __device__ __forceinline__ int get_i(const int l) {
-            if constexpr (I == 8 && (J == 4 || J == 8)) {
+            if constexpr (I == 8 && J == 4) {
+                return threadIdx.x / 4;
+            } else if constexpr (I == 8 && J == 8) {
                 return threadIdx.x / 4;
             } else if constexpr (I == 16 && J == 8) {
-                return (l / 2) * 8 + threadIdx.x / 4;
+                return ((l / 2) * 8) | (threadIdx.x / 4);
             } else if constexpr (I == 16 && J == 16) {
-                return ((l / 2) % 2) * 8 + threadIdx.x / 4;
+                return (((l / 2) % 2) * 8) | (threadIdx.x / 4);
+            } else if constexpr (I == 32 && J == 8) {
+                return tile<16, 8, T>::get_i(l); // Memory layout simply repeated with same pattern in i direction.
             } else {
-                static_assert(I == -1 && J == -1, "template specialization not implemented");
+                NO_DEVICE_CODE;
+                return -1;
             }
         }
 
@@ -124,13 +183,16 @@ namespace ggml_cuda_mma {
             if constexpr (I == 8 && J == 4) {
                 return threadIdx.x % 4;
             } else if constexpr (I == 8 && J == 8) {
-                return 4 * l + threadIdx.x % 4;
+                return (l * 4) | (threadIdx.x % 4);
             } else if constexpr (I == 16 && J == 8) {
-                return 2 * (threadIdx.x % 4) + l % 2;
+                return ((threadIdx.x % 4) * 2) | (l % 2);
             } else if constexpr (I == 16 && J == 16) {
-                return 8 * (l / 4) + 2 * (threadIdx.x % 4) + l % 2;
+                return ((l / 4) * 8) | ((threadIdx.x % 4) * 2) | (l % 2);
+            } else if constexpr (I == 32 && J == 8) {
+                return tile<16, 8, T>::get_j(l); // Memory layout simply repeated with same pattern in i direction.
             } else {
-                static_assert(I == -1 && J == -1, "template specialization not implemented");
+                NO_DEVICE_CODE;
+                return -1;
             }
         }
 #endif // defined(GGML_USE_HIP)
@@ -140,32 +202,83 @@ namespace ggml_cuda_mma {
     struct tile<I_, J_, half2> {
         static constexpr int I  = I_;
         static constexpr int J  = J_;
+
+#if __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
+        static constexpr int ne = I == 8 && J == 8 ? I * J / (WARP_SIZE/4) : I * J / WARP_SIZE;
+        half2 x[ne] = {{0.0f, 0.0f}};
+
+        static constexpr __device__ bool supported() {
+            if (I ==  8 && J ==  8) return true;
+            if (I == 32 && J ==  8) return true;
+            return false;
+        }
+
+        static __device__ __forceinline__ int get_i(const int l) {
+            if constexpr (I == 8 && J == 8) {
+                return ((threadIdx.x / 16) * 4) | (threadIdx.x % 4);
+            } else if constexpr (I == 32 && J == 8) {
+#ifdef GGML_CUDA_MMA_NO_VOLTA_PERM
+                return (((threadIdx.x % 16) / 4) * 8) | ((threadIdx.x / 16) * 4) | (threadIdx.x % 4);
+#else
+                return threadIdx.x;
+#endif // GGML_CUDA_MMA_NO_VOLTA_PERM
+            } else {
+                NO_DEVICE_CODE;
+                return -1;
+            }
+        }
+
+        static __device__ __forceinline__ int get_j(const int l) {
+            if constexpr ((I == 8 || I == 32) && J == 8) {
+                return l;
+            } else {
+                NO_DEVICE_CODE;
+                return -1;
+            }
+        }
+#else
         static constexpr int ne = I * J / WARP_SIZE;
         half2 x[ne] = {{0.0f, 0.0f}};
 
+        static constexpr __device__ bool supported() {
+            if (I ==  8 && J ==  4) return true;
+            if (I ==  8 && J ==  8) return true;
+            if (I == 16 && J ==  8) return true;
+            if (I == 16 && J == 16) return true;
+            if (I == 32 && J ==  8) return true;
+            return false;
+        }
+
         static __device__ __forceinline__ int get_i(const int l) {
             if constexpr (I == 8 && J == 8) {
                 return threadIdx.x / 4;
             } else if constexpr (I == 16 && J == 4) {
-                return l * 8 + threadIdx.x / 4;
+                return (l * 8) | (threadIdx.x / 4);
             } else if constexpr (I == 16 && J == 8) {
-                return (l % 2) * 8 + threadIdx.x / 4;
+                return ((l % 2) * 8) | (threadIdx.x / 4);
+            } else if constexpr (I == 32 && J == 8) {
+                return ((l / 4) * 16) | ((l % 2) * 8) | (threadIdx.x / 4);
             } else {
-                static_assert(I == -1 && J == -1, "template specialization not implemented");
+                NO_DEVICE_CODE;
+                return -1;
             }
         }
 
         static __device__ __forceinline__ int get_j(const int l) {
             if constexpr (I == 8 && J == 8) {
-                return l * 4 + threadIdx.x % 4;
+                return (l * 4) | (threadIdx.x % 4);
             } else if constexpr (I == 16 && J == 4) {
                 return threadIdx.x % 4;
             } else if constexpr (I == 16 && J == 8) {
-                return (l / 2) * 4 + threadIdx.x % 4;
+                return ((l / 2) * 4) | (threadIdx.x % 4);
+            } else if constexpr (I == 32 && J == 8) {
+                return ((l & 2) * 2) | (threadIdx.x % 4);
             } else {
-                static_assert(I == -1 && J == -1, "template specialization not implemented");
+                NO_DEVICE_CODE;
+                return -1;
             }
         }
+#endif // __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
     };
 
     template <int I_, int J_>
@@ -175,27 +288,36 @@ namespace ggml_cuda_mma {
         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;
+            if (I == 16 && J ==  8) return true;
+            return false;
+        }
+
         static __device__ __forceinline__ int get_i(const int l) {
             if constexpr (I == 8 && J == 8) {
                 return threadIdx.x / 4;
             } else if constexpr (I == 16 && J == 4) {
-                return l * 8 + threadIdx.x / 4;
+                return (l * 8) | (threadIdx.x / 4);
             } else if constexpr (I == 16 && J == 8) {
-                return (l % 2) * 8 + threadIdx.x / 4;
+                return ((l % 2) * 8) | (threadIdx.x / 4);
             } else {
-                static_assert(I == -1 && J == -1, "template specialization not implemented");
+                NO_DEVICE_CODE;
+                return -1;
             }
         }
 
         static __device__ __forceinline__ int get_j(const int l) {
             if constexpr (I == 8 && J == 8) {
-                return l * 4 + threadIdx.x % 4;
+                return (l * 4) | (threadIdx.x % 4);
             } else if constexpr (I == 16 && J == 4) {
                 return threadIdx.x % 4;
             } else if constexpr (I == 16 && J == 8) {
-                return (l / 2) * 4 + threadIdx.x % 4;
+                return ((l / 2) * 4) | (threadIdx.x % 4);
             } else {
-                static_assert(I == -1 && J == -1, "template specialization not implemented");
+                NO_DEVICE_CODE;
+                return -1;
             }
         }
     };
@@ -263,8 +385,12 @@ namespace ggml_cuda_mma {
             : "=r"(xi[0]), "=r"(xi[1])
             : "l"(xs));
 #else
-        load_generic(xs0, stride);
-        GGML_UNUSED(t);
+#if __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
+        GGML_UNUSED_VARS(t, xs0, stride);
+        NO_DEVICE_CODE;
+#else
+        load_generic(t, xs0, stride);
+#endif // __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
 #endif // TURING_MMA_AVAILABLE
     }
 
@@ -277,11 +403,35 @@ namespace ggml_cuda_mma {
         asm volatile("ldmatrix.sync.aligned.m8n8.x4.b16 {%0, %1, %2, %3}, [%4];"
             : "=r"(xi[0]), "=r"(xi[1]), "=r"(xi[2]), "=r"(xi[3])
             : "l"(xs));
+#else
+#if __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
+        GGML_UNUSED_VARS(t, xs0, stride);
+        NO_DEVICE_CODE;
 #else
         load_generic(t, xs0, stride);
+#endif // __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
 #endif // TURING_MMA_AVAILABLE
     }
 
+    template <typename T>
+    static __device__ __forceinline__ void load_ldmatrix(
+            tile<32, 8, T> & t, const T * __restrict__ xs0, const int stride) {
+#if __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
+#if 1
+        // TODO: more generic handling
+        static_assert(sizeof(T) == 4, "bad type size");
+        ggml_cuda_memcpy_1<4*sizeof(T)>(t.x + 0, xs0 + t.get_i(0)*stride + 0);
+        ggml_cuda_memcpy_1<4*sizeof(T)>(t.x + 4, xs0 + t.get_i(4)*stride + 4);
+#else
+        load_generic(t, xs0, stride);
+#endif // 1
+#else
+        tile<16, 8, T> * t16 = (tile<16, 8, T> *) &t;
+        load_ldmatrix(t16[0], xs0 +  0*stride, stride);
+        load_ldmatrix(t16[1], xs0 + 16*stride, stride);
+#endif // __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
+    }
+
     template <typename T>
     static __device__ __forceinline__ void load_ldmatrix_trans(
             tile<16, 8, T> & t, const T * __restrict__ xs0, const int stride) {
@@ -546,4 +696,43 @@ namespace ggml_cuda_mma {
         NO_DEVICE_CODE;
 #endif // AMD_MFMA_AVAILABLE
     }
+
+    template <typename T1, typename T2, int J, int K>
+    static __device__ __forceinline__ void mma(
+            tile<32, J, T1> & D, const tile<32, K, T2> & A, const tile<J, K, T2> & B) {
+        tile<16, J, T1> * D16 = (tile<16, J, T1> *) &D;
+        tile<16, K, T2> * A16 = (tile<16, K, T2> *) &A;
+        mma(D16[0], A16[0], B);
+        mma(D16[1], A16[1], B);
+    }
+
+    static __device__ __forceinline__ void mma(
+            tile<32, 8, float> & D, const tile<32, 8, half2> & A, const tile<8, 8, half2> & B) {
+#if __CUDA_ARCH__ == GGML_CUDA_CC_VOLTA
+        const int * Axi = (const int *) A.x;
+        const int * Bxi = (const int *) B.x;
+        int       * Dxi = (int       *) D.x;
+        asm("mma.sync.aligned.m8n8k4.row.col.f32.f16.f16.f32 "
+            "{%0, %1, %2, %3, %4, %5, %6, %7}, {%8, %9}, {%10, %11}, {%0, %1, %2, %3, %4, %5, %6, %7};"
+            : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3]), "+r"(Dxi[4]), "+r"(Dxi[5]), "+r"(Dxi[6]), "+r"(Dxi[7])
+            : "r"(Axi[0]), "r"(Axi[1]), "r"(Bxi[0]), "r"(Bxi[1]));
+        asm("mma.sync.aligned.m8n8k4.row.col.f32.f16.f16.f32 "
+            "{%0, %1, %2, %3, %4, %5, %6, %7}, {%8, %9}, {%10, %11}, {%0, %1, %2, %3, %4, %5, %6, %7};"
+            : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3]), "+r"(Dxi[4]), "+r"(Dxi[5]), "+r"(Dxi[6]), "+r"(Dxi[7])
+            : "r"(Axi[2]), "r"(Axi[3]), "r"(Bxi[2]), "r"(Bxi[3]));
+        asm("mma.sync.aligned.m8n8k4.row.col.f32.f16.f16.f32 "
+            "{%0, %1, %2, %3, %4, %5, %6, %7}, {%8, %9}, {%10, %11}, {%0, %1, %2, %3, %4, %5, %6, %7};"
+            : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3]), "+r"(Dxi[4]), "+r"(Dxi[5]), "+r"(Dxi[6]), "+r"(Dxi[7])
+            : "r"(Axi[4]), "r"(Axi[5]), "r"(Bxi[4]), "r"(Bxi[5]));
+        asm("mma.sync.aligned.m8n8k4.row.col.f32.f16.f16.f32 "
+            "{%0, %1, %2, %3, %4, %5, %6, %7}, {%8, %9}, {%10, %11}, {%0, %1, %2, %3, %4, %5, %6, %7};"
+            : "+r"(Dxi[0]), "+r"(Dxi[1]), "+r"(Dxi[2]), "+r"(Dxi[3]), "+r"(Dxi[4]), "+r"(Dxi[5]), "+r"(Dxi[6]), "+r"(Dxi[7])
+            : "r"(Axi[6]), "r"(Axi[7]), "r"(Bxi[6]), "r"(Bxi[7]));
+#else
+        tile<16, 8, float> * D16 = (tile<16, 8, float> *) &D;
+        tile<16, 8, half2> * A16 = (tile<16, 8, half2> *) &A;
+        mma(D16[0], A16[0], B);
+        mma(D16[1], A16[1], B);
+#endif // __CUDA_ARCH__ >= GGML_CUDA_CC_AMPERE
+    }
 }
diff --git a/ggml/src/ggml-cuda/mmf.cu b/ggml/src/ggml-cuda/mmf.cu
index 9e2aaf52d6..2b0a61395b 100644
--- a/ggml/src/ggml-cuda/mmf.cu
+++ b/ggml/src/ggml-cuda/mmf.cu
@@ -148,7 +148,7 @@ 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 turing_mma_available(cc);
+            return volta_mma_available(cc) || turing_mma_available(cc);
         case GGML_TYPE_BF16:
             return ampere_mma_available(cc);
         default:
diff --git a/ggml/src/ggml-cuda/mmf.cuh b/ggml/src/ggml-cuda/mmf.cuh
index 49d5295be0..f7e46e2f63 100644
--- a/ggml/src/ggml-cuda/mmf.cuh
+++ b/ggml/src/ggml-cuda/mmf.cuh
@@ -28,9 +28,19 @@ static __global__ void mul_mat_f(
         const int channel_ratio, const int stride_channel_x, const int stride_channel_y, const int stride_channel_dst,
         const int sample_ratio, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst) {
 #if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-    typedef tile<16, 8, T>     tile_A;
-    typedef tile< 8, 8, T>     tile_B;
-    typedef tile<16, 8, float> tile_C;
+    constexpr bool I_16_supported = tile<16, 8, T>::supported() && tile<16, 8, float>::supported();
+    constexpr bool I_32_supported = tile<32, 8, T>::supported() && tile<32, 8, float>::supported();
+
+    if (!I_16_supported && !I_32_supported) {
+        NO_DEVICE_CODE;
+        return;
+    }
+
+    constexpr int I_preferred = I_16_supported ? 16 : 32; // For Turing MMA both work but 16 is ~1% faster.
+
+    typedef tile<I_preferred, 8, T>     tile_A;
+    typedef tile<8,           8, T>     tile_B;
+    typedef tile<I_preferred, 8, float> tile_C;
 
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
     constexpr int tile_k_padded = warp_size + 4;
@@ -232,7 +242,6 @@ static __global__ void mul_mat_f(
 #endif // !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
 }
 
-
 //This kernel is for larger batch sizes of mul_mat_id
 template <typename T, int rows_per_block, int cols_per_block, int nwarps>
 __launch_bounds__(ggml_cuda_get_physical_warp_size()*nwarps, 1)
@@ -245,9 +254,19 @@ static __global__ void mul_mat_f_ids(
         const int sample_ratio, const int stride_sample_x, const int stride_sample_y, const int stride_sample_dst,
         const uint3 sis1_fd, const uint3 nch_fd) {
 #if !defined(GGML_USE_HIP) && !defined(GGML_USE_MUSA)
-    typedef tile<16, 8, T>     tile_A;
-    typedef tile< 8, 8, T>     tile_B;
-    typedef tile<16, 8, float> tile_C;
+    constexpr bool I_16_supported = tile<16, 8, T>::supported() && tile<16, 8, float>::supported();
+    constexpr bool I_32_supported = tile<32, 8, T>::supported() && tile<32, 8, float>::supported();
+
+    if (!I_16_supported && !I_32_supported) {
+        NO_DEVICE_CODE;
+        return;
+    }
+
+    constexpr int I_preferred = I_16_supported ? 16 : 32; // For Turing MMA both work butr 16 is ~1% faster.
+
+    typedef tile<I_preferred, 8, T>     tile_A;
+    typedef tile<8,           8, T>     tile_B;
+    typedef tile<I_preferred, 8, float> tile_C;
 
     constexpr int warp_size = ggml_cuda_get_physical_warp_size();
     constexpr int tile_k_padded = warp_size + 4;
@@ -533,7 +552,8 @@ void mul_mat_f_cuda(
         const int64_t stride_channel_x, const int64_t stride_channel_y, const int64_t stride_channel_dst, const int64_t nsamples_x,
         const int64_t nsamples_dst, const int64_t stride_sample_x, const int64_t stride_sample_y, const int64_t stride_sample_dst,
         cudaStream_t stream, const mmf_ids_data * ids_data) {
-    typedef tile<16, 8, T>     tile_A;
+    typedef tile<16, 8, T>     tile_A_16;
+    typedef tile<32, 8, T>     tile_A_32;
     typedef tile< 8, 8, T>     tile_B;
 
     GGML_ASSERT(ncols_x      % 2 == 0);
@@ -544,7 +564,8 @@ void mul_mat_f_cuda(
     const int64_t channel_ratio = nchannels_dst / nchannels_x;
     const int64_t sample_ratio  = nsamples_dst  / nsamples_x;
 
-    const int device = ggml_cuda_get_device();
+    const int device    = ggml_cuda_get_device();
+    const int cc        = ggml_cuda_info().devices[device].cc;
     const int warp_size = ggml_cuda_info().devices[device].warp_size;
 
     int64_t nwarps_best     = 1;
@@ -559,7 +580,7 @@ void mul_mat_f_cuda(
     }
 
     constexpr int rows_per_block = MMF_ROWS_PER_BLOCK;
-    const int nbytes_shared_iter = nwarps_best * tile_A::I * (warp_size + 4) * 4;
+    const int nbytes_shared_iter = nwarps_best * (volta_mma_available(cc) ? tile_A_32::I : tile_A_16::I) * (warp_size + 4) * 4;
     const int nbytes_shared_combine = GGML_PAD(cols_per_block, tile_B::I) * (nwarps_best*rows_per_block + 4) * 4;
     const int nbytes_shared = std::max(nbytes_shared_iter, nbytes_shared_combine);
     const int nbytes_slotmap = ids ? GGML_PAD(cols_per_block, 16) * sizeof(int) : 0;

From e58d585604bbbbbc24f8149effe444621b5191c6 Mon Sep 17 00:00:00 2001
From: Giuseppe Scrivano <gscrivan@redhat.com>
Date: Fri, 31 Oct 2025 21:20:07 +0100
Subject: [PATCH 17/30] model : add Granite Hybrid nano types (#16896)

Signed-off-by: Giuseppe Scrivano <gscrivan@redhat.com>
---
 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 1e0680bee9..89a6a51b2c 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -1898,7 +1898,8 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
 
                 switch (hparams.n_embd) {
-                    case 1536: type = LLM_TYPE_7B_A1B; break;
+                    case 768: type = LLM_TYPE_350M; break;
+                    case 1536: type = (hparams.n_embd == 2048 ? LLM_TYPE_7B_A1B : LLM_TYPE_1B); break;
                     case 2048: case 2560: type = LLM_TYPE_3B; break;
                     case 4096: type = LLM_TYPE_32B; break;
                     default: type = LLM_TYPE_UNKNOWN;

From 0de0a01576772032008a689afc4d7c80685074c4 Mon Sep 17 00:00:00 2001
From: "Piotr Wilkin (ilintar)" <piotr.wilkin@syndatis.com>
Date: Fri, 31 Oct 2025 21:20:47 +0100
Subject: [PATCH 18/30] model : Minimax M2 (#16831)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Model: Minimax M2

* Cleanup

* Cleanup pt. 2

* Cleanup pt. 3

* Update convert_hf_to_gguf_update.py - merge catch blocks

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

* Remove vocab models and test

* Remove all redundant hparam settings covered by TextModel

* Move super to start, don't set block_count

* Update src/llama-model.cpp

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

* Update gguf-py/gguf/constants.py

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

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---
 convert_hf_to_gguf.py          |  61 ++++++++++++
 convert_hf_to_gguf_update.py   |   3 +-
 gguf-py/gguf/constants.py      |  20 ++++
 gguf-py/gguf/tensor_mapping.py |   1 +
 src/llama-arch.cpp             |  22 +++++
 src/llama-arch.h               |   1 +
 src/llama-model.cpp            | 170 +++++++++++++++++++++++++++++++++
 src/llama-model.h              |   1 +
 src/llama-vocab.cpp            |   5 +
 src/llama-vocab.h              |   1 +
 10 files changed, 284 insertions(+), 1 deletion(-)

diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py
index 5abc177958..f186c2167d 100755
--- a/convert_hf_to_gguf.py
+++ b/convert_hf_to_gguf.py
@@ -1054,6 +1054,9 @@ class TextModel(ModelBase):
         if chkhsh == "53e325976a6e142379c19b09afcae354f2f496f147afa8f9e189a33fe4e3024e":
             # ref: https://huggingface.co/ibm-granite/granite-docling-258M
             res = "granite-docling"
+        if chkhsh == "f4f37b6c8eb9ea29b3eac6bb8c8487c5ab7885f8d8022e67edc1c68ce8403e95":
+            # ref: https://huggingface.co/MiniMaxAI/MiniMax-M2
+            res = "minimax-m2"
 
         if res is None:
             logger.warning("\n")
@@ -7126,6 +7129,64 @@ class DeepseekV2Model(TextModel):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@ModelBase.register("MiniMaxM2ForCausalLM")
+class MiniMaxM2Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.MINIMAXM2
+    _experts_cache: dict[int, dict[str, Tensor]] = {}
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.hparams["num_experts"] = self.hparams["num_local_experts"]
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        if self.hparams["scoring_func"] == "sigmoid":
+            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID)
+        elif self.hparams["scoring_func"] == "softmax":
+            self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX)
+        else:
+            raise ValueError(f"Unsupported scoring_func value: {self.hparams['scoring_func']}")
+
+        self.gguf_writer.add_expert_feed_forward_length(self.find_hparam(["intermediate_size"]))
+        self.gguf_writer.add_rope_dimension_count(self.find_hparam(["rotary_dim"]))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None):
+        if name.endswith("e_score_correction_bias"):
+            name = name.replace("e_score_correction_bias", "e_score_correction.bias")
+
+        # merge expert weights
+        if 'experts' in name:
+            n_experts = self.hparams["num_experts"]
+            assert bid is not None
+
+            expert_cache = self._experts_cache.setdefault(bid, {})
+            expert_cache[name] = data_torch
+            expert_weights = ["w1", "w2", "w3"]
+
+            # not enough expert weights to merge
+            if len(expert_cache) < n_experts * len(expert_weights):
+                return []
+
+            tensors: list[tuple[str, Tensor]] = []
+            for w_name in expert_weights:
+                datas: list[Tensor] = []
+
+                for xid in range(n_experts):
+                    ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{w_name}.weight"
+                    datas.append(expert_cache[ename])
+                    del expert_cache[ename]
+
+                data_torch = torch.stack(datas, dim=0)
+                merged_name = f"model.layers.{bid}.block_sparse_moe.experts.{w_name}.weight"
+                new_name = self.map_tensor_name(merged_name)
+                tensors.append((new_name, data_torch))
+
+            del self._experts_cache[bid]
+            return tensors
+
+        return super().modify_tensors(data_torch, name, bid)
+
+
 @ModelBase.register("Dots1ForCausalLM")
 class Dots1Model(Qwen2MoeModel):
     model_arch = gguf.MODEL_ARCH.DOTS1
diff --git a/convert_hf_to_gguf_update.py b/convert_hf_to_gguf_update.py
index 0ebc1b160f..7df96eb083 100755
--- a/convert_hf_to_gguf_update.py
+++ b/convert_hf_to_gguf_update.py
@@ -141,6 +141,7 @@ models = [
     {"name": "mellum",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum-4b-base", },
     {"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", },
 ]
 
 # some models are known to be broken upstream, so we will skip them as exceptions
@@ -435,7 +436,7 @@ for model in models:
             tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
         else:
             tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
-    except OSError as e:
+    except (OSError, TypeError) as e:
         logger.error(f"Failed to load tokenizer for model {name}. Error: {e}")
         continue  # Skip this model and continue with the next one in the loop
 
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index d75ac704fe..0d5afa01ed 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -425,6 +425,7 @@ class MODEL_ARCH(IntEnum):
     GROVEMOE         = auto()
     APERTUS          = auto()
     COGVLM           = auto()
+    MINIMAXM2        = auto()
 
 
 class VISION_PROJECTOR_TYPE(IntEnum):
@@ -790,6 +791,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.SEED_OSS:         "seed_oss",
     MODEL_ARCH.GROVEMOE:         "grovemoe",
     MODEL_ARCH.APERTUS:          "apertus",
+    MODEL_ARCH.MINIMAXM2:        "minimax-m2",
     MODEL_ARCH.COGVLM:           "cogvlm",
 }
 
@@ -2921,6 +2923,24 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_CHEXP,
         MODEL_TENSOR.FFN_UP_CHEXP,
     ],
+    MODEL_ARCH.MINIMAXM2: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+        MODEL_TENSOR.FFN_EXP_PROBS_B,
+    ],
     MODEL_ARCH.COGVLM: [
         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 6fe6f22358..cef5acec75 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -381,6 +381,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.moe_statics.e_score_correction",        # ernie4.5-moe
             "model.layers.{bid}.mlp.gate.expert_bias",                      # bailingmoe2
             "model.layers.{bid}.feed_forward.expert_bias",                  # lfm2moe
+            "model.layers.{bid}.block_sparse_moe.e_score_correction",       # minimax-m2
         ),
 
         # Feed-forward up
diff --git a/src/llama-arch.cpp b/src/llama-arch.cpp
index ad2880034c..7c7953b83d 100644
--- a/src/llama-arch.cpp
+++ b/src/llama-arch.cpp
@@ -105,6 +105,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_SEED_OSS,         "seed_oss"         },
     { LLM_ARCH_GROVEMOE,         "grovemoe"         },
     { LLM_ARCH_APERTUS,          "apertus"          },
+    { LLM_ARCH_MINIMAX_M2,       "minimax-m2"       },
     { LLM_ARCH_COGVLM,           "cogvlm"           },
     { LLM_ARCH_UNKNOWN,          "(unknown)"        },
 };
@@ -2355,6 +2356,27 @@ static const std::map<llm_arch, std::map<llm_tensor, const char *>> LLM_TENSOR_N
             { 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_COGVLM,
         {
diff --git a/src/llama-arch.h b/src/llama-arch.h
index 4dae35e292..3f893a2dc6 100644
--- a/src/llama-arch.h
+++ b/src/llama-arch.h
@@ -109,6 +109,7 @@ enum llm_arch {
     LLM_ARCH_SEED_OSS,
     LLM_ARCH_GROVEMOE,
     LLM_ARCH_APERTUS,
+    LLM_ARCH_MINIMAX_M2,
     LLM_ARCH_COGVLM,
     LLM_ARCH_UNKNOWN,
 };
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index 89a6a51b2c..db53652575 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_30B_A3B:       return "30B.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";
         case LLM_TYPE_235B_A22B:     return "235B.A22B";
         case LLM_TYPE_300B_A47B:     return "300B.A47B";
         case LLM_TYPE_355B_A32B:     return "355B.A32B";
@@ -2155,6 +2156,17 @@ void llama_model::load_hparams(llama_model_loader & ml) {
                     default: type = LLM_TYPE_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_MINIMAX_M2:
+            {
+                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_EXPERT_GATING_FUNC,           hparams.expert_gating_func, false);
+
+                switch (hparams.n_layer) {
+                    case 62: type = LLM_TYPE_230B_A10B; break;
+                    default: type = LLM_TYPE_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_COGVLM:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
@@ -6185,6 +6197,35 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
                         layer.attn_k_norm_b = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "bias",   i), { n_embd_head_k }, TENSOR_NOT_REQUIRED);
                     }
                 } break;
+            case LLM_ARCH_MINIMAX_M2:
+                {
+                    tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
+
+                    // output
+                    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+                    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, 0);
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        auto & layer = layers[i];
+
+                        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, 0);
+                        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_gqa }, 0);
+                        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_gqa }, 0);
+                        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, 0);
+
+                        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+                        layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k * n_head}, 0);
+                        layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_k_gqa}, 0);
+
+                        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+
+                        layer.ffn_gate_inp = create_tensor(tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}, 0);
+                        layer.ffn_gate_exps = create_tensor(tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff,   n_expert}, 0);
+                        layer.ffn_down_exps = create_tensor(tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff,   n_embd, n_expert}, 0);
+                        layer.ffn_up_exps   = create_tensor(tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd, n_ff,   n_expert}, 0);
+                        layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, 0);
+                    }
+                } break;
             case LLM_ARCH_COGVLM:
                 {
                     tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
@@ -20024,6 +20065,130 @@ struct llm_build_apertus : public llm_graph_context {
     }
 };
 
+struct llm_build_minimax_m2 : public llm_graph_context {
+    llm_build_minimax_m2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        // GGML_ASSERT(n_embd_head == hparams.n_rot); this is wrong in case of minimax, head_dim = 128, n_rot = 64
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        ggml_tensor * inp_pos = build_inp_pos();
+        auto inp_attn = build_attn_inp_kv();
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            cur = inpL;
+
+            // self_attention
+            {
+                cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+                cb(cur, "attn_norm", il);
+
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_normed", il);
+
+                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur_normed", 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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // MoE branch
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_moe_ffn(cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    model.layers[il].ffn_exp_probs_b,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU, true,
+                    false, 0.0,
+                    (llama_expert_gating_func_type) hparams.expert_gating_func,
+                    il);
+            cb(cur, "ffn_moe_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+};
+
 struct llm_build_cogvlm : public llm_graph_context {
     llm_build_cogvlm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
         const int64_t n_embd_head = hparams.n_embd_head_v;
@@ -20654,6 +20819,10 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
             {
                 llm = std::make_unique<llm_build_apertus>(*this, params);
             } break;
+        case LLM_ARCH_MINIMAX_M2:
+            {
+                llm = std::make_unique<llm_build_minimax_m2>(*this, params);
+            } break;
         case LLM_ARCH_COGVLM:
             {
                 llm = std::make_unique<llm_build_cogvlm>(*this, params);
@@ -20875,6 +21044,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_SEED_OSS:
         case LLM_ARCH_GROVEMOE:
         case LLM_ARCH_APERTUS:
+        case LLM_ARCH_MINIMAX_M2:
         case LLM_ARCH_COGVLM:
             return LLAMA_ROPE_TYPE_NEOX;
 
diff --git a/src/llama-model.h b/src/llama-model.h
index a5affda1c9..71ff148e07 100644
--- a/src/llama-model.h
+++ b/src/llama-model.h
@@ -114,6 +114,7 @@ enum llm_type {
     LLM_TYPE_30B_A3B,
     LLM_TYPE_100B_A6B,
     LLM_TYPE_106B_A12B, // GLM-4.5-Air
+    LLM_TYPE_230B_A10B, // Minimax M2
     LLM_TYPE_235B_A22B,
     LLM_TYPE_300B_A47B, // Ernie MoE big
     LLM_TYPE_355B_A32B, // GLM-4.5
diff --git a/src/llama-vocab.cpp b/src/llama-vocab.cpp
index 639fecbd31..735c5d547f 100644
--- a/src/llama-vocab.cpp
+++ b/src/llama-vocab.cpp
@@ -401,6 +401,7 @@ struct llm_tokenizer_bpe : llm_tokenizer {
                 };
                 break;
             case LLAMA_VOCAB_PRE_TYPE_GPT4O:
+            case LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2:
                 regex_exprs = {
                     // original regex from tokenizer.json
                     // "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
@@ -1992,6 +1993,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
                 tokenizer_pre == "grok-2") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_GROK_2;
                 clean_spaces = false;
+            } else if (
+                tokenizer_pre == "minimax-m2") {
+                pre_type = LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2;
+                clean_spaces = false;
             } else {
                 throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
             }
diff --git a/src/llama-vocab.h b/src/llama-vocab.h
index 5e468675e4..1194ec473d 100644
--- a/src/llama-vocab.h
+++ b/src/llama-vocab.h
@@ -49,6 +49,7 @@ enum llama_vocab_pre_type {
     LLAMA_VOCAB_PRE_TYPE_HUNYUAN_DENSE   = 38,
     LLAMA_VOCAB_PRE_TYPE_GROK_2          = 39,
     LLAMA_VOCAB_PRE_TYPE_GRANITE_DOCLING = 40,
+    LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2      = 41,
 };
 
 struct LLM_KV;

From bea04522ff1a0d8559ccfd353aa018dcfbb608cc Mon Sep 17 00:00:00 2001
From: "Piotr Wilkin (ilintar)" <piotr.wilkin@syndatis.com>
Date: Fri, 31 Oct 2025 23:40:23 +0100
Subject: [PATCH 19/30] refactor : llama-model.cpp (#16252)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Sqashed: llama-model.cpp refactoring

* Fix formatting of attn / ffn / ffn_moe calls

* Fix import regression / unify spacing in models.h

* totally DID NOT miss those!

* Add missing qwen3vl(moe) models

* Add missing new .cpp files to build

* Remove extra semicolons

* Editor checker

* Update src/models/models.h

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

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---
 src/CMakeLists.txt                 |    94 +
 src/llama-model.cpp                | 13565 +--------------------------
 src/models/apertus.cpp             |   125 +
 src/models/arcee.cpp               |   135 +
 src/models/arctic.cpp              |   138 +
 src/models/arwkv7.cpp              |    86 +
 src/models/baichuan.cpp            |   122 +
 src/models/bailingmoe.cpp          |   144 +
 src/models/bailingmoe2.cpp         |   135 +
 src/models/bert.cpp                |   176 +
 src/models/bitnet.cpp              |   160 +
 src/models/bloom.cpp               |   101 +
 src/models/chameleon.cpp           |   178 +
 src/models/chatglm.cpp             |   132 +
 src/models/codeshell.cpp           |   111 +
 src/models/cogvlm.cpp              |   100 +
 src/models/cohere2-iswa.cpp        |   131 +
 src/models/command-r.cpp           |   122 +
 src/models/dbrx.cpp                |   123 +
 src/models/deci.cpp                |   135 +
 src/models/deepseek.cpp            |   144 +
 src/models/deepseek2.cpp           |   236 +
 src/models/dots1.cpp               |   134 +
 src/models/dream.cpp               |   105 +
 src/models/ernie4-5-moe.cpp        |   150 +
 src/models/ernie4-5.cpp            |   111 +
 src/models/exaone.cpp              |   114 +
 src/models/exaone4.cpp             |   123 +
 src/models/falcon-h1.cpp           |   113 +
 src/models/falcon.cpp              |   120 +
 src/models/gemma-embedding.cpp     |   120 +
 src/models/gemma.cpp               |   112 +
 src/models/gemma2-iswa.cpp         |   125 +
 src/models/gemma3-iswa.cpp         |   131 +
 src/models/gemma3n-iswa.cpp        |   377 +
 src/models/glm4-moe.cpp            |   153 +
 src/models/glm4.cpp                |   127 +
 src/models/gpt2.cpp                |   105 +
 src/models/gptneox.cpp             |   144 +
 src/models/granite-hybrid.cpp      |   196 +
 src/models/granite.cpp             |   211 +
 src/models/graph-context-mamba.cpp |   283 +
 src/models/grok.cpp                |   160 +
 src/models/grovemoe.cpp            |   141 +
 src/models/hunyuan-dense.cpp       |   132 +
 src/models/hunyuan-moe.cpp         |   154 +
 src/models/internlm2.cpp           |   121 +
 src/models/jais.cpp                |    86 +
 src/models/jamba.cpp               |   107 +
 src/models/lfm2.cpp                |   173 +
 src/models/llada-moe.cpp           |   123 +
 src/models/llada.cpp               |   101 +
 src/models/llama-iswa.cpp          |   174 +
 src/models/llama.cpp               |   156 +
 src/models/mamba.cpp               |    55 +
 src/models/minicpm3.cpp            |   200 +
 src/models/minimax-m2.cpp          |   124 +
 src/models/models.h                |   477 +
 src/models/mpt.cpp                 |   126 +
 src/models/nemotron-h.cpp          |   121 +
 src/models/nemotron.cpp            |   122 +
 src/models/neo-bert.cpp            |   104 +
 src/models/olmo.cpp                |   121 +
 src/models/olmo2.cpp               |   151 +
 src/models/olmoe.cpp               |   124 +
 src/models/openai-moe-iswa.cpp     |   123 +
 src/models/openelm.cpp             |   124 +
 src/models/orion.cpp               |   123 +
 src/models/phi2.cpp                |   121 +
 src/models/phi3.cpp                |   153 +
 src/models/plamo.cpp               |   110 +
 src/models/plamo2.cpp              |   316 +
 src/models/plm.cpp                 |   168 +
 src/models/qwen.cpp                |   108 +
 src/models/qwen2.cpp               |   118 +
 src/models/qwen2moe.cpp            |   151 +
 src/models/qwen2vl.cpp             |   117 +
 src/models/qwen3.cpp               |   117 +
 src/models/qwen3moe.cpp            |   124 +
 src/models/qwen3vl-moe.cpp         |   150 +
 src/models/qwen3vl.cpp             |   144 +
 src/models/refact.cpp              |    94 +
 src/models/rwkv6-base.cpp          |   162 +
 src/models/rwkv6.cpp               |    94 +
 src/models/rwkv6qwen2.cpp          |    86 +
 src/models/rwkv7-base.cpp          |   135 +
 src/models/rwkv7.cpp               |    90 +
 src/models/seed-oss.cpp            |   124 +
 src/models/smallthinker.cpp        |   120 +
 src/models/smollm3.cpp             |   128 +
 src/models/stablelm.cpp            |   146 +
 src/models/starcoder.cpp           |   100 +
 src/models/starcoder2.cpp          |   121 +
 src/models/t5-dec.cpp              |   166 +
 src/models/t5-enc.cpp              |    96 +
 src/models/wavtokenizer-dec.cpp    |   149 +
 src/models/xverse.cpp              |   108 +
 97 files changed, 13423 insertions(+), 13563 deletions(-)
 create mode 100644 src/models/apertus.cpp
 create mode 100644 src/models/arcee.cpp
 create mode 100644 src/models/arctic.cpp
 create mode 100644 src/models/arwkv7.cpp
 create mode 100644 src/models/baichuan.cpp
 create mode 100644 src/models/bailingmoe.cpp
 create mode 100644 src/models/bailingmoe2.cpp
 create mode 100644 src/models/bert.cpp
 create mode 100644 src/models/bitnet.cpp
 create mode 100644 src/models/bloom.cpp
 create mode 100644 src/models/chameleon.cpp
 create mode 100644 src/models/chatglm.cpp
 create mode 100644 src/models/codeshell.cpp
 create mode 100644 src/models/cogvlm.cpp
 create mode 100644 src/models/cohere2-iswa.cpp
 create mode 100644 src/models/command-r.cpp
 create mode 100644 src/models/dbrx.cpp
 create mode 100644 src/models/deci.cpp
 create mode 100644 src/models/deepseek.cpp
 create mode 100644 src/models/deepseek2.cpp
 create mode 100644 src/models/dots1.cpp
 create mode 100644 src/models/dream.cpp
 create mode 100644 src/models/ernie4-5-moe.cpp
 create mode 100644 src/models/ernie4-5.cpp
 create mode 100644 src/models/exaone.cpp
 create mode 100644 src/models/exaone4.cpp
 create mode 100644 src/models/falcon-h1.cpp
 create mode 100644 src/models/falcon.cpp
 create mode 100644 src/models/gemma-embedding.cpp
 create mode 100644 src/models/gemma.cpp
 create mode 100644 src/models/gemma2-iswa.cpp
 create mode 100644 src/models/gemma3-iswa.cpp
 create mode 100644 src/models/gemma3n-iswa.cpp
 create mode 100644 src/models/glm4-moe.cpp
 create mode 100644 src/models/glm4.cpp
 create mode 100644 src/models/gpt2.cpp
 create mode 100644 src/models/gptneox.cpp
 create mode 100644 src/models/granite-hybrid.cpp
 create mode 100644 src/models/granite.cpp
 create mode 100644 src/models/graph-context-mamba.cpp
 create mode 100644 src/models/grok.cpp
 create mode 100644 src/models/grovemoe.cpp
 create mode 100644 src/models/hunyuan-dense.cpp
 create mode 100644 src/models/hunyuan-moe.cpp
 create mode 100644 src/models/internlm2.cpp
 create mode 100644 src/models/jais.cpp
 create mode 100644 src/models/jamba.cpp
 create mode 100644 src/models/lfm2.cpp
 create mode 100644 src/models/llada-moe.cpp
 create mode 100644 src/models/llada.cpp
 create mode 100644 src/models/llama-iswa.cpp
 create mode 100644 src/models/llama.cpp
 create mode 100644 src/models/mamba.cpp
 create mode 100644 src/models/minicpm3.cpp
 create mode 100644 src/models/minimax-m2.cpp
 create mode 100644 src/models/models.h
 create mode 100644 src/models/mpt.cpp
 create mode 100644 src/models/nemotron-h.cpp
 create mode 100644 src/models/nemotron.cpp
 create mode 100644 src/models/neo-bert.cpp
 create mode 100644 src/models/olmo.cpp
 create mode 100644 src/models/olmo2.cpp
 create mode 100644 src/models/olmoe.cpp
 create mode 100644 src/models/openai-moe-iswa.cpp
 create mode 100644 src/models/openelm.cpp
 create mode 100644 src/models/orion.cpp
 create mode 100644 src/models/phi2.cpp
 create mode 100644 src/models/phi3.cpp
 create mode 100644 src/models/plamo.cpp
 create mode 100644 src/models/plamo2.cpp
 create mode 100644 src/models/plm.cpp
 create mode 100644 src/models/qwen.cpp
 create mode 100644 src/models/qwen2.cpp
 create mode 100644 src/models/qwen2moe.cpp
 create mode 100644 src/models/qwen2vl.cpp
 create mode 100644 src/models/qwen3.cpp
 create mode 100644 src/models/qwen3moe.cpp
 create mode 100644 src/models/qwen3vl-moe.cpp
 create mode 100644 src/models/qwen3vl.cpp
 create mode 100644 src/models/refact.cpp
 create mode 100644 src/models/rwkv6-base.cpp
 create mode 100644 src/models/rwkv6.cpp
 create mode 100644 src/models/rwkv6qwen2.cpp
 create mode 100644 src/models/rwkv7-base.cpp
 create mode 100644 src/models/rwkv7.cpp
 create mode 100644 src/models/seed-oss.cpp
 create mode 100644 src/models/smallthinker.cpp
 create mode 100644 src/models/smollm3.cpp
 create mode 100644 src/models/stablelm.cpp
 create mode 100644 src/models/starcoder.cpp
 create mode 100644 src/models/starcoder2.cpp
 create mode 100644 src/models/t5-dec.cpp
 create mode 100644 src/models/t5-enc.cpp
 create mode 100644 src/models/wavtokenizer-dec.cpp
 create mode 100644 src/models/xverse.cpp

diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index 18cfc76564..832b58e315 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -35,6 +35,100 @@ add_library(llama
             unicode-data.cpp
             unicode.cpp
             unicode.h
+            models/apertus.cpp
+            models/arcee.cpp
+            models/arctic.cpp
+            models/arwkv7.cpp
+            models/baichuan.cpp
+            models/bailingmoe.cpp
+            models/bailingmoe2.cpp
+            models/bert.cpp
+            models/bitnet.cpp
+            models/bloom.cpp
+            models/chameleon.cpp
+            models/chatglm.cpp
+            models/codeshell.cpp
+            models/cogvlm.cpp
+            models/cohere2-iswa.cpp
+            models/command-r.cpp
+            models/dbrx.cpp
+            models/deci.cpp
+            models/deepseek.cpp
+            models/deepseek2.cpp
+            models/dots1.cpp
+            models/dream.cpp
+            models/ernie4-5-moe.cpp
+            models/ernie4-5.cpp
+            models/exaone.cpp
+            models/exaone4.cpp
+            models/falcon-h1.cpp
+            models/falcon.cpp
+            models/gemma-embedding.cpp
+            models/gemma.cpp
+            models/gemma2-iswa.cpp
+            models/gemma3-iswa.cpp
+            models/gemma3n-iswa.cpp
+            models/glm4-moe.cpp
+            models/glm4.cpp
+            models/gpt2.cpp
+            models/gptneox.cpp
+            models/granite-hybrid.cpp
+            models/granite.cpp
+            models/grok.cpp
+            models/grovemoe.cpp
+            models/hunyuan-dense.cpp
+            models/hunyuan-moe.cpp
+            models/internlm2.cpp
+            models/jais.cpp
+            models/jamba.cpp
+            models/lfm2.cpp
+            models/llada-moe.cpp
+            models/llada.cpp
+            models/llama-iswa.cpp
+            models/llama.cpp
+            models/mamba.cpp
+            models/minicpm3.cpp
+            models/minimax-m2.cpp
+            models/mpt.cpp
+            models/nemotron-h.cpp
+            models/nemotron.cpp
+            models/neo-bert.cpp
+            models/olmo.cpp
+            models/olmo2.cpp
+            models/olmoe.cpp
+            models/openai-moe-iswa.cpp
+            models/openelm.cpp
+            models/orion.cpp
+            models/phi2.cpp
+            models/phi3.cpp
+            models/plamo.cpp
+            models/plamo2.cpp
+            models/plm.cpp
+            models/qwen.cpp
+            models/qwen2.cpp
+            models/qwen2moe.cpp
+            models/qwen2vl.cpp
+            models/qwen3.cpp
+            models/qwen3vl.cpp
+            models/qwen3vl-moe.cpp
+            models/qwen3moe.cpp
+            models/refact.cpp
+            models/rwkv6-base.cpp
+            models/rwkv6.cpp
+            models/rwkv6qwen2.cpp
+            models/rwkv7-base.cpp
+            models/rwkv7.cpp
+            models/seed-oss.cpp
+            models/smallthinker.cpp
+            models/smollm3.cpp
+            models/stablelm.cpp
+            models/starcoder.cpp
+            models/starcoder2.cpp
+            models/t5-dec.cpp
+            models/t5-enc.cpp
+            models/wavtokenizer-dec.cpp
+            models/xverse.cpp
+            models/graph-context-mamba.cpp
             )
 
 target_include_directories(llama PRIVATE .)
diff --git a/src/llama-model.cpp b/src/llama-model.cpp
index db53652575..04239181c7 100644
--- a/src/llama-model.cpp
+++ b/src/llama-model.cpp
@@ -13,6 +13,8 @@
 
 #include "ggml-cpp.h"
 
+#include "models/models.h"
+
 #include <algorithm>
 #include <cassert>
 #include <cfloat>
@@ -6724,13569 +6726,6 @@ ggml_tensor * llama_model::get_rope_factors(const llama_cparams & cparams, int i
     return layers[il].rope_short;
 }
 
-struct llm_build_llama : public llm_graph_context {
-    llm_build_llama(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // rope freq factors for llama3; may return nullptr for llama2 and other models
-                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = ggml_rope_ext(
-                        ctx0, Qcur, inp_pos, rope_factors,
-                        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, rope_factors,
-                        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);
-                cb(Vcur, "Vcur", il);
-
-                if (hparams.use_kq_norm) {
-                    // Llama4TextL2Norm
-                    Qcur = ggml_rms_norm(ctx0, Qcur, hparams.f_norm_rms_eps);
-                    Kcur = ggml_rms_norm(ctx0, Kcur, hparams.f_norm_rms_eps);
-                    cb(Qcur, "Qcur_normed", il);
-                    cb(Kcur, "Kcur_normed", il);
-                }
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-                cb(cur, "attn_out", il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network (non-MoE)
-            if (model.layers[il].ffn_gate_inp == nullptr) {
-
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
-                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
-                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            } else {
-                // MoE branch
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = build_moe_ffn(cur,
-                        model.layers[il].ffn_gate_inp,
-                        model.layers[il].ffn_up_exps,
-                        model.layers[il].ffn_gate_exps,
-                        model.layers[il].ffn_down_exps,
-                        nullptr,
-                        n_expert, n_expert_used,
-                        LLM_FFN_SILU, true,
-                        false, 0.0,
-                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                        il);
-                cb(cur, "ffn_moe_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_llama_iswa : public llm_graph_context {
-    llm_build_llama_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        // temperature tuning
-        ggml_tensor * inp_attn_scale = nullptr;
-        inp_attn_scale = build_inp_attn_scale();
-
-        auto * inp_attn = build_attn_inp_kv_iswa();
-
-        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            const bool use_rope = hparams.n_no_rope_layer_step > 0 &&
-                                  (il + 1) % hparams.n_no_rope_layer_step != 0;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // rope freq factors for llama3; may return nullptr for llama2 and other models
-                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                if (use_rope) {
-                    Qcur = ggml_rope_ext(
-                            ctx0, Qcur, inp_pos, rope_factors,
-                            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, rope_factors,
-                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                            ext_factor, attn_factor, beta_fast, beta_slow
-                            );
-                } else if (inp_attn_scale) {
-                    Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);
-                }
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                if (use_rope && hparams.use_kq_norm) {
-                    // Llama4TextL2Norm
-                    Qcur = ggml_rms_norm(ctx0, Qcur, hparams.f_norm_rms_eps);
-                    Kcur = ggml_rms_norm(ctx0, Kcur, hparams.f_norm_rms_eps);
-                    cb(Qcur, "Qcur_normed", il);
-                    cb(Kcur, "Kcur_normed", il);
-                }
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-                cb(cur, "attn_out", il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network (non-MoE)
-            if (model.layers[il].ffn_gate_inp == nullptr) {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
-                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
-                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            } else {
-                ggml_tensor * ffn_inp_normed = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                ggml_tensor * moe_out = build_moe_ffn(ffn_inp_normed,
-                        model.layers[il].ffn_gate_inp,
-                        model.layers[il].ffn_up_exps,
-                        model.layers[il].ffn_gate_exps,
-                        model.layers[il].ffn_down_exps,
-                        nullptr,
-                        n_expert, n_expert_used,
-                        LLM_FFN_SILU, false,
-                        false, 0.0,
-                        LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID,
-                        il);
-
-                // Shared experts
-                ggml_tensor * shexp_out = build_ffn(ffn_inp_normed,
-                    model.layers[il].ffn_up_shexp,   NULL, NULL,
-                    model.layers[il].ffn_gate_shexp, NULL, NULL,
-                    model.layers[il].ffn_down_shexp, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(shexp_out, "ffn_moe_shexp", il);
-
-                cur = ggml_add(ctx0, moe_out, shexp_out);
-                cb(cur, "ffn_moe_out_merged", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_deci : public llm_graph_context {
-    llm_build_deci(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-            const int64_t n_head_kv = hparams.n_head_kv(il);
-            const int64_t n_head    = hparams.n_head(il);
-            const int64_t n_ff      = hparams.n_ff(il);
-
-            if (n_head == 0) {
-                // attention-free layer of Llama-3_1-Nemotron-51B
-                cur = inpL;
-            } else {
-                // norm
-                cur = build_norm(inpL,
-                        model.layers[il].attn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "attn_norm", il);
-            }
-
-            if (n_head > 0 && n_head_kv == 0) {
-                // "linear attention" of Llama-3_1-Nemotron-51B
-                cur = build_lora_mm(model.layers[il].wo, cur);
-                cb(cur, "wo", il);
-            } else if (n_head > 0) {
-                // self-attention
-                // rope freq factors for llama3; may return nullptr for llama2 and other models
-                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = ggml_rope_ext(
-                        ctx0, Qcur, inp_pos, rope_factors,
-                        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, rope_factors,
-                        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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            // FFN-free layer of Llama-3_1-Nemotron-Ultra-253B
-            if (n_ff == 0) {
-                continue;
-            }
-
-            // modified to support attention-free layer of Llama-3_1-Nemotron-51B
-            ggml_tensor * ffn_inp = cur;
-            if (n_head > 0) {
-                ffn_inp = ggml_add(ctx0, cur, inpSA);
-                cb(ffn_inp, "ffn_inp", il);
-            }
-
-            // feed-forward network
-            if (model.layers[il].ffn_gate_inp == nullptr) {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
-                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
-                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_baichuan : public llm_graph_context {
-    llm_build_baichuan(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = model.type == LLM_TYPE_7B ? build_inp_pos() : nullptr;
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                switch (model.type) {
-                    case LLM_TYPE_7B:
-                        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
-                                );
-                        break;
-                    case LLM_TYPE_13B:
-                        break;
-                    default:
-                        GGML_ABORT("fatal error");
-                }
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_xverse : public llm_graph_context {
-    llm_build_xverse(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_falcon : public llm_graph_context {
-    llm_build_falcon(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * attn_norm;
-
-            attn_norm = build_norm(inpL,
-                    model.layers[il].attn_norm,
-                    model.layers[il].attn_norm_b,
-                    LLM_NORM, il);
-            cb(attn_norm, "attn_norm", il);
-
-            // self-attention
-            {
-                if (model.layers[il].attn_norm_2) {
-                    // Falcon-40B
-                    cur = build_norm(inpL,
-                            model.layers[il].attn_norm_2,
-                            model.layers[il].attn_norm_2_b,
-                            LLM_NORM, il);
-                    cb(cur, "attn_norm_2", il);
-                } else {
-                    cur = attn_norm;
-                }
-
-                cur = build_lora_mm(model.layers[il].wqkv, cur);
-                cb(cur, "wqkv", il);
-
-                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
-                ggml_tensor * Kcur = 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));
-                ggml_tensor * 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));
-
-                // using mode = 2 for neox mode
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur       = ggml_get_rows(ctx0,       cur, inp_out_ids);
-                inpL      = ggml_get_rows(ctx0,      inpL, inp_out_ids);
-                attn_norm = ggml_get_rows(ctx0, attn_norm, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = cur;
-
-            // feed forward
-            {
-                cur = build_ffn(attn_norm, // !! use the attn norm, not the result
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        NULL,                      NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_GELU, LLM_FFN_SEQ, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cur = ggml_add(ctx0, cur, inpL);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        // norm
-        cur = build_norm(cur,
-                model.output_norm,
-                model.output_norm_b,
-                LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_grok : public llm_graph_context {
-    llm_build_grok(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            cur = build_norm(cur,
-                    model.layers[il].attn_out_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_out_norm", il);
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            // MoE branch
-            ggml_tensor * moe_out = build_moe_ffn(cur,
-                    model.layers[il].ffn_gate_inp,
-                    model.layers[il].ffn_up_exps,
-                    model.layers[il].ffn_gate_exps,
-                    model.layers[il].ffn_down_exps,
-                    nullptr,
-                    n_expert, n_expert_used,
-                    LLM_FFN_GELU, true,
-                    false, 0.0,
-                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                    il);
-            cb(moe_out, "ffn_moe_out", il);
-
-            if (model.layers[il].ffn_up) {
-                ggml_tensor * ffn_out = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_GELU, LLM_FFN_PAR, il);
-                cb(ffn_out, "ffn_out", il);
-
-                cur = ggml_scale(ctx0, ggml_add(ctx0, ffn_out, moe_out), std::sqrt(2) / 2);
-                cb(cur, "ffn_out", il);
-            } else {
-                cur = moe_out;
-            }
-
-            cur = build_norm(cur,
-                    model.layers[il].ffn_post_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_post_norm", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cur = ggml_scale(ctx0, cur, hparams.f_logit_scale);
-
-        // final logit soft-capping
-        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);
-    }
-};
-
-struct llm_build_dbrx : public llm_graph_context {
-    llm_build_dbrx(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                ggml_tensor * Qcur = nullptr;
-                ggml_tensor * Kcur = nullptr;
-                ggml_tensor * Vcur = nullptr;
-
-                cur = build_lora_mm(model.layers[il].wqkv, cur);
-                cb(cur, "wqkv", il);
-
-                cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
-                cb(cur, "wqkv_clamped", il);
-
-                Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
-                Kcur = 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));
-                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
-                        );
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            // MoE branch
-            cur = build_norm(ffn_inp,
-                    model.layers[il].attn_out_norm, NULL,
-                    LLM_NORM, il);
-            cb(cur, "attn_out_norm", il);
-
-            cur = build_moe_ffn(cur,
-                    model.layers[il].ffn_gate_inp,
-                    model.layers[il].ffn_up_exps,
-                    model.layers[il].ffn_gate_exps,
-                    model.layers[il].ffn_down_exps,
-                    nullptr,
-                    n_expert, n_expert_used,
-                    LLM_FFN_SILU, true,
-                    false, 0.0,
-                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                    il);
-            cb(cur, "ffn_moe_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_starcoder : public llm_graph_context {
-    llm_build_starcoder(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
-        cb(pos, "pos_embd", -1);
-
-        inpL = ggml_add(ctx0, inpL, pos);
-        cb(inpL, "inpL", -1);
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm,
-                    model.layers[il].attn_norm_b,
-                    LLM_NORM, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                cur = build_lora_mm(model.layers[il].wqkv, cur);
-                cb(cur, "wqkv", il);
-
-                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-                cb(cur, "bqkv", il);
-
-                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
-                ggml_tensor * Kcur = 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));
-                ggml_tensor * 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));
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
-            }
-
-            // add the input
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // FF
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm,
-                        model.layers[il].ffn_norm_b,
-                        LLM_NORM, il);
-                cb(cur, "ffn_norm", 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_GELU, LLM_FFN_SEQ, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = build_norm(inpL,
-                model.output_norm,
-                model.output_norm_b,
-                LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_refact : public llm_graph_context {
-    llm_build_refact(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_bert : public llm_graph_context {
-    llm_build_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-        ggml_tensor * inp_pos = nullptr;
-
-        if (model.arch != LLM_ARCH_JINA_BERT_V2) {
-            inp_pos = build_inp_pos();
-        }
-
-        // construct input embeddings (token, type, position)
-        inpL = build_inp_embd(model.tok_embd);
-
-        // token types are hardcoded to zero ("Sentence A")
-        if (model.type_embd) {
-            ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0);
-            inpL = ggml_add(ctx0, inpL, type_row0);
-        }
-        if (model.arch == LLM_ARCH_BERT) {
-            inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL);
-        }
-        cb(inpL, "inp_embd", -1);
-
-        // embed layer norm
-        inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
-        cb(inpL, "inp_norm", -1);
-
-        auto * inp_attn = build_attn_inp_no_cache();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * cur = inpL;
-
-            {
-                ggml_tensor * Qcur;
-                ggml_tensor * Kcur;
-                ggml_tensor * Vcur;
-
-                // self-attention
-                if (model.layers[il].wqkv) {
-                    cur = build_lora_mm(model.layers[il].wqkv, cur);
-                    cb(cur, "wqkv", il);
-
-                    if (model.layers[il].bqkv) {
-                        cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-                        cb(cur, "bqkv", il);
-                    }
-
-                    Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
-                    Kcur = 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));
-                    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));
-                } else {
-                    Qcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wq, cur), model.layers[il].bq);
-                    Kcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wk, cur), model.layers[il].bk);
-                    Vcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wv, cur), model.layers[il].bv);
-
-                    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);
-                    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-                }
-
-                if (model.layers[il].attn_q_norm) {
-                    Qcur = ggml_reshape_2d(ctx0, Qcur, n_embd_head*n_head, n_tokens);
-
-                    Qcur = build_norm(Qcur,
-                            model.layers[il].attn_q_norm,
-                            model.layers[il].attn_q_norm_b,
-                            LLM_NORM, il);
-
-                    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
-                }
-
-                if (model.layers[il].attn_k_norm) {
-                    Kcur = ggml_reshape_2d(ctx0, Kcur, n_embd_head*n_head_kv, n_tokens);
-
-                    Kcur = build_norm(Kcur,
-                            model.layers[il].attn_k_norm,
-                            model.layers[il].attn_k_norm_b,
-                            LLM_NORM, il);
-
-                    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-                }
-
-                // RoPE
-                if (model.arch == LLM_ARCH_NOMIC_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE || model.arch == LLM_ARCH_JINA_BERT_V3) {
-                    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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-                cb(cur, "kqv_out", il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
-            }
-
-            // re-add the layer input
-            cur = ggml_add(ctx0, cur, inpL);
-
-            // attention layer norm
-            cur = build_norm(cur, model.layers[il].attn_out_norm, model.layers[il].attn_out_norm_b, LLM_NORM, il);
-
-            if (model.layers[il].attn_norm_2 != nullptr) {
-                cur = ggml_add(ctx0, cur, inpL); // re-add the layer input
-                cur = build_norm(cur, model.layers[il].attn_norm_2, model.layers[il].attn_norm_2_b, LLM_NORM, il);
-            }
-
-            ggml_tensor * ffn_inp = cur;
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            if (hparams.moe_every_n_layers > 0 && il % hparams.moe_every_n_layers == 1) {
-                // MoE branch
-                cur = build_moe_ffn(cur,
-                        model.layers[il].ffn_gate_inp,
-                        model.layers[il].ffn_up_exps,
-                        nullptr,
-                        model.layers[il].ffn_down_exps,
-                        nullptr,
-                        hparams.n_expert,
-                        hparams.n_expert_used,
-                        LLM_FFN_GELU,
-                        false, false,
-                        0.0f,
-                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
-                cb(cur, "ffn_moe_out", il);
-            } else if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE || model.arch == LLM_ARCH_JINA_BERT_V3) {
-                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_GELU, LLM_FFN_SEQ, il);
-                cb(cur, "ffn_out", il);
-            } else if (model.arch == LLM_ARCH_JINA_BERT_V2) {
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL,                        NULL,
-                        model.layers[il].ffn_gate, NULL,                        NULL,
-                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
-                        NULL,
-                        model.layers[il].ffn_gate ? LLM_FFN_GELU : LLM_FFN_GEGLU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            } else {
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            // attentions bypass the intermediate layer
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            // output layer norm
-            cur = build_norm(cur, model.layers[il].layer_out_norm, model.layers[il].layer_out_norm_b, LLM_NORM, il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cb(cur, "result_embd", -1);
-        res->t_embd = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_neo_bert : public llm_graph_context {
-    llm_build_neo_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        // construct input embeddings (token, type, position)
-        inpL = build_inp_embd(model.tok_embd);
-        cb(inpL, "inp_embd", -1);
-
-        auto * inp_attn = build_attn_inp_no_cache();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * cur = inpL;
-
-            // pre-norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-
-            {
-                ggml_tensor * Qcur;
-                ggml_tensor * Kcur;
-                ggml_tensor * Vcur;
-
-                // self-attention
-                cur = build_lora_mm(model.layers[il].wqkv, cur);
-                cb(cur, "wqkv", il);
-
-                Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
-                Kcur = 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));
-                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));
-
-                // 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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, nullptr,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-                cb(cur, "kqv_out", il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
-            }
-
-            // re-add the layer input
-            cur = ggml_add(ctx0, cur, inpL);
-
-            ggml_tensor * ffn_inp = cur;
-            cb(ffn_inp, "ffn_inp", il);
-
-            // pre-norm
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            // feed-forward network
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,
-                    NULL, NULL, NULL, NULL, NULL,
-                    model.layers[il].ffn_down,
-                    NULL, NULL, NULL,
-                    LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
-
-            // attentions bypass the intermediate layer
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm_enc, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_embd", -1);
-        res->t_embd = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_bloom : public llm_graph_context {
-    llm_build_bloom(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        inpL = build_norm(inpL,
-                model.tok_norm,
-                model.tok_norm_b,
-                LLM_NORM, -1);
-        cb(inpL, "inp_norm", -1);
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm,
-                    model.layers[il].attn_norm_b,
-                    LLM_NORM, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                cur = build_lora_mm(model.layers[il].wqkv, cur);
-                cb(cur, "wqkv", il);
-
-                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-                cb(cur, "bqkv", il);
-
-                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
-                ggml_tensor * Kcur = 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));
-                ggml_tensor * 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));
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
-            }
-
-            // Add the input
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // FF
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm,
-                        model.layers[il].ffn_norm_b,
-                        LLM_NORM, il);
-                cb(cur, "ffn_norm", 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_GELU, LLM_FFN_SEQ, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = build_norm(inpL,
-                model.output_norm,
-                model.output_norm_b,
-                LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_mpt : public llm_graph_context {
-    llm_build_mpt(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * pos;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        if (model.pos_embd) {
-            // inp_pos - contains the positions
-            ggml_tensor * inp_pos = build_inp_pos();
-            pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
-            cb(pos, "pos_embd", -1);
-
-            inpL = ggml_add(ctx0, inpL, pos);
-            cb(inpL, "inpL", -1);
-        }
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * attn_norm;
-
-            attn_norm = build_norm(inpL,
-                    model.layers[il].attn_norm,
-                    model.layers[il].attn_norm_b,
-                    LLM_NORM, il);
-            cb(attn_norm, "attn_norm", il);
-
-            // self-attention
-            {
-                cur = attn_norm;
-
-                cur = build_lora_mm(model.layers[il].wqkv, cur);
-                cb(cur, "wqkv", il);
-
-                if (model.layers[il].bqkv){
-                    cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-                    cb(cur, "bqkv", il);
-                }
-
-                if (hparams.f_clamp_kqv > 0.0f) {
-                    cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
-                    cb(cur, "wqkv_clamped", il);
-                }
-
-                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
-                ggml_tensor * Kcur = 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));
-                ggml_tensor * 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));
-
-                // Q/K Layernorm
-                if (model.layers[il].attn_q_norm) {
-                    Qcur = ggml_reshape_2d(ctx0, Qcur, n_embd_head*n_head,    n_tokens);
-                    Kcur = ggml_reshape_2d(ctx0, Kcur, n_embd_head*n_head_kv, n_tokens);
-
-                    Qcur = build_norm(Qcur,
-                            model.layers[il].attn_q_norm,
-                            model.layers[il].attn_q_norm_b,
-                            LLM_NORM, il);
-
-                    Kcur = build_norm(Kcur,
-                            model.layers[il].attn_k_norm,
-                            model.layers[il].attn_k_norm_b,
-                            LLM_NORM, 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);
-                }
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
-            }
-
-            // Add the input
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed forward
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm,
-                        model.layers[il].ffn_norm_b,
-                        LLM_NORM, il);
-                cb(cur, "ffn_norm", 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,
-                        model.layers[il].ffn_act,
-                        LLM_FFN_GELU, LLM_FFN_SEQ, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm,
-                model.output_norm_b,
-                LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_stablelm : public llm_graph_context {
-    llm_build_stablelm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm,
-                    model.layers[il].attn_norm_b,
-                    LLM_NORM, il);
-            cb(cur, "attn_norm", il);
-
-            ggml_tensor * inpSA = cur;
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                if (model.layers[il].attn_q_norm) {
-                    Qcur = build_norm(Qcur,
-                            model.layers[il].attn_q_norm,
-                            NULL,
-                            LLM_NORM, il);
-                    cb(Qcur, "Qcur", il);
-                }
-
-                if (model.layers[il].attn_k_norm) {
-                    Kcur = build_norm(Kcur,
-                            model.layers[il].attn_k_norm,
-                            NULL,
-                            LLM_NORM, il);
-                    cb(Kcur, "Kcur", 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
-                        );
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpL  = ggml_get_rows(ctx0,  inpL, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            {
-                if (model.layers[il].ffn_norm) {
-                    cur = build_norm(ffn_inp,
-                            model.layers[il].ffn_norm,
-                            model.layers[il].ffn_norm_b,
-                            LLM_NORM, il);
-                    cb(cur, "ffn_norm", il);
-                } else {
-                    // parallel residual
-                    cur = inpSA;
-                }
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm,
-                model.output_norm_b,
-                LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_qwen : public llm_graph_context {
-    llm_build_qwen(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                cur = build_lora_mm(model.layers[il].wqkv, cur);
-                cb(cur, "wqkv", il);
-
-                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-                cb(cur, "bqkv", il);
-
-                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
-                ggml_tensor * Kcur = 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));
-                ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 2*sizeof(float)*(n_embd));
-
-                // using mode = 2 for neox mode
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward forward
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_qwen2 : public llm_graph_context {
-    llm_build_qwen2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // 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);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                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);
-
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        if (model.output_b != nullptr) {
-            cur = ggml_add(ctx0, cur, model.output_b);
-        }
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_dream : public llm_graph_context {
-    llm_build_dream(const llama_model & model, const llm_graph_params & params) :
-        llm_graph_context(params) {
-        //copied from qwen2
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_no_cache();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // 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);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                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);
-
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur, model.layers[il].ffn_up, NULL, NULL, model.layers[il].ffn_gate, NULL, NULL,
-                            model.layers[il].ffn_down, NULL, NULL, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_llada : public llm_graph_context {
-    llm_build_llada(const llama_model & model, const llm_graph_params & params) :
-        llm_graph_context(params) {
-        // LLaDA is similar to LLaMA but uses non-causal attention for diffusion
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        // Non-causal attention for diffusion
-        auto * inp_attn = build_attn_inp_no_cache();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // compute separate Q, K, V projections without bias, matching LLaDALlamaBlock
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur, model.layers[il].ffn_up, NULL, NULL, model.layers[il].ffn_gate, NULL, NULL,
-                            model.layers[il].ffn_down, NULL, NULL, NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_qwen2vl : public llm_graph_context {
-    llm_build_qwen2vl(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        int sections[4];
-        std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // 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);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                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);
-
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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
-                        );
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_qwen2moe : public llm_graph_context {
-    llm_build_qwen2moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self_attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // MoE branch
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            ggml_tensor * moe_out =
-                build_moe_ffn(cur,
-                        model.layers[il].ffn_gate_inp,
-                        model.layers[il].ffn_up_exps,
-                        model.layers[il].ffn_gate_exps,
-                        model.layers[il].ffn_down_exps,
-                        nullptr,
-                        n_expert, n_expert_used,
-                        LLM_FFN_SILU, false,
-                        false, 0.0,
-                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                        il);
-            cb(moe_out, "ffn_moe_out", il);
-
-            // FFN shared expert
-            {
-                ggml_tensor * cur_gate_inp = build_lora_mm(model.layers[il].ffn_gate_inp_shexp, cur);
-                cb(cur_gate_inp, "ffn_shexp_gate_inp", il);
-
-                // sigmoid
-                ggml_tensor * cur_gate = ggml_div(ctx0, ggml_silu(ctx0, cur_gate_inp), cur_gate_inp);
-                cb(cur_gate, "ffn_shexp_gate", il);
-
-                ggml_tensor * cur_ffn = build_ffn(cur,
-                        model.layers[il].ffn_up_shexp,   NULL, NULL,
-                        model.layers[il].ffn_gate_shexp, NULL, NULL,
-                        model.layers[il].ffn_down_shexp, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur_ffn, "ffn_shexp", il);
-
-                ggml_tensor * ffn_shexp_out = ggml_mul(ctx0, cur_ffn, cur_gate);
-                cb(ffn_shexp_out, "ffn_shexp_out", il);
-
-                moe_out = ggml_add(ctx0, moe_out, ffn_shexp_out);
-                cb(moe_out, "ffn_out", il);
-
-                cur = moe_out;
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_qwen3 : public llm_graph_context {
-    llm_build_qwen3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
-                cb(Kcur, "Kcur_normed", il);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_qwen3moe : public llm_graph_context {
-    llm_build_qwen3moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self_attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
-                cb(Kcur, "Kcur_normed", il);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // MoE branch
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            ggml_tensor * moe_out =
-                build_moe_ffn(cur,
-                        model.layers[il].ffn_gate_inp,
-                        model.layers[il].ffn_up_exps,
-                        model.layers[il].ffn_gate_exps,
-                        model.layers[il].ffn_down_exps,
-                        nullptr,
-                        n_expert, n_expert_used,
-                        LLM_FFN_SILU, true,
-                        false, 0.0,
-                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                        il);
-            cb(moe_out, "ffn_moe_out", il);
-            cur = moe_out;
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_qwen3vl : public llm_graph_context {
-    llm_build_qwen3vl(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-
-        const int64_t n_embd_full = hparams.n_embd; // main embd + deepstack embds
-        const size_t n_deepstack_layers = hparams.n_deepstack_layers;
-        const int64_t n_embd = n_embd_full / (n_deepstack_layers + 1);
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        int sections[4];
-        std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
-
-        std::vector<ggml_tensor *> deepstack_features(n_deepstack_layers, nullptr);
-
-        if (ubatch.embd) {
-            // Image input: split main embd and deepstack embds
-            ggml_tensor * inpL_main = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], 0);
-            for (size_t i = 0; i < n_deepstack_layers; i++) {
-                deepstack_features[i] = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], (i + 1) * n_embd * sizeof(float));
-            }
-            inpL = inpL_main;
-        }
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_normed", il);
-
-                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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
-                cb(Kcur, "Kcur_normed", il);
-
-                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
-                        );
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            if (ubatch.embd && (size_t)il < n_deepstack_layers) {
-                cur = ggml_add(ctx0, cur, deepstack_features[il]);
-                cb(cur, "deepstack_out", il);
-            }
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_qwen3vlmoe : public llm_graph_context {
-    llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_full = hparams.n_embd; // main embd + deepstack embds
-        const size_t n_deepstack_layers = hparams.n_deepstack_layers;
-        const int64_t n_embd = n_embd_full / (n_deepstack_layers + 1);
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        int sections[4];
-        std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
-
-        std::vector<ggml_tensor *> deepstack_features(n_deepstack_layers, nullptr);
-
-        if (ubatch.embd) {
-            // Image input: split main embd and deepstack embds
-            ggml_tensor * inpL_main = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], 0);
-            for (size_t i = 0; i < n_deepstack_layers; i++) {
-                deepstack_features[i] = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], (i + 1) * n_embd * sizeof(float));
-            }
-            inpL = inpL_main;
-        }
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self_attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_normed", il);
-
-                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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
-                cb(Kcur, "Kcur_normed", il);
-
-                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
-                        );
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // MoE branch
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            ggml_tensor * moe_out =
-                build_moe_ffn(cur,
-                        model.layers[il].ffn_gate_inp,
-                        model.layers[il].ffn_up_exps,
-                        model.layers[il].ffn_gate_exps,
-                        model.layers[il].ffn_down_exps,
-                        nullptr,
-                        n_expert, n_expert_used,
-                        LLM_FFN_SILU, true,
-                        false, 0.0,
-                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                        il);
-            cb(moe_out, "ffn_moe_out", il);
-            cur = moe_out;
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            if (ubatch.embd && (size_t)il < n_deepstack_layers) {
-                cur = ggml_add(ctx0, cur, deepstack_features[il]);
-                cb(cur, "deepstack_out", il);
-            }
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_phi2 : public llm_graph_context {
-    llm_build_phi2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * attn_norm_output;
-        ggml_tensor * ffn_output;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            attn_norm_output = build_norm(inpL,
-                    model.layers[il].attn_norm,
-                    model.layers[il].attn_norm_b,
-                    LLM_NORM, il);
-            cb(attn_norm_output, "attn_norm", il);
-
-            // self-attention
-            {
-                ggml_tensor * Qcur = nullptr;
-                ggml_tensor * Kcur = nullptr;
-                ggml_tensor * Vcur = nullptr;
-
-                if (model.layers[il].wqkv) {
-                    cur = build_lora_mm(model.layers[il].wqkv, attn_norm_output);
-                    cb(cur, "wqkv", il);
-
-                    cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-                    cb(cur, "bqkv", il);
-
-                    Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
-                    Kcur = 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));
-                    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));
-                } else {
-                    Qcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wq, attn_norm_output), model.layers[il].bq);
-                    Kcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wk, attn_norm_output), model.layers[il].bk);
-                    Vcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wv, attn_norm_output), model.layers[il].bv);
-
-                    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);
-                    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-                }
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                // with phi2, we scale the Q to avoid precision issues
-                // ref: https://github.com/ml-explore/mlx-examples/blob/08e862336ade809bc37d1035f94b359e7d1a5152/phi2/phi2.py#L64-L66
-                Qcur = ggml_scale(ctx0, Qcur, 1.0f/sqrtf(float(n_embd_head)));
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur              = ggml_get_rows(ctx0,              cur, inp_out_ids);
-                inpL             = ggml_get_rows(ctx0,             inpL, inp_out_ids);
-                attn_norm_output = ggml_get_rows(ctx0, attn_norm_output, inp_out_ids);
-            }
-
-            // FF
-            {
-                ffn_output = build_ffn(attn_norm_output,
-                        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_GELU, LLM_FFN_SEQ, il);
-                cb(ffn_output, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_output);
-            cur = ggml_add(ctx0, cur, inpL);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = build_norm(inpL,
-                model.output_norm,
-                model.output_norm_b,
-                LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-        cb(cur, "result_output_no_bias", -1);
-
-        cur = ggml_add(ctx0, cur, model.output_b);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-template<bool iswa>
-struct llm_build_phi3 : public llm_graph_context {
-    llm_build_phi3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        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) {
-            auto * residual = inpL;
-
-            // self-attention
-            {
-                // rope freq factors for 128k context
-                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-
-                ggml_tensor* attn_norm_output = build_norm(inpL,
-                        model.layers[il].attn_norm,
-                        model.layers[il].attn_norm_b,
-                        LLM_NORM_RMS, il);
-                cb(attn_norm_output, "attn_norm", il);
-
-                ggml_tensor * Qcur = nullptr;
-                ggml_tensor * Kcur = nullptr;
-                ggml_tensor * Vcur = nullptr;
-
-                if (model.layers[il].wqkv) {
-                    cur = build_lora_mm(model.layers[il].wqkv, attn_norm_output);
-                    cb(cur, "wqkv", il);
-
-                    Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head * sizeof(float), cur->nb[1], 0 * sizeof(float) * (n_embd));
-                    Kcur = 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));
-                    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));
-                } else {
-                    Qcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wq, attn_norm_output), model.layers[il].bq);
-                    Kcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wk, attn_norm_output), model.layers[il].bk);
-                    Vcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wv, attn_norm_output), model.layers[il].bv);
-
-                    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);
-                    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-                }
-
-                Qcur = ggml_rope_ext(
-                        ctx0, Qcur, inp_pos, rope_factors,
-                        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, rope_factors,
-                        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);
-                cb(Vcur, "Vcur", il);
-
-                Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head)));
-                cb(Qcur, "Qcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur      = ggml_get_rows(ctx0, cur,      inp_out_ids);
-                residual = ggml_get_rows(ctx0, residual, inp_out_ids);
-            }
-
-            cur = ggml_add(ctx0, cur, residual);
-            residual = cur;
-
-            cur = build_norm(cur,
-                    model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            // feed-forward network
-            if (model.layers[il].ffn_gate_inp == nullptr) {
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        NULL,                      NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
-                cb(cur, "ffn_out", il);
-            } else {
-                // MoE branch
-                cur = build_moe_ffn(cur,
-                        model.layers[il].ffn_gate_inp,
-                        model.layers[il].ffn_up_exps,
-                        model.layers[il].ffn_gate_exps,
-                        model.layers[il].ffn_down_exps,
-                        nullptr,
-                        n_expert, n_expert_used,
-                        LLM_FFN_SILU, true,
-                        false, 0.0,
-                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                        il);
-                cb(cur, "ffn_moe_out", il);
-            }
-
-            cur = ggml_add(ctx0, residual, cur);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = build_norm(inpL,
-                model.output_norm,
-                model.output_norm_b,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-
-        if (model.output_b != nullptr) {
-            cb(cur, "result_output_no_bias", -1);
-            cur = ggml_add(ctx0, cur, model.output_b);
-        }
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_plamo : public llm_graph_context {
-    llm_build_plamo(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            ggml_tensor * sa_inp = cur;
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = ggml_rope_ext(
-                        ctx0, Qcur, inp_pos, nullptr,
-                        n_embd_head, 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_embd_head, 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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur    = ggml_get_rows(ctx0,    cur, inp_out_ids);
-                sa_inp = ggml_get_rows(ctx0, sa_inp, inp_out_ids);
-                inpL   = ggml_get_rows(ctx0,   inpL, inp_out_ids);
-            }
-
-            ggml_tensor * sa_out = cur;
-
-            cur = sa_inp;
-
-            // feed-forward network
-            {
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, sa_out);
-            cur = ggml_add(ctx0, cur, inpL);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_gpt2 : public llm_graph_context {
-    llm_build_gpt2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * pos;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
-        cb(pos, "pos_embd", -1);
-
-        inpL = ggml_add(ctx0, inpL, pos);
-        cb(inpL, "inpL", -1);
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm,
-                    model.layers[il].attn_norm_b,
-                    LLM_NORM, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                cur = build_lora_mm(model.layers[il].wqkv, cur);
-                cb(cur, "wqkv", il);
-
-                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-                cb(cur, "bqkv", il);
-
-                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
-                ggml_tensor * Kcur = 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));
-                ggml_tensor * 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));
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
-            }
-
-            // add the input
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // FF
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm,
-                        model.layers[il].ffn_norm_b,
-                        LLM_NORM, il);
-                cb(cur, "ffn_norm", 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_GELU, LLM_FFN_SEQ, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = build_norm(inpL,
-                model.output_norm,
-                model.output_norm_b,
-                LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_codeshell : public llm_graph_context {
-    llm_build_codeshell(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm,
-                    model.layers[il].attn_norm_b,
-                    LLM_NORM, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                cur = build_lora_mm(model.layers[il].wqkv, cur);
-                cb(cur, "wqkv", il);
-
-                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-                cb(cur, "bqkv", il);
-
-                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
-                ggml_tensor * Kcur = 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));
-                ggml_tensor * 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
-                        );
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
-            }
-
-            // add the input
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // FF
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm,
-                        model.layers[il].ffn_norm_b,
-                        LLM_NORM, il);
-                cb(cur, "ffn_norm", 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_GELU, LLM_FFN_SEQ, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = build_norm(inpL,
-                model.output_norm,
-                model.output_norm_b,
-                LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_orion : public llm_graph_context {
-    llm_build_orion(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, model.layers[il].attn_norm_b,
-                    LLM_NORM, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
-                    LLM_NORM, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, model.output_norm_b,
-                LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_internlm2 : public llm_graph_context {
-    llm_build_internlm2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_minicpm3 : public llm_graph_context {
-    llm_build_minicpm3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        //TODO: if the model varies, these parameters need to be read from the model
-        const int64_t n_embd_base = 256;
-        const float scale_embd  = 12.0f;
-        const float scale_depth = 1.4f;
-        const float kq_scale = 1.0f / sqrtf(float(hparams.n_embd_head_k));
-
-        const uint32_t n_embd_head_qk_rope = hparams.n_rot;
-        const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
-        const uint32_t kv_lora_rank = hparams.n_lora_kv;
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // scale the input embeddings
-        inpL = ggml_scale(ctx0, inpL, scale_embd);
-        cb(inpL, "inp_scaled", -1);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self_attention
-            {
-                ggml_tensor * q = NULL;
-                // {n_embd, q_lora_rank} * {n_embd, n_tokens} -> {q_lora_rank, n_tokens}
-                q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
-                cb(q, "q", il);
-
-                q = build_norm(q,
-                        model.layers[il].attn_q_a_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(q, "q", il);
-
-                // {q_lora_rank, n_head * hparams.n_embd_head_k} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k, n_tokens}
-                q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
-                cb(q, "q", il);
-
-                // split into {n_head * n_embd_head_qk_nope, n_tokens}
-                ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
-                        ggml_row_size(q->type, hparams.n_embd_head_k),
-                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
-                        0);
-                cb(q_nope, "q_nope", il);
-
-                // and {n_head * n_embd_head_qk_rope, n_tokens}
-                ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
-                        ggml_row_size(q->type, hparams.n_embd_head_k),
-                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
-                        ggml_row_size(q->type, n_embd_head_qk_nope));
-                cb(q_pe, "q_pe", il);
-
-                // {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens}
-                ggml_tensor * kv_pe_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
-                cb(kv_pe_compresseed, "kv_pe_compresseed", il);
-
-                // split into {kv_lora_rank, n_tokens}
-                ggml_tensor * kv_compressed = ggml_view_2d(ctx0, kv_pe_compresseed, kv_lora_rank, n_tokens,
-                        kv_pe_compresseed->nb[1],
-                        0);
-                cb(kv_compressed, "kv_compressed", il);
-
-                // and {n_embd_head_qk_rope, n_tokens}
-                ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_pe_compresseed, n_embd_head_qk_rope, 1, n_tokens,
-                        kv_pe_compresseed->nb[1],
-                        kv_pe_compresseed->nb[1],
-                        ggml_row_size(kv_pe_compresseed->type, kv_lora_rank));
-                cb(k_pe, "k_pe", il);
-
-                kv_compressed = build_norm(kv_compressed,
-                        model.layers[il].attn_kv_a_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(kv_compressed, "kv_compressed", il);
-
-                // {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)} * {kv_lora_rank, n_tokens} -> {n_head * (n_embd_head_qk_nope + n_embd_head_v), n_tokens}
-                ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_compressed);
-                cb(kv, "kv", il);
-
-                // split into {n_head * n_embd_head_qk_nope, n_tokens}
-                ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
-                        ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v),
-                        ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
-                        0);
-                cb(k_nope, "k_nope", il);
-
-                // and {n_head * n_embd_head_v, n_tokens}
-                ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens,
-                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)),
-                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head),
-                        ggml_row_size(kv->type, (n_embd_head_qk_nope)));
-                cb(v_states, "v_states", il);
-
-                v_states = ggml_cont(ctx0, v_states);
-                cb(v_states, "v_states", il);
-
-                q_pe = ggml_rope_ext(
-                        ctx0, q_pe, inp_pos, rope_factors,
-                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                        ext_factor, attn_factor, beta_fast, beta_slow
-                        );
-                cb(q_pe, "q_pe", il);
-
-                // shared RoPE key
-                k_pe = ggml_rope_ext(
-                        ctx0, k_pe, inp_pos, rope_factors,
-                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                        ext_factor, attn_factor, beta_fast, beta_slow
-                        );
-                cb(k_pe, "k_pe", il);
-
-                ggml_tensor * q_states = ggml_concat(ctx0, q_nope, q_pe, 0);
-                cb(q_states, "q_states", il);
-
-                ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);
-                cb(k_states, "k_states", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        q_states, k_states, v_states, nullptr, nullptr, nullptr, kq_scale, il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            // scale_res - scale the hidden states for residual connection
-            const float scale_res = scale_depth/sqrtf(float(n_layer)); // TODO: is this correct?
-            cur = ggml_scale(ctx0, cur, scale_res);
-            cb(cur, "hidden_scaled", il);
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            // scale the hidden states for residual connection
-            cur = ggml_scale(ctx0, cur, scale_res);
-            cb(cur, "hidden_scaled_ffn", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head scaling
-        const float scale_lmhead = float(n_embd_base)/float(n_embd);
-        cur = ggml_scale(ctx0, cur, scale_lmhead);
-        cb(cur, "lmhead_scaling", -1);
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_gemma : public llm_graph_context {
-    llm_build_gemma(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
-        cb(inpL, "inp_scaled", -1);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head)));
-                cb(Qcur, "Qcur_scaled", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
-            }
-
-            ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
-            cb(sa_out, "sa_out", il);
-
-            cur = build_norm(sa_out,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            // feed-forward network
-            {
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_GELU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, sa_out);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_gemma2_iswa : public llm_graph_context {
-    llm_build_gemma2_iswa(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;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
-        cb(inpL, "inp_scaled", -1);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv_iswa();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                Qcur = ggml_scale(ctx0, Qcur, hparams.f_attention_scale);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
-            }
-
-            cur = build_norm(cur,
-                    model.layers[il].attn_post_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_post_norm", il);
-
-            ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
-            cb(sa_out, "sa_out", il);
-
-            cur = build_norm(sa_out,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            // feed-forward network
-            {
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_GELU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = build_norm(cur,
-                    model.layers[il].ffn_post_norm, NULL,
-                    LLM_NORM_RMS, -1);
-            cb(cur, "ffn_post_norm", -1);
-
-            cur = ggml_add(ctx0, cur, sa_out);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        // final logit soft-capping
-        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);
-    }
-};
-
-struct llm_build_gemma3_iswa : public llm_graph_context {
-    llm_build_gemma3_iswa(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;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
-        if (ubatch.token) {
-            inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
-            cb(inpL, "inp_scaled", -1);
-        }
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        // TODO: is causal == true correct? might need some changes
-        auto * inp_attn = build_attn_inp_kv_iswa();
-
-        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);
-
-            // norm
-            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_normed", il);
-
-                Qcur = ggml_rope_ext(
-                        ctx0, Qcur, inp_pos, nullptr,
-                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
-                        ext_factor, attn_factor, beta_fast, beta_slow);
-
-                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
-                cb(Kcur, "Kcur_normed", il);
-
-                Kcur = ggml_rope_ext(
-                        ctx0, Kcur, inp_pos, nullptr,
-                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
-                        ext_factor, attn_factor, beta_fast, beta_slow);
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/model.py#L315
-                Qcur = ggml_scale(ctx0, Qcur, hparams.f_attention_scale);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
-            }
-
-            cur = build_norm(cur,
-                    model.layers[il].attn_post_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_post_norm", il);
-
-            ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
-            cb(sa_out, "sa_out", il);
-
-            cur = build_norm(sa_out,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            // feed-forward network
-            {
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_GELU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = build_norm(cur,
-                    model.layers[il].ffn_post_norm, NULL,
-                    LLM_NORM_RMS, -1);
-            cb(cur, "ffn_post_norm", -1);
-
-            cur = ggml_add(ctx0, cur, sa_out);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_gemma3n_iswa : public llm_graph_context {
-    const llama_model & model;
-
-    const int64_t n_embd_head;
-    const int64_t n_embd_altup;
-    const int64_t n_altup;
-    const int     i_altup_act;
-    const int     n_layer_sparsity = 10; // number of layers using activation sparsity
-    const float   f_sparsity_std_mul = 1.6448533535003662f; // std_multiplier = normal_dist.icdf(0.95)
-
-    llm_build_gemma3n_iswa(const llama_model & model, const llm_graph_params & params)
-            : llm_graph_context(params),
-              model(model),
-              n_embd_head(model.hparams.n_embd_head_k),
-              n_embd_altup(model.hparams.n_embd_altup),
-              n_altup(model.hparams.n_altup),
-              i_altup_act(model.hparams.i_altup_act) {
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
-        if (ubatch.token) {
-            inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
-            cb(inpL, "inp_scaled", -1);
-        }
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        // TODO: is causal == true correct? might need some changes
-        auto * inp_attn = build_attn_inp_kv_iswa();
-
-        // inp_per_layer shape: [n_embd_altup, n_tokens, n_layer]
-        ggml_tensor * inp_per_layer = project_per_layer_inputs(inpL, get_per_layer_inputs());
-
-        // inpL now has only 1 altup, project it to the rest of the altups
-        // these "added" altups will be concat to the last dim of inpL
-        {
-            ggml_tensor * target_magnitude = calc_magnitude(inpL);
-            ggml_tensor * inp_repeated = ggml_repeat_4d(ctx0, inpL, n_embd, n_tokens, n_altup - 1, 1);
-            ggml_tensor * altup_added = ggml_mul_mat(ctx0, model.altup_proj, inp_repeated); // shape: [n_embd, n_tokens, n_altup - 1]
-            ggml_tensor * new_magnitude = calc_magnitude(altup_added);
-            altup_added = ggml_div(ctx0,
-                                ggml_mul(ctx0, altup_added, target_magnitude),
-                                new_magnitude);
-            inpL = ggml_concat(ctx0, inpL, altup_added, 2); // shape: [n_embd, n_tokens, n_altup]
-            cb(inpL, "inp_stacked", -1);
-        }
-
-        // inpL now has shape:          [n_embd,       n_tokens, n_altup]
-        // inp_per_layer now has shape: [n_embd_altup, n_tokens, n_layer]
-
-        for (int il = 0; il < n_layer; ++il) {
-            // this block is made to be closely resemble Gemma3p5DecoderLayer on python code
-            const float freq_base_l  = model.get_rope_freq_base (cparams, il);
-            const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
-
-            ggml_tensor * cur = inpL; // [n_embd, n_tokens, n_altup]
-            ggml_tensor * predictions = altup_predict(cur, il); // [n_embd, n_tokens, n_altup]
-
-            // predicted value will go through self-attention and laurel
-            ggml_tensor * active_prediction = view_2d_slice(predictions, i_altup_act); // [n_embd, n_tokens]
-            cur = active_prediction;
-            cb(cur, "active_prediction", il);
-
-            // norm
-            cur = build_norm(cur, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // laurel
-            ggml_tensor * laurel_out = laurel(cur, il); // [n_embd, n_tokens]
-
-            // self-attention
-            if (hparams.has_kv(il)) {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
-                Vcur = ggml_rms_norm(ctx0, Vcur, hparams.f_norm_rms_eps);
-
-                cb(Qcur, "Qcur_normed", il);
-                cb(Kcur, "Kcur_normed", il);
-                cb(Vcur, "Vcur_normed", il);
-
-                Qcur = ggml_rope_ext(
-                        ctx0, Qcur, inp_pos, nullptr,
-                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
-                        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_l, freq_scale_l,
-                        ext_factor, attn_factor, beta_fast, beta_slow);
-
-                cb(Qcur, "Qcur_pos", il);
-                cb(Kcur, "Kcur_pos", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, hparams.f_attention_scale, il);
-            } else {
-                // reuse KV cache of earlier layers
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
-
-                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_normed", il);
-
-                Qcur = ggml_rope_ext(
-                        ctx0, Qcur, inp_pos, nullptr,
-                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
-                        ext_factor, attn_factor, beta_fast, beta_slow);
-                cb(Qcur, "Qcur_pos", il);
-
-                cur = build_attn(inp_attn,
-                    model.layers[il].wo, NULL,
-                    Qcur, nullptr, nullptr, nullptr, nullptr, nullptr, hparams.f_attention_scale, il);
-            }
-
-            cur = build_norm(cur,
-                    model.layers[il].attn_post_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_post_norm", il);
-
-            cur = ggml_add(ctx0, cur, active_prediction); // [n_embd, n_tokens]
-            cb(cur, "attn_gated", il);
-
-            ggml_tensor * attn_laurel = ggml_scale(ctx0,
-                                            ggml_add(ctx0, cur, laurel_out),
-                                            1.0f / sqrtf(2.0f)); // [n_embd, n_tokens]
-            cb(attn_laurel, "attn_laurel", il);
-
-            cur = build_norm(attn_laurel,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            // feed-forward network
-            {
-                ggml_tensor * up_proj   = build_lora_mm(model.layers[il].ffn_up,   cur);
-                ggml_tensor * gate_proj = build_lora_mm(model.layers[il].ffn_gate, cur);
-
-                if (il < n_layer_sparsity) {
-                    // apply activation sparsity
-                    gate_proj = gaussian_topk(gate_proj);
-                }
-                gate_proj = ggml_gelu(ctx0, gate_proj);
-
-                cur = ggml_mul(ctx0, up_proj, gate_proj);
-                cur = build_lora_mm(model.layers[il].ffn_down, cur);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = build_norm(cur,
-                    model.layers[il].ffn_post_norm, NULL,
-                    LLM_NORM_RMS, -1);
-            cb(cur, "ffn_post_norm", il);
-
-            ggml_tensor * attn_ffw_laurel_gated = ggml_add(ctx0, cur, attn_laurel); // [n_embd, n_tokens]
-            cb(attn_ffw_laurel_gated, "attn_ffw_laurel_gated", il);
-
-            ggml_tensor * corrected = altup_correct(predictions, attn_ffw_laurel_gated, il); // [n_embd, n_tokens, n_altup]
-
-            ggml_tensor * first_prediction; // [n_embd, n_tokens]
-            {
-                first_prediction = view_2d_slice(corrected, i_altup_act); // [n_embd, n_tokens]
-                first_prediction = ggml_mul(ctx0, first_prediction, model.layers[il].altup_correct_scale);
-                first_prediction = build_lora_mm(model.layers[il].per_layer_inp_gate, first_prediction);
-                first_prediction = ggml_gelu(ctx0, first_prediction); // [n_embd_altup, n_tokens]
-                cb(first_prediction, "first_prediction_gated", il);
-                ggml_tensor * inp_this_layer = view_2d_slice(inp_per_layer, il); // [n_embd_altup, n_tokens]
-                first_prediction = ggml_mul(ctx0, first_prediction, inp_this_layer); // [n_embd_altup, n_tokens]
-                cb(first_prediction, "first_prediction_scaled", il);
-
-                first_prediction = build_lora_mm(model.layers[il].per_layer_proj, first_prediction); // [n_embd, n_tokens]
-                first_prediction = build_norm(first_prediction,
-                        model.layers[il].per_layer_post_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(first_prediction, "first_prediction_out", il);
-            }
-
-            // equivalent to python code: corrected_predictions[1:] += first_prediction
-            {
-                ggml_tensor * slice_first = view_2d_slice(corrected, 0);
-                ggml_tensor * slice_rest  = ggml_view_3d(ctx0, corrected, n_embd, n_tokens, n_altup - 1,
-                                                    ggml_row_size(corrected->type, n_embd),
-                                                    ggml_row_size(corrected->type, n_embd*n_tokens),
-                                                    n_embd*n_tokens*ggml_element_size(corrected));
-                ggml_tensor * tmp = ggml_add(ctx0, slice_rest, first_prediction); // [n_embd, n_tokens, n_altup - 1]
-                corrected = ggml_concat(ctx0, slice_first, tmp, 2); // [n_embd, n_tokens, n_altup]
-            }
-
-            cur = corrected; // [n_embd, n_tokens, n_altup]
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL; // [n_embd, n_tokens, n_altup]
-
-        // cur now has multiple altup(s), we want to merge them back to 1 altup
-        {
-            ggml_tensor * target_magnitude = calc_magnitude(view_2d_slice(cur, i_altup_act)); // [n_embd, n_tokens]
-            // do a view to skip the first slice (active altup)
-            ggml_tensor * alt_slice = ggml_view_3d(ctx0, cur, n_embd, n_tokens, n_altup - 1,
-                                                    ggml_row_size(cur->type, n_embd),
-                                                    ggml_row_size(cur->type, n_embd*n_tokens),
-                                                    n_embd*n_tokens*ggml_element_size(cur));
-            ggml_tensor * altup_unembd = ggml_mul_mat(ctx0, model.altup_unembd_proj, alt_slice); // shape: [n_embd, n_tokens, n_altup - 1]
-            ggml_tensor * new_magnitude = calc_magnitude(altup_unembd);
-            altup_unembd = ggml_div(ctx0,
-                                ggml_mul(ctx0, altup_unembd, target_magnitude),
-                                new_magnitude);
-            cb(altup_unembd, "altup_unembd", -1);
-
-            // equivalent to torch.mean(hidden_states, dim=0)
-            cur = view_2d_slice(cur, 0); // [n_embd, n_tokens]
-            for (int i = 0; i < n_altup - 1; ++i) {
-                cur = ggml_add(ctx0, cur, view_2d_slice(altup_unembd, i));
-            }
-            cur = ggml_scale(ctx0, cur, 1.0f / float(n_altup)); // [n_embd, n_tokens]
-            cb(cur, "unembd_merged", -1);
-        }
-
-        // cur now has shape: [n_embd, n_tokens]
-
-        // TODO: move this to right after the last KV layer
-        {
-            // skip computing output for unused tokens
-            ggml_tensor * inp_out_ids = build_inp_out_ids();
-            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-        }
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-
-        {
-            // final logit soft-capping
-            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);
-    }
-
-    ggml_tensor * calc_magnitude(ggml_tensor * x) {
-        return ggml_sqrt(ctx0, ggml_sum_rows(ctx0, ggml_sqr(ctx0, x)));
-    }
-
-    // get 2D slice view from a 3D tensor, the idx corresponds to the 3rd dim
-    ggml_tensor * view_2d_slice(ggml_tensor * x, int idx) {
-        GGML_ASSERT(idx < (int)x->ne[2]);
-        return ggml_view_2d(ctx0, x, x->ne[0], x->ne[1],
-                            ggml_row_size(x->type, x->ne[0]),
-                            idx * x->ne[0] * x->ne[1] * ggml_element_size(x));
-    }
-
-    // equivalent to get_per_layer_inputs() in python code
-    // output shape: [n_embd_altup, n_layer, n_tokens]
-    ggml_tensor * get_per_layer_inputs() {
-        auto inp = std::make_unique<llm_graph_input_embd>();
-        ggml_tensor * inp_per_layer;
-        if (ubatch.token) {
-            inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
-            ggml_set_input(inp->tokens);
-            res->t_tokens = inp->tokens;
-            inp_per_layer = ggml_get_rows(ctx0, model.tok_embd_per_layer, inp->tokens);
-            inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_altup, n_layer, n_tokens);
-            inp_per_layer = ggml_scale(ctx0, inp_per_layer, sqrtf((float)n_embd_altup));
-            cb(inp_per_layer, "inp_per_layer_selected", -1);
-        } else {
-            GGML_ABORT("TODO: support embd input");
-        }
-        res->add_input(std::move(inp));
-        return inp_per_layer;
-    }
-
-    // equivalent to project_per_layer_inputs() in python code
-    // this calculates the per-layer inputs, so the final tensor shape will have n_layer as the last dim
-    // output shape: [n_embd_altup, n_tokens, n_layer]
-    ggml_tensor * project_per_layer_inputs(ggml_tensor * inputs_embeds, ggml_tensor * inp_per_layer) {
-        const float per_layer_projection_scale = 1.0f / sqrtf((float)n_embd);
-        const float per_layer_input_scale      = 1.0f / sqrtf(2.0f);
-
-        ggml_tensor * per_layer_proj = ggml_mul_mat(ctx0, model.per_layer_model_proj, inputs_embeds);
-        per_layer_proj = ggml_scale(ctx0, per_layer_proj, per_layer_projection_scale);
-        per_layer_proj = ggml_reshape_3d(ctx0, per_layer_proj, n_embd_altup, n_layer, n_tokens);
-        per_layer_proj = build_norm(per_layer_proj,
-                                    model.per_layer_proj_norm, NULL,
-                                    LLM_NORM_RMS, -1); // [n_embd_altup, n_layer, n_tokens]
-        cb(per_layer_proj, "per_layer_proj", -1);
-
-        inp_per_layer = ggml_add(ctx0, inp_per_layer, per_layer_proj);
-        inp_per_layer = ggml_scale(ctx0, inp_per_layer, per_layer_input_scale);
-        cb(inp_per_layer, "inp_per_layer", -1);
-
-        // permute to shape: [n_embd_altup, n_tokens, n_layer]
-        inp_per_layer = ggml_cont(ctx0, ggml_permute(ctx0, inp_per_layer, 0, 2, 1, 3));
-        return inp_per_layer;
-    }
-
-    // input cur shape: [n_altup, n_tokens]
-    // output    shape: [n_altup, n_tokens]
-    ggml_tensor * laurel(ggml_tensor * cur, int il) {
-        ggml_tensor * tmp = cur;
-        tmp = build_lora_mm(model.layers[il].laurel_l, tmp);
-        tmp = build_lora_mm(model.layers[il].laurel_r, tmp);
-        tmp = build_norm(tmp, model.layers[il].laurel_post_norm, NULL, LLM_NORM_RMS, il);
-        tmp = ggml_add(ctx0, tmp, cur);
-        cb(tmp, "laurel_out", il);
-        return tmp;
-    }
-
-    // input x shape: [n_embd, n_tokens]
-    // output  shape: [n_embd, n_tokens]
-    ggml_tensor * gaussian_topk(ggml_tensor * x) {
-        ggml_tensor * mean = ggml_mean(ctx0, x);
-        ggml_tensor * std  = ggml_sqrt(ctx0, ggml_scale(ctx0,
-            ggml_sum_rows(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, x, mean))),
-            1.0f / (float)(x->ne[0] - 1)
-        ));
-        ggml_tensor * cutoff_x = ggml_add(ctx0, mean, ggml_scale(ctx0, std, f_sparsity_std_mul));
-        return ggml_relu(ctx0, ggml_sub(ctx0, x, cutoff_x));
-    }
-
-    //
-    // altup functions
-    //
-
-    // equivalent to compute_router_modalities() in python code
-    // input x shape: [n_embd,  n_tokens]
-    // output  shape: [n_altup, n_tokens]
-    ggml_tensor * altup_compute_router_modalities(ggml_tensor * x, int il) {
-        ggml_tensor * router_inputs = build_norm(x,
-            model.layers[il].altup_router_norm, NULL,
-            LLM_NORM_RMS, il);
-
-        // router_input_scale
-        router_inputs = ggml_scale(ctx0, router_inputs, 1.0f / (float)n_embd);
-
-        ggml_tensor * output = ggml_mul_mat(ctx0, model.layers[il].altup_router, router_inputs);
-        return ggml_tanh(ctx0, output); // [n_altup, n_tokens]
-    }
-
-    // input cur shape: [n_embd, n_tokens, n_altup]
-    // output    shape: [n_embd, n_tokens, n_altup]
-    ggml_tensor * altup_predict(ggml_tensor * cur, int il) {
-        ggml_tensor * activated = view_2d_slice(cur, i_altup_act); // [n_embd, n_tokens]
-        ggml_tensor * modalities = altup_compute_router_modalities(activated, il); // [n_altup, n_tokens]
-        cb(modalities, "modalities", il);
-
-        ggml_tensor * all_coefs = build_lora_mm(model.layers[il].altup_predict_coef, modalities);
-        cb(all_coefs, "all_coefs", il);
-        // first dim now having n_altup^2 elements, we reshape it to 2D (so we end up with 3D tensor)
-        all_coefs = ggml_reshape_3d(ctx0, all_coefs, n_altup, n_altup, n_tokens);
-
-        // permute to [n_altup, n_embd, n_tokens]
-        ggml_tensor * cur_permuted = ggml_cont(ctx0, ggml_permute(ctx0, cur, 1, 2, 0, 3));
-        ggml_tensor * predictions = ggml_mul_mat(ctx0, cur_permuted, all_coefs); // [n_altup, n_embd, n_tokens]
-
-        // final shape must be the same as cur: [n_embd, n_tokens, n_altup]
-        predictions = ggml_cont(ctx0, ggml_permute(ctx0, predictions, 0, 2, 1, 3));
-        predictions = ggml_add(ctx0, predictions, cur);
-        cb(predictions, "predictions", il);
-
-        return predictions;
-    }
-
-    // input predictions       shape: [n_embd, n_tokens, n_altup]
-    // input activated         shape: [n_embd, n_tokens]
-    // output                  shape: [n_embd, n_tokens, n_altup]
-    ggml_tensor * altup_correct(ggml_tensor * predictions, ggml_tensor * activated, int il) {
-        ggml_tensor * modalities = altup_compute_router_modalities(activated, il); // [n_altup, n_tokens]
-        cb(modalities, "modalities", il);
-
-        ggml_tensor * active_prediction = view_2d_slice(predictions, i_altup_act);
-        ggml_tensor * innovation = ggml_sub(ctx0, activated, active_prediction); // [n_embd, n_tokens]
-        cb(innovation, "innovation", il);
-
-        ggml_tensor * all_coefs = build_lora_mm(model.layers[il].altup_correct_coef, modalities); // [n_altup, n_tokens]
-        all_coefs = ggml_scale_bias(ctx0, all_coefs, 1.0f, 1.0f); // + 1.0
-        cb(all_coefs, "all_coefs", il);
-        all_coefs = ggml_transpose(ctx0, all_coefs); // [n_tokens, n_altup]
-        all_coefs = ggml_cont_3d(ctx0, all_coefs, 1, n_tokens, n_altup); // [1, n_tokens, n_altup]
-
-        innovation = ggml_repeat_4d(ctx0, innovation, n_embd, n_tokens, n_altup, 1);
-        ggml_tensor * corrected = ggml_mul(ctx0, innovation, all_coefs); // [n_embd, n_tokens, n_altup]
-        corrected = ggml_add(ctx0, corrected, predictions); // [n_embd, n_tokens, n_altup]
-        cb(corrected, "corrected", il);
-
-        return corrected;
-    }
-};
-
-struct llm_build_gemma_embedding : public llm_graph_context {
-    llm_build_gemma_embedding(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;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
-        if (ubatch.token) {
-            inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
-            cb(inpL, "inp_scaled", -1);
-        }
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_no_cache();
-
-        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);
-
-            // norm
-            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_normed", il);
-
-                Qcur = ggml_rope_ext(
-                        ctx0, Qcur, inp_pos, nullptr,
-                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
-                        ext_factor, attn_factor, beta_fast, beta_slow);
-
-                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
-                cb(Kcur, "Kcur_normed", il);
-
-                Kcur = ggml_rope_ext(
-                        ctx0, Kcur, inp_pos, nullptr,
-                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
-                        ext_factor, attn_factor, beta_fast, beta_slow);
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/model.py#L315
-                Qcur = ggml_scale(ctx0, Qcur, hparams.f_attention_scale);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
-            }
-
-            cur = build_norm(cur,
-                    model.layers[il].attn_post_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_post_norm", il);
-
-            ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
-            cb(sa_out, "sa_out", il);
-
-            cur = build_norm(sa_out,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            // feed-forward network
-            {
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_GELU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = build_norm(cur,
-                    model.layers[il].ffn_post_norm, NULL,
-                    LLM_NORM_RMS, -1);
-            cb(cur, "ffn_post_norm", -1);
-
-            cur = ggml_add(ctx0, cur, sa_out);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-// TODO: move up next to build_starcoder
-struct llm_build_starcoder2 : public llm_graph_context {
-    llm_build_starcoder2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, model.layers[il].attn_norm_b,
-                    LLM_NORM, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
-                    LLM_NORM, il);
-            cb(cur, "ffn_norm", 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_GELU, LLM_FFN_SEQ, il);
-            cb(cur, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, model.output_norm_b,
-                LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_graph_context_mamba : public llm_graph_context {
-    llm_graph_context_mamba(const llm_graph_params & params) : llm_graph_context(params) {}
-
-    ggml_tensor * build_mamba_layer(
-        llm_graph_input_rs * inp,
-               ggml_tensor * cur,
-         const llama_model & model,
-        const llama_ubatch & ubatch,
-                       int   il) {
-
-        const auto * mctx_cur = inp->mctx;
-
-        const auto kv_head = mctx_cur->get_head();
-
-        const auto & layer = model.layers[il];
-
-        const int64_t d_conv  = hparams.ssm_d_conv;
-        const int64_t d_inner = hparams.ssm_d_inner;
-        const int64_t d_state = hparams.ssm_d_state;
-        const int64_t dt_rank = hparams.ssm_dt_rank;
-        const int64_t n_head  = d_inner;
-        const int64_t head_dim = 1;
-        const int64_t n_seqs  = ubatch.n_seqs;
-        // Some variants of Mamba arch (e.g. FalconMamba do apply layer norm on B and Dt layers)
-        const bool ssm_dt_b_c_rms = hparams.ssm_dt_b_c_rms;
-
-        const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-
-        GGML_ASSERT(n_seqs != 0);
-        GGML_ASSERT(ubatch.equal_seqs());
-        GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
-
-        ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
-        ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
-
-        ggml_tensor * conv = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
-        conv = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner, n_seqs);
-
-        // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs}
-        cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
-
-        // {n_embd, 2*d_inner} @ {n_embd, n_seq_tokens, n_seqs} => {2*d_inner, n_seq_tokens, n_seqs}
-        ggml_tensor * xz = build_lora_mm(layer.ssm_in, cur);
-        // split the above in two
-        // => {d_inner, n_seq_tokens, n_seqs}
-        ggml_tensor * x = ggml_view_3d(ctx0, xz, d_inner, xz->ne[1], xz->ne[2], xz->nb[1], xz->nb[2], 0);
-        ggml_tensor * z = ggml_view_3d(ctx0, xz, d_inner, xz->ne[1], xz->ne[2], xz->nb[1], xz->nb[2], d_inner*ggml_element_size(xz));
-
-        // conv
-        {
-            // => {d_conv - 1 + n_seq_tokens, d_inner, n_seqs}
-            ggml_tensor * conv_x = ggml_concat(ctx0, conv, ggml_transpose(ctx0, x), 0);
-
-            // copy last (d_conv - 1) columns back into the state cache
-            ggml_tensor * last_conv = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner, n_seqs, conv_x->nb[1], conv_x->nb[2], n_seq_tokens*(conv_x->nb[0]));
-
-            ggml_build_forward_expand(gf,
-                ggml_cpy(ctx0, last_conv,
-                    ggml_view_1d(ctx0, conv_states_all,
-                        (d_conv - 1)*(d_inner)*(n_seqs),
-                        kv_head*(d_conv - 1)*(d_inner)*ggml_element_size(conv_states_all))));
-
-            // 1D convolution
-            // The equivalent is to make a self-overlapping view of conv_x
-            // over d_conv columns at each stride in the 3rd dimension,
-            // then element-wise multiply that with the conv1d weight,
-            // then sum the elements of each row,
-            // (the last two steps are a dot product over rows (also doable with mul_mat))
-            // then permute away the ne[0] dimension,
-            // and then you're left with the resulting x tensor.
-            // For simultaneous sequences, all sequences need to have the same length.
-            x = ggml_ssm_conv(ctx0, conv_x, layer.ssm_conv1d);
-
-            // bias
-            x = ggml_add(ctx0, x, layer.ssm_conv1d_b);
-
-            x = ggml_silu(ctx0, x);
-        }
-
-        // ssm
-        {
-            // {d_inner, dt_rank + 2*d_state} @ {d_inner, n_seq_tokens, n_seqs} => {dt_rank + 2*d_state, n_seq_tokens, n_seqs}
-            ggml_tensor * x_db = build_lora_mm(layer.ssm_x, x);
-            // split
-            ggml_tensor * dt = ggml_view_3d(ctx0, x_db, dt_rank, n_seq_tokens, n_seqs, x_db->nb[1], x_db->nb[2], 0);
-            ggml_tensor * B  = ggml_view_4d(ctx0, x_db, d_state, /* n_group */ 1, n_seq_tokens, n_seqs, d_state*x_db->nb[0], x_db->nb[1], x_db->nb[2], ggml_element_size(x_db)*dt_rank);
-            ggml_tensor * C  = ggml_view_4d(ctx0, x_db, d_state, /* n_group */ 1, n_seq_tokens, n_seqs, d_state*x_db->nb[0], x_db->nb[1], x_db->nb[2], ggml_element_size(x_db)*(dt_rank+d_state));
-
-            // Some Mamba variants (e.g. FalconMamba, Jamba) apply RMS norm in B, C & Dt layers
-            if (ssm_dt_b_c_rms || (layer.ssm_dt_norm && layer.ssm_b_norm && layer.ssm_c_norm)) {
-                dt = build_norm(dt, layer.ssm_dt_norm, NULL, LLM_NORM_RMS, il);
-                B  = build_norm(B,  layer.ssm_b_norm,  NULL, LLM_NORM_RMS, il);
-                C  = build_norm(C,  layer.ssm_c_norm,  NULL, LLM_NORM_RMS, il);
-            }
-
-            // {dt_rank, d_inner} @ {dt_rank, n_seq_tokens, n_seqs} => {d_inner, n_seq_tokens, n_seqs}
-            dt = build_lora_mm(layer.ssm_dt, dt);
-            dt = ggml_add(ctx0, dt, layer.ssm_dt_b);
-
-            cur = x;
-            x = ggml_reshape_4d(ctx0, x, head_dim, n_head, n_seq_tokens, n_seqs);
-
-            ggml_tensor * A = layer.ssm_a;
-
-            // use the states and the indices provided by build_recurrent_state
-            // (this is necessary in order to properly use the states before they are overwritten,
-            //  while avoiding to make unnecessary copies of the states)
-            auto get_ssm_rows = [&](ggml_context * ctx, ggml_tensor * states, ggml_tensor * ids) {
-                ggml_tensor * ssm = ggml_reshape_4d(ctx, states, d_state, head_dim, n_head, mctx_cur->get_size());
-
-                // Custom operator to optimize the parallel associative scan
-                // as described in the Annex D of the Mamba paper.
-                // => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs}
-                return ggml_ssm_scan(ctx, ssm, x, dt, A, B, C, ids);
-            };
-
-            ggml_tensor * y_ssm = build_rs(inp, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows);
-
-            // store last states
-            ggml_build_forward_expand(gf,
-                ggml_cpy(ctx0,
-                    ggml_view_1d(ctx0, y_ssm, d_state*d_inner*n_seqs, x->nb[3]*x->ne[3]),
-                    ggml_view_1d(ctx0, ssm_states_all, d_state*d_inner*n_seqs, kv_head*d_state*d_inner*ggml_element_size(ssm_states_all))));
-
-            ggml_tensor * y = ggml_view_3d(ctx0, y_ssm, d_inner, n_seq_tokens, n_seqs, x->nb[2], x->nb[3], 0);
-
-            // TODO: skip computing output earlier for unused tokens
-
-            y = ggml_add(ctx0, y, ggml_mul(ctx0, cur, layer.ssm_d));
-            y = ggml_swiglu_split(ctx0, ggml_cont(ctx0, z), y);
-
-            // {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs}
-            cur = build_lora_mm(layer.ssm_out, y);
-        }
-
-        // {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
-        cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs);
-
-        return cur;
-    }
-
-    ggml_tensor * build_mamba2_layer(
-        llm_graph_input_rs * inp,
-               ggml_tensor * cur,
-         const llama_model & model,
-        const llama_ubatch & ubatch,
-                       int   il) const {
-
-        const auto * mctx_cur = inp->mctx;
-
-        const auto kv_head = mctx_cur->get_head();
-
-        const int64_t d_conv  = hparams.ssm_d_conv;
-        const int64_t d_inner = hparams.ssm_d_inner;
-        const int64_t d_state = hparams.ssm_d_state;
-        const int64_t n_head  = hparams.ssm_dt_rank;
-        const int64_t head_dim = d_inner / n_head;
-        const int64_t n_group = hparams.ssm_n_group;
-        const int64_t n_seqs  = ubatch.n_seqs;
-
-        const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-
-        GGML_ASSERT(n_seqs != 0);
-        GGML_ASSERT(ubatch.equal_seqs());
-        GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
-
-        ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
-        ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
-
-        ggml_tensor * conv = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
-        conv = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner + 2*n_group*d_state, n_seqs);
-
-        // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs}
-        cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
-
-        // d_in_proj = 2 * self.d_inner + 2 * self.ngroups * self.d_state + self.nheads
-
-        // {n_embd, d_in_proj} @ {n_embd, n_seq_tokens, n_seqs} => {d_in_proj, n_seq_tokens, n_seqs}
-        ggml_tensor * zxBCdt = build_lora_mm(model.layers[il].ssm_in, cur);
-
-        // split the above in three
-        ggml_tensor * z = ggml_view_4d(ctx0, zxBCdt, head_dim, n_head, n_seq_tokens, n_seqs, head_dim*zxBCdt->nb[0], zxBCdt->nb[1], zxBCdt->nb[2], 0);
-        ggml_tensor * xBC = ggml_view_3d(ctx0, zxBCdt, d_inner + 2*n_group*d_state, n_seq_tokens, n_seqs, zxBCdt->nb[1], zxBCdt->nb[2], d_inner*ggml_element_size(zxBCdt));
-        ggml_tensor * dt = ggml_view_3d(ctx0, zxBCdt, n_head, n_seq_tokens, n_seqs, zxBCdt->nb[1], zxBCdt->nb[2], (2*d_inner + 2*n_group*d_state)*ggml_element_size(zxBCdt));
-
-        // conv
-        {
-            // => {d_conv - 1 + n_seq_tokens, d_inner + 2*n_group*d_state, n_seqs}
-            ggml_tensor * conv_x = ggml_concat(ctx0, conv, ggml_transpose(ctx0, xBC), 0);
-
-            // copy last (d_conv - 1) columns back into the state cache
-            ggml_tensor * last_conv = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner + 2*n_group*d_state, n_seqs, conv_x->nb[1], conv_x->nb[2], n_seq_tokens*(conv_x->nb[0]));
-
-            ggml_build_forward_expand(gf,
-                ggml_cpy(ctx0, last_conv,
-                    ggml_view_1d(ctx0, conv_states_all,
-                        (d_conv - 1)*(d_inner + 2*n_group*d_state)*(n_seqs),
-                        kv_head*(d_conv - 1)*(d_inner + 2*n_group*d_state)*ggml_element_size(conv_states_all))));
-
-            // 1D convolution
-            // The equivalent is to make a self-overlapping view of conv_x
-            // over d_conv columns at each stride in the 3rd dimension,
-            // then element-wise multiply that with the conv1d weight,
-            // then sum the elements of each row,
-            // (the last two steps are a dot product over rows (also doable with mul_mat))
-            // then permute away the ne[0] dimension,
-            // and then you're left with the resulting x tensor.
-            // For simultaneous sequences, all sequences need to have the same length.
-            xBC = ggml_ssm_conv(ctx0, conv_x, model.layers[il].ssm_conv1d);
-
-            // bias
-            xBC = ggml_add(ctx0, xBC, model.layers[il].ssm_conv1d_b);
-
-            xBC = ggml_silu(ctx0, xBC);
-        }
-
-        // ssm
-        {
-            // These correspond to V K Q in SSM/attention duality
-            ggml_tensor * x = ggml_view_4d(ctx0, xBC, head_dim, n_head, n_seq_tokens, n_seqs, head_dim*xBC->nb[0], xBC->nb[1], xBC->nb[2], 0);
-            ggml_tensor * B = ggml_view_4d(ctx0, xBC, d_state, n_group, n_seq_tokens, n_seqs, d_state*xBC->nb[0], xBC->nb[1], xBC->nb[2], d_inner*ggml_element_size(xBC));
-            ggml_tensor * C = ggml_view_4d(ctx0, xBC, d_state, n_group, n_seq_tokens, n_seqs, d_state*xBC->nb[0], xBC->nb[1], xBC->nb[2], (d_inner + n_group*d_state)*ggml_element_size(xBC));
-
-            // {n_head, n_seq_tokens, n_seqs}
-            dt = ggml_add(ctx0, ggml_cont(ctx0, dt), model.layers[il].ssm_dt_b);
-
-            ggml_tensor * A = model.layers[il].ssm_a;
-
-            // use the states and the indices provided by build_recurrent_state
-            // (this is necessary in order to properly use the states before they are overwritten,
-            //  while avoiding to make unnecessary copies of the states)
-            auto get_ssm_rows = [&](ggml_context * ctx, ggml_tensor * states, ggml_tensor * ids) {
-                ggml_tensor * ssm = ggml_reshape_4d(ctx, states, d_state, head_dim, n_head, mctx_cur->get_size());
-
-                // TODO: use semistructured matrices to implement state-space duality
-                // => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs}
-                return ggml_ssm_scan(ctx, ssm, x, dt, A, B, C, ids);
-            };
-
-            ggml_tensor * y_ssm = build_rs(inp, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows);
-
-            // store last states
-            ggml_build_forward_expand(gf,
-                ggml_cpy(ctx0,
-                    ggml_view_1d(ctx0, y_ssm, d_state*d_inner*n_seqs, ggml_nelements(x)*x->nb[0]),
-                    ggml_view_1d(ctx0, ssm_states_all, d_state*d_inner*n_seqs, kv_head*d_state*d_inner*ggml_element_size(ssm_states_all))));
-
-            ggml_tensor * y = ggml_view_4d(ctx0, y_ssm, head_dim, n_head, n_seq_tokens, n_seqs, x->nb[1], n_head*x->nb[1], n_seq_tokens*n_head*x->nb[1], 0);
-
-            // TODO: skip computing output earlier for unused tokens
-
-            y = ggml_add(ctx0, y, ggml_mul(ctx0, x, model.layers[il].ssm_d));
-            cb(y, "mamba2_y_add_d", il);
-            y = ggml_swiglu_split(ctx0, ggml_cont(ctx0, z), y);
-
-            // grouped RMS norm
-            if (model.layers[il].ssm_norm) {
-                y = ggml_reshape_4d(ctx0, y, d_inner / n_group, n_group, n_seq_tokens, n_seqs);
-                y = build_norm(y, model.layers[il].ssm_norm, NULL, LLM_NORM_RMS, il);
-            }
-
-            y = ggml_reshape_3d(ctx0, y, d_inner, n_seq_tokens, n_seqs);
-
-            // {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs}
-            cur = build_lora_mm(model.layers[il].ssm_out, y);
-        }
-
-        // {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
-        cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs);
-        cb(cur, "mamba_out", il);
-
-        return cur;
-    }
-};
-
-struct llm_build_mamba : public llm_graph_context_mamba {
-    llm_build_mamba(const llama_model & model, const llm_graph_params & params) : llm_graph_context_mamba(params) {
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        // {n_embd, n_tokens}
-        inpL = build_inp_embd(model.tok_embd);
-
-        auto * rs_inp = build_rs_inp();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            if (model.arch == LLM_ARCH_MAMBA2) {
-                cur = build_mamba2_layer(rs_inp, cur, model, ubatch, il);
-            } else {
-                cur = build_mamba_layer(rs_inp, cur, model, ubatch, il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
-            }
-
-            // residual
-            cur = ggml_add(ctx0, cur, inpL);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        // final rmsnorm
-        cur = build_norm(inpL, model.output_norm, NULL, LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-
-};
-
-struct llm_build_jamba : public llm_graph_context_mamba {
-    llm_build_jamba(const llama_model & model, const llm_graph_params & params) : llm_graph_context_mamba(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        // {n_embd, n_tokens}
-        inpL = build_inp_embd(model.tok_embd);
-
-        auto * inp_hybrid = build_inp_mem_hybrid();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            const int64_t n_head_kv = hparams.n_head_kv(il);
-
-            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            if (n_head_kv == 0) {
-                cur = build_mamba_layer(inp_hybrid->get_recr(), cur, model, ubatch, il);
-            } else {
-                // Attention
-
-                struct ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                struct ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                struct ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                // No RoPE :)
-                cur = build_attn(inp_hybrid->get_attn(),
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, NULL, NULL, NULL, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
-            }
-
-            // residual
-            struct ggml_tensor * ffn_inp = ggml_add(ctx0, inpL, cur);
-            cb(cur, "ffn_inp", il);
-
-            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            // feed-forward network
-            if (model.layers[il].ffn_gate_inp == nullptr) {
-                // FFN
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            } else {
-                // MoE branch
-                cur = build_moe_ffn(cur,
-                        model.layers[il].ffn_gate_inp,
-                        model.layers[il].ffn_up_exps,
-                        model.layers[il].ffn_gate_exps,
-                        model.layers[il].ffn_down_exps,
-                        nullptr,
-                        n_expert, n_expert_used,
-                        LLM_FFN_SILU, false,
-                        false, 0.0,
-                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                        il);
-                cb(cur, "ffn_moe_out", il);
-            }
-
-            // residual
-            cur = ggml_add(ctx0, ffn_inp, cur);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        // final rmsnorm
-        cur = build_norm(inpL, model.output_norm, NULL, LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_command_r : public llm_graph_context {
-    llm_build_command_r(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        const float f_logit_scale = hparams.f_logit_scale;
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM, il);
-            cb(cur, "attn_norm", il);
-
-            ggml_tensor * ffn_inp = cur;
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                if (model.layers[il].attn_q_norm) {
-                    Qcur = build_norm(Qcur,
-                            model.layers[il].attn_q_norm,
-                            NULL,
-                            LLM_NORM, il);
-                    cb(Qcur, "Qcur", 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
-                        );
-
-                if (model.layers[il].attn_k_norm) {
-                    Kcur = build_norm(Kcur,
-                            model.layers[il].attn_k_norm,
-                            NULL,
-                            LLM_NORM, il);
-                    cb(Kcur, "Kcur", il);
-                }
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur     = ggml_get_rows(ctx0,     cur, inp_out_ids);
-                inpL    = ggml_get_rows(ctx0,    inpL, inp_out_ids);
-                ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
-            }
-
-            ggml_tensor * attn_out = cur;
-
-            // feed-forward network
-            {
-                cur = build_ffn(ffn_inp,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            // add together residual + FFN + self-attention
-            cur = ggml_add(ctx0, cur, inpL);
-            cur = ggml_add(ctx0, cur, attn_out);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        if (f_logit_scale) {
-            cur = ggml_scale(ctx0, cur, f_logit_scale);
-        }
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_cohere2_iswa : public llm_graph_context {
-    llm_build_cohere2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        const float f_logit_scale = hparams.f_logit_scale;
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv_iswa();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            const bool is_swa = hparams.is_swa(il);
-
-            // norm
-            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM, il);
-            cb(cur, "attn_norm", il);
-            ggml_tensor * ffn_inp = cur;
-
-            // self-attention
-            {
-                // rope freq factors for 128k context
-                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                if (is_swa) {
-                    Qcur = ggml_rope_ext(
-                            ctx0, Qcur, inp_pos, rope_factors,
-                            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, rope_factors,
-                            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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur     = ggml_get_rows(ctx0, cur, inp_out_ids);
-                inpL    = ggml_get_rows(ctx0, inpL, inp_out_ids);
-                ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
-            }
-
-            ggml_tensor * attn_out = cur;
-
-            // feed-forward network
-            {
-                cur = build_ffn(ffn_inp, model.layers[il].ffn_up, NULL, NULL, model.layers[il].ffn_gate,
-                        NULL, NULL, model.layers[il].ffn_down, NULL, NULL, NULL, LLM_FFN_SILU, LLM_FFN_PAR,
-                        il);
-                cb(cur, "ffn_out", il);
-            }
-
-            // add together residual + FFN + self-attention
-            cur = ggml_add(ctx0, cur, inpL);
-            cur = ggml_add(ctx0, cur, attn_out);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur, model.output_norm, NULL, LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        if (f_logit_scale) {
-            cur = ggml_scale(ctx0, cur, f_logit_scale);
-        }
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-// ref: https://allenai.org/olmo
-// based on the original build_llama() function, changes:
-//   * non-parametric layer norm
-//   * clamp qkv
-//   * removed bias
-//   * removed MoE
-struct llm_build_olmo : public llm_graph_context {
-    llm_build_olmo(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    NULL, NULL,
-                    LLM_NORM, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-                if (hparams.f_clamp_kqv > 0.0f) {
-                    Qcur = ggml_clamp(ctx0, Qcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
-                    cb(Qcur, "Qcur", il);
-                }
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-                if (hparams.f_clamp_kqv > 0.0f) {
-                    Kcur = ggml_clamp(ctx0, Kcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
-                    cb(Kcur, "Kcur", il);
-                }
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                cb(Vcur, "Vcur", il);
-                if (hparams.f_clamp_kqv > 0.0f) {
-                    Vcur = ggml_clamp(ctx0, Vcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
-                    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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, nullptr,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            cur = build_norm(ffn_inp,
-                    NULL, NULL,
-                    LLM_NORM, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                NULL, NULL,
-                LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-template <bool iswa>
-struct llm_build_olmo2 : public llm_graph_context {
-    llm_build_olmo2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        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) {
-            ggml_tensor * inpSA = inpL;
-
-            cur = inpL;
-
-            // self_attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                cb(Vcur, "Vcur", il);
-
-                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_normed", il);
-
-                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(Kcur, "Kcur_normed", 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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                const bool is_swa = hparams.is_swa(il);
-
-                if (is_swa) {
-                    // For sliding window layers, Olmo3 use regular rope with no yarn rope scaling.
-                    // This is achieved here by setting freq_scale and attn_factor to 1.
-                    // We also set ext_factor to 0 to avoid a few unnecessary computations.
-                    Qcur = ggml_rope_ext(
-                        ctx0, Qcur, inp_pos, nullptr,
-                        n_rot, rope_type, n_ctx_orig, freq_base, 1.0,
-                        0.0, 1.0, beta_fast, beta_slow
-                        );
-
-                    Kcur = ggml_rope_ext(
-                        ctx0, Kcur, inp_pos, nullptr,
-                        n_rot, rope_type, n_ctx_orig, freq_base, 1.0,
-                        0.0, 1.0, beta_fast, beta_slow
-                        );
-                } else {
-                    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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            cur = build_norm(cur,
-                    model.layers[il].attn_post_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_post_norm", il);
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            cur = build_ffn(ffn_inp,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            cur = build_norm(cur,
-                    model.layers[il].ffn_post_norm, NULL,
-                    LLM_NORM_RMS, -1);
-            cb(cur, "ffn_post_norm", -1);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-// based on the build_qwen2moe() function, changes:
-//   * removed shared experts
-//   * removed bias
-//   * added q, k norm
-struct llm_build_olmoe : public llm_graph_context {
-    llm_build_olmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self_attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                cb(Vcur, "Vcur", il);
-
-                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_normed", il);
-
-                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(Kcur, "Kcur_normed", 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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // MoE branch
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_moe_ffn(cur,
-                    model.layers[il].ffn_gate_inp,
-                    model.layers[il].ffn_up_exps,
-                    model.layers[il].ffn_gate_exps,
-                    model.layers[il].ffn_down_exps,
-                    nullptr,
-                    n_expert, n_expert_used,
-                    LLM_FFN_SILU, false,
-                    false, 0.0,
-                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                    il);
-            cb(cur, "ffn_moe_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_llada_moe : public llm_graph_context {
-    llm_build_llada_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_no_cache();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self_attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_normed", il);
-
-                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
-                        );
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // MoE branch
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_moe_ffn(cur,
-                    model.layers[il].ffn_gate_inp,
-                    model.layers[il].ffn_up_exps,
-                    model.layers[il].ffn_gate_exps,
-                    model.layers[il].ffn_down_exps,
-                    nullptr,
-                    n_expert, n_expert_used,
-                    LLM_FFN_SILU, false,
-                    false, 0.0,
-                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                    il);
-            cb(cur, "ffn_moe_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_openelm : public llm_graph_context {
-    llm_build_openelm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            const int64_t n_head    = hparams.n_head(il);
-            const int64_t n_head_kv = hparams.n_head_kv(il);
-            const int64_t n_head_qkv = 2*n_head_kv + n_head;
-
-            cur = inpL;
-            ggml_tensor * residual = cur;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                cur = build_lora_mm(model.layers[il].wqkv, cur);
-                cb(cur, "wqkv", il);
-
-                cur = ggml_reshape_3d(ctx0, cur, n_embd_head_k, n_head_qkv, n_tokens);
-
-                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, cur->nb[1], cur->nb[2], 0);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*n_head);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*(n_head+n_head_kv)));
-                cb(Vcur, "Vcur", il);
-
-                Qcur = build_norm(Qcur,
-                        model.layers[il].attn_q_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur", il);
-
-                Kcur = build_norm(Kcur,
-                        model.layers[il].attn_k_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(Kcur, "Kcur", il);
-
-                Qcur = ggml_rope_ext(
-                        ctx0, Qcur, inp_pos, NULL,
-                        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, NULL,
-                        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);
-                cb(Qcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                residual = ggml_get_rows(ctx0, residual, inp_out_ids);
-                cur      = ggml_get_rows(ctx0, cur,      inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        // norm
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_gptneox : public llm_graph_context {
-    llm_build_gptneox(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm,
-                    model.layers[il].attn_norm_b,
-                    LLM_NORM, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                cur = build_lora_mm(model.layers[il].wqkv, cur);
-                cb(cur, "wqkv", il);
-
-                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-                cb(cur, "bqkv", il);
-
-                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
-                ggml_tensor * Kcur = 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));
-                ggml_tensor * 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
-                        );
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
-            }
-
-            // ffn
-            if (hparams.use_par_res) {
-                // attention and ffn are computed in parallel
-                // x = x + attn(ln1(x)) + ffn(ln2(x))
-
-                ggml_tensor * attn_out = cur;
-
-                cur = build_norm(inpL,
-                        model.layers[il].ffn_norm,
-                        model.layers[il].ffn_norm_b,
-                        LLM_NORM, il);
-                cb(cur, "ffn_norm", 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_GELU, LLM_FFN_SEQ, il);
-                cb(cur, "ffn_out", il);
-
-                cur = ggml_add(ctx0, cur, inpL);
-                cb(cur, "ffn_out", il);
-
-                cur = ggml_add(ctx0, cur, attn_out);
-
-                cur = build_cvec(cur, il);
-                cb(cur, "l_out", il);
-
-                // input for next layer
-                inpL = cur;
-            } else {
-                // attention and ffn are computed sequentially
-                // x = x + attn(ln1(x))
-                // x = x + ffn(ln2(x))
-
-                ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
-                cb(ffn_inp, "ffn_inp", il);
-
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm,
-                        model.layers[il].ffn_norm_b,
-                        LLM_NORM, il);
-                cb(cur, "ffn_norm", 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_GELU, LLM_FFN_SEQ, il);
-                cb(cur, "ffn_out", il);
-
-                cur = ggml_add(ctx0, cur, ffn_inp);
-
-                cur = build_cvec(cur, il);
-                cb(cur, "l_out", il);
-
-                // input for next layer
-                inpL = cur;
-            }
-        }
-
-        cur = build_norm(inpL,
-                model.output_norm,
-                model.output_norm_b,
-                LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_arctic : public llm_graph_context {
-    llm_build_arctic(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            ggml_tensor * ffn_out = ggml_add(ctx0, cur, ffn_inp);
-            cb(ffn_out, "ffn_out", il);
-
-            // MoE
-            cur = build_norm(inpSA,
-                    model.layers[il].ffn_norm_exps, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm_exps", il);
-
-            cur = build_moe_ffn(cur,
-                    model.layers[il].ffn_gate_inp,
-                    model.layers[il].ffn_up_exps,
-                    model.layers[il].ffn_gate_exps,
-                    model.layers[il].ffn_down_exps,
-                    nullptr,
-                    n_expert, n_expert_used,
-                    LLM_FFN_SILU, true,
-                    false, 0.0,
-                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                    il);
-            cb(cur, "ffn_moe_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_out);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_deepseek : public llm_graph_context {
-    llm_build_deepseek(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // rope freq factors for llama3; may return nullptr for llama2 and other models
-                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = ggml_rope_ext(
-                        ctx0, Qcur, inp_pos, rope_factors,
-                        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, rope_factors,
-                        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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            if ((uint32_t) il < hparams.n_layer_dense_lead) {
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            } else {
-                // MoE branch
-                ggml_tensor * moe_out =
-                    build_moe_ffn(cur,
-                            model.layers[il].ffn_gate_inp,
-                            model.layers[il].ffn_up_exps,
-                            model.layers[il].ffn_gate_exps,
-                            model.layers[il].ffn_down_exps,
-                            nullptr,
-                            n_expert, n_expert_used,
-                            LLM_FFN_SILU, false,
-                            false, hparams.expert_weights_scale,
-                            LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                            il);
-                cb(moe_out, "ffn_moe_out", il);
-
-                // FFN shared expert
-                {
-                    ggml_tensor * ffn_shexp = build_ffn(cur,
-                            model.layers[il].ffn_up_shexp,   NULL, NULL,
-                            model.layers[il].ffn_gate_shexp, NULL, NULL,
-                            model.layers[il].ffn_down_shexp, NULL, NULL,
-                            NULL,
-                            LLM_FFN_SILU, LLM_FFN_PAR, il);
-                    cb(ffn_shexp, "ffn_shexp", il);
-
-                    cur = ggml_add(ctx0, moe_out, ffn_shexp);
-                    cb(cur, "ffn_out", il);
-                }
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_deepseek2 : public llm_graph_context {
-    llm_build_deepseek2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        bool is_lite = (hparams.n_layer == 27);
-
-        const bool is_mla = (hparams.n_embd_head_k_mla != 0 && hparams.n_embd_head_v_mla != 0);
-
-        // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
-        const int64_t n_embd_head_k = is_mla ? hparams.n_embd_head_k_mla : hparams.n_embd_head_k;
-        const int64_t n_embd_head_v = is_mla ? hparams.n_embd_head_v_mla : hparams.n_embd_head_v;
-
-        const int64_t n_embd_head_qk_rope = hparams.n_rot;
-        const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;
-
-        const uint32_t kv_lora_rank = hparams.n_lora_kv;
-
-        // 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));
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        // {n_embd, n_tokens}
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self_attention
-            {
-                ggml_tensor * q = NULL;
-                if (!is_lite) {
-                    q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
-                    cb(q, "q", il);
-
-                    q = build_norm(q,
-                            model.layers[il].attn_q_a_norm, nullptr,
-                            LLM_NORM_RMS, il);
-                    cb(q, "q", il);
-
-                    q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
-                    cb(q, "q", il);
-                } else {
-                    q = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
-                    cb(q, "q", il);
-                }
-
-                // split into {n_embd_head_qk_nope, n_head, n_tokens}
-                ggml_tensor * q_nope = ggml_view_3d(ctx0, q,
-                        n_embd_head_qk_nope, n_head, n_tokens,
-                        ggml_row_size(q->type, n_embd_head_k),
-                        ggml_row_size(q->type, n_embd_head_k) * n_head,
-                        0);
-                cb(q_nope, "q_nope", il);
-
-                // and {n_embd_head_qk_rope, n_head, n_tokens}
-                ggml_tensor * q_pe = ggml_view_3d(ctx0, q,
-                        n_embd_head_qk_rope, n_head, n_tokens,
-                        ggml_row_size(q->type, n_embd_head_k),
-                        ggml_row_size(q->type, n_embd_head_k) * n_head,
-                        ggml_row_size(q->type, n_embd_head_qk_nope));
-                cb(q_pe, "q_pe", il);
-
-                ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
-                cb(kv_cmpr_pe, "kv_cmpr_pe", il);
-
-                // split into {kv_lora_rank, n_tokens}
-                ggml_tensor * kv_cmpr = ggml_view_2d(ctx0, kv_cmpr_pe,
-                        kv_lora_rank, n_tokens,
-                        ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
-                        0);
-                cb(kv_cmpr, "kv_cmpr", il);
-
-                // and {n_embd_head_qk_rope, 1, n_tokens}
-                ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe,
-                        n_embd_head_qk_rope, 1, n_tokens,
-                        ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
-                        ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
-                        ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
-                cb(k_pe, "k_pe", il);
-
-                q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr,
-                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                        ext_factor, attn_factor, beta_fast, beta_slow
-                );
-                cb(q_pe, "q_pe", il);
-
-                k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr,
-                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                        ext_factor, attn_factor, beta_fast, beta_slow
-                );
-                cb(k_pe, "k_pe", il);
-
-                kv_cmpr = build_norm(kv_cmpr,
-                        model.layers[il].attn_kv_a_norm, nullptr,
-                        LLM_NORM_RMS, il);
-                cb(kv_cmpr, "kv_cmpr", il);
-
-                if (is_mla) {
-                    // {n_embd_head_qk_nope, n_tokens, n_head}
-                    q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
-                    cb(q_nope, "q_nope_perm", il);
-
-                    // {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head}
-                    ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, model.layers[il].wk_b, q_nope);
-                    cb(q_nope_absorbed, "q_nope_absorbed", il);
-
-                    // {kv_lora_rank, n_head, n_tokens}
-                    q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
-                    cb(q_nope_absorbed, "q_nope_absorbed_perm", il);
-
-                    // {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens}
-                    // note: rope must go first for in-place context shifting in build_rope_shift()
-                    ggml_tensor * Qcur = ggml_concat(ctx0, q_pe, q_nope_absorbed, 0);
-                    cb(Qcur, "Qcur", il);
-
-                    kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
-                    cb(kv_cmpr, "kv_cmpr_reshape", il);
-
-                    // {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}
-                    ggml_tensor * Kcur = ggml_concat(ctx0, k_pe, kv_cmpr, 0);
-                    cb(Kcur, "Kcur", il);
-
-                    // {kv_lora_rank, 1, n_tokens}
-                    ggml_tensor * Vcur = kv_cmpr;
-                    cb(Vcur, "Vcur", 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,
-                            Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, kq_scale, il);
-                } else {
-                    ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_cmpr);
-                    cb(kv, "kv", il);
-
-                    // split into {n_embd_head_qk_nope, n_head, n_tokens}
-                    ggml_tensor * k_nope = ggml_view_3d(ctx0, kv,
-                            n_embd_head_qk_nope, n_head, n_tokens,
-                            ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v),
-                            ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v) * n_head,
-                            0);
-                    cb(k_nope, "k_nope_view", il);
-
-                    // and {n_embd_head_v, n_head, n_tokens}
-                    ggml_tensor * Vcur = ggml_view_3d(ctx0, kv,
-                            n_embd_head_v, n_head, n_tokens,
-                            ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v),
-                            ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v) * n_head,
-                            ggml_row_size(kv->type, n_embd_head_qk_nope));
-                    cb(Vcur, "Vcur_view", il);
-
-                    Vcur = ggml_cont(ctx0, Vcur);
-                    cb(Vcur, "Vcur_cont", il);
-
-                    // note: rope must go first for in-place context shifting in build_rope_shift()
-                    ggml_tensor * Qcur = ggml_concat(ctx0, q_pe, q_nope, 0);
-                    cb(Qcur, "Qcur", il);
-
-                    ggml_tensor * Kcur = ggml_concat(ctx0, ggml_repeat(ctx0, k_pe, q_pe), k_nope, 0);
-                    cb(Kcur, "Kcur", 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,
-                            Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-                }
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            if ((uint32_t) il < hparams.n_layer_dense_lead) {
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            } else {
-                // MoE branch
-                ggml_tensor * moe_out =
-                    build_moe_ffn(cur,
-                            model.layers[il].ffn_gate_inp,
-                            model.layers[il].ffn_up_exps,
-                            model.layers[il].ffn_gate_exps,
-                            model.layers[il].ffn_down_exps,
-                            model.layers[il].ffn_exp_probs_b,
-                            n_expert, n_expert_used,
-                            LLM_FFN_SILU, hparams.expert_weights_norm,
-                            true, hparams.expert_weights_scale,
-                            (llama_expert_gating_func_type) hparams.expert_gating_func,
-                            il);
-                cb(moe_out, "ffn_moe_out", il);
-
-                // FFN shared expert
-                {
-                    ggml_tensor * ffn_shexp = build_ffn(cur,
-                            model.layers[il].ffn_up_shexp,   NULL, NULL,
-                            model.layers[il].ffn_gate_shexp, NULL, NULL,
-                            model.layers[il].ffn_down_shexp, NULL, NULL,
-                            NULL,
-                            LLM_FFN_SILU, LLM_FFN_PAR, il);
-                    cb(ffn_shexp, "ffn_shexp", il);
-
-                    cur = ggml_add(ctx0, moe_out, ffn_shexp);
-                    cb(cur, "ffn_out", il);
-                }
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = ggml_mul_mat(ctx0, model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_bitnet : public llm_graph_context {
-    llm_build_bitnet(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                if (model.layers[il].wq_scale) {
-                    Qcur = ggml_mul(ctx0, Qcur, model.layers[il].wq_scale);
-                }
-                cb(Qcur, "Qcur", il);
-                if (model.layers[il].bq) {
-                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
-                    cb(Qcur, "Qcur", il);
-                }
-
-                // B1.K
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                if (model.layers[il].wk_scale) {
-                    Kcur = ggml_mul(ctx0, Kcur, model.layers[il].wk_scale);
-                }
-                cb(Kcur, "Kcur", il);
-                if (model.layers[il].bk) {
-                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
-                    cb(Kcur, "Kcur", il);
-                }
-
-                // B1.V
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                if (model.layers[il].wv_scale) {
-                    Vcur = ggml_mul(ctx0, Vcur, model.layers[il].wv_scale);
-                }
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        NULL, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-
-                cur = build_norm(cur,
-                        model.layers[il].attn_sub_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "attn_sub_norm", il);
-
-                cur = build_lora_mm(model.layers[il].wo, cur);
-                if (model.layers[il].wo_scale) {
-                    cur = ggml_mul(ctx0, cur, model.layers[il].wo_scale);
-                }
-                if (model.layers[il].bo) {
-                    cur = ggml_add(ctx0, cur, model.layers[il].bo);
-                }
-                cb(cur, "attn_o_out", il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward forward
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, model.layers[il].ffn_up_scale,
-                    model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_scale,
-                    NULL,                      NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_sub_out", il);
-
-            cur = build_norm(cur,
-                    model.layers[il].ffn_sub_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_sub_norm", il);
-
-            cur = build_lora_mm(model.layers[il].ffn_down, cur);
-            if (model.layers[il].ffn_down_scale) {
-                cur = ggml_mul(ctx0, cur, model.layers[il].ffn_down_scale);
-            }
-            cb(cur, "ffn_down", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        // FIXME: do not use model.tok_embd directly, duplicate as model.output
-        cur = build_lora_mm(model.tok_embd, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_t5_enc : public llm_graph_context {
-    llm_build_t5_enc(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        ggml_tensor * pos_bucket_enc = build_inp_pos_bucket_enc();
-
-        auto * inp_attn = build_attn_inp_no_cache();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm_enc, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq_enc, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk_enc, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv_enc, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b_enc ? model.layers[il].attn_rel_b_enc : model.layers[0].attn_rel_b_enc;
-                ggml_tensor * kq_b = build_pos_bias(pos_bucket_enc, attn_rel_b);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo_enc, nullptr,
-                        Qcur, Kcur, Vcur, kq_b, nullptr, nullptr, 1.0f, il);
-                cb(cur, "kqv_out", il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm_enc, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                // T5 uses relu, flan-T5 uses gelu-gated
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up_enc,   NULL, NULL,
-                        model.layers[il].ffn_gate_enc, NULL, NULL,
-                        model.layers[il].ffn_down_enc, NULL, NULL,
-                        NULL,
-                        model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
-                        model.layers[il].ffn_gate_enc ? LLM_FFN_PAR  : LLM_FFN_SEQ,
-                        il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-        cb(cur, "result_embd", -1);
-
-        cur = build_norm(cur,
-                model.output_norm_enc, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_t5_dec : public llm_graph_context {
-    llm_build_t5_dec(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        //const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        ggml_tensor * embd_enc       = build_inp_cross_embd();
-        ggml_tensor * pos_bucket_dec = build_inp_pos_bucket_dec();
-
-        const int64_t n_outputs_enc = embd_enc->ne[1];
-
-        auto * inp_attn_self  = build_attn_inp_kv();
-        auto * inp_attn_cross = build_attn_inp_cross();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        const int64_t dec_n_layer = hparams.dec_n_layer;
-
-        for (int il = 0; il < dec_n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b ? model.layers[il].attn_rel_b : model.layers[0].attn_rel_b;
-                ggml_tensor * kq_b = build_pos_bias(pos_bucket_dec, attn_rel_b);
-
-                cur = build_attn(inp_attn_self,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, kq_b, nullptr, nullptr, 1.0f, il);
-                cb(cur, "kqv_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, inpSA);
-            cb(cur, "cross_inp", il);
-
-            ggml_tensor * inpCA = cur;
-
-            // norm
-            cur = build_norm(cur,
-                    model.layers[il].attn_norm_cross, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm_cross", il);
-
-            // cross-attention
-            {
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq_cross, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk_cross, embd_enc);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv_cross, embd_enc);
-                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_outputs_enc);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_outputs_enc);
-
-                cur = build_attn(inp_attn_cross,
-                        model.layers[il].wo_cross, nullptr,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
-                cb(cur, "kqv_out", il);
-
-                //ggml_tensor * q =                 ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
-                //ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
-
-                //ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
-                //cb(kq, "kq", il);
-
-                //kq = ggml_soft_max_ext(ctx0, kq, KQ_mask_cross, 1.0f, hparams.f_max_alibi_bias);
-                //cb(kq, "kq_soft_max_ext", il);
-
-                //ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_outputs_enc)));
-                //cb(v, "v", il);
-
-                //ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_outputs_enc, n_embd_head, n_head_kv), kq);
-                //cb(kqv, "kqv", il);
-
-                //ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
-                //cb(kqv_merged, "kqv_merged", il);
-
-                //cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
-                //cb(cur, "kqv_merged_cont", il);
-
-                //ggml_build_forward_expand(gf, cur);
-
-                //cur = build_lora_mm(model.layers[il].wo_cross, cur);
-                //cb(cur, "kqv_out", il);
-            }
-
-            if (il == dec_n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpCA = ggml_get_rows(ctx0, inpCA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpCA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                // T5 uses relu, flan-T5 uses gelu-gated
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        model.layers[il].ffn_gate ? LLM_FFN_GELU : LLM_FFN_RELU,
-                        model.layers[il].ffn_gate ? LLM_FFN_PAR : LLM_FFN_SEQ,
-                        il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-        cb(cur, "result_embd", -1);
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_jais : public llm_graph_context {
-    llm_build_jais(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm,
-                    model.layers[il].attn_norm_b,
-                    LLM_NORM, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                cur = build_lora_mm(model.layers[il].wqkv, cur);
-                cb(cur, "wqkv", il);
-
-                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-                cb(cur, "bqkv", il);
-
-                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*cur->nb[0]*(n_embd));
-                ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*cur->nb[0]*(n_embd));
-                ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*cur->nb[0]*(n_embd + n_embd_gqa));
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/float(n_embd_head), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
-            }
-
-            // add the input
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // FF
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm,
-                        model.layers[il].ffn_norm_b,
-                        LLM_NORM, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
-                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
-                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            inpL = ggml_add(ctx0, cur, ffn_inp);
-            cb(inpL, "l_out", il);
-        }
-
-        cur = build_norm(inpL,
-                model.output_norm,
-                model.output_norm_b,
-                LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_chatglm : public llm_graph_context {
-    llm_build_chatglm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm,
-                    NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                ggml_tensor * Qcur = nullptr;
-                ggml_tensor * Kcur = nullptr;
-                ggml_tensor * Vcur = nullptr;
-
-                if (model.layers[il].wqkv == nullptr) {
-                    Qcur = build_lora_mm(model.layers[il].wq, cur);
-                    if (model.layers[il].bq) {
-                        Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
-                    }
-                    Kcur = build_lora_mm(model.layers[il].wk, cur);
-                    if (model.layers[il].bk) {
-                        Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
-                    }
-                    Vcur = build_lora_mm(model.layers[il].wv, cur);
-                    if (model.layers[il].bv) {
-                        Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
-                    }
-                    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);
-                    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-                } else {
-                    cur = build_lora_mm(model.layers[il].wqkv, cur);
-                    cb(cur, "wqkv", il);
-                    if (model.layers[il].bqkv) {
-                        cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-                        cb(cur, "bqkv", il);
-                    }
-                    Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
-                    Kcur = 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));
-                    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));
-                }
-
-                //printf("freq_base: %f freq_scale: %f ext_factor: %f attn_factor: %f\n", freq_base, freq_scale, ext_factor, attn_factor);
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            // Add the input
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // FF
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm,
-                        NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        NULL,                      NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
-                cb(cur, "ffn_out", il);
-
-            }
-
-            inpL = ggml_add(ctx0, cur, ffn_inp);
-            cb(inpL, "l_out", il);
-        }
-
-        cur = build_norm(inpL,
-                model.output_norm,
-                NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_glm4 : public llm_graph_context {
-    llm_build_glm4(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // Pre-attention norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm,
-                    NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                ggml_tensor * Qcur = nullptr;
-                ggml_tensor * Kcur = nullptr;
-                ggml_tensor * Vcur = nullptr;
-
-                if (model.layers[il].wqkv == nullptr) {
-                    Qcur = build_lora_mm(model.layers[il].wq, cur);
-                    if (model.layers[il].bq) {
-                        Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
-                    }
-                    Kcur = build_lora_mm(model.layers[il].wk, cur);
-                    if (model.layers[il].bk) {
-                        Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
-                    }
-                    Vcur = build_lora_mm(model.layers[il].wv, cur);
-                    if (model.layers[il].bv) {
-                        Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
-                    }
-                    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);
-                    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-                } else {
-                    cur = build_lora_mm(model.layers[il].wqkv, cur);
-                    cb(cur, "wqkv", il);
-                    if (model.layers[il].bqkv) {
-                        cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
-                        cb(cur, "bqkv", il);
-                    }
-                    Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
-                    Kcur = 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));
-                    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
-                        );
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            // Post-attention norm (new!)
-            cur = build_norm(cur,
-                    model.layers[il].attn_post_norm,
-                    NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "post_attn_norm", il);
-
-            // Add the input (residual connection after post-attention norm)
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // FF
-            {
-                // Pre-MLP norm
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm,
-                        NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                // MLP
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        NULL,                      NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
-                cb(cur, "ffn_out", il);
-
-                // Post-MLP norm
-                cur = build_norm(cur,
-                        model.layers[il].ffn_post_norm,
-                        NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "post_mlp_norm", il);
-            }
-
-            // Add residual connection after post-MLP norm
-            inpL = ggml_add(ctx0, cur, ffn_inp);
-            cb(inpL, "l_out", il);
-        }
-
-        // Final norm
-        cur = build_norm(inpL,
-                model.output_norm,
-                NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // Output projection
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_glm4_moe : public llm_graph_context {
-    llm_build_glm4_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        // Only process up to last layer (skip final NextN layer)
-        // Final layer tensors are loaded but not processed in forward pass
-        const int n_transformer_layers = n_layer - hparams.nextn_predict_layers;
-        for (int il = 0; il < n_transformer_layers; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // Pre-attention norm
-            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                // Apply Q/K norm if available (GLM-4.5 355B variant)
-                if (model.layers[il].attn_q_norm) {
-                    Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-                    cb(Qcur, "Qcur_normed", il);
-                }
-                if (model.layers[il].attn_k_norm) {
-                    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
-                        );
-
-                cb(Qcur, "Qcur", il);
-                cb(Kcur, "Kcur", il);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_transformer_layers - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // Post-attention norm
-            cur = build_norm(ffn_inp, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "post_attn_norm", il);
-
-            // Check if this is a dense layer (n_layer_dense_lead=1, so layer 0 is dense)
-            if (static_cast<uint32_t>(il) < hparams.n_layer_dense_lead) {
-                // Dense FFN layer
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            } else {
-                // Process routed experts using existing MoE infrastructure
-                ggml_tensor * routed_out = build_moe_ffn(cur,
-                        model.layers[il].ffn_gate_inp,
-                        model.layers[il].ffn_up_exps,
-                        model.layers[il].ffn_gate_exps,
-                        model.layers[il].ffn_down_exps,
-                        model.layers[il].ffn_exp_probs_b,
-                        n_expert, n_expert_used,
-                        LLM_FFN_SILU, hparams.expert_weights_norm,
-                        true, hparams.expert_weights_scale,
-                        (llama_expert_gating_func_type) hparams.expert_gating_func,
-                        il);
-                cb(routed_out, "ffn_moe_out", il);
-
-                // Process shared expert on original input
-                ggml_tensor * shared_out = build_ffn(cur,
-                        model.layers[il].ffn_up_shexp,   NULL, NULL,
-                        model.layers[il].ffn_gate_shexp, NULL, NULL,
-                        model.layers[il].ffn_down_shexp, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(shared_out, "ffn_shexp_out", il);
-
-                // Final output: routed_output + shared_output
-                cur = ggml_add(ctx0, routed_out, shared_out);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-        cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_nemotron : public llm_graph_context {
-    llm_build_nemotron(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        //GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm,
-                    model.layers[il].attn_norm_b,
-                    LLM_NORM, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm,
-                    model.layers[il].ffn_norm_b,
-                    LLM_NORM, il);
-            cb(cur, "ffn_norm", 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_SEQ, il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, model.output_norm_b,
-                LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_nemotron_h : public llm_graph_context_mamba {
-    llm_build_nemotron_h(
-            const llama_model      & model,
-            const llm_graph_params & params) :
-        llm_graph_context_mamba(params) {
-
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-        ggml_build_forward_expand(gf, inpL);
-
-        auto * inp = build_inp_mem_hybrid();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            if (hparams.is_recurrent(il)) {
-                // ssm layer //
-                cur = build_mamba2_layer(inp->get_recr(), cur, model, ubatch, il);
-            } else if (hparams.n_ff(il) == 0) {
-                // attention layer //
-                cur = build_attention_layer(cur, inp->get_attn(), model, n_embd_head, il);
-            } else {
-                cur = build_ffn_layer(cur, model, il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            // add residual
-            cur = ggml_add(ctx0, cur, inpSA);
-            cb(cur, "nemotron_h_block_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-
-    ggml_tensor * build_attention_layer(
-              ggml_tensor             * cur,
-              llm_graph_input_attn_kv * inp_attn,
-        const llama_model             & model,
-        const int64_t                   n_embd_head,
-        const int                       il) {
-
-        // compute Q and K and (optionally) RoPE them
-        ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-        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);
-        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);
-        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, hparams.n_head(il),    n_tokens);
-        Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
-        Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
-
-        cb(Qcur, "Qcur", il);
-        cb(Kcur, "Kcur", il);
-        cb(Vcur, "Vcur", il);
-
-        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
-        cur = build_attn(inp_attn,
-                model.layers[il].wo, model.layers[il].bo,
-                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-                cb(cur, "attn_out", il);
-        return cur;
-    }
-
-    ggml_tensor * 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);
-
-        cur = build_cvec(cur, il);
-        cb(cur, "l_out", il);
-
-        return cur;
-    }
-};
-
-struct llm_build_exaone : public llm_graph_context {
-    llm_build_exaone(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // rope freq factors for llama3; may return nullptr for llama2 and other models
-                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = ggml_rope_ext(
-                        ctx0, Qcur, inp_pos, rope_factors,
-                        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, rope_factors,
-                        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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-template <bool iswa>
-struct llm_build_exaone4 : public llm_graph_context {
-    llm_build_exaone4(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_ASSERT(n_embd_head == hparams.n_embd_head_v);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        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) {
-            ggml_tensor * inpSA = inpL;
-
-            // use RoPE for SWA layers or non-SWA models
-            const bool use_rope = hparams.is_swa(il) || hparams.swa_type == LLAMA_SWA_TYPE_NONE;
-
-            cur = inpL;
-
-            // self-attention
-            {
-                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_normed", il);
-                cb(Kcur, "Kcur_normed", il);
-
-                if (use_rope) {
-                    Qcur = ggml_rope_ext(
-                            ctx0, Qcur, inp_pos, rope_factors,
-                            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, rope_factors,
-                            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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-                cb(cur, "attn_out", il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            cur = build_norm(cur,
-                    model.layers[il].attn_post_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_post_norm", il);
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            cur = build_ffn(ffn_inp,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            cur = build_norm(cur,
-                    model.layers[il].ffn_post_norm, NULL,
-                    LLM_NORM_RMS, -1);
-            cb(cur, "ffn_post_norm", -1);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_rwkv6_base : public llm_graph_context {
-    const llama_model & model;
-
-    llm_build_rwkv6_base(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params), model(model) {
-    }
-
-    ggml_tensor * build_rwkv6_channel_mix(
-            const llama_layer * layer,
-            ggml_tensor * cur,
-            ggml_tensor * x_prev,
-            llm_arch arch) const {
-        ggml_tensor * sx = ggml_sub(ctx0, x_prev, cur);
-        switch (arch) {
-            case LLM_ARCH_RWKV6:
-                {
-                    ggml_tensor * xk = ggml_add(ctx0, ggml_mul(ctx0, sx, layer->channel_mix_lerp_k), cur);
-                    ggml_tensor * xr = ggml_add(ctx0, ggml_mul(ctx0, sx, layer->channel_mix_lerp_r), cur);
-
-                    ggml_tensor * r = ggml_sigmoid(ctx0, build_lora_mm(layer->channel_mix_receptance, xr));
-                    ggml_tensor * k = ggml_sqr(
-                            ctx0,
-                            ggml_relu(
-                                ctx0,
-                                build_lora_mm(layer->channel_mix_key, xk)
-                                )
-                            );
-                    cur = ggml_mul(ctx0, r, build_lora_mm(layer->channel_mix_value, k));
-                } break;
-            default:
-                GGML_ABORT("fatal error");
-        }
-
-        return cur;
-    }
-
-    ggml_tensor * build_rwkv6_time_mix(
-            llm_graph_input_rs * inp,
-            ggml_tensor * cur,
-            ggml_tensor * x_prev,
-            const llama_ubatch & ubatch,
-            int   il) const {
-        const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
-
-        const auto n_tokens = ubatch.n_tokens;
-        const auto n_seqs = ubatch.n_seqs;
-        const auto n_seq_tokens = ubatch.n_seq_tokens;
-        const auto n_embd = hparams.n_embd;
-        const auto head_size = hparams.wkv_head_size;
-        const auto n_head = n_embd / head_size;
-        const auto n_head_kv = hparams.n_head_kv(il);
-
-        const auto kv_head = mctx_cur->get_head();
-
-        const auto & layer = model.layers[il];
-
-        bool is_qrwkv = layer.time_mix_first == nullptr;
-
-        ggml_tensor * sx = ggml_sub(ctx0, x_prev, cur);
-
-        sx  = ggml_reshape_2d(ctx0, sx,  n_embd, n_tokens);
-        cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
-
-        ggml_tensor * xxx = ggml_add(ctx0, ggml_mul(ctx0, sx, layer.time_mix_lerp_x), cur);
-
-        xxx = ggml_reshape_4d(
-                ctx0,
-                ggml_tanh(
-                    ctx0,
-                    ggml_mul_mat(ctx0, layer.time_mix_w1, xxx)
-                    ),
-                layer.time_mix_w1->ne[1] / 5, 1, 5, n_tokens
-                );
-
-        xxx = ggml_cont(ctx0, ggml_permute(ctx0, xxx, 0, 1, 3, 2));
-
-        xxx = ggml_mul_mat(
-                ctx0,
-                ggml_reshape_4d(
-                    ctx0,
-                    layer.time_mix_w2,
-                    layer.time_mix_w2->ne[0], layer.time_mix_w2->ne[1], 1, 5
-                    ),
-                xxx
-                );
-
-        ggml_tensor *xw, *xk, *xv, *xr, *xg;
-        if (layer.time_mix_lerp_fused) {
-            // fusing these weights makes some performance improvement
-            sx  = ggml_reshape_3d(ctx0, sx,  n_embd, 1, n_tokens);
-            cur = ggml_reshape_3d(ctx0, cur, n_embd, 1, n_tokens);
-            xxx = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xxx, layer.time_mix_lerp_fused), sx), cur);
-            xw = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], 0);
-            xk = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * sizeof(float));
-            xv = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 2 * sizeof(float));
-            xr = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 3 * sizeof(float));
-            xg = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 4 * sizeof(float));
-        } else {
-            // for backward compatibility
-            xw = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], 0);
-            xk = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * sizeof(float));
-            xv = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 2 * sizeof(float));
-            xr = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 3 * sizeof(float));
-            xg = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 4 * sizeof(float));
-
-            xw = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xw, layer.time_mix_lerp_w), sx), cur);
-            xk = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xk, layer.time_mix_lerp_k), sx), cur);
-            xv = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xv, layer.time_mix_lerp_v), sx), cur);
-            xr = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xr, layer.time_mix_lerp_r), sx), cur);
-            xg = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xg, layer.time_mix_lerp_g), sx), cur);
-        }
-
-        ggml_tensor * r = build_lora_mm(layer.time_mix_receptance, xr);
-        ggml_tensor * k = build_lora_mm(layer.time_mix_key,        xk);
-        ggml_tensor * v = build_lora_mm(layer.time_mix_value,      xv);
-        if (layer.time_mix_receptance_b) {
-            r = ggml_add(ctx0, r, layer.time_mix_receptance_b);
-        }
-        if (layer.time_mix_key_b) {
-            k = ggml_add(ctx0, k, layer.time_mix_key_b);
-        }
-        if (layer.time_mix_value_b) {
-            v = ggml_add(ctx0, v, layer.time_mix_value_b);
-        }
-
-        ggml_tensor * g = build_lora_mm(layer.time_mix_gate, xg);
-        if (is_qrwkv) {
-            g = ggml_sigmoid(ctx0, g);
-        } else {
-            g = ggml_silu(ctx0, g);
-        }
-
-        if (n_head_kv != 0 && n_head_kv != n_head) {
-            GGML_ASSERT(n_head % n_head_kv == 0);
-            k = ggml_reshape_4d(ctx0, k, head_size, 1, n_head_kv, n_tokens);
-            v = ggml_reshape_4d(ctx0, v, head_size, 1, n_head_kv, n_tokens);
-            ggml_tensor * tmp = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, head_size, n_head / n_head_kv, n_head_kv, n_tokens);
-            k = ggml_repeat(ctx0, k, tmp);
-            v = ggml_repeat(ctx0, v, tmp);
-        }
-
-        k = ggml_reshape_3d(ctx0, k, head_size, n_head, n_tokens);
-        v = ggml_reshape_3d(ctx0, v, head_size, n_head, n_tokens);
-        r = ggml_reshape_3d(ctx0, r, head_size, n_head, n_tokens);
-
-        ggml_tensor * w = ggml_mul_mat(
-                ctx0,
-                layer.time_mix_decay_w2,
-                ggml_tanh(
-                    ctx0,
-                    ggml_mul_mat(ctx0, layer.time_mix_decay_w1, xw)
-                    )
-                );
-
-        w = ggml_add(ctx0, w, layer.time_mix_decay);
-        w = ggml_exp(ctx0, ggml_neg(ctx0, ggml_exp(ctx0, w)));
-        w = ggml_reshape_3d(ctx0, w, head_size, n_head, n_tokens);
-
-        if (is_qrwkv) {
-            // k = k * (1 - w)
-            k = ggml_sub(ctx0, k, ggml_mul(ctx0, k, w));
-        }
-
-        ggml_tensor * wkv_state = build_rs(
-                inp, mctx_cur->get_s_l(il),
-                hparams.n_embd_s(), n_seqs);
-
-        ggml_tensor * wkv_output;
-        if (is_qrwkv) {
-            wkv_output = ggml_gated_linear_attn(ctx0, k, v, r, w, wkv_state, pow(head_size, -0.5f));
-        } else {
-            wkv_output = ggml_rwkv_wkv6(ctx0, k, v, r, layer.time_mix_first, w, wkv_state);
-        }
-        cur = ggml_view_1d(ctx0, wkv_output, n_embd * n_tokens, 0);
-        wkv_state = ggml_view_1d(ctx0, wkv_output, n_embd * head_size * n_seqs, n_embd * n_tokens * sizeof(float));
-
-        ggml_build_forward_expand(
-                gf,
-                ggml_cpy(
-                    ctx0,
-                    wkv_state,
-                    ggml_view_1d(
-                        ctx0,
-                        mctx_cur->get_s_l(il),
-                        hparams.n_embd_s() * n_seqs,
-                        hparams.n_embd_s() * kv_head * ggml_element_size(mctx_cur->get_s_l(il))
-                        )
-                    )
-                );
-
-        if (!is_qrwkv) {
-            // group norm with head_count groups
-            cur = ggml_reshape_3d(ctx0, cur, n_embd / n_head, n_head, n_tokens);
-            cur = ggml_norm(ctx0, cur, 64e-5f);
-
-            // Convert back to regular vectors.
-            cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
-            cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.time_mix_ln), layer.time_mix_ln_b);
-        } else {
-            cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
-        }
-
-        cur = ggml_mul(ctx0, cur, g);
-        cur = build_lora_mm(layer.time_mix_output, cur);
-
-        return ggml_reshape_3d(ctx0, cur, n_embd, n_seq_tokens, n_seqs);
-    }
-};
-
-struct llm_build_rwkv6 : public llm_build_rwkv6_base {
-    llm_build_rwkv6(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv6_base(model, params) {
-        GGML_ASSERT(hparams.token_shift_count == 2);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-        inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
-
-        auto * rs_inp = build_rs_inp();
-
-        const auto n_embd = hparams.n_embd;
-        const auto n_seq_tokens = ubatch.n_seq_tokens;
-        const auto n_seqs = ubatch.n_seqs;
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            const llama_layer * layer = &model.layers[il];
-            inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
-
-            ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il);
-
-            ggml_tensor * att_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], 0);
-            ggml_tensor * ffn_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], n_embd * ggml_element_size(token_shift));
-
-            ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM, il);
-            cb(att_norm, "attn_norm", il);
-
-            ggml_tensor * x_prev = ggml_concat(
-                    ctx0,
-                    att_shift,
-                    ggml_view_3d(ctx0, att_norm, n_embd, n_seq_tokens - 1, n_seqs, att_norm->nb[1], att_norm->nb[2], 0),
-                    1
-                    );
-
-            cur = build_rwkv6_time_mix(rs_inp, att_norm, x_prev, ubatch, il);
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
-            cb(ffn_inp, "ffn_inp", il);
-
-            ggml_tensor * ffn_norm = build_norm(ffn_inp, layer->attn_norm_2, layer->attn_norm_2_b, LLM_NORM, il);
-            cb(ffn_norm, "ffn_norm", il);
-
-            x_prev = ggml_concat(
-                    ctx0,
-                    ffn_shift,
-                    ggml_view_3d(ctx0, ffn_norm, n_embd, n_seq_tokens - 1, n_seqs, ffn_norm->nb[1], ffn_norm->nb[2], 0),
-                    1
-                    );
-
-            token_shift = ggml_concat(ctx0,
-                    ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(att_norm)),
-                    ggml_view_3d(ctx0, ffn_norm, n_embd, 1, n_seqs, ffn_norm->nb[1], ffn_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(ffn_norm)),
-                    1
-                    );
-            ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));
-
-            ffn_inp  = ggml_reshape_2d(ctx0, ffn_inp,  n_embd, n_tokens);
-            ffn_norm = ggml_reshape_2d(ctx0, ffn_norm, n_embd, n_tokens);
-            x_prev   = ggml_reshape_2d(ctx0, x_prev,   n_embd, n_tokens);
-            cur      = ggml_reshape_2d(ctx0, cur,      n_embd, n_tokens);
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                ffn_inp  = ggml_get_rows(ctx0, ffn_inp,  inp_out_ids);
-                ffn_norm = ggml_get_rows(ctx0, ffn_norm, inp_out_ids);
-                x_prev   = ggml_get_rows(ctx0, x_prev,   inp_out_ids);
-                cur      = ggml_get_rows(ctx0, cur,      inp_out_ids);
-            }
-
-            cur = build_rwkv6_channel_mix(layer, ffn_norm, x_prev, LLM_ARCH_RWKV6);
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            if (hparams.rescale_every_n_layers != 0 && (il + 1) % hparams.rescale_every_n_layers == 0) {
-                cur = ggml_scale(ctx0, cur, 0.5F);
-            }
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-        cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-// ref: https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1/blob/main/modeling_rwkv6qwen2.py
-struct llm_build_rwkv6qwen2 : public llm_build_rwkv6_base {
-    llm_build_rwkv6qwen2(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv6_base(model, params) {
-        GGML_ASSERT(n_embd == hparams.n_embd_r());
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        auto * rs_inp = build_rs_inp();
-
-        const auto n_embd = hparams.n_embd;
-        const auto n_seq_tokens = ubatch.n_seq_tokens;
-        const auto n_seqs = ubatch.n_seqs;
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            const llama_layer * layer = &model.layers[il];
-            inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
-
-            ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il);
-
-            ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM_RMS, il);
-            cb(att_norm, "attn_norm", il);
-
-            ggml_tensor * x_prev = ggml_concat(
-                    ctx0,
-                    token_shift,
-                    ggml_view_3d(ctx0, att_norm, n_embd, n_seq_tokens - 1, n_seqs, att_norm->nb[1], att_norm->nb[2], 0),
-                    1
-                    );
-
-            cur = build_rwkv6_time_mix(rs_inp, att_norm, x_prev, ubatch, il);
-
-            token_shift = ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(att_norm));
-            ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
-            cb(ffn_inp, "ffn_inp", il);
-
-            cur     = ggml_reshape_2d(ctx0, cur,     n_embd, n_tokens);
-            ffn_inp = ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens);
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur     = ggml_get_rows(ctx0, cur,     inp_out_ids);
-                ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
-            }
-
-            // feed-forward network
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-        cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_rwkv7_base : public llm_graph_context {
-    const llama_model & model;
-
-    llm_build_rwkv7_base(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params), model(model) {
-    }
-
-    ggml_tensor * build_rwkv7_channel_mix(
-            const llama_layer * layer,
-            ggml_tensor * cur,
-            ggml_tensor * x_prev,
-            llm_arch arch) const {
-        ggml_tensor * sx = ggml_sub(ctx0, x_prev, cur);
-        switch (arch) {
-            case LLM_ARCH_RWKV7:
-                {
-                    ggml_tensor * xk = ggml_add(ctx0, ggml_mul(ctx0, sx, layer->channel_mix_lerp_k), cur);
-
-                    ggml_tensor * k = ggml_sqr(
-                        ctx0,
-                        ggml_relu(
-                            ctx0,
-                            build_lora_mm(layer->channel_mix_key, xk)
-                        )
-                    );
-
-                    cur = build_lora_mm(layer->channel_mix_value, k);
-                } break;
-            default:
-                GGML_ABORT("fatal error");
-        }
-
-        return cur;
-    }
-
-    ggml_tensor * build_rwkv7_time_mix(
-            llm_graph_input_rs * inp,
-            ggml_tensor * cur,
-            ggml_tensor * x_prev,
-            ggml_tensor *& first_layer_value,
-            const llama_ubatch & ubatch,
-            int   il) const {
-        const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
-
-        const auto n_tokens = ubatch.n_tokens;
-        const auto n_seqs = ubatch.n_seqs;
-        const auto n_embd = hparams.n_embd;
-        const auto head_size = hparams.wkv_head_size;
-        const auto head_count = n_embd / head_size;
-        const auto n_seq_tokens = ubatch.n_seq_tokens;
-
-        const auto kv_head = mctx_cur->get_head();
-
-        const auto & layer = model.layers[il];
-
-        bool has_gating = layer.time_mix_g1 && layer.time_mix_g2;
-
-        ggml_tensor * sx = ggml_sub(ctx0, x_prev, cur);
-        ggml_tensor * dummy = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_embd, n_seq_tokens, n_seqs, has_gating ? 6 : 5);
-        sx = ggml_repeat(ctx0, sx, dummy);
-
-        ggml_tensor * xxx = ggml_add(ctx0, ggml_mul(ctx0, sx, layer.time_mix_lerp_fused), cur);
-
-        ggml_tensor * xr = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], 0);
-        ggml_tensor * xw = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * sizeof(float));
-        ggml_tensor * xk = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 2 * sizeof(float));
-        ggml_tensor * xv = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 3 * sizeof(float));
-        ggml_tensor * xa = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 4 * sizeof(float));
-        ggml_tensor * xg = has_gating ? ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 5 * sizeof(float)) : nullptr;
-
-        ggml_tensor * r = build_lora_mm(layer.time_mix_receptance, xr);
-        ggml_tensor * w = ggml_add(
-            ctx0,
-            ggml_mul_mat(ctx0, layer.time_mix_w2, ggml_tanh(ctx0, ggml_mul_mat(ctx0, layer.time_mix_w1, xw))),
-            layer.time_mix_w0
-        );
-        w = ggml_exp(ctx0, ggml_scale(ctx0, ggml_sigmoid(ctx0, w), -0.606531));
-
-        ggml_tensor * k = build_lora_mm(layer.time_mix_key, xk);
-        ggml_tensor * v = build_lora_mm(layer.time_mix_value, xv);
-        if (first_layer_value == nullptr) {
-            first_layer_value = v;
-        } else {
-            // Add the first layer value as a residual connection.
-            v = ggml_add(ctx0, v,
-                ggml_mul(ctx0,
-                    ggml_sub(ctx0, first_layer_value, v),
-                    ggml_sigmoid(ctx0, ggml_add(ctx0,
-                            ggml_mul_mat(ctx0, layer.time_mix_v2, ggml_mul_mat(ctx0, layer.time_mix_v1, xv)),
-                            layer.time_mix_v0
-                        )
-                    )
-                )
-            );
-        }
-
-        ggml_tensor * g = nullptr;
-        if (layer.time_mix_g1 && layer.time_mix_g2) {
-            g = ggml_mul_mat(ctx0, layer.time_mix_g2, ggml_sigmoid(ctx0, ggml_mul_mat(ctx0, layer.time_mix_g1, xg)));
-        }
-
-        ggml_tensor * a = ggml_sigmoid(ctx0,
-            ggml_add(
-                ctx0,
-                ggml_mul_mat(ctx0, layer.time_mix_a2, ggml_mul_mat(ctx0, layer.time_mix_a1, xa)),
-                layer.time_mix_a0
-            )
-        );
-
-        ggml_tensor * kk = ggml_reshape_3d(ctx0, ggml_mul(ctx0, k, layer.time_mix_k_k), head_size, head_count, n_tokens);
-        kk = ggml_l2_norm(ctx0, kk, 1e-12);
-
-        ggml_tensor * ka = ggml_mul(ctx0, k, layer.time_mix_k_a);
-        k = ggml_add(ctx0, k, ggml_sub(ctx0, ggml_mul(ctx0, a, ka), ka));
-
-        r = ggml_reshape_3d(ctx0, r, head_size, head_count, n_tokens);
-        w = ggml_reshape_3d(ctx0, w, head_size, head_count, n_tokens);
-        k = ggml_reshape_3d(ctx0, k, head_size, head_count, n_tokens);
-        v = ggml_reshape_3d(ctx0, v, head_size, head_count, n_tokens);
-        a = ggml_reshape_3d(ctx0, a, head_size, head_count, n_tokens);
-
-        ggml_tensor * wkv_state = build_rs(
-                inp, mctx_cur->get_s_l(il),
-                hparams.n_embd_s(), n_seqs);
-
-        ggml_tensor * wkv_output = ggml_rwkv_wkv7(ctx0, r, w, k, v, ggml_neg(ctx0, kk), ggml_mul(ctx0, kk, a), wkv_state);
-        cur = ggml_view_1d(ctx0, wkv_output, n_embd * n_tokens, 0);
-        wkv_state = ggml_view_1d(ctx0, wkv_output, n_embd * head_size * n_seqs, n_embd * n_tokens * sizeof(float));
-
-        ggml_build_forward_expand(
-                gf,
-                ggml_cpy(
-                    ctx0,
-                    wkv_state,
-                    ggml_view_1d(
-                        ctx0,
-                        mctx_cur->get_s_l(il),
-                        hparams.n_embd_s() * n_seqs,
-                        hparams.n_embd_s() * kv_head * ggml_element_size(mctx_cur->get_s_l(il))
-                        )
-                    )
-                );
-
-        if (layer.time_mix_ln && layer.time_mix_ln_b) {
-            // group norm with head_count groups
-            cur = ggml_reshape_3d(ctx0, cur, n_embd / head_count, head_count, n_tokens);
-            cur = ggml_norm(ctx0, cur, 64e-5f);
-
-            // Convert back to regular vectors.
-            cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
-            cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.time_mix_ln), layer.time_mix_ln_b);
-        } else {
-            cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
-        }
-
-        ggml_tensor * rk = ggml_sum_rows(ctx0,
-                ggml_mul(ctx0, ggml_mul(ctx0, k, r), ggml_reshape_2d(ctx0, layer.time_mix_r_k, head_size, head_count)));
-        cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, ggml_mul(ctx0, v, rk), n_embd, n_tokens));
-
-        if (has_gating) {
-            cur = ggml_mul(ctx0, cur, g);
-        }
-        cur = build_lora_mm(layer.time_mix_output, cur);
-
-        return ggml_reshape_3d(ctx0, cur, n_embd, n_seq_tokens, n_seqs);
-    }
-};
-
-struct llm_build_rwkv7 : public llm_build_rwkv7_base {
-    llm_build_rwkv7(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv7_base(model, params) {
-        GGML_ASSERT(hparams.token_shift_count == 2);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-        ggml_tensor * v_first = nullptr;
-
-        inpL = build_inp_embd(model.tok_embd);
-        inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
-
-        auto * rs_inp = build_rs_inp();
-
-        const auto n_embd = hparams.n_embd;
-        const auto n_seq_tokens = ubatch.n_seq_tokens;
-        const auto n_seqs = ubatch.n_seqs;
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            const llama_layer * layer = &model.layers[il];
-            inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
-
-            ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il);
-
-            ggml_tensor * att_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], 0);
-            ggml_tensor * ffn_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], n_embd * ggml_element_size(token_shift));
-
-            ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM, il);
-            cb(att_norm, "attn_norm", il);
-
-            ggml_tensor * x_prev = ggml_concat(
-                    ctx0,
-                    att_shift,
-                    ggml_view_3d(ctx0, att_norm, n_embd, n_seq_tokens - 1, n_seqs, att_norm->nb[1], att_norm->nb[2], 0),
-                    1
-                    );
-
-            cur = build_rwkv7_time_mix(rs_inp, att_norm, x_prev, v_first, ubatch, il);
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
-            cb(ffn_inp, "ffn_inp", il);
-
-            ggml_tensor * ffn_norm = build_norm(ffn_inp, layer->attn_norm_2, layer->attn_norm_2_b, LLM_NORM, il);
-            cb(ffn_norm, "ffn_norm", il);
-
-            x_prev = ggml_concat(
-                    ctx0,
-                    ffn_shift,
-                    ggml_view_3d(ctx0, ffn_norm, n_embd, n_seq_tokens - 1, n_seqs, ffn_norm->nb[1], ffn_norm->nb[2], 0),
-                    1
-                    );
-
-            token_shift = ggml_concat(ctx0,
-                    ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(att_norm)),
-                    ggml_view_3d(ctx0, ffn_norm, n_embd, 1, n_seqs, ffn_norm->nb[1], ffn_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(ffn_norm)),
-                    1
-                    );
-            ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));
-
-            ffn_inp  = ggml_reshape_2d(ctx0, ffn_inp,  n_embd, n_tokens);
-            ffn_norm = ggml_reshape_2d(ctx0, ffn_norm, n_embd, n_tokens);
-            x_prev   = ggml_reshape_2d(ctx0, x_prev,   n_embd, n_tokens);
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                ffn_inp  = ggml_get_rows(ctx0, ffn_inp,  inp_out_ids);
-                ffn_norm = ggml_get_rows(ctx0, ffn_norm, inp_out_ids);
-                x_prev   = ggml_get_rows(ctx0, x_prev,   inp_out_ids);
-            }
-
-            cur = build_rwkv7_channel_mix(layer, ffn_norm, x_prev, LLM_ARCH_RWKV7);
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-        cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-
-struct llm_build_arwkv7 : public llm_build_rwkv7_base {
-    llm_build_arwkv7(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv7_base(model, params) {
-        GGML_ASSERT(n_embd == hparams.n_embd_r());
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-        ggml_tensor * v_first = nullptr;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        auto * rs_inp = build_rs_inp();
-
-        const auto n_embd = hparams.n_embd;
-        const auto n_seq_tokens = ubatch.n_seq_tokens;
-        const auto n_seqs = ubatch.n_seqs;
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            const llama_layer * layer = &model.layers[il];
-            inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
-
-            ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il);
-
-            ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM_RMS, il);
-            cb(att_norm, "attn_norm", il);
-
-            ggml_tensor * x_prev = ggml_concat(
-                    ctx0,
-                    token_shift,
-                    ggml_view_3d(ctx0, att_norm, n_embd, n_seq_tokens - 1, n_seqs, att_norm->nb[1], att_norm->nb[2], 0),
-                    1
-                    );
-
-            cur = build_rwkv7_time_mix(rs_inp, att_norm, x_prev, v_first, ubatch, il);
-
-            token_shift = ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(att_norm));
-            ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
-            cb(ffn_inp, "ffn_inp", il);
-
-            cur     = ggml_reshape_2d(ctx0, cur,     n_embd, n_tokens);
-            ffn_inp = ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens);
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur     = ggml_get_rows(ctx0, cur,     inp_out_ids);
-                ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
-            }
-
-            // feed-forward network
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-        cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_granite : public llm_graph_context {
-    llm_build_granite(
-        const llama_model & model,
-        const llm_graph_params & params)
-        : llm_graph_context(params) {
-
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - built only if rope enabled
-        ggml_tensor * inp_pos = nullptr;
-        if (hparams.rope_finetuned) {
-            inp_pos = build_inp_pos();
-        }
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            cur = build_attention_layer(
-                cur, inp_pos, inp_attn,
-                model, n_embd_head, il);
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            // ffn
-            cur = build_layer_ffn(cur, inpSA, model, il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        // For Granite architectures - scale logits
-        cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-
-    ggml_tensor * build_attention_layer(
-              ggml_tensor             * cur,
-              ggml_tensor             * inp_pos,
-              llm_graph_input_attn_kv * inp_attn,
-        const llama_model             & model,
-        const int64_t                 n_embd_head,
-        const int                     il) {
-
-        // compute Q and K and (optionally) RoPE them
-        ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-        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);
-        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);
-        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, hparams.n_head(il),    n_tokens);
-        Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
-        Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
-
-        const bool use_rope = hparams.rope_finetuned;
-        if (use_rope) {
-            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-            Qcur = ggml_rope_ext(
-                    ctx0, Qcur, inp_pos, rope_factors,
-                    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, rope_factors,
-                    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);
-        cb(Vcur, "Vcur", il);
-
-        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
-        cur = build_attn(inp_attn,
-                model.layers[il].wo, model.layers[il].bo,
-                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-                cb(cur, "attn_out", il);
-        return cur;
-    }
-
-    ggml_tensor * build_layer_ffn(
-              ggml_tensor       * cur,
-              ggml_tensor       * inpSA,
-        const llama_model       & model,
-        const int                 il) {
-
-        // For Granite architectures - scale residual
-        if (hparams.f_residual_scale) {
-            cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
-        }
-        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-        cb(ffn_inp, "ffn_inp", il);
-
-        // feed-forward network (non-MoE)
-        if (model.layers[il].ffn_gate_inp == nullptr) {
-
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-                    cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
-                    model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
-                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-                    cb(cur, "ffn_out", il);
-
-        } else {
-            // MoE branch
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-                    cb(cur, "ffn_norm", il);
-
-            ggml_tensor * moe_out = build_moe_ffn(cur,
-                    model.layers[il].ffn_gate_inp,
-                    model.layers[il].ffn_up_exps,
-                    model.layers[il].ffn_gate_exps,
-                    model.layers[il].ffn_down_exps,
-                    nullptr,
-                    n_expert, n_expert_used,
-                    LLM_FFN_SILU, true,
-                    false, 0.0,
-                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                    il);
-            cb(moe_out, "ffn_moe_out", il);
-
-            // For Granite MoE Shared
-            if (hparams.n_ff_shexp > 0) {
-                ggml_tensor * ffn_shexp = build_ffn(cur,
-                    model.layers[il].ffn_up_shexp,   NULL, NULL,
-                    model.layers[il].ffn_gate_shexp, NULL, NULL,
-                    model.layers[il].ffn_down_shexp, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(ffn_shexp, "ffn_shexp", il);
-
-                cur = ggml_add(ctx0, moe_out, ffn_shexp);
-                cb(cur, "ffn_out", il);
-            } else {
-                cur = moe_out;
-            }
-        }
-
-        // For Granite architectures - scale residual
-        if (hparams.f_residual_scale) {
-            cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
-        }
-        cur = ggml_add(ctx0, cur, ffn_inp);
-        cb(cur, "ffn_out", il);
-
-        cur = build_cvec(cur, il);
-        cb(cur, "l_out", il);
-
-        return cur;
-    }
-};
-
-struct llm_build_granite_hybrid : public llm_graph_context_mamba {
-    llm_build_granite_hybrid(
-                 const llama_model & model,
-            const llm_graph_params & params) :
-        llm_graph_context_mamba(params) {
-
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        auto * inp = build_inp_mem_hybrid();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        // Positional embeddings populated if rope enabled
-        ggml_tensor * inp_pos = nullptr;
-        if (hparams.rope_finetuned) {
-            inp_pos = build_inp_pos();
-        }
-
-        for (int il = 0; il < n_layer; ++il) {
-            struct ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            if (hparams.is_recurrent(il)) {
-                // ssm layer //
-                cur = build_mamba2_layer(inp->get_recr(), cur, model, ubatch, il);
-            } else {
-                // attention layer //
-                cur = build_attention_layer(
-                    cur, inp_pos, inp->get_attn(), model,
-                    n_embd_head, il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            // ffn
-            cur = build_layer_ffn(cur, inpSA, model, il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        // For Granite architectures - scale logits
-        if (hparams.f_logit_scale) {
-            cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
-        }
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-
-    ggml_tensor * build_attention_layer(
-              ggml_tensor             * cur,
-              ggml_tensor             * inp_pos,
-              llm_graph_input_attn_kv * inp_attn,
-        const llama_model             & model,
-        const int64_t                 n_embd_head,
-        const int                     il) {
-
-        // compute Q and K and (optionally) RoPE them
-        ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-        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);
-        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);
-        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, hparams.n_head(il),    n_tokens);
-        Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
-        Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
-
-        const bool use_rope = hparams.rope_finetuned;
-        if (use_rope) {
-            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-            Qcur = ggml_rope_ext(
-                    ctx0, Qcur, inp_pos, rope_factors,
-                    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, rope_factors,
-                    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);
-        cb(Vcur, "Vcur", il);
-
-        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
-        cur = build_attn(inp_attn,
-                model.layers[il].wo, model.layers[il].bo,
-                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-                cb(cur, "attn_out", il);
-        return cur;
-    }
-
-    ggml_tensor * build_layer_ffn(
-              ggml_tensor * cur,
-              ggml_tensor * inpSA,
-        const llama_model & model,
-        const int           il) {
-
-        // For Granite architectures - scale residual
-        if (hparams.f_residual_scale) {
-            cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
-        }
-        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-        cb(ffn_inp, "ffn_inp", il);
-
-        // feed-forward network (non-MoE)
-        if (model.layers[il].ffn_gate_inp == nullptr) {
-
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-                    cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
-                    model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
-                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-                    cb(cur, "ffn_out", il);
-
-        } else {
-            // MoE branch
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-                    cb(cur, "ffn_norm", il);
-
-            ggml_tensor * moe_out = build_moe_ffn(cur,
-                    model.layers[il].ffn_gate_inp,
-                    model.layers[il].ffn_up_exps,
-                    model.layers[il].ffn_gate_exps,
-                    model.layers[il].ffn_down_exps,
-                    nullptr,
-                    n_expert, n_expert_used,
-                    LLM_FFN_SILU, true,
-                    false, 0.0,
-                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                    il);
-            cb(moe_out, "ffn_moe_out", il);
-
-            // For Granite MoE Shared
-            if (hparams.n_ff_shexp > 0) {
-                ggml_tensor * ffn_shexp = build_ffn(cur,
-                    model.layers[il].ffn_up_shexp,   NULL, NULL,
-                    model.layers[il].ffn_gate_shexp, NULL, NULL,
-                    model.layers[il].ffn_down_shexp, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(ffn_shexp, "ffn_shexp", il);
-
-                cur = ggml_add(ctx0, moe_out, ffn_shexp);
-                cb(cur, "ffn_out", il);
-            } else {
-                cur = moe_out;
-            }
-        }
-
-        // For Granite architectures - scale residual
-        if (hparams.f_residual_scale) {
-            cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
-        }
-        cur = ggml_add(ctx0, cur, ffn_inp);
-        cb(cur, "ffn_out", il);
-
-        cur = build_cvec(cur, il);
-        cb(cur, "l_out", il);
-
-        return cur;
-    }
-};
-
-// ref: https://github.com/facebookresearch/chameleon
-// based on the original build_llama() function, changes:
-//   * qk-norm
-//   * swin-norm
-//   * removed bias
-//   * removed MoE
-struct llm_build_chameleon : public llm_graph_context {
-    llm_build_chameleon(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            if (hparams.swin_norm) {
-                cur = inpL;
-            } else {
-                cur = build_norm(inpL,
-                        model.layers[il].attn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "attn_norm", il);
-            }
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                cb(Vcur, "Vcur", il);
-
-                if (model.layers[il].attn_q_norm) {
-                    Qcur = ggml_view_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens,
-                            ggml_element_size(Qcur) * n_embd_head,
-                            ggml_element_size(Qcur) * n_embd_head * n_head,
-                            0);
-                    cb(Qcur, "Qcur", il);
-
-                    Qcur = build_norm(Qcur,
-                            model.layers[il].attn_q_norm,
-                            model.layers[il].attn_q_norm_b,
-                            LLM_NORM, il);
-                    cb(Qcur, "Qcur", il);
-                }
-
-                if (model.layers[il].attn_k_norm) {
-                    Kcur = ggml_view_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens,
-                            ggml_element_size(Kcur) * n_embd_head,
-                            ggml_element_size(Kcur) * n_embd_head * n_head_kv,
-                            0);
-                    cb(Kcur, "Kcur", il);
-
-                    Kcur = build_norm(Kcur,
-                            model.layers[il].attn_k_norm,
-                            model.layers[il].attn_k_norm_b,
-                            LLM_NORM, il);
-                    cb(Kcur, "Kcur", 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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, nullptr,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            if (hparams.swin_norm) {
-                cur = build_norm(cur,
-                        model.layers[il].attn_norm, NULL,
-                        LLM_NORM_RMS, il);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            if (!hparams.swin_norm) {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-            }
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            if (hparams.swin_norm) {
-                cur = build_norm(cur,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-        cb(cur, "result_output_with_img_logits", -1);
-
-        // TODO: this suppresses the output of image tokens, which is required to enable text-only outputs.
-        // Needs to be removed once image outputs are supported.
-        int img_token_end_idx = 8196;
-        int img_token_start_idx = 4;
-        int num_img_tokens = img_token_end_idx - img_token_start_idx;
-        // creates 1d tensor of size num_img_tokens and values -FLT_MAX,
-        // which ensures that text token values are always at least larger than image token values
-        ggml_tensor * img_logits = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, num_img_tokens);
-        img_logits = ggml_clamp(ctx0, img_logits, -FLT_MAX, -FLT_MAX);
-        cb(img_logits, "img_logits", -1);
-
-        cur = ggml_set_1d(ctx0, cur, img_logits, ggml_element_size(cur) * img_token_start_idx);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_wavtokenizer_dec : public llm_graph_context {
-    llm_build_wavtokenizer_dec(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        cur = ggml_cont(ctx0, ggml_transpose(ctx0, inpL));
-
-        cur = ggml_conv_1d_ph(ctx0, model.conv1d, cur, 1, 1);
-        cur = ggml_add(ctx0, cur, model.conv1d_b);
-
-        // posnet
-        for (uint32_t il = 0; il < hparams.posnet.n_layer; ++il) {
-            const auto & layer = model.layers[il].posnet;
-
-            inpL = cur;
-
-            switch (il) {
-                case 0:
-                case 1:
-                case 3:
-                case 4:
-                    {
-                        cur = build_norm(cur,
-                                layer.norm1,
-                                layer.norm1_b,
-                                LLM_NORM_GROUP, 0);
-
-                        cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur);
-
-                        cur = ggml_conv_1d_ph(ctx0, layer.conv1, cur, 1, 1);
-                        cur = ggml_add(ctx0, cur, layer.conv1_b);
-
-                        cur = build_norm(cur,
-                                layer.norm2,
-                                layer.norm2_b,
-                                LLM_NORM_GROUP, 0);
-
-                        cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur);
-
-                        cur = ggml_conv_1d_ph(ctx0, layer.conv2, cur, 1, 1);
-                        cur = ggml_add(ctx0, cur, layer.conv2_b);
-
-                        cur = ggml_add(ctx0, cur, inpL);
-                    } break;
-                case 2:
-                    {
-                        cur = build_norm(cur,
-                                layer.attn_norm,
-                                layer.attn_norm_b,
-                                LLM_NORM_GROUP, 0);
-
-                        ggml_tensor * q;
-                        ggml_tensor * k;
-                        ggml_tensor * v;
-
-                        q = ggml_conv_1d_ph(ctx0, layer.attn_q, cur, 1, 1);
-                        k = ggml_conv_1d_ph(ctx0, layer.attn_k, cur, 1, 1);
-                        v = ggml_conv_1d_ph(ctx0, layer.attn_v, cur, 1, 1);
-
-                        q = ggml_add(ctx0, q, layer.attn_q_b);
-                        k = ggml_add(ctx0, k, layer.attn_k_b);
-                        v = ggml_add(ctx0, v, layer.attn_v_b);
-
-                        q = ggml_cont(ctx0, ggml_transpose(ctx0, q));
-                        k = ggml_cont(ctx0, ggml_transpose(ctx0, k));
-
-                        ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
-
-                        kq = ggml_soft_max_ext(ctx0, kq, nullptr, 1.0f/sqrtf(float(hparams.posnet.n_embd)), 0.0f);
-
-                        cur = ggml_mul_mat(ctx0, kq, v);
-
-                        cur = ggml_conv_1d_ph(ctx0, layer.attn_o, cur, 1, 1);
-                        cur = ggml_add(ctx0, cur, layer.attn_o_b);
-
-                        cur = ggml_add(ctx0, cur, inpL);
-                    } break;
-                case 5:
-                    {
-                        cur = build_norm(cur,
-                                layer.norm,
-                                layer.norm_b,
-                                LLM_NORM_GROUP, 0);
-                    } break;
-                default: GGML_ABORT("unknown posnet layer");
-            };
-        }
-
-        cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
-
-        cur = build_norm(cur,
-                model.tok_norm,
-                model.tok_norm_b,
-                LLM_NORM, -1);
-
-        cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
-
-        inpL = cur;
-
-        // convnext
-        for (uint32_t il = 0; il < hparams.convnext.n_layer; ++il) {
-            const auto & layer = model.layers[il].convnext;
-
-            cur = inpL;
-
-            cur = ggml_conv_1d_dw_ph(ctx0, layer.dw, cur, 1, 1);
-            cur = ggml_add(ctx0, cur, layer.dw_b);
-
-            cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
-
-            cur = build_norm(cur,
-                    layer.norm,
-                    layer.norm_b,
-                    LLM_NORM, -1);
-
-            cur = build_ffn(cur,
-                    layer.pw1, layer.pw1_b, NULL,
-                    NULL,      NULL,        NULL,
-                    layer.pw2, layer.pw2_b, NULL,
-                    NULL,
-                    LLM_FFN_GELU, LLM_FFN_SEQ, il);
-
-            cur = ggml_mul(ctx0, cur, layer.gamma);
-
-            cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
-
-            inpL = ggml_add(ctx0, cur, inpL);
-        }
-
-        cur = inpL;
-
-        cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
-
-        cur = build_norm(cur,
-                model.output_norm,
-                model.output_norm_b,
-                LLM_NORM, -1);
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cur = ggml_add(ctx0, cur, model.output_b);
-
-        cb(cur, "result_embd", -1);
-        res->t_embd = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_plm : public llm_graph_context {
-    llm_build_plm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const float kq_scale = 1.0f/sqrtf(float(hparams.n_embd_head_k));
-
-        const uint32_t n_embd_head_qk_rope = hparams.n_rot;
-        const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
-        const uint32_t kv_lora_rank = hparams.n_lora_kv;
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        // {n_embd, n_tokens}
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self_attention
-            {
-                ggml_tensor * q = NULL;
-                q = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
-                cb(q, "q", il);
-
-                // split into {n_head * n_embd_head_qk_nope, n_tokens}
-                ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
-                        ggml_row_size(q->type, hparams.n_embd_head_k),
-                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
-                        0);
-                cb(q_nope, "q_nope", il);
-
-                // and {n_head * n_embd_head_qk_rope, n_tokens}
-                ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
-                        ggml_row_size(q->type, hparams.n_embd_head_k),
-                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
-                        ggml_row_size(q->type, n_embd_head_qk_nope));
-                cb(q_pe, "q_pe", il);
-
-                // {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens}
-                ggml_tensor * kv_pe_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
-                cb(kv_pe_compresseed, "kv_pe_compresseed", il);
-
-                // split into {kv_lora_rank, n_tokens}
-                ggml_tensor * kv_compressed = ggml_view_2d(ctx0, kv_pe_compresseed, kv_lora_rank, n_tokens,
-                        kv_pe_compresseed->nb[1],
-                        0);
-                cb(kv_compressed, "kv_compressed", il);
-
-                // and {n_embd_head_qk_rope, n_tokens}
-                ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_pe_compresseed, n_embd_head_qk_rope, 1, n_tokens,
-                        kv_pe_compresseed->nb[1],
-                        kv_pe_compresseed->nb[1],
-                        ggml_row_size(kv_pe_compresseed->type, kv_lora_rank));
-                cb(k_pe, "k_pe", il);
-
-                kv_compressed = build_norm(kv_compressed,
-                        model.layers[il].attn_kv_a_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(kv_compressed, "kv_compressed", il);
-
-                // {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)} * {kv_lora_rank, n_tokens} -> {n_head * (n_embd_head_qk_nope + n_embd_head_v), n_tokens}
-                ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_compressed);
-                cb(kv, "kv", il);
-
-                // split into {n_head * n_embd_head_qk_nope, n_tokens}
-                ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
-                        ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v),
-                        ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
-                        0);
-                cb(k_nope, "k_nope", il);
-
-                // and {n_head * n_embd_head_v, n_tokens}
-                ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens,
-                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)),
-                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head),
-                        ggml_row_size(kv->type, (n_embd_head_qk_nope)));
-                cb(v_states, "v_states", il);
-
-                v_states = ggml_cont(ctx0, v_states);
-                cb(v_states, "v_states", il);
-
-                v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v * n_head, n_tokens,
-                        ggml_row_size(kv->type, hparams.n_embd_head_v * n_head),
-                        0);
-                cb(v_states, "v_states", il);
-
-                q_pe = ggml_rope_ext(
-                        ctx0, q_pe, inp_pos, nullptr,
-                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                        ext_factor, attn_factor, beta_fast, beta_slow
-                        );
-                cb(q_pe, "q_pe", il);
-
-                // shared RoPE key
-                k_pe = ggml_rope_ext(
-                        ctx0, k_pe, inp_pos, nullptr,
-                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                        ext_factor, attn_factor, beta_fast, beta_slow
-                        );
-                cb(k_pe, "k_pe", il);
-
-                ggml_tensor * q_states = ggml_concat(ctx0, q_nope, q_pe, 0);
-                cb(q_states, "q_states", il);
-
-                ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);
-                cb(k_states, "k_states", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        q_states, k_states, v_states, nullptr, nullptr, nullptr, kq_scale, il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    NULL, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_RELU_SQR, LLM_FFN_SEQ, il);
-            cb(cur, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_bailingmoe : public llm_graph_context {
-    llm_build_bailingmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // rope freq factors for llama3; may return nullptr for llama2 and other models
-                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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_rot, n_head,    n_tokens);
-                Kcur = ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_rot, n_head_kv, n_tokens);
-
-                Qcur = ggml_rope_ext(
-                        ctx0, Qcur, inp_pos, rope_factors,
-                        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, rope_factors,
-                        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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_rot)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            ggml_tensor * moe_out =
-                build_moe_ffn(cur,
-                        model.layers[il].ffn_gate_inp,
-                        model.layers[il].ffn_up_exps,
-                        model.layers[il].ffn_gate_exps,
-                        model.layers[il].ffn_down_exps,
-                        nullptr,
-                        n_expert, n_expert_used,
-                        LLM_FFN_SILU, hparams.expert_weights_norm,
-                        false, hparams.expert_weights_scale,
-                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                        il);
-            cb(moe_out, "ffn_moe_out", il);
-
-            // FFN shared expert
-            {
-                ggml_tensor * ffn_shexp = build_ffn(cur,
-                        model.layers[il].ffn_up_shexp,   NULL, NULL,
-                        model.layers[il].ffn_gate_shexp, NULL, NULL,
-                        model.layers[il].ffn_down_shexp, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(ffn_shexp, "ffn_shexp", il);
-
-                cur = ggml_add(ctx0, moe_out, ffn_shexp);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_bailingmoe2 : public llm_graph_context {
-    llm_build_bailingmoe2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        const int n_transformer_layers = n_layer - hparams.nextn_predict_layers;
-        for (int il = 0; il < n_transformer_layers; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self_attention
-            {
-                cur = build_lora_mm(model.layers[il].wqkv, cur);
-                cb(cur, "wqkv", il);
-
-                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
-                ggml_tensor * Kcur = 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));
-                ggml_tensor * 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 = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
-                cb(Kcur, "Kcur_normed", il);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_transformer_layers - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * sa_out = ggml_add(ctx0, cur, inpSA);
-            cb(sa_out, "sa_out", il);
-
-            // MoE branch
-            cur = build_norm(sa_out,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            if (static_cast<uint32_t>(il) < hparams.n_layer_dense_lead) {
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            } else {
-                ggml_tensor * moe_out =
-                    build_moe_ffn(cur,
-                            model.layers[il].ffn_gate_inp,
-                            model.layers[il].ffn_up_exps,
-                            model.layers[il].ffn_gate_exps,
-                            model.layers[il].ffn_down_exps,
-                            model.layers[il].ffn_exp_probs_b,
-                            n_expert, n_expert_used,
-                            LLM_FFN_SILU, hparams.expert_weights_norm,
-                            true, hparams.expert_weights_scale,
-                            (llama_expert_gating_func_type) hparams.expert_gating_func,
-                            il);
-                cb(moe_out, "ffn_moe_out", il);
-
-                {
-                    ggml_tensor * ffn_shexp = build_ffn(cur,
-                            model.layers[il].ffn_up_shexp,   NULL, NULL,
-                            model.layers[il].ffn_gate_shexp, NULL, NULL,
-                            model.layers[il].ffn_down_shexp, NULL, NULL,
-                            NULL,
-                            LLM_FFN_SILU, LLM_FFN_PAR, il);
-                    cb(ffn_shexp, "ffn_shexp", il);
-
-                    cur = ggml_add(ctx0, moe_out, ffn_shexp);
-                    cb(cur, "ffn_out", il);
-                }
-            }
-
-            cur = ggml_add(ctx0, cur, sa_out);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_dots1 : public llm_graph_context {
-    llm_build_dots1(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self_attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
-                cb(Kcur, "Kcur_normed", il);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // MoE branch
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            if ((uint32_t) il < hparams.n_layer_dense_lead) {
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            } else {
-                ggml_tensor * moe_out =
-                    build_moe_ffn(cur,
-                            model.layers[il].ffn_gate_inp,
-                            model.layers[il].ffn_up_exps,
-                            model.layers[il].ffn_gate_exps,
-                            model.layers[il].ffn_down_exps,
-                            model.layers[il].ffn_exp_probs_b,
-                            n_expert, n_expert_used,
-                            LLM_FFN_SILU, hparams.expert_weights_norm,
-                            true, hparams.expert_weights_scale,
-                            (llama_expert_gating_func_type) hparams.expert_gating_func,
-                            il);
-                cb(moe_out, "ffn_moe_out", il);
-
-                {
-                    ggml_tensor * ffn_shexp = build_ffn(cur,
-                            model.layers[il].ffn_up_shexp,   NULL, NULL,
-                            model.layers[il].ffn_gate_shexp, NULL, NULL,
-                            model.layers[il].ffn_down_shexp, NULL, NULL,
-                            NULL,
-                            LLM_FFN_SILU, LLM_FFN_PAR, il);
-                    cb(ffn_shexp, "ffn_shexp", il);
-
-                    cur = ggml_add(ctx0, moe_out, ffn_shexp);
-                    cb(cur, "ffn_out", il);
-                }
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_ernie4_5 : public llm_graph_context {
-    llm_build_ernie4_5(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            {
-                cur = build_norm(inpL,
-                        model.layers[il].attn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "attn_norm", il);
-            }
-
-            // self-attention
-            {
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1) {
-                // skip computing output for unused tokens
-                ggml_tensor * inp_out_ids = build_inp_out_ids();
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_ernie4_5_moe : public llm_graph_context {
-    llm_build_ernie4_5_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        GGML_ASSERT(hparams.n_moe_layer_step > 0 && "Ernie 4.5 MoE requires n_moe_layer_step > 0");
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-            // norm
-            {
-                cur = build_norm(inpL,
-                        model.layers[il].attn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "attn_norm", il);
-            }
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-                cb(cur, "attn_out", il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            bool is_moe_layer = static_cast<uint32_t>(il) >= hparams.n_layer_dense_lead && (il + 1) % hparams.n_moe_layer_step == 0;
-
-            if (!is_moe_layer) {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            } else {
-                // MoE branch
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                ggml_tensor * moe_out = build_moe_ffn(cur,
-                        model.layers[il].ffn_gate_inp,
-                        model.layers[il].ffn_up_exps,
-                        model.layers[il].ffn_gate_exps,
-                        model.layers[il].ffn_down_exps,
-                        model.layers[il].ffn_exp_probs_b,
-                        n_expert, n_expert_used,
-                        LLM_FFN_SILU, true,
-                        false, 0.0,
-                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                        il);
-                cb(moe_out, "ffn_moe_out", il);
-
-                // Shared expert (if present)
-                if (hparams.n_ff_shexp > 0) {
-                    ggml_tensor * ffn_shexp = build_ffn(cur,
-                        model.layers[il].ffn_up_shexp,   NULL, NULL,
-                        model.layers[il].ffn_gate_shexp, NULL, NULL,
-                        model.layers[il].ffn_down_shexp, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                    cb(ffn_shexp, "ffn_shexp", il);
-
-                    cur = ggml_add(ctx0, moe_out, ffn_shexp);
-                } else {
-                    cur = moe_out;
-                }
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_falcon_h1 : public llm_graph_context_mamba {
-    llm_build_falcon_h1(const llama_model & model, const llm_graph_params & params) : llm_graph_context_mamba(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        // Build the inputs in the recurrent & kv cache
-        auto * inp = build_inp_mem_hybrid();
-
-        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-            cb(Qcur, "Qcur", il);
-
-            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-            cb(Kcur, "Kcur", il);
-
-            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-            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);
-
-            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-            Qcur = ggml_rope_ext(
-                    ctx0, Qcur, inp_pos, nullptr,
-                    n_rot, hparams.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, hparams.rope_type, n_ctx_orig, freq_base, freq_scale,
-                    ext_factor, attn_factor, beta_fast, beta_slow
-                    );
-
-            cb(Qcur, "Qcur-post-rope", il);
-            cb(Kcur, "Kcur-post-rope", il);
-            cb(Vcur, "Vcur-post-rope", il);
-
-            ggml_tensor * attn_out = build_attn(inp->get_attn(),
-                    model.layers[il].wo, NULL,
-                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-            cb(attn_out, "attn_out", il);
-
-            cur = build_norm(inpL,
-                model.layers[il].attn_norm, NULL,
-                LLM_NORM_RMS, il);
-            // Mamba2 layer
-            cb(cur, "ssm_in", il);
-
-            ggml_tensor * ssm_out = build_mamba2_layer(inp->get_recr(), cur, model, ubatch, il);
-            cb(ssm_out, "ssm_out", il);
-
-            // // Aggregation
-            cur = ggml_add(ctx0, attn_out, ssm_out);
-            inpSA = ggml_add(ctx0, cur, inpSA);
-            cb(cur, "layer_out", il);
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = inpSA;
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b, NULL,
-                    model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
-                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, inpSA);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_plamo2 : public llm_graph_context_mamba {
-    llm_build_plamo2(const llama_model & model, const llm_graph_params & params) : llm_graph_context_mamba(params) {
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        // {n_embd, n_tokens}
-        inpL = build_inp_embd(model.tok_embd);
-        cb(inpL, "embedding_output", -1);
-
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_hybrid = build_inp_mem_hybrid();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * residual = inpL;
-
-            // ggml_graph_add_node(gf, model.layers[il].attn_norm);
-            // cb(model.layers[il].attn_norm, "attn_norm", il);
-
-            // pre_mixer_norm
-            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
-
-            // check if this layer is Mamba or Attention
-            bool is_mamba_layer = hparams.is_recurrent(il);
-
-            if (is_mamba_layer) {
-                // PLaMo-2 Mamba layer
-                cur = build_plamo2_mamba_layer(inp_hybrid->get_recr(), cur, model, ubatch, il);
-            } else {
-                // PLaMo-2 Attention layer
-                cur = build_plamo2_attn_layer(inp_hybrid->get_attn(), inp_pos, cur, model, il);
-            }
-
-            // post_mixer_norm
-            cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "attn_post_norm", il);
-
-            // residual connection
-            cur = ggml_add(ctx0, cur, residual);
-            cb(cur, "attn_residual", il);
-            residual = cur;
-
-            // pre-ffn norm
-            cur = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "ffn_pre_norm", il);
-
-            // feed-forward network
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    NULL,                      NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
-            cb(cur, "ffn_out", il);
-
-            // post ffn norm
-            cur = build_norm(cur, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "ffn_post_norm", il);
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
-                residual = ggml_get_rows(ctx0, residual, inp_out_ids);
-            }
-
-            // residual connection
-            cur = ggml_add(ctx0, cur, residual);
-            cb(cur, "ffn_residual", il);
-
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        // final norm
-        cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
-        cb(cur, "result_norm", -1);
-
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-        cb(cur, "result_output", -1);
-
-        // Explicitly mark as output tensor to ensure proper backend assignment
-        ggml_set_output(cur);
-
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-
-private:
-    ggml_tensor * build_plamo2_attn_layer(
-            llm_graph_input_attn_kv * inp,
-            ggml_tensor * inp_pos,
-            ggml_tensor * cur,
-            const llama_model & model,
-            int il) {
-
-        // self-attention
-        {
-            // PLaMo-2 uses combined QKV tensor
-            ggml_tensor * qkv = build_lora_mm(model.layers[il].wqkv, cur);
-            cb(qkv, "wqkv", il);
-
-            // split QKV tensor into Q, K, V
-            const int64_t n_embd_head_q = hparams.n_embd_head_k;
-            const int64_t n_embd_head_k = hparams.n_embd_head_k;
-            const int64_t n_embd_head_v = hparams.n_embd_head_v;
-            int32_t n_head = hparams.n_head(il);
-            int32_t n_head_kv = hparams.n_head_kv(il);
-
-            const int64_t q_offset = 0;
-            const int64_t k_offset = n_embd_head_q * n_head;
-            const int64_t v_offset = k_offset + n_embd_head_k * n_head_kv;
-
-            ggml_tensor * Qcur = ggml_view_3d(ctx0, qkv, n_embd_head_q, n_head,    n_tokens, n_embd_head_q * sizeof(float), qkv->nb[1], q_offset * ggml_element_size(qkv));
-            ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_kv, n_tokens, n_embd_head_k * sizeof(float), qkv->nb[1], k_offset * ggml_element_size(qkv));
-            ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, n_embd_head_v, n_head_kv, n_tokens, n_embd_head_v * sizeof(float), qkv->nb[1], v_offset * ggml_element_size(qkv));
-
-            cb(Qcur, "Qcur", il);
-            cb(Kcur, "Kcur", il);
-            cb(Vcur, "Vcur", il);
-
-            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-            cb(Qcur, "Qcur_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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
-            cb(Kcur, "Kcur_normed", il);
-
-            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
-                    );
-
-            cur = build_attn(inp,
-                    model.layers[il].wo, NULL,
-                    Qcur, Kcur, Vcur, NULL, NULL, NULL, 1.0f/sqrtf(float(n_embd_head_v)), il);
-        }
-
-        cb(cur, "attn_out", il);
-
-        return cur;
-    }
-
-    ggml_tensor * build_plamo2_mamba_layer(
-         llm_graph_input_rs * inp,
-               ggml_tensor * cur,
-         const llama_model & model,
-        const llama_ubatch & ubatch,
-                       int   il) {
-
-        const auto * mctx_cur = inp->mctx;
-
-        const auto kv_head = mctx_cur->get_head();
-
-        const int64_t d_conv   = hparams.ssm_d_conv;
-        const int64_t d_inner  = hparams.ssm_d_inner;
-        const int64_t d_state  = hparams.ssm_d_state;
-        const int64_t n_heads  = hparams.ssm_dt_rank;
-        const int64_t head_dim = d_inner / n_heads;
-        const int64_t n_group  = hparams.ssm_n_group;
-        const int64_t n_seqs   = ubatch.n_seqs;
-
-        const int64_t n_seq_tokens = ubatch.n_seq_tokens;
-
-        GGML_ASSERT(n_seqs != 0);
-        GGML_ASSERT(ubatch.equal_seqs());
-        GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
-
-        ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
-        ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
-
-        ggml_tensor * conv = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
-        conv = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner + 2*n_group*d_state, n_seqs);
-
-        // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs}
-        cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
-
-        // in_proj: {n_embd, 2*d_inner} @ {n_embd, n_seq_tokens, n_seqs} => {2*d_inner, n_seq_tokens, n_seqs}
-        ggml_tensor * zx = build_lora_mm(model.layers[il].ssm_in, cur);
-        cb(zx, "mamba_in_proj", il);
-        // {8192, 5, 1, 1} -> {8192, 1, 5, 1}
-        zx = ggml_permute(ctx0, zx, 0, 2, 1, 3);
-        zx = ggml_cont_4d(ctx0, zx, head_dim * 2, n_heads, n_seq_tokens, n_seqs);
-        cb(zx, "mamba_in_proj_out", il);
-
-        // split into z and x
-        // => {head_dim * n_heads, n_seq_tokens, n_seqs}
-        ggml_tensor * x = ggml_view_4d(ctx0, zx, head_dim, n_heads, n_seq_tokens, n_seqs, zx->nb[1], zx->nb[2], zx->nb[3], head_dim*ggml_element_size(zx));
-        x = ggml_cont_3d(ctx0, x, head_dim * n_heads, n_seq_tokens, n_seqs);
-        // x = ggml_permute(ctx0, x, 0, 2, 1, 3);
-        cb(x, "mamba_x_split", il);
-
-        ggml_tensor * z = ggml_view_4d(ctx0, zx, head_dim, n_heads, n_seq_tokens, n_seqs, zx->nb[1], zx->nb[2], zx->nb[3], 0);
-        cb(z, "mamba_z_split", il);
-
-        // conv1d
-        {
-            // => {d_conv - 1 + n_seq_tokens, d_inner, n_seqs}
-            ggml_tensor * conv_x = ggml_concat(ctx0, conv, ggml_transpose(ctx0, x), 0);
-            cb(conv_x, "mamba_conv1d_input", il);
-
-            // copy last (d_conv - 1) columns back into the state cache
-            ggml_tensor * last_conv = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner, n_seqs,
-                    conv_x->nb[1], conv_x->nb[2], n_seq_tokens*(conv_x->nb[0]));
-
-            ggml_build_forward_expand(gf,
-                ggml_cpy(ctx0, last_conv,
-                    ggml_view_1d(ctx0, conv_states_all,
-                        (d_conv - 1)*(d_inner + 2*n_group*d_state)*(n_seqs),
-                        kv_head*(d_conv - 1)*(d_inner + 2*n_group*d_state)*ggml_element_size(conv_states_all))));
-            cb(conv_states_all, "mamba_conv1d_state", il);
-
-            // 1D convolution
-            x = ggml_ssm_conv(ctx0, conv_x, model.layers[il].ssm_conv1d);
-            cb(x, "mamba_conv1d", il);
-
-            x = ggml_silu(ctx0, x);
-            cb(x, "mamba_conv1d_silu", il);
-        }
-
-        // SSM
-        {
-            // bcdt_proj: {d_inner, dt_rank + 2*d_state} @ {d_inner, n_seq_tokens, n_seqs} => {dt_rank + 2*d_state, n_seq_tokens, n_seqs}
-            ggml_tensor * x_bcdt = build_lora_mm(model.layers[il].ssm_x, x);
-            cb(x_bcdt, "mamba_bcdt_proj", il);
-
-            // split into dt, B, C
-            const int64_t dt_dim = std::max(64, int(hparams.n_embd / 16));
-            ggml_tensor * B = ggml_view_3d(ctx0, x_bcdt, d_state, n_seq_tokens, n_seqs, x_bcdt->nb[1], x_bcdt->nb[2], 0);
-            ggml_tensor * C  = ggml_view_3d(ctx0, x_bcdt, d_state, n_seq_tokens, n_seqs, x_bcdt->nb[1], x_bcdt->nb[2], ggml_element_size(x_bcdt)*d_state);
-            ggml_tensor * dt  = ggml_view_3d(ctx0, x_bcdt, dt_dim, n_seq_tokens, n_seqs, x_bcdt->nb[1], x_bcdt->nb[2], ggml_element_size(x_bcdt)*(2*d_state));
-            cb(B, "mamba_B_raw", il);
-            cb(C, "mamba_C_raw", il);
-            cb(dt, "mamba_dt_raw", il);
-
-            // Apply RMS norm to dt, B, C (PLaMo-2 specific)
-            B = build_norm(B, model.layers[il].ssm_b_norm, NULL, LLM_NORM_RMS, il);
-            C = build_norm(C, model.layers[il].ssm_c_norm, NULL, LLM_NORM_RMS, il);
-            dt = build_norm(dt, model.layers[il].ssm_dt_norm, NULL, LLM_NORM_RMS, il);
-            cb(B, "mamba_B_normed", il);
-            cb(C, "mamba_C_normed", il);
-            cb(dt, "mamba_dt_normed", il);
-
-            // dt_proj: {dt_rank, d_inner} @ {dt_rank, n_seq_tokens, n_seqs} => {d_inner, n_seq_tokens, n_seqs}
-            dt = build_lora_mm(model.layers[il].ssm_dt, dt);
-            dt = ggml_add(ctx0, dt, model.layers[il].ssm_dt_b);
-            cb(dt, "mamba_dt_proj", il);
-
-            ggml_tensor * A = ggml_reshape_2d(ctx0, model.layers[il].ssm_a, 1, n_heads);
-            cb(A, "mamba_A", il);
-
-            x = ggml_view_4d(ctx0, x, head_dim, n_heads, n_seq_tokens, n_seqs, head_dim * ggml_element_size(x), head_dim * n_heads * ggml_element_size(x), head_dim * n_heads * n_seq_tokens * ggml_element_size(x), 0);
-            B = ggml_view_4d(ctx0, B, d_state, 1, n_seq_tokens, n_seqs, d_state * B->nb[0], B->nb[1], B->nb[2], 0);
-            C = ggml_view_4d(ctx0, C, d_state, 1, n_seq_tokens, n_seqs, d_state * C->nb[0], C->nb[1], C->nb[2], 0);
-
-            // use the states and the indices provided by build_recurrent_state
-            // (this is necessary in order to properly use the states before they are overwritten,
-            //  while avoiding to make unnecessary copies of the states)
-            auto get_ssm_rows = [&](ggml_context * ctx, ggml_tensor * states, ggml_tensor * ids) {
-                ggml_tensor * ssm = ggml_reshape_4d(ctx, states, d_state, head_dim, n_heads, mctx_cur->get_size());
-
-                // Custom operator to optimize the parallel associative scan
-                // as described in the Annex D of the Mamba paper.
-                // => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs}
-                return ggml_ssm_scan(ctx, ssm, x, dt, A, B, C, ids);
-            };
-
-            ggml_tensor * y_ssm = build_rs(inp, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows);
-            cb(y_ssm, "mamba_ssm_scan", il);
-
-            // store last states
-            ggml_build_forward_expand(gf,
-                ggml_cpy(ctx0,
-                    ggml_view_1d(ctx0, y_ssm, n_heads*head_dim*d_state*n_seqs, n_heads*head_dim*n_seq_tokens*n_seqs*ggml_element_size(y_ssm)),
-                    ggml_view_1d(ctx0, ssm_states_all, n_heads*head_dim*d_state*n_seqs, kv_head*n_seqs*n_heads*head_dim*d_state*ggml_element_size(ssm_states_all))));
-            cb(ssm_states_all, "mamba_ssm_states", il);
-
-            ggml_tensor * y = ggml_view_4d(ctx0, y_ssm, head_dim, n_heads, n_seq_tokens, n_seqs, head_dim * ggml_element_size(x), head_dim * n_heads * ggml_element_size(x), head_dim * n_heads * n_seq_tokens * ggml_element_size(x), 0);
-            cb(y, "mamba_y_view", il);
-
-            // Add D parameter and apply gating with z
-            // {d_inner, n_seq_tokens, n_seqs} * {d_inner} => {d_inner, n_seq_tokens, n_seqs}
-            ggml_tensor * D = ggml_reshape_2d(ctx0, model.layers[il].ssm_d, 1, n_heads);
-            y = ggml_add(ctx0, y, ggml_mul(ctx0, x, D));
-            cb(y, "mamba_y_add_d", il);
-
-            y = ggml_swiglu_split(ctx0, ggml_cont(ctx0, z), y);
-            cb(y, "mamba_y_swiglu_z", il);
-
-            // out_proj: {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs}
-            y = ggml_view_3d(ctx0, y, head_dim * n_heads, n_seq_tokens, n_seqs, y->nb[2], y->nb[3], 0);
-            cur = build_lora_mm(model.layers[il].ssm_out, y);
-            cb(cur, "mamba_out_proj", il);
-        }
-
-        // {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
-        cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs);
-        cb(cur, "mamba_out", il);
-
-        return cur;
-    }
-};
-
-struct llm_build_arcee : public llm_graph_context {
-    llm_build_arcee(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // rope freq factors for llama3; may return nullptr for llama2 and other models
-                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = ggml_rope_ext(
-                        ctx0, Qcur, inp_pos, rope_factors,
-                        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, rope_factors,
-                        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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-                cb(cur, "attn_out", il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            // ARCEE uses relu^2 instead of silu
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    NULL,                      NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_RELU_SQR, LLM_FFN_SEQ, il);
-            cb(cur, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_hunyuan_moe : public llm_graph_context {
-    llm_build_hunyuan_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // rope freq factors for llama3; may return nullptr for llama2 and other models
-                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = ggml_rope_ext(
-                        ctx0, Qcur, inp_pos, rope_factors,
-                        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);
-                cb(Vcur, "Vcur", il);
-
-                Kcur = ggml_rope_ext(
-                        ctx0, Kcur, inp_pos, rope_factors,
-                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                        ext_factor, attn_factor, beta_fast, beta_slow
-                        );
-
-                Kcur = build_norm(Kcur,
-                        model.layers[il].attn_k_norm, nullptr,
-                        LLM_NORM_RMS, il);
-                cb(Kcur, "Kcur_norm", il);
-
-                Qcur = build_norm(Qcur,
-                        model.layers[il].attn_q_norm, nullptr,
-                        LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_norm", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-                cb(cur, "attn_out", il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            cur = build_norm(ffn_inp,
-                model.layers[il].ffn_norm, NULL,
-                LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            // feed-forward network (non-MoE)
-            ggml_tensor * cur_mlp = build_ffn(cur,
-                    model.layers[il].ffn_up_shexp,   NULL, NULL,
-                    model.layers[il].ffn_gate_shexp, NULL, NULL,
-                    model.layers[il].ffn_down_shexp, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur_mlp, "ffn_mlp", il);
-
-            // MoE branch
-            ggml_tensor * cur_moe = build_moe_ffn(cur,
-                    model.layers[il].ffn_gate_inp,
-                    model.layers[il].ffn_up_exps,
-                    model.layers[il].ffn_gate_exps,
-                    model.layers[il].ffn_down_exps,
-                    nullptr,
-                    n_expert, n_expert_used,
-                    LLM_FFN_SILU,
-                    true, // norm_topk_prob
-                    false,
-                    0.0,
-                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                    il);
-            cb(cur_moe, "ffn_moe_out", il);
-
-            ggml_tensor * ffn_out = ggml_add(ctx0, cur_moe, cur_mlp);
-            cb(ffn_out, "ffn_out", il);
-
-            cur = ggml_add(ctx0, ffn_out, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_hunyuan_dense : public llm_graph_context {
-    llm_build_hunyuan_dense(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-            // self-attention
-            {
-                // rope freq factors for llama3; may return nullptr for llama2 and other models
-                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = ggml_rope_ext(
-                         ctx0, Qcur, inp_pos, rope_factors,
-                         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);
-                cb(Vcur, "Vcur", il);
-
-                Kcur = ggml_rope_ext(
-                         ctx0, Kcur, inp_pos, rope_factors,
-                         n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                         ext_factor, attn_factor, beta_fast, beta_slow
-                         );
-
-                Kcur = build_norm(Kcur,
-                         model.layers[il].attn_k_norm, nullptr,
-                         LLM_NORM_RMS, il);
-                cb(Kcur, "Kcur_norm", il);
-
-                Qcur = build_norm(Qcur,
-                         model.layers[il].attn_q_norm, nullptr,
-                         LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_norm", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-                cb(cur, "attn_out", il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-            // feed-forward network (non-MoE)
-            ggml_tensor * cur_mlp = build_ffn(cur,
-                        model.layers[il].ffn_up,   NULL, NULL,
-                        model.layers[il].ffn_gate, NULL, NULL,
-                        model.layers[il].ffn_down, NULL, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur_mlp, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur_mlp, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_smollm3 : public llm_graph_context {
-    llm_build_smollm3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            const bool use_rope = (il + 1) % hparams.n_no_rope_layer_step != 0;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                if (use_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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-                cb(cur, "attn_out", il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                cur = build_ffn(cur,
-                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
-                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
-                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
-                        NULL,
-                        LLM_FFN_SILU, LLM_FFN_PAR, il);
-                cb(cur, "ffn_out", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_openai_moe_iswa : public llm_graph_context {
-    llm_build_openai_moe_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv_iswa();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, nullptr,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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_rot, n_head,    n_tokens);
-                Kcur = ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_rot, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, model.layers[il].attn_sinks, nullptr, 1.0f/sqrtf(float(n_rot)), il);
-
-                cb(cur, "attn_out", il);
-            }
-
-            if (il == n_layer - 1) {
-                // skip computing output for unused tokens
-                ggml_tensor * inp_out_ids = build_inp_out_ids();
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            cur = ffn_inp;
-            cur = build_norm(cur,
-                    model.layers[il].attn_post_norm, nullptr,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_post_norm", il);
-
-            // MoE branch
-            cur = build_moe_ffn(cur,
-                    model.layers[il].ffn_gate_inp,  model.layers[il].ffn_gate_inp_b,
-                    model.layers[il].ffn_up_exps,   model.layers[il].ffn_up_exps_b,
-                    model.layers[il].ffn_gate_exps, model.layers[il].ffn_gate_exps_b,
-                    model.layers[il].ffn_down_exps, model.layers[il].ffn_down_exps_b,
-                    nullptr,
-                    n_expert, n_expert_used,
-                    LLM_FFN_SWIGLU_OAI_MOE, false,
-                    false, 0.0,
-                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX_WEIGHT,
-                    il);
-            cb(cur, "ffn_moe_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_lfm2 : public llm_graph_context {
-    const llama_model & model;
-
-    llm_build_lfm2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params), model(model) {
-
-        ggml_tensor * cur = build_inp_embd(model.tok_embd);
-        cb(cur, "model.embed_tokens", -1);
-
-        ggml_tensor * inp_pos     = build_inp_pos();
-        auto        * inp_hybrid  = build_inp_mem_hybrid();
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            const bool is_moe_layer = il >= static_cast<int>(hparams.n_layer_dense_lead);
-
-            auto * prev_cur = cur;
-            cur = build_norm(cur, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "model.layers.{}.operator_norm", il);
-
-            cur = hparams.is_recurrent(il) ?
-                build_shortconv_block(cur, inp_hybrid->get_recr(), il) :
-                build_attn_block(cur, inp_pos, inp_hybrid->get_attn(), il) ;
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur      = ggml_get_rows(ctx0,      cur, inp_out_ids);
-                prev_cur = ggml_get_rows(ctx0, prev_cur, inp_out_ids);
-            }
-
-            cur = ggml_add(ctx0, prev_cur, cur);
-
-            auto * ffn_norm_out = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
-            cb(ffn_norm_out, "model.layers.{}.ffn_norm", il);
-
-            ggml_tensor * ffn_out = is_moe_layer ?
-                build_moe_feed_forward(ffn_norm_out, il) :
-                build_dense_feed_forward(ffn_norm_out, il);
-            cb(ffn_norm_out, "model.layers.{}.ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_out);
-        }
-
-        cur = build_norm(cur, model.tok_norm, NULL, LLM_NORM_RMS, -1);
-        cb(cur, "model.embedding_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-        cb(cur, "lm_head", -1);
-
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-
-    ggml_tensor * build_moe_feed_forward(ggml_tensor * cur,
-                                         int           il) const {
-        return build_moe_ffn(cur,
-                    model.layers[il].ffn_gate_inp,
-                    model.layers[il].ffn_up_exps,
-                    model.layers[il].ffn_gate_exps,
-                    model.layers[il].ffn_down_exps,
-                    model.layers[il].ffn_exp_probs_b,
-                    n_expert, n_expert_used,
-                    LLM_FFN_SILU, true,
-                    false, 0.0,
-                    static_cast<llama_expert_gating_func_type>(hparams.expert_gating_func),
-                    il);
-    }
-
-    ggml_tensor * build_dense_feed_forward(ggml_tensor * cur,
-                                           int           il) const {
-        GGML_ASSERT(!model.layers[il].ffn_up_b);
-        GGML_ASSERT(!model.layers[il].ffn_gate_b);
-        GGML_ASSERT(!model.layers[il].ffn_down_b);
-        return build_ffn(cur,
-                model.layers[il].ffn_up,   NULL, NULL,
-                model.layers[il].ffn_gate, NULL, NULL,
-                model.layers[il].ffn_down, NULL, NULL,
-                NULL,
-                LLM_FFN_SILU, LLM_FFN_PAR, il);
-    }
-
-    ggml_tensor * build_attn_block(ggml_tensor             * cur,
-                                   ggml_tensor             * inp_pos,
-                                   llm_graph_input_attn_kv * inp_attn,
-                                   int                     il) const {
-        GGML_ASSERT(hparams.n_embd_v_gqa(il) == hparams.n_embd_k_gqa(il));
-        auto const n_embd_head = hparams.n_embd_head_v;
-        auto const n_head_kv = hparams.n_head_kv(il);
-
-        auto * q = build_lora_mm(model.layers[il].wq, cur);
-        cb(q, "model.layers.{}.self_attn.q_proj", il);
-        auto * k = build_lora_mm(model.layers[il].wk, cur);
-        cb(k, "model.layers.{}.self_attn.k_proj", il);
-        auto * v = build_lora_mm(model.layers[il].wv, cur);
-        cb(v, "model.layers.{}.self_attn.v_proj", il);
-
-        q = ggml_reshape_3d(ctx0, q, n_embd_head, n_head,    n_tokens);
-        k = ggml_reshape_3d(ctx0, k, n_embd_head, n_head_kv, n_tokens);
-        v = ggml_reshape_3d(ctx0, v, n_embd_head, n_head_kv, n_tokens);
-
-        // qk norm
-        q = build_norm(q, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-        cb(q, "model.layers.{}.self_attn.q_layernorm", il);
-        k = build_norm(k, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
-        cb(k, "model.layers.{}.self_attn.k_layernorm", il);
-
-        // RoPE
-        q = ggml_rope_ext(
-                ctx0, q, inp_pos, nullptr,
-                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                ext_factor, attn_factor, beta_fast, beta_slow
-                );
-        k = ggml_rope_ext(
-                ctx0, k, inp_pos, nullptr,
-                n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                ext_factor, attn_factor, beta_fast, beta_slow
-                );
-
-        cur = build_attn(inp_attn, model.layers[il].wo, NULL,
-                q, k, v, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-
-        cb(cur, "model.layers.{}.self_attn.out_proj", il);
-
-        return cur;
-    }
-
-    ggml_tensor * build_shortconv_block(ggml_tensor        * cur,
-                                        llm_graph_input_rs * inp_recr,
-                                        int                il) {
-        const auto *   mctx_cur     = static_cast<const llama_memory_hybrid_context *>(mctx)->get_recr();
-        const uint32_t kv_head      = mctx_cur->get_head();
-        const int64_t  n_seq_tokens = ubatch.n_seq_tokens;
-        const int64_t  n_seqs       = ubatch.n_seqs;
-        GGML_ASSERT(n_seqs != 0);
-        GGML_ASSERT(ubatch.equal_seqs());
-        GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
-
-        GGML_ASSERT(hparams.n_shortconv_l_cache > 1);
-        const uint32_t d_conv = hparams.n_shortconv_l_cache - 1;
-
-        // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs}
-        cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
-
-        auto * bcx = build_lora_mm(model.layers[il].shortconv.in_proj, cur);
-        cb(bcx, "model.layers.{}.conv.in_proj", il);
-
-        constexpr auto n_chunks = 3;
-        GGML_ASSERT(bcx->ne[0] % n_chunks == 0);
-        auto const chunk_size = bcx->ne[0] / n_chunks;
-        auto * b = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2], 0*chunk_size*ggml_element_size(bcx));
-        auto * c = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2], 1*chunk_size*ggml_element_size(bcx));
-        auto * x = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2], 2*chunk_size*ggml_element_size(bcx));
-
-        auto * bx = ggml_transpose(ctx0, ggml_mul(ctx0, b, x));
-
-        // read conv state
-        auto * conv_state = mctx_cur->get_r_l(il);
-        auto * conv_rs    = build_rs(inp_recr, conv_state, hparams.n_embd_r(), n_seqs);
-        auto * conv       = ggml_reshape_3d(ctx0, conv_rs, d_conv, hparams.n_embd, n_seqs);
-
-        bx = ggml_concat(ctx0, conv, bx, 0);
-        GGML_ASSERT(bx->ne[0] > conv->ne[0]);
-
-        // last d_conv columns is a new conv state
-        auto * new_conv = ggml_view_3d(ctx0, bx, conv->ne[0], bx->ne[1], bx->ne[2], bx->nb[1], bx->nb[2], (bx->ne[0] - conv->ne[0])*ggml_element_size(bx));
-        GGML_ASSERT(ggml_are_same_shape(conv, new_conv));
-
-        // write new conv conv state
-        ggml_build_forward_expand(
-                gf,
-                ggml_cpy(
-                    ctx0,
-                    new_conv,
-                    ggml_view_1d(
-                        ctx0,
-                        conv_state,
-                        ggml_nelements(new_conv),
-                        kv_head*d_conv*n_embd*ggml_element_size(new_conv)
-                        )
-                    )
-                );
-
-        auto * conv_kernel = model.layers[il].shortconv.conv;
-        auto * conv_out = ggml_ssm_conv(ctx0, bx, conv_kernel);
-        cb(conv_out, "model.layers.{}.conv.conv", il);
-
-        auto * y = ggml_mul(ctx0, c, conv_out);
-        y = build_lora_mm(model.layers[il].shortconv.out_proj, y);
-        cb(y, "model.layers.{}.conv.out_proj", il);
-        // {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
-        y = ggml_reshape_2d(ctx0, y, y->ne[0], n_seq_tokens * n_seqs);
-
-        return y;
-    }
-};
-
-struct llm_build_seed_oss : public llm_graph_context {
-    llm_build_seed_oss(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                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);
-                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);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-                cb(cur, "attn_out", il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network
-            cur = build_norm(ffn_inp,
-                    model.layers[il].attn_post_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_post_norm", il);
-
-            cur = build_ffn(cur,
-                    model.layers[il].ffn_up,   NULL, NULL,
-                    model.layers[il].ffn_gate, NULL, NULL,
-                    model.layers[il].ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-            cb(cur, "ffn_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-template <bool iswa>
-struct llm_build_smallthinker : public llm_graph_context{
-    llm_build_smallthinker(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params){
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        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) {
-            ggml_tensor * inpSA  = inpL;
-            ggml_tensor * probs  = nullptr;
-
-            probs = build_lora_mm(model.layers[il].ffn_gate_inp, inpL);  // [n_expert, n_tokens]
-            cb(probs, "ffn_moe_logits", il);
-
-            // norm
-            cur = build_norm(inpL,model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self_attention
-            {
-                // compute Q and K and RoPE them
-                struct ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                struct ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                struct ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                if (hparams.n_no_rope_layer_step == n_layer || il % hparams.n_no_rope_layer_step != 0) {
-                    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);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur = ggml_get_rows(ctx0, cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-                probs = ggml_get_rows(ctx0, probs, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // MoE branch
-            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            ggml_tensor * ffn_out =
-                build_moe_ffn(cur,
-                        nullptr,
-                        model.layers[il].ffn_up_exps,
-                        model.layers[il].ffn_gate_exps,
-                        model.layers[il].ffn_down_exps,
-                        nullptr,
-                        n_expert, n_expert_used,
-                        LLM_FFN_RELU, true,
-                        false, 0.0,
-                        static_cast<llama_expert_gating_func_type>(hparams.expert_gating_func),
-                        il, probs);
-
-            cb(ffn_out, "ffn_out", il);
-            cur = ffn_out;
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_grovemoe : public llm_graph_context {
-    llm_build_grovemoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        const int64_t n_chunk_expert = n_expert / hparams.n_group_experts;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        // inp_pos - contains the positions
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            // norm
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self_attention
-            {
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
-                cb(Kcur, "Kcur_normed", il);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // MoE branch
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            ggml_tensor * probs = build_lora_mm(model.layers[il].ffn_gate_inp, cur); // [n_expert, n_tokens]
-            cb(probs, "ffn_moe_logits", il);
-
-            ggml_tensor * moe_out =
-                build_moe_ffn(cur,
-                        nullptr,
-                        model.layers[il].ffn_up_exps,
-                        model.layers[il].ffn_gate_exps,
-                        model.layers[il].ffn_down_exps,
-                        nullptr,
-                        n_expert, n_expert_used,
-                        LLM_FFN_SILU, true,
-                        false, 0.0,
-                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                        il, probs);
-            cb(moe_out, "ffn_moe_out", il);
-            cur = moe_out;
-
-            // TODO: Only do the expert selection and weights once
-            moe_out =
-                build_moe_ffn(cur,
-                        nullptr,
-                        model.layers[il].ffn_up_chexps,
-                        model.layers[il].ffn_gate_chexps,
-                        model.layers[il].ffn_down_chexps,
-                        nullptr,
-                        n_chunk_expert, n_expert_used > n_chunk_expert ? n_chunk_expert : n_expert_used,
-                        LLM_FFN_SILU, true,
-                        false, 0.0,
-                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
-                        il, probs);
-            cb(moe_out, "ffn_adj_moe_out", il);
-
-            cur = ggml_add(ctx0, cur, ggml_scale(ctx0, moe_out, hparams.expert_group_scale));
-            cb(cur, "ffn_final_moe_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_apertus : public llm_graph_context {
-    llm_build_apertus(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        ggml_tensor * inp_pos = build_inp_pos();
-        auto * inp_attn = build_attn_inp_kv();
-
-        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
-
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            cur = build_norm(inpL,
-                    model.layers[il].attn_norm, nullptr,
-                    LLM_NORM_RMS, il);
-            cb(cur, "attn_norm", il);
-
-            // self-attention
-            {
-                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
-
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                cb(Vcur, "Vcur", il);
-
-                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
-                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_normed", il);
-
-                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
-                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
-                cb(Kcur, "Kcur_normed", il);
-
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                Qcur = ggml_rope_ext(
-                        ctx0, Qcur, inp_pos, rope_factors,
-                        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, rope_factors,
-                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
-                        ext_factor, attn_factor, beta_fast, beta_slow
-                        );
-
-                cb(Qcur, "Qcur_pos", il);
-                cb(Kcur, "Kcur_pos", il);
-                cb(Vcur, "Vcur_pos", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, model.layers[il].bo,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-                cb(cur, "attn_out", il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // feed-forward network with xIELU activation
-            {
-                cur = build_norm(ffn_inp,
-                        model.layers[il].ffn_norm, nullptr,
-                        LLM_NORM_RMS, il);
-                cb(cur, "ffn_norm", il);
-
-                // Up projection
-                ggml_tensor * up = build_lora_mm(model.layers[il].ffn_up, cur);
-                cb(up, "ffn_up", il);
-
-                float alpha_n_val = hparams.xielu_alpha_n[il];
-                float alpha_p_val = hparams.xielu_alpha_p[il];
-                float beta_val = hparams.xielu_beta[il];
-                float eps_val = hparams.xielu_eps[il];
-
-                // Apply xIELU activation
-                ggml_tensor * activated = ggml_xielu(ctx0, up, alpha_n_val, alpha_p_val, beta_val, eps_val);
-                cb(activated, "ffn_xielu", il);
-
-                // Down projection
-                cur = build_lora_mm(model.layers[il].ffn_down, activated);
-                cb(cur, "ffn_down", il);
-            }
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, nullptr,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_minimax_m2 : public llm_graph_context {
-    llm_build_minimax_m2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        // GGML_ASSERT(n_embd_head == hparams.n_rot); this is wrong in case of minimax, head_dim = 128, n_rot = 64
-
-        ggml_tensor * cur;
-        ggml_tensor * inpL;
-
-        inpL = build_inp_embd(model.tok_embd);
-
-        ggml_tensor * inp_pos = build_inp_pos();
-        auto inp_attn = build_attn_inp_kv();
-        ggml_tensor * inp_out_ids = build_inp_out_ids();
-
-        for (int il = 0; il < n_layer; ++il) {
-            ggml_tensor * inpSA = inpL;
-
-            cur = inpL;
-
-            // self_attention
-            {
-                cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
-                cb(cur, "attn_norm", il);
-
-                // compute Q and K and RoPE them
-                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
-                cb(Qcur, "Qcur", il);
-
-                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
-
-                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
-                cb(Vcur, "Vcur", il);
-
-                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(Qcur, "Qcur_normed", il);
-
-                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL,
-                        LLM_NORM_RMS, il);
-                cb(Kcur, "Kcur_normed", 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);
-                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
-
-                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);
-                cb(Vcur, "Vcur", il);
-
-                cur = build_attn(inp_attn,
-                        model.layers[il].wo, NULL,
-                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
-            }
-
-            if (il == n_layer - 1 && inp_out_ids) {
-                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
-                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            // MoE branch
-            cur = build_norm(ffn_inp,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_moe_ffn(cur,
-                    model.layers[il].ffn_gate_inp,
-                    model.layers[il].ffn_up_exps,
-                    model.layers[il].ffn_gate_exps,
-                    model.layers[il].ffn_down_exps,
-                    model.layers[il].ffn_exp_probs_b,
-                    n_expert, n_expert_used,
-                    LLM_FFN_SILU, true,
-                    false, 0.0,
-                    (llama_expert_gating_func_type) hparams.expert_gating_func,
-                    il);
-            cb(cur, "ffn_moe_out", il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-
-            cur = build_cvec(cur, il);
-            cb(cur, "l_out", il);
-
-            // input for next layer
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur,
-                model.output_norm, NULL,
-                LLM_NORM_RMS, -1);
-
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        // lm_head
-        cur = build_lora_mm(model.output, cur);
-
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-
-        ggml_build_forward_expand(gf, cur);
-    }
-};
-
-struct llm_build_cogvlm : public llm_graph_context {
-    llm_build_cogvlm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
-        const int64_t n_embd_head = hparams.n_embd_head_v;
-        float kq_scale = 1.0f / sqrtf(float(n_embd_head));
-
-        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
-        GGML_ASSERT(n_embd_head == hparams.n_rot);
-
-        ggml_tensor * inpL, * cur;
-        inpL = build_inp_embd(model.tok_embd);
-
-        ggml_tensor * inp_pos = build_inp_pos();
-
-        auto * inp_attn = build_attn_inp_kv();
-
-        // check ubatch to see if we have input tokens (text)
-        // or an input embedding vector (image)
-        bool is_text;
-        if (ubatch.token) {
-            is_text = true;
-        } else {
-            is_text = false;
-        }
-
-        for (int il = 0; il < n_layer; ++il) {
-            // get either the text or image weight tensors
-            ggml_tensor * wqkv, * wo;
-            ggml_tensor * ffn_gate, * ffn_down, * ffn_up;
-
-            if (is_text) {
-                wqkv = model.layers[il].wqkv;
-                wo = model.layers[il].wo;
-                ffn_gate = model.layers[il].ffn_gate;
-                ffn_down = model.layers[il].ffn_down;
-                ffn_up = model.layers[il].ffn_up;
-            } else {
-                wqkv = model.layers[il].visexp_attn_wqkv;
-                wo = model.layers[il].visexp_attn_wo;
-                ffn_gate = model.layers[il].visexp_ffn_gate;
-                ffn_down = model.layers[il].visexp_ffn_down;
-                ffn_up = model.layers[il].visexp_ffn_up;
-            }
-
-            ggml_tensor * inpSA = inpL;
-            cur = build_norm(inpSA, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
-
-            // build self attention
-            {
-                ggml_tensor * qkv = build_lora_mm(wqkv, cur);
-
-                // split qkv into Q, K, V along the first dimension
-                ggml_tensor * Qcur = ggml_view_3d(ctx0, qkv, n_embd_head, n_head, n_tokens, n_embd_head * sizeof(float),
-                    qkv->nb[1], 0);
-                ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),
-                    qkv->nb[1], n_embd * ggml_element_size(qkv));
-                ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),
-                    qkv->nb[1], 2 * n_embd * ggml_element_size(qkv));
-
-                Qcur = ggml_rope(ctx0, Qcur, inp_pos, n_embd_head, rope_type);
-                Kcur = ggml_rope(ctx0, Kcur, inp_pos, n_embd_head, rope_type);
-
-                cur = build_attn(inp_attn, wo, nullptr, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
-                cb(cur, "attn_out", il);
-            }
-
-            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
-            cb(ffn_inp, "ffn_inp", il);
-
-            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = build_ffn(cur,
-                    ffn_up,   NULL, NULL,
-                    ffn_gate, NULL, NULL,
-                    ffn_down, NULL, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, il);
-
-            cur = ggml_add(ctx0, cur, ffn_inp);
-            cb(cur, "ffn_out", il);
-
-            inpL = cur;
-        }
-
-        cur = inpL;
-
-        cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
-        cb(cur, "result_norm", -1);
-        res->t_embd = cur;
-
-        cur = build_lora_mm(model.output, cur);
-        cb(cur, "result_output", -1);
-        res->t_logits = cur;
-        ggml_build_forward_expand(gf, cur);
-
-    }
-};
-
 llama_memory_i * llama_model::create_memory(const llama_memory_params & params, const llama_cparams & cparams) const {
     llama_memory_i * res;
 
diff --git a/src/models/apertus.cpp b/src/models/apertus.cpp
new file mode 100644
index 0000000000..9af19c1bfe
--- /dev/null
+++ b/src/models/apertus.cpp
@@ -0,0 +1,125 @@
+#include "models.h"
+
+
+
+llm_build_apertus::llm_build_apertus(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    ggml_tensor * inp_pos  = build_inp_pos();
+    auto *        inp_attn = build_attn_inp_kv();
+
+    const float kq_scale =
+        hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            cb(Vcur, "Vcur", il);
+
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+            cb(Qcur, "Qcur_normed", il);
+
+            Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+            cb(Kcur, "Kcur_normed", il);
+
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors, 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, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                                 ext_factor, attn_factor, beta_fast, beta_slow);
+
+            cb(Qcur, "Qcur_pos", il);
+            cb(Kcur, "Kcur_pos", il);
+            cb(Vcur, "Vcur_pos", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+            cb(cur, "attn_out", il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network with xIELU activation
+        {
+            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, nullptr, LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            // Up projection
+            ggml_tensor * up = build_lora_mm(model.layers[il].ffn_up, cur);
+            cb(up, "ffn_up", il);
+
+            float alpha_n_val = hparams.xielu_alpha_n[il];
+            float alpha_p_val = hparams.xielu_alpha_p[il];
+            float beta_val    = hparams.xielu_beta[il];
+            float eps_val     = hparams.xielu_eps[il];
+
+            // Apply xIELU activation
+            ggml_tensor * activated = ggml_xielu(ctx0, up, alpha_n_val, alpha_p_val, beta_val, eps_val);
+            cb(activated, "ffn_xielu", il);
+
+            // Down projection
+            cur = build_lora_mm(model.layers[il].ffn_down, activated);
+            cb(cur, "ffn_down", il);
+        }
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "ffn_out", il);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, nullptr, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/arcee.cpp b/src/models/arcee.cpp
new file mode 100644
index 0000000000..aa6167dba1
--- /dev/null
+++ b/src/models/arcee.cpp
@@ -0,0 +1,135 @@
+#include "models.h"
+
+
+llm_build_arcee::llm_build_arcee(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            // rope freq factors for llama3; may return nullptr for llama2 and other models
+            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            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);
+            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);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, rope_factors,
+                    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, rope_factors,
+                    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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+            cb(cur, "attn_out", il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network
+        // ARCEE uses relu^2 instead of silu
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up,   NULL, NULL,
+                NULL,                      NULL, NULL,
+                model.layers[il].ffn_down, NULL, NULL,
+                NULL,
+                LLM_FFN_RELU_SQR, LLM_FFN_SEQ, il);
+        cb(cur, "ffn_out", il);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "ffn_out", il);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/arctic.cpp b/src/models/arctic.cpp
new file mode 100644
index 0000000000..e8f028a723
--- /dev/null
+++ b/src/models/arctic.cpp
@@ -0,0 +1,138 @@
+#include "models.h"
+
+
+llm_build_arctic::llm_build_arctic(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, NULL,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up,   NULL, NULL,
+                model.layers[il].ffn_gate, NULL, NULL,
+                model.layers[il].ffn_down, NULL, NULL,
+                NULL,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+
+        ggml_tensor * ffn_out = ggml_add(ctx0, cur, ffn_inp);
+        cb(ffn_out, "ffn_out", il);
+
+        // MoE
+        cur = build_norm(inpSA,
+                model.layers[il].ffn_norm_exps, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm_exps", il);
+
+        cur = build_moe_ffn(cur,
+                model.layers[il].ffn_gate_inp,
+                model.layers[il].ffn_up_exps,
+                model.layers[il].ffn_gate_exps,
+                model.layers[il].ffn_down_exps,
+                nullptr,
+                n_expert, n_expert_used,
+                LLM_FFN_SILU, true,
+                false, 0.0,
+                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                il);
+        cb(cur, "ffn_moe_out", il);
+
+        cur = ggml_add(ctx0, cur, ffn_out);
+        cb(cur, "ffn_out", il);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/arwkv7.cpp b/src/models/arwkv7.cpp
new file mode 100644
index 0000000000..107a3bef8d
--- /dev/null
+++ b/src/models/arwkv7.cpp
@@ -0,0 +1,86 @@
+#include "models.h"
+
+
+llm_build_arwkv7::llm_build_arwkv7(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv7_base(model, params) {
+    GGML_ASSERT(n_embd == hparams.n_embd_r());
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+    ggml_tensor * v_first = nullptr;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    auto * rs_inp = build_rs_inp();
+
+    const auto n_embd = hparams.n_embd;
+    const auto n_seq_tokens = ubatch.n_seq_tokens;
+    const auto n_seqs = ubatch.n_seqs;
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        const llama_layer * layer = &model.layers[il];
+        inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
+
+        ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il);
+
+        ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM_RMS, il);
+        cb(att_norm, "attn_norm", il);
+
+        ggml_tensor * x_prev = ggml_concat(
+                ctx0,
+                token_shift,
+                ggml_view_3d(ctx0, att_norm, n_embd, n_seq_tokens - 1, n_seqs, att_norm->nb[1], att_norm->nb[2], 0),
+                1
+                );
+
+        cur = build_rwkv7_time_mix(rs_inp, att_norm, x_prev, v_first, ubatch, il);
+
+        token_shift = ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(att_norm));
+        ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+        cb(ffn_inp, "ffn_inp", il);
+
+        cur     = ggml_reshape_2d(ctx0, cur,     n_embd, n_tokens);
+        ffn_inp = ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens);
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur     = ggml_get_rows(ctx0, cur,     inp_out_ids);
+            ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
+        }
+        // feed-forward network
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up,   NULL, NULL,
+                model.layers[il].ffn_gate, NULL, NULL,
+                model.layers[il].ffn_down, NULL, NULL,
+                NULL,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+    cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/baichuan.cpp b/src/models/baichuan.cpp
new file mode 100644
index 0000000000..c04b0c98b0
--- /dev/null
+++ b/src/models/baichuan.cpp
@@ -0,0 +1,122 @@
+#include "models.h"
+
+
+llm_build_baichuan::llm_build_baichuan(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = model.type == LLM_TYPE_7B ? build_inp_pos() : nullptr;
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            switch (model.type) {
+                case LLM_TYPE_7B:
+                    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
+                            );
+                    break;
+                case LLM_TYPE_13B:
+                    break;
+                default:
+                    GGML_ABORT("fatal error");
+            }
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, NULL,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network
+        {
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        }
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/bailingmoe.cpp b/src/models/bailingmoe.cpp
new file mode 100644
index 0000000000..ed56b9c471
--- /dev/null
+++ b/src/models/bailingmoe.cpp
@@ -0,0 +1,144 @@
+#include "models.h"
+
+
+llm_build_bailingmoe::llm_build_bailingmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            // rope freq factors for llama3; may return nullptr for llama2 and other models
+            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            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);
+            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);
+            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_rot, n_head,    n_tokens);
+            Kcur = ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_rot, n_head_kv, n_tokens);
+
+            Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, rope_factors,
+                    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, rope_factors,
+                    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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_rot)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        ggml_tensor * moe_out =
+            build_moe_ffn(cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    nullptr,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU, hparams.expert_weights_norm,
+                    false, hparams.expert_weights_scale,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                    il);
+        cb(moe_out, "ffn_moe_out", il);
+
+        // FFN shared expert
+        {
+            ggml_tensor * ffn_shexp = build_ffn(cur,
+                    model.layers[il].ffn_up_shexp,   NULL, NULL,
+                    model.layers[il].ffn_gate_shexp, NULL, NULL,
+                    model.layers[il].ffn_down_shexp, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(ffn_shexp, "ffn_shexp", il);
+
+            cur = ggml_add(ctx0, moe_out, ffn_shexp);
+            cb(cur, "ffn_out", il);
+        }
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/bailingmoe2.cpp b/src/models/bailingmoe2.cpp
new file mode 100644
index 0000000000..fbf7b210c4
--- /dev/null
+++ b/src/models/bailingmoe2.cpp
@@ -0,0 +1,135 @@
+#include "models.h"
+
+
+
+llm_build_bailingmoe2::llm_build_bailingmoe2(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    const int n_transformer_layers = n_layer - hparams.nextn_predict_layers;
+    for (int il = 0; il < n_transformer_layers; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self_attention
+        {
+            cur = build_lora_mm(model.layers[il].wqkv, cur);
+            cb(cur, "wqkv", il);
+
+            ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, n_embd_head * sizeof(float),
+                                              cur->nb[1], 0 * sizeof(float) * (n_embd));
+            ggml_tensor * Kcur = 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));
+            ggml_tensor * 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 = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+            cb(Qcur, "Qcur_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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+            cb(Kcur, "Kcur_normed", il);
+
+            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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_transformer_layers - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * sa_out = ggml_add(ctx0, cur, inpSA);
+        cb(sa_out, "sa_out", il);
+
+        // MoE branch
+        cur = build_norm(sa_out, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        if (static_cast<uint32_t>(il) < hparams.n_layer_dense_lead) {
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up, NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        } else {
+            ggml_tensor * moe_out = build_moe_ffn(cur,
+                model.layers[il].ffn_gate_inp,
+                model.layers[il].ffn_up_exps,
+                model.layers[il].ffn_gate_exps,
+                model.layers[il].ffn_down_exps,
+                model.layers[il].ffn_exp_probs_b,
+                n_expert, n_expert_used,
+                LLM_FFN_SILU, hparams.expert_weights_norm,
+                true, hparams.expert_weights_scale,
+                (llama_expert_gating_func_type) hparams.expert_gating_func,
+                il);
+            cb(moe_out, "ffn_moe_out", il);
+
+            {
+                ggml_tensor * ffn_shexp =
+                    build_ffn(cur,
+                        model.layers[il].ffn_up_shexp, NULL, NULL,
+                        model.layers[il].ffn_gate_shexp, NULL, NULL,
+                        model.layers[il].ffn_down_shexp, NULL, NULL,
+                        NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(ffn_shexp, "ffn_shexp", il);
+
+                cur = ggml_add(ctx0, moe_out, ffn_shexp);
+                cb(cur, "ffn_out", il);
+            }
+        }
+
+        cur = ggml_add(ctx0, cur, sa_out);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/bert.cpp b/src/models/bert.cpp
new file mode 100644
index 0000000000..3274fa3b99
--- /dev/null
+++ b/src/models/bert.cpp
@@ -0,0 +1,176 @@
+#include "models.h"
+
+
+
+llm_build_bert::llm_build_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+    ggml_tensor * inp_pos = nullptr;
+
+    if (model.arch != LLM_ARCH_JINA_BERT_V2) {
+        inp_pos = build_inp_pos();
+    }
+
+    // construct input embeddings (token, type, position)
+    inpL = build_inp_embd(model.tok_embd);
+
+    // token types are hardcoded to zero ("Sentence A")
+    if (model.type_embd) {
+        ggml_tensor * type_row0 = ggml_view_1d(ctx0, model.type_embd, n_embd, 0);
+        inpL                    = ggml_add(ctx0, inpL, type_row0);
+    }
+    if (model.arch == LLM_ARCH_BERT) {
+        inpL = ggml_add(ctx0, ggml_get_rows(ctx0, model.pos_embd, inp_pos), inpL);
+    }
+    cb(inpL, "inp_embd", -1);
+
+    // embed layer norm
+    inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
+    cb(inpL, "inp_norm", -1);
+
+    auto * inp_attn = build_attn_inp_no_cache();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * cur = inpL;
+
+        {
+            ggml_tensor * Qcur;
+            ggml_tensor * Kcur;
+            ggml_tensor * Vcur;
+
+            // self-attention
+            if (model.layers[il].wqkv) {
+                cur = build_lora_mm(model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
+
+                if (model.layers[il].bqkv) {
+                    cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                    cb(cur, "bqkv", il);
+                }
+
+                Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, n_embd_head * sizeof(float), cur->nb[1],
+                                    0 * sizeof(float) * (n_embd));
+                Kcur = 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));
+                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));
+            } else {
+                Qcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wq, cur), model.layers[il].bq);
+                Kcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wk, cur), model.layers[il].bk);
+                Vcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wv, cur), model.layers[il].bv);
+
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+            }
+
+            if (model.layers[il].attn_q_norm) {
+                Qcur = ggml_reshape_2d(ctx0, Qcur, n_embd_head * n_head, n_tokens);
+
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, model.layers[il].attn_q_norm_b, LLM_NORM, il);
+
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+            }
+
+            if (model.layers[il].attn_k_norm) {
+                Kcur = ggml_reshape_2d(ctx0, Kcur, n_embd_head * n_head_kv, n_tokens);
+
+                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, model.layers[il].attn_k_norm_b, LLM_NORM, il);
+
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+            }
+
+            // RoPE
+            if (model.arch == LLM_ARCH_NOMIC_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE ||
+                model.arch == LLM_ARCH_JINA_BERT_V3) {
+                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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+            cb(cur, "kqv_out", il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur  = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+        }
+
+        // re-add the layer input
+        cur = ggml_add(ctx0, cur, inpL);
+
+        // attention layer norm
+        cur = build_norm(cur, model.layers[il].attn_out_norm, model.layers[il].attn_out_norm_b, LLM_NORM, il);
+
+        if (model.layers[il].attn_norm_2 != nullptr) {
+            cur = ggml_add(ctx0, cur, inpL);  // re-add the layer input
+            cur = build_norm(cur, model.layers[il].attn_norm_2, model.layers[il].attn_norm_2_b, LLM_NORM, il);
+        }
+
+        ggml_tensor * ffn_inp = cur;
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network
+        if (hparams.moe_every_n_layers > 0 && il % hparams.moe_every_n_layers == 1) {
+            // MoE branch
+            cur = build_moe_ffn(cur, model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps, nullptr,
+                                model.layers[il].ffn_down_exps, nullptr, hparams.n_expert, hparams.n_expert_used,
+                                LLM_FFN_GELU, false, false, 0.0f, LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX, il);
+            cb(cur, "ffn_moe_out", il);
+        } else if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_NOMIC_BERT_MOE ||
+                   model.arch == LLM_ARCH_JINA_BERT_V3) {
+            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_GELU, LLM_FFN_SEQ, il);
+            cb(cur, "ffn_out", il);
+        } else if (model.arch == LLM_ARCH_JINA_BERT_V2) {
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up, NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, NULL,
+                    model.layers[il].ffn_gate ? LLM_FFN_GELU : LLM_FFN_GEGLU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        } else {
+            cur = build_ffn(cur,
+                model.layers[il].ffn_up, NULL, NULL,
+                model.layers[il].ffn_gate, NULL, NULL,
+                model.layers[il].ffn_down, NULL, NULL,
+                NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        }
+
+        // attentions bypass the intermediate layer
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        // output layer norm
+        cur = build_norm(cur, model.layers[il].layer_out_norm, model.layers[il].layer_out_norm_b, LLM_NORM, il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cb(cur, "result_embd", -1);
+    res->t_embd = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/bitnet.cpp b/src/models/bitnet.cpp
new file mode 100644
index 0000000000..331a3f1119
--- /dev/null
+++ b/src/models/bitnet.cpp
@@ -0,0 +1,160 @@
+#include "models.h"
+
+
+llm_build_bitnet::llm_build_bitnet(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            if (model.layers[il].wq_scale) {
+                Qcur = ggml_mul(ctx0, Qcur, model.layers[il].wq_scale);
+            }
+            cb(Qcur, "Qcur", il);
+            if (model.layers[il].bq) {
+                Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                cb(Qcur, "Qcur", il);
+            }
+
+            // B1.K
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            if (model.layers[il].wk_scale) {
+                Kcur = ggml_mul(ctx0, Kcur, model.layers[il].wk_scale);
+            }
+            cb(Kcur, "Kcur", il);
+            if (model.layers[il].bk) {
+                Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                cb(Kcur, "Kcur", il);
+            }
+
+            // B1.V
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            if (model.layers[il].wv_scale) {
+                Vcur = ggml_mul(ctx0, Vcur, model.layers[il].wv_scale);
+            }
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    NULL, NULL,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+
+            cur = build_norm(cur,
+                    model.layers[il].attn_sub_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_sub_norm", il);
+
+            cur = build_lora_mm(model.layers[il].wo, cur);
+            if (model.layers[il].wo_scale) {
+                cur = ggml_mul(ctx0, cur, model.layers[il].wo_scale);
+            }
+            if (model.layers[il].bo) {
+                cur = ggml_add(ctx0, cur, model.layers[il].bo);
+            }
+            cb(cur, "attn_out", il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward forward
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up,   NULL, model.layers[il].ffn_up_scale,
+                model.layers[il].ffn_gate, NULL, model.layers[il].ffn_gate_scale,
+                NULL,                      NULL, NULL,
+                NULL,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur, "ffn_sub_out", il);
+
+        cur = build_norm(cur,
+                model.layers[il].ffn_sub_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_sub_norm", il);
+
+        cur = build_lora_mm(model.layers[il].ffn_down, cur);
+        if (model.layers[il].ffn_down_scale) {
+            cur = ggml_mul(ctx0, cur, model.layers[il].ffn_down_scale);
+        }
+        cb(cur, "ffn_down", il);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    // FIXME: do not use model.tok_embd directly, duplicate as model.output
+    cur = build_lora_mm(model.tok_embd, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/bloom.cpp b/src/models/bloom.cpp
new file mode 100644
index 0000000000..2c552d1d15
--- /dev/null
+++ b/src/models/bloom.cpp
@@ -0,0 +1,101 @@
+#include "models.h"
+
+llm_build_bloom::llm_build_bloom(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    inpL = build_norm(inpL,
+            model.tok_norm,
+            model.tok_norm_b,
+            LLM_NORM, -1);
+    cb(inpL, "inp_norm", -1);
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm,
+                model.layers[il].attn_norm_b,
+                LLM_NORM, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            cur = build_lora_mm(model.layers[il].wqkv, cur);
+            cb(cur, "wqkv", il);
+
+            cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+            cb(cur, "bqkv", il);
+
+            ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
+            ggml_tensor * Kcur = 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));
+            ggml_tensor * 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));
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+        }
+
+        // Add the input
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // FF
+        {
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm,
+                    model.layers[il].ffn_norm_b,
+                    LLM_NORM, il);
+            cb(cur, "ffn_norm", 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_GELU, LLM_FFN_SEQ, il);
+            cb(cur, "ffn_out", il);
+        }
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = build_norm(inpL,
+            model.output_norm,
+            model.output_norm_b,
+            LLM_NORM, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/chameleon.cpp b/src/models/chameleon.cpp
new file mode 100644
index 0000000000..184511aed4
--- /dev/null
+++ b/src/models/chameleon.cpp
@@ -0,0 +1,178 @@
+#include "models.h"
+
+#include <float.h>
+
+llm_build_chameleon::llm_build_chameleon(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        if (hparams.swin_norm) {
+            cur = inpL;
+        } else {
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+        }
+
+        // self-attention
+        {
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            cb(Vcur, "Vcur", il);
+
+            if (model.layers[il].attn_q_norm) {
+                Qcur = ggml_view_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens,
+                        ggml_element_size(Qcur) * n_embd_head,
+                        ggml_element_size(Qcur) * n_embd_head * n_head,
+                        0);
+                cb(Qcur, "Qcur", il);
+
+                Qcur = build_norm(Qcur,
+                        model.layers[il].attn_q_norm,
+                        model.layers[il].attn_q_norm_b,
+                        LLM_NORM, il);
+                cb(Qcur, "Qcur", il);
+            }
+
+            if (model.layers[il].attn_k_norm) {
+                Kcur = ggml_view_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens,
+                        ggml_element_size(Kcur) * n_embd_head,
+                        ggml_element_size(Kcur) * n_embd_head * n_head_kv,
+                        0);
+                cb(Kcur, "Kcur", il);
+
+                Kcur = build_norm(Kcur,
+                        model.layers[il].attn_k_norm,
+                        model.layers[il].attn_k_norm_b,
+                        LLM_NORM, il);
+                cb(Kcur, "Kcur", 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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, nullptr,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        if (hparams.swin_norm) {
+            cur = build_norm(cur,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network
+        if (!hparams.swin_norm) {
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+        }
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up,   NULL, NULL,
+                model.layers[il].ffn_gate, NULL, NULL,
+                model.layers[il].ffn_down, NULL, NULL,
+                NULL,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+
+        if (hparams.swin_norm) {
+            cur = build_norm(cur,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+        }
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "ffn_out", il);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+    cb(cur, "result_output_with_img_logits", -1);
+
+    // TODO: this suppresses the output of image tokens, which is required to enable text-only outputs.
+    // Needs to be removed once image outputs are supported.
+    int img_token_end_idx = 8196;
+    int img_token_start_idx = 4;
+    int num_img_tokens = img_token_end_idx - img_token_start_idx;
+    // creates 1d tensor of size num_img_tokens and values -FLT_MAX,
+    // which ensures that text token values are always at least larger than image token values
+    ggml_tensor * img_logits = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, num_img_tokens);
+    img_logits = ggml_clamp(ctx0, img_logits, -FLT_MAX, -FLT_MAX);
+    cb(img_logits, "img_logits", -1);
+
+    cur = ggml_set_1d(ctx0, cur, img_logits, ggml_element_size(cur) * img_token_start_idx);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/chatglm.cpp b/src/models/chatglm.cpp
new file mode 100644
index 0000000000..2685d4fbcb
--- /dev/null
+++ b/src/models/chatglm.cpp
@@ -0,0 +1,132 @@
+#include "models.h"
+
+
+llm_build_chatglm::llm_build_chatglm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm,
+                NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            ggml_tensor * Qcur = nullptr;
+            ggml_tensor * Kcur = nullptr;
+            ggml_tensor * Vcur = nullptr;
+
+            if (model.layers[il].wqkv == nullptr) {
+                Qcur = build_lora_mm(model.layers[il].wq, cur);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                }
+                Kcur = build_lora_mm(model.layers[il].wk, cur);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                }
+                Vcur = build_lora_mm(model.layers[il].wv, cur);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                }
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+            } else {
+                cur = build_lora_mm(model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
+                if (model.layers[il].bqkv) {
+                    cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                    cb(cur, "bqkv", il);
+                }
+                Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
+                Kcur = 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));
+                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));
+            }
+
+            //printf("freq_base: %f freq_scale: %f ext_factor: %f attn_factor: %f\n", freq_base, freq_scale, ext_factor, attn_factor);
+            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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, NULL,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        // Add the input
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // FF
+        {
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm,
+                    NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    NULL,                      NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
+            cb(cur, "ffn_out", il);
+
+        }
+
+        inpL = ggml_add(ctx0, cur, ffn_inp);
+        cb(inpL, "l_out", il);
+    }
+
+    cur = build_norm(inpL,
+            model.output_norm,
+            NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/codeshell.cpp b/src/models/codeshell.cpp
new file mode 100644
index 0000000000..0b3bdbff52
--- /dev/null
+++ b/src/models/codeshell.cpp
@@ -0,0 +1,111 @@
+#include "models.h"
+
+llm_build_codeshell::llm_build_codeshell(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm,
+                model.layers[il].attn_norm_b,
+                LLM_NORM, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            cur = build_lora_mm(model.layers[il].wqkv, cur);
+            cb(cur, "wqkv", il);
+
+            cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+            cb(cur, "bqkv", il);
+
+            ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
+            ggml_tensor * Kcur = 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));
+            ggml_tensor * 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
+                    );
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+        }
+
+        // add the input
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // FF
+        {
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm,
+                    model.layers[il].ffn_norm_b,
+                    LLM_NORM, il);
+            cb(cur, "ffn_norm", 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_GELU, LLM_FFN_SEQ, il);
+            cb(cur, "ffn_out", il);
+        }
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = build_norm(inpL,
+            model.output_norm,
+            model.output_norm_b,
+            LLM_NORM, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/cogvlm.cpp b/src/models/cogvlm.cpp
new file mode 100644
index 0000000000..edf0d1424c
--- /dev/null
+++ b/src/models/cogvlm.cpp
@@ -0,0 +1,100 @@
+#include "models.h"
+
+llm_build_cogvlm::llm_build_cogvlm(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    float         kq_scale    = 1.0f / sqrtf(float(n_embd_head));
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor *inpL, *cur;
+    inpL = build_inp_embd(model.tok_embd);
+
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    // check ubatch to see if we have input tokens (text)
+    // or an input embedding vector (image)
+    bool is_text;
+    if (ubatch.token) {
+        is_text = true;
+    } else {
+        is_text = false;
+    }
+
+    for (int il = 0; il < n_layer; ++il) {
+        // get either the text or image weight tensors
+        ggml_tensor *wqkv, *wo;
+        ggml_tensor *ffn_gate, *ffn_down, *ffn_up;
+
+        if (is_text) {
+            wqkv     = model.layers[il].wqkv;
+            wo       = model.layers[il].wo;
+            ffn_gate = model.layers[il].ffn_gate;
+            ffn_down = model.layers[il].ffn_down;
+            ffn_up   = model.layers[il].ffn_up;
+        } else {
+            wqkv     = model.layers[il].visexp_attn_wqkv;
+            wo       = model.layers[il].visexp_attn_wo;
+            ffn_gate = model.layers[il].visexp_ffn_gate;
+            ffn_down = model.layers[il].visexp_ffn_down;
+            ffn_up   = model.layers[il].visexp_ffn_up;
+        }
+
+        ggml_tensor * inpSA = inpL;
+        cur                 = build_norm(inpSA, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+
+        // build self attention
+        {
+            ggml_tensor * qkv = build_lora_mm(wqkv, cur);
+
+            // split qkv into Q, K, V along the first dimension
+            ggml_tensor * Qcur =
+                ggml_view_3d(ctx0, qkv, n_embd_head, n_head, n_tokens, n_embd_head * sizeof(float), qkv->nb[1], 0);
+            ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),
+                                              qkv->nb[1], n_embd * ggml_element_size(qkv));
+            ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, n_embd_head, n_head_kv, n_tokens, n_embd_head * sizeof(float),
+                                              qkv->nb[1], 2 * n_embd * ggml_element_size(qkv));
+
+            Qcur = ggml_rope(ctx0, Qcur, inp_pos, n_embd_head, rope_type);
+            Kcur = ggml_rope(ctx0, Kcur, inp_pos, n_embd_head, rope_type);
+
+            cur = build_attn(inp_attn,
+                wo, nullptr,
+                Qcur, Kcur, Vcur,
+                nullptr, nullptr, nullptr,
+                kq_scale, il);
+            cb(cur, "attn_out", il);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                ffn_up, NULL, NULL,
+                ffn_gate, NULL, NULL,
+                ffn_down, NULL, NULL,
+                NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "ffn_out", il);
+
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/cohere2-iswa.cpp b/src/models/cohere2-iswa.cpp
new file mode 100644
index 0000000000..b18aa8c4e6
--- /dev/null
+++ b/src/models/cohere2-iswa.cpp
@@ -0,0 +1,131 @@
+#include "models.h"
+
+llm_build_cohere2_iswa::llm_build_cohere2_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    const float f_logit_scale = hparams.f_logit_scale;
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv_iswa();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        const bool is_swa = hparams.is_swa(il);
+
+        // norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM, il);
+        cb(cur, "attn_norm", il);
+        ggml_tensor * ffn_inp = cur;
+
+        // self-attention
+        {
+            // rope freq factors for 128k context
+            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            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);
+            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);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            if (is_swa) {
+                Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, rope_factors,
+                        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, rope_factors,
+                        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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur     = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpL    = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
+        }
+
+        ggml_tensor * attn_out = cur;
+
+        // feed-forward network
+        {
+            cur = build_ffn(ffn_inp,
+                    model.layers[il].ffn_up, NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        }
+
+        // add together residual + FFN + self-attention
+        cur = ggml_add(ctx0, cur, inpL);
+        cur = ggml_add(ctx0, cur, attn_out);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    if (f_logit_scale) {
+        cur = ggml_scale(ctx0, cur, f_logit_scale);
+    }
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/command-r.cpp b/src/models/command-r.cpp
new file mode 100644
index 0000000000..4d3b643b44
--- /dev/null
+++ b/src/models/command-r.cpp
@@ -0,0 +1,122 @@
+#include "models.h"
+
+
+
+llm_build_command_r::llm_build_command_r(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    const float f_logit_scale = hparams.f_logit_scale;
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        // norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM, il);
+        cb(cur, "attn_norm", il);
+
+        ggml_tensor * ffn_inp = cur;
+
+        // self-attention
+        {
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            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);
+            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);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            if (model.layers[il].attn_q_norm) {
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM, il);
+                cb(Qcur, "Qcur", 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);
+
+            if (model.layers[il].attn_k_norm) {
+                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM, il);
+                cb(Kcur, "Kcur", il);
+            }
+            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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+        }
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur     = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpL    = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
+        }
+        ggml_tensor * attn_out = cur;
+
+        // feed-forward network
+        {
+            cur = build_ffn(ffn_inp,
+                    model.layers[il].ffn_up, NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        }
+        // add together residual + FFN + self-attention
+        cur = ggml_add(ctx0, cur, inpL);
+        cur = ggml_add(ctx0, cur, attn_out);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    if (f_logit_scale) {
+        cur = ggml_scale(ctx0, cur, f_logit_scale);
+    }
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/dbrx.cpp b/src/models/dbrx.cpp
new file mode 100644
index 0000000000..6d2a0ebf1b
--- /dev/null
+++ b/src/models/dbrx.cpp
@@ -0,0 +1,123 @@
+#include "models.h"
+
+
+llm_build_dbrx::llm_build_dbrx(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            ggml_tensor * Qcur = nullptr;
+            ggml_tensor * Kcur = nullptr;
+            ggml_tensor * Vcur = nullptr;
+
+            cur = build_lora_mm(model.layers[il].wqkv, cur);
+            cb(cur, "wqkv", il);
+
+            cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+            cb(cur, "wqkv_clamped", il);
+
+            Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
+            Kcur = 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));
+            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
+                    );
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, NULL,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network
+        // MoE branch
+        cur = build_norm(ffn_inp,
+                model.layers[il].attn_out_norm, NULL,
+                LLM_NORM, il);
+        cb(cur, "attn_out_norm", il);
+
+        cur = build_moe_ffn(cur,
+                model.layers[il].ffn_gate_inp,
+                model.layers[il].ffn_up_exps,
+                model.layers[il].ffn_gate_exps,
+                model.layers[il].ffn_down_exps,
+                nullptr,
+                n_expert, n_expert_used,
+                LLM_FFN_SILU, true,
+                false, 0.0,
+                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                il);
+        cb(cur, "ffn_moe_out", il);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "ffn_out", il);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/deci.cpp b/src/models/deci.cpp
new file mode 100644
index 0000000000..7410a3a46d
--- /dev/null
+++ b/src/models/deci.cpp
@@ -0,0 +1,135 @@
+#include "models.h"
+
+
+
+llm_build_deci::llm_build_deci(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    const float kq_scale =
+        hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA     = inpL;
+        const int64_t n_head_kv = hparams.n_head_kv(il);
+        const int64_t n_head    = hparams.n_head(il);
+        const int64_t n_ff      = hparams.n_ff(il);
+
+        if (n_head == 0) {
+            // attention-free layer of Llama-3_1-Nemotron-51B
+            cur = inpL;
+        } else {
+            // norm
+            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+        }
+        if (n_head > 0 && n_head_kv == 0) {
+            // "linear attention" of Llama-3_1-Nemotron-51B
+            cur = build_lora_mm(model.layers[il].wo, cur);
+            cb(cur, "wo", il);
+        } else if (n_head > 0) {
+            // self-attention
+            // rope freq factors for llama3; may return nullptr for llama2 and other models
+            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            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);
+            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);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors, 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, rope_factors, 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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+        }
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        // FFN-free layer of Llama-3_1-Nemotron-Ultra-253B
+        if (n_ff == 0) {
+            continue;
+        }
+        // modified to support attention-free layer of Llama-3_1-Nemotron-51B
+        ggml_tensor * ffn_inp = cur;
+        if (n_head > 0) {
+            ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+        }
+        // feed-forward network
+        if (model.layers[il].ffn_gate_inp == nullptr) {
+            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_ffn(cur,
+                model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+                model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        }
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "ffn_out", il);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/deepseek.cpp b/src/models/deepseek.cpp
new file mode 100644
index 0000000000..17866c0d88
--- /dev/null
+++ b/src/models/deepseek.cpp
@@ -0,0 +1,144 @@
+#include "models.h"
+
+
+
+llm_build_deepseek::llm_build_deepseek(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    const float kq_scale =
+        hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            // rope freq factors for llama3; may return nullptr for llama2 and other models
+            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            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);
+            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);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors, 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, rope_factors, 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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+        }
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        if ((uint32_t) il < hparams.n_layer_dense_lead) {
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up, NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        } else {
+            // MoE branch
+            ggml_tensor * moe_out = build_moe_ffn(cur,
+                model.layers[il].ffn_gate_inp,
+                model.layers[il].ffn_up_exps,
+                model.layers[il].ffn_gate_exps,
+                model.layers[il].ffn_down_exps,
+                nullptr,
+                n_expert, n_expert_used,
+                LLM_FFN_SILU, false,
+                false, hparams.expert_weights_scale,
+                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                il);
+            cb(moe_out, "ffn_moe_out", il);
+
+            // FFN shared expert
+            {
+                ggml_tensor * ffn_shexp =
+                    build_ffn(cur,
+                        model.layers[il].ffn_up_shexp, NULL, NULL,
+                        model.layers[il].ffn_gate_shexp, NULL, NULL,
+                        model.layers[il].ffn_down_shexp, NULL, NULL,
+                        NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(ffn_shexp, "ffn_shexp", il);
+
+                cur = ggml_add(ctx0, moe_out, ffn_shexp);
+                cb(cur, "ffn_out", il);
+            }
+        }
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/deepseek2.cpp b/src/models/deepseek2.cpp
new file mode 100644
index 0000000000..68f72f72bb
--- /dev/null
+++ b/src/models/deepseek2.cpp
@@ -0,0 +1,236 @@
+#include "models.h"
+
+
+
+llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params) {
+    bool is_lite = (hparams.n_layer == 27);
+
+    const bool is_mla = (hparams.n_embd_head_k_mla != 0 && hparams.n_embd_head_v_mla != 0);
+
+    // note: these are the actual head sizes you get when treating as MHA or after "decompression" using wv_b for MLA
+    const int64_t n_embd_head_k = is_mla ? hparams.n_embd_head_k_mla : hparams.n_embd_head_k;
+    const int64_t n_embd_head_v = is_mla ? hparams.n_embd_head_v_mla : hparams.n_embd_head_v;
+
+    const int64_t n_embd_head_qk_rope = hparams.n_rot;
+    const int64_t n_embd_head_qk_nope = n_embd_head_k - n_embd_head_qk_rope;
+
+    const uint32_t kv_lora_rank = hparams.n_lora_kv;
+
+    // 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));
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    // {n_embd, n_tokens}
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self_attention
+        {
+            ggml_tensor * q = NULL;
+            if (!is_lite) {
+                q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
+                cb(q, "q", il);
+
+                q = build_norm(q, model.layers[il].attn_q_a_norm, nullptr, LLM_NORM_RMS, il);
+                cb(q, "q", il);
+
+                q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
+                cb(q, "q", il);
+            } else {
+                q = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(q, "q", il);
+            }
+            // split into {n_embd_head_qk_nope, n_head, n_tokens}
+            ggml_tensor * q_nope =
+                ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
+                             ggml_row_size(q->type, n_embd_head_k) * n_head, 0);
+            cb(q_nope, "q_nope", il);
+
+            // and {n_embd_head_qk_rope, n_head, n_tokens}
+            ggml_tensor * q_pe = ggml_view_3d(
+                ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_row_size(q->type, n_embd_head_k),
+                ggml_row_size(q->type, n_embd_head_k) * n_head, ggml_row_size(q->type, n_embd_head_qk_nope));
+            cb(q_pe, "q_pe", il);
+
+            ggml_tensor * kv_cmpr_pe = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
+            cb(kv_cmpr_pe, "kv_cmpr_pe", il);
+
+            // split into {kv_lora_rank, n_tokens}
+            ggml_tensor * kv_cmpr =
+                ggml_view_2d(ctx0, kv_cmpr_pe, kv_lora_rank, n_tokens,
+                             ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope), 0);
+            cb(kv_cmpr, "kv_cmpr", il);
+
+            // and {n_embd_head_qk_rope, 1, n_tokens}
+            ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_cmpr_pe, n_embd_head_qk_rope, 1, n_tokens,
+                                              ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
+                                              ggml_row_size(kv_cmpr_pe->type, kv_lora_rank + n_embd_head_qk_rope),
+                                              ggml_row_size(kv_cmpr_pe->type, kv_lora_rank));
+            cb(k_pe, "k_pe", il);
+
+            q_pe = ggml_rope_ext(ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                                 ext_factor, attn_factor, beta_fast, beta_slow);
+            cb(q_pe, "q_pe", il);
+
+            k_pe = ggml_rope_ext(ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                                 ext_factor, attn_factor, beta_fast, beta_slow);
+            cb(k_pe, "k_pe", il);
+
+            kv_cmpr = build_norm(kv_cmpr, model.layers[il].attn_kv_a_norm, nullptr, LLM_NORM_RMS, il);
+            cb(kv_cmpr, "kv_cmpr", il);
+
+            if (is_mla) {
+                // {n_embd_head_qk_nope, n_tokens, n_head}
+                q_nope = ggml_permute(ctx0, q_nope, 0, 2, 1, 3);
+                cb(q_nope, "q_nope_perm", il);
+
+                // {n_embd_head_qk_nope, kv_lora_rank, n_head} x {n_embd_head_qk_nope, n_tokens, n_head}
+                ggml_tensor * q_nope_absorbed = ggml_mul_mat(ctx0, model.layers[il].wk_b, q_nope);
+                cb(q_nope_absorbed, "q_nope_absorbed", il);
+
+                // {kv_lora_rank, n_head, n_tokens}
+                q_nope_absorbed = ggml_permute(ctx0, q_nope_absorbed, 0, 2, 1, 3);
+                cb(q_nope_absorbed, "q_nope_absorbed_perm", il);
+
+                // {n_embd_head_qk_rope + kv_lora_rank, n_head, n_tokens}
+                // note: rope must go first for in-place context shifting in build_rope_shift()
+                ggml_tensor * Qcur = ggml_concat(ctx0, q_pe, q_nope_absorbed, 0);
+                cb(Qcur, "Qcur", il);
+
+                kv_cmpr = ggml_reshape_3d(ctx0, kv_cmpr, kv_lora_rank, 1, n_tokens);
+                cb(kv_cmpr, "kv_cmpr_reshape", il);
+
+                // {n_embd_head_qk_rope + kv_lora_rank, 1, n_tokens}
+                ggml_tensor * Kcur = ggml_concat(ctx0, k_pe, kv_cmpr, 0);
+                cb(Kcur, "Kcur", il);
+
+                // {kv_lora_rank, 1, n_tokens}
+                ggml_tensor * Vcur = kv_cmpr;
+                cb(Vcur, "Vcur", 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,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, model.layers[il].wv_b, kq_scale, il);
+            } else {
+                ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_cmpr);
+                cb(kv, "kv", il);
+
+                // split into {n_embd_head_qk_nope, n_head, n_tokens}
+                ggml_tensor * k_nope =
+                    ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
+                                 ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v),
+                                 ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v) * n_head, 0);
+                cb(k_nope, "k_nope_view", il);
+
+                // and {n_embd_head_v, n_head, n_tokens}
+                ggml_tensor * Vcur = ggml_view_3d(ctx0, kv, n_embd_head_v, n_head, n_tokens,
+                                                  ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v),
+                                                  ggml_row_size(kv->type, n_embd_head_qk_nope + n_embd_head_v) * n_head,
+                                                  ggml_row_size(kv->type, n_embd_head_qk_nope));
+                cb(Vcur, "Vcur_view", il);
+
+                Vcur = ggml_cont(ctx0, Vcur);
+                cb(Vcur, "Vcur_cont", il);
+
+                // note: rope must go first for in-place context shifting in build_rope_shift()
+                ggml_tensor * Qcur = ggml_concat(ctx0, q_pe, q_nope, 0);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = ggml_concat(ctx0, ggml_repeat(ctx0, k_pe, q_pe), k_nope, 0);
+                cb(Kcur, "Kcur", 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,
+                            Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+            }
+        }
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        if ((uint32_t) il < hparams.n_layer_dense_lead) {
+            cur = build_ffn(cur,
+                model.layers[il].ffn_up, NULL, NULL,
+                model.layers[il].ffn_gate, NULL, NULL,
+                model.layers[il].ffn_down, NULL, NULL,
+                NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        } else {
+            // MoE branch
+            ggml_tensor * moe_out = build_moe_ffn(cur,
+                model.layers[il].ffn_gate_inp,
+                model.layers[il].ffn_up_exps,
+                model.layers[il].ffn_gate_exps,
+                model.layers[il].ffn_down_exps,
+                model.layers[il].ffn_exp_probs_b,
+                n_expert, n_expert_used,
+                LLM_FFN_SILU, hparams.expert_weights_norm,
+                true, hparams.expert_weights_scale,
+                (llama_expert_gating_func_type) hparams.expert_gating_func,
+                il);
+            cb(moe_out, "ffn_moe_out", il);
+
+            // FFN shared expert
+            {
+                ggml_tensor * ffn_shexp =
+                    build_ffn(cur,
+                        model.layers[il].ffn_up_shexp, NULL, NULL,
+                        model.layers[il].ffn_gate_shexp, NULL, NULL,
+                        model.layers[il].ffn_down_shexp, NULL, NULL,
+                        NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(ffn_shexp, "ffn_shexp", il);
+
+                cur = ggml_add(ctx0, moe_out, ffn_shexp);
+                cb(cur, "ffn_out", il);
+            }
+        }
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = ggml_mul_mat(ctx0, model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/dots1.cpp b/src/models/dots1.cpp
new file mode 100644
index 0000000000..09c36f82fe
--- /dev/null
+++ b/src/models/dots1.cpp
@@ -0,0 +1,134 @@
+#include "models.h"
+
+
+
+llm_build_dots1::llm_build_dots1(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self_attention
+        {
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+            cb(Qcur, "Qcur_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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+            cb(Kcur, "Kcur_normed", il);
+
+            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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+        }
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // MoE branch
+        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        if ((uint32_t) il < hparams.n_layer_dense_lead) {
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up, NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        } else {
+            ggml_tensor * moe_out = build_moe_ffn(cur,
+                model.layers[il].ffn_gate_inp,
+                model.layers[il].ffn_up_exps,
+                model.layers[il].ffn_gate_exps,
+                model.layers[il].ffn_down_exps,
+                model.layers[il].ffn_exp_probs_b,
+                n_expert, n_expert_used,
+                LLM_FFN_SILU, hparams.expert_weights_norm,
+                true, hparams.expert_weights_scale,
+                (llama_expert_gating_func_type) hparams.expert_gating_func,
+                il);
+            cb(moe_out, "ffn_moe_out", il);
+
+            {
+                ggml_tensor * ffn_shexp =
+                    build_ffn(cur,
+                        model.layers[il].ffn_up_shexp, NULL, NULL,
+                        model.layers[il].ffn_gate_shexp, NULL, NULL,
+                        model.layers[il].ffn_down_shexp, NULL, NULL,
+                        NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(ffn_shexp, "ffn_shexp", il);
+
+                cur = ggml_add(ctx0, moe_out, ffn_shexp);
+                cb(cur, "ffn_out", il);
+            }
+        }
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/dream.cpp b/src/models/dream.cpp
new file mode 100644
index 0000000000..2aafbae139
--- /dev/null
+++ b/src/models/dream.cpp
@@ -0,0 +1,105 @@
+#include "models.h"
+
+
+
+llm_build_dream::llm_build_dream(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params) {
+    //copied from qwen2
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_no_cache();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            // 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);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            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);
+
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+        }
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network
+        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+            model.layers[il].ffn_up, NULL, NULL,
+            model.layers[il].ffn_gate, NULL, NULL,
+            model.layers[il].ffn_down, NULL, NULL,
+            NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/ernie4-5-moe.cpp b/src/models/ernie4-5-moe.cpp
new file mode 100644
index 0000000000..0d96d14e6f
--- /dev/null
+++ b/src/models/ernie4-5-moe.cpp
@@ -0,0 +1,150 @@
+#include "models.h"
+
+
+
+llm_build_ernie4_5_moe::llm_build_ernie4_5_moe(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    GGML_ASSERT(hparams.n_moe_layer_step > 0 && "Ernie 4.5 MoE requires n_moe_layer_step > 0");
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+        // norm
+        {
+            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+        }
+        // self-attention
+        {
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            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);
+            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);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, NULL,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+            cb(cur, "attn_out", il);
+        }
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network
+        bool is_moe_layer =
+            static_cast<uint32_t>(il) >= hparams.n_layer_dense_lead && (il + 1) % hparams.n_moe_layer_step == 0;
+
+        if (!is_moe_layer) {
+            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up, NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        } else {
+            // MoE branch
+            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            ggml_tensor * moe_out = build_moe_ffn(cur,
+                                        model.layers[il].ffn_gate_inp,
+                                        model.layers[il].ffn_up_exps,
+                                        model.layers[il].ffn_gate_exps,
+                                        model.layers[il].ffn_down_exps,
+                                        model.layers[il].ffn_exp_probs_b,
+                                        n_expert, n_expert_used,
+                                        LLM_FFN_SILU, true,
+                                        false, 0.0,
+                                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                                        il);
+            cb(moe_out, "ffn_moe_out", il);
+
+            // Shared expert (if present)
+            if (hparams.n_ff_shexp > 0) {
+                ggml_tensor * ffn_shexp =
+                    build_ffn(cur,
+                        model.layers[il].ffn_up_shexp, NULL, NULL,
+                        model.layers[il].ffn_gate_shexp, NULL, NULL,
+                        model.layers[il].ffn_down_shexp, NULL, NULL,
+                        NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(ffn_shexp, "ffn_shexp", il);
+
+                cur = ggml_add(ctx0, moe_out, ffn_shexp);
+            } else {
+                cur = moe_out;
+            }
+            cb(cur, "ffn_out", il);
+        }
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "ffn_out", il);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/ernie4-5.cpp b/src/models/ernie4-5.cpp
new file mode 100644
index 0000000000..99962af111
--- /dev/null
+++ b/src/models/ernie4-5.cpp
@@ -0,0 +1,111 @@
+#include "models.h"
+
+
+
+llm_build_ernie4_5::llm_build_ernie4_5(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        {
+            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+        }
+        // self-attention
+        {
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            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);
+            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);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, NULL,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+        }
+        if (il == n_layer - 1) {
+            // skip computing output for unused tokens
+            ggml_tensor * inp_out_ids = build_inp_out_ids();
+            cur                       = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA                     = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network
+        {
+            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up, NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        }
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/exaone.cpp b/src/models/exaone.cpp
new file mode 100644
index 0000000000..62602b284d
--- /dev/null
+++ b/src/models/exaone.cpp
@@ -0,0 +1,114 @@
+#include "models.h"
+
+
+
+llm_build_exaone::llm_build_exaone(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            // rope freq factors for llama3; may return nullptr for llama2 and other models
+            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            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);
+            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);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors, 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, rope_factors, 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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+        }
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network
+        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up, NULL, NULL,
+                model.layers[il].ffn_gate, NULL, NULL,
+                model.layers[il].ffn_down, NULL, NULL,
+                NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "ffn_out", il);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/exaone4.cpp b/src/models/exaone4.cpp
new file mode 100644
index 0000000000..8b7e3dc06e
--- /dev/null
+++ b/src/models/exaone4.cpp
@@ -0,0 +1,123 @@
+#include "models.h"
+
+
+template <bool iswa>
+llm_build_exaone4<iswa>::llm_build_exaone4(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_ASSERT(n_embd_head == hparams.n_embd_head_v);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    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) {
+        ggml_tensor * inpSA = inpL;
+
+        // use RoPE for SWA layers or non-SWA models
+        const bool use_rope = hparams.is_swa(il) || hparams.swa_type == LLAMA_SWA_TYPE_NONE;
+
+        cur = inpL;
+
+        // self-attention
+        {
+            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+            cb(Qcur, "Qcur_normed", il);
+            cb(Kcur, "Kcur_normed", il);
+
+            if (use_rope) {
+                Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors, 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, rope_factors, 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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, NULL,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+            cb(cur, "attn_out", il);
+        }
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_post_norm", il);
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network
+        cur = build_ffn(ffn_inp,
+                model.layers[il].ffn_up, NULL, NULL,
+                model.layers[il].ffn_gate, NULL, NULL,
+                model.layers[il].ffn_down, NULL, NULL, NULL,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+
+        cur = build_norm(cur, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, -1);
+        cb(cur, "ffn_post_norm", -1);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+// Explicit template instantiations
+template struct llm_build_exaone4<false>;
+template struct llm_build_exaone4<true>;
diff --git a/src/models/falcon-h1.cpp b/src/models/falcon-h1.cpp
new file mode 100644
index 0000000000..b641a09407
--- /dev/null
+++ b/src/models/falcon-h1.cpp
@@ -0,0 +1,113 @@
+#include "models.h"
+
+
+
+llm_build_falcon_h1::llm_build_falcon_h1(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context_mamba(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    // Build the inputs in the recurrent & kv cache
+    auto * inp = build_inp_mem_hybrid();
+
+    const float kq_scale =
+        hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+        cb(Qcur, "Qcur", il);
+
+        ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+        cb(Kcur, "Kcur", il);
+
+        ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+        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);
+
+        Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+        Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, hparams.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, hparams.rope_type, n_ctx_orig, freq_base, freq_scale,
+                             ext_factor, attn_factor, beta_fast, beta_slow);
+
+        cb(Qcur, "Qcur-post-rope", il);
+        cb(Kcur, "Kcur-post-rope", il);
+        cb(Vcur, "Vcur-post-rope", il);
+
+        ggml_tensor * attn_out = build_attn(inp->get_attn(),
+                                    model.layers[il].wo, NULL,
+                                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+        cb(attn_out, "attn_out", il);
+
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        // Mamba2 layer
+        cb(cur, "ssm_in", il);
+
+        ggml_tensor * ssm_out = build_mamba2_layer(inp->get_recr(), cur, model, ubatch, il);
+        cb(ssm_out, "ssm_out", il);
+
+        // // Aggregation
+        cur   = ggml_add(ctx0, attn_out, ssm_out);
+        inpSA = ggml_add(ctx0, cur, inpSA);
+        cb(cur, "layer_out", il);
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        ggml_tensor * ffn_inp = inpSA;
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network
+        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+                model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+
+        cur = ggml_add(ctx0, cur, inpSA);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/falcon.cpp b/src/models/falcon.cpp
new file mode 100644
index 0000000000..db1ccdb500
--- /dev/null
+++ b/src/models/falcon.cpp
@@ -0,0 +1,120 @@
+#include "models.h"
+
+
+llm_build_falcon::llm_build_falcon(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * attn_norm;
+
+        attn_norm = build_norm(inpL,
+                model.layers[il].attn_norm,
+                model.layers[il].attn_norm_b,
+                LLM_NORM, il);
+        cb(attn_norm, "attn_norm", il);
+
+        // self-attention
+        {
+            if (model.layers[il].attn_norm_2) {
+                // Falcon-40B
+                cur = build_norm(inpL,
+                        model.layers[il].attn_norm_2,
+                        model.layers[il].attn_norm_2_b,
+                        LLM_NORM, il);
+                cb(cur, "attn_norm_2", il);
+            } else {
+                cur = attn_norm;
+            }
+
+            cur = build_lora_mm(model.layers[il].wqkv, cur);
+            cb(cur, "wqkv", il);
+
+            ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
+            ggml_tensor * Kcur = 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));
+            ggml_tensor * 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));
+
+            // using mode = 2 for neox mode
+            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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, NULL,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur       = ggml_get_rows(ctx0,       cur, inp_out_ids);
+            inpL      = ggml_get_rows(ctx0,      inpL, inp_out_ids);
+            attn_norm = ggml_get_rows(ctx0, attn_norm, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = cur;
+
+        // feed forward
+        {
+            cur = build_ffn(attn_norm, // !! use the attn norm, not the result
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    NULL,                      NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_GELU, LLM_FFN_SEQ, il);
+            cb(cur, "ffn_out", il);
+        }
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cur = ggml_add(ctx0, cur, inpL);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    // norm
+    cur = build_norm(cur,
+            model.output_norm,
+            model.output_norm_b,
+            LLM_NORM, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/gemma-embedding.cpp b/src/models/gemma-embedding.cpp
new file mode 100644
index 0000000000..90a98f7abf
--- /dev/null
+++ b/src/models/gemma-embedding.cpp
@@ -0,0 +1,120 @@
+#include "models.h"
+
+
+
+llm_build_gemma_embedding::llm_build_gemma_embedding(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;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
+    if (ubatch.token) {
+        inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
+        cb(inpL, "inp_scaled", -1);
+    }
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_no_cache();
+
+    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);
+
+        // norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+            cb(Qcur, "Qcur_normed", il);
+
+            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                                 ext_factor, attn_factor, beta_fast, beta_slow);
+
+            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+            cb(Kcur, "Kcur_normed", il);
+
+            Kcur = ggml_rope_ext(ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                                 ext_factor, attn_factor, beta_fast, beta_slow);
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/model.py#L315
+            Qcur = ggml_scale(ctx0, Qcur, hparams.f_attention_scale);
+
+            cur =
+                build_attn(inp_attn,
+                    model.layers[il].wo, NULL,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur  = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+        }
+
+        cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_post_norm", il);
+
+        ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
+        cb(sa_out, "sa_out", il);
+
+        cur = build_norm(sa_out, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        // feed-forward network
+        {
+            cur = build_ffn(cur,
+                model.layers[il].ffn_up, NULL, NULL,
+                model.layers[il].ffn_gate, NULL, NULL,
+                model.layers[il].ffn_down, NULL, NULL,
+                NULL, LLM_FFN_GELU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        }
+
+        cur = build_norm(cur, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, -1);
+        cb(cur, "ffn_post_norm", -1);
+
+        cur = ggml_add(ctx0, cur, sa_out);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/gemma.cpp b/src/models/gemma.cpp
new file mode 100644
index 0000000000..4893d9af4b
--- /dev/null
+++ b/src/models/gemma.cpp
@@ -0,0 +1,112 @@
+#include "models.h"
+
+
+llm_build_gemma::llm_build_gemma(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
+    cb(inpL, "inp_scaled", -1);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        // norm
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            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);
+            cb(Vcur, "Vcur", il);
+
+            Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head)));
+            cb(Qcur, "Qcur_scaled", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, NULL,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
+        }
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+        }
+        ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
+        cb(sa_out, "sa_out", il);
+
+        cur = build_norm(sa_out,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        // feed-forward network
+        {
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_GELU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        }
+        cur = ggml_add(ctx0, cur, sa_out);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/gemma2-iswa.cpp b/src/models/gemma2-iswa.cpp
new file mode 100644
index 0000000000..1f2b597c65
--- /dev/null
+++ b/src/models/gemma2-iswa.cpp
@@ -0,0 +1,125 @@
+#include "models.h"
+
+llm_build_gemma2_iswa::llm_build_gemma2_iswa(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;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
+        cb(inpL, "inp_scaled", -1);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv_iswa();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_scale(ctx0, Qcur, hparams.f_attention_scale);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            }
+            cur = build_norm(cur,
+                    model.layers[il].attn_post_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_post_norm", il);
+
+            ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
+            cb(sa_out, "sa_out", il);
+
+            cur = build_norm(sa_out,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            // feed-forward network
+            {
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_GELU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+            }
+            cur = build_norm(cur,
+                    model.layers[il].ffn_post_norm, NULL,
+                    LLM_NORM_RMS, -1);
+            cb(cur, "ffn_post_norm", -1);
+
+            cur = ggml_add(ctx0, cur, sa_out);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        // final logit soft-capping
+        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);
+}
diff --git a/src/models/gemma3-iswa.cpp b/src/models/gemma3-iswa.cpp
new file mode 100644
index 0000000000..84badc38f1
--- /dev/null
+++ b/src/models/gemma3-iswa.cpp
@@ -0,0 +1,131 @@
+#include "models.h"
+
+llm_build_gemma3_iswa::llm_build_gemma3_iswa(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;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
+        if (ubatch.token) {
+            inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
+            cb(inpL, "inp_scaled", -1);
+        }
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        // TODO: is causal == true correct? might need some changes
+        auto * inp_attn = build_attn_inp_kv_iswa();
+
+        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);
+
+            // norm
+            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_normed", il);
+
+                Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+
+                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur_normed", il);
+
+                Kcur = ggml_rope_ext(
+                        ctx0, Kcur, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                        ext_factor, attn_factor, beta_fast, beta_slow);
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                // ref: https://github.com/google/gemma_pytorch/blob/014acb7ac4563a5f77c76d7ff98f31b568c16508/gemma/model.py#L315
+                Qcur = ggml_scale(ctx0, Qcur, hparams.f_attention_scale);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            }
+            cur = build_norm(cur,
+                    model.layers[il].attn_post_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_post_norm", il);
+
+            ggml_tensor * sa_out = ggml_add(ctx0, cur, inpL);
+            cb(sa_out, "sa_out", il);
+
+            cur = build_norm(sa_out,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            // feed-forward network
+            {
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_GELU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+            }
+            cur = build_norm(cur,
+                    model.layers[il].ffn_post_norm, NULL,
+                    LLM_NORM_RMS, -1);
+            cb(cur, "ffn_post_norm", -1);
+
+            cur = ggml_add(ctx0, cur, sa_out);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/gemma3n-iswa.cpp b/src/models/gemma3n-iswa.cpp
new file mode 100644
index 0000000000..a0bdd6a15a
--- /dev/null
+++ b/src/models/gemma3n-iswa.cpp
@@ -0,0 +1,377 @@
+#include "models.h"
+
+
+
+llm_build_gemma3n_iswa::llm_build_gemma3n_iswa(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params),
+    model(model),
+    n_embd_head(model.hparams.n_embd_head_k),
+    n_embd_altup(model.hparams.n_embd_altup),
+    n_altup(model.hparams.n_altup),
+    i_altup_act(model.hparams.i_altup_act) {
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
+    if (ubatch.token) {
+        inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
+        cb(inpL, "inp_scaled", -1);
+    }
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    // TODO: is causal == true correct? might need some changes
+    auto * inp_attn = build_attn_inp_kv_iswa();
+
+    // inp_per_layer shape: [n_embd_altup, n_tokens, n_layer]
+    ggml_tensor * inp_per_layer = project_per_layer_inputs(inpL, get_per_layer_inputs());
+
+    // inpL now has only 1 altup, project it to the rest of the altups
+    // these "added" altups will be concat to the last dim of inpL
+    {
+        ggml_tensor * target_magnitude = calc_magnitude(inpL);
+        ggml_tensor * inp_repeated     = ggml_repeat_4d(ctx0, inpL, n_embd, n_tokens, n_altup - 1, 1);
+        ggml_tensor * altup_added =
+            ggml_mul_mat(ctx0, model.altup_proj, inp_repeated);  // shape: [n_embd, n_tokens, n_altup - 1]
+        ggml_tensor * new_magnitude = calc_magnitude(altup_added);
+        altup_added                 = ggml_div(ctx0, ggml_mul(ctx0, altup_added, target_magnitude), new_magnitude);
+        inpL                        = ggml_concat(ctx0, inpL, altup_added, 2);  // shape: [n_embd, n_tokens, n_altup]
+        cb(inpL, "inp_stacked", -1);
+    }
+    // inpL now has shape:          [n_embd,       n_tokens, n_altup]
+    // inp_per_layer now has shape: [n_embd_altup, n_tokens, n_layer]
+
+    for (int il = 0; il < n_layer; ++il) {
+        // this block is made to be closely resemble Gemma3p5DecoderLayer on python code
+        const float freq_base_l  = model.get_rope_freq_base(cparams, il);
+        const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
+
+        ggml_tensor * cur         = inpL;                    // [n_embd, n_tokens, n_altup]
+        ggml_tensor * predictions = altup_predict(cur, il);  // [n_embd, n_tokens, n_altup]
+
+        // predicted value will go through self-attention and laurel
+        ggml_tensor * active_prediction = view_2d_slice(predictions, i_altup_act);  // [n_embd, n_tokens]
+        cur                             = active_prediction;
+        cb(cur, "active_prediction", il);
+
+        // norm
+        cur = build_norm(cur, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // laurel
+        ggml_tensor * laurel_out = laurel(cur, il);  // [n_embd, n_tokens]
+
+        // self-attention
+        if (hparams.has_kv(il)) {
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+            Vcur = ggml_rms_norm(ctx0, Vcur, hparams.f_norm_rms_eps);
+
+            cb(Qcur, "Qcur_normed", il);
+            cb(Kcur, "Kcur_normed", il);
+            cb(Vcur, "Vcur_normed", il);
+
+            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                                 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_l, freq_scale_l,
+                                 ext_factor, attn_factor, beta_fast, beta_slow);
+
+            cb(Qcur, "Qcur_pos", il);
+            cb(Kcur, "Kcur_pos", il);
+
+            cur = build_attn(inp_attn, model.layers[il].wo,
+                    NULL, Qcur, Kcur, Vcur, nullptr, nullptr, nullptr,
+                    hparams.f_attention_scale, il);
+        } else {
+            // reuse KV cache of earlier layers
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+            Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+
+            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+            cb(Qcur, "Qcur_normed", il);
+
+            Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base_l, freq_scale_l,
+                                 ext_factor, attn_factor, beta_fast, beta_slow);
+            cb(Qcur, "Qcur_pos", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, NULL,
+                    Qcur, nullptr, nullptr, nullptr, nullptr, nullptr, hparams.f_attention_scale, il);
+        }
+        cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_post_norm", il);
+
+        cur = ggml_add(ctx0, cur, active_prediction);  // [n_embd, n_tokens]
+        cb(cur, "attn_gated", il);
+
+        ggml_tensor * attn_laurel = ggml_scale(ctx0, ggml_add(ctx0, cur, laurel_out),
+                                               1.0f / sqrtf(2.0f));  // [n_embd, n_tokens]
+        cb(attn_laurel, "attn_laurel", il);
+
+        cur = build_norm(attn_laurel, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        // feed-forward network
+        {
+            ggml_tensor * up_proj   = build_lora_mm(model.layers[il].ffn_up, cur);
+            ggml_tensor * gate_proj = build_lora_mm(model.layers[il].ffn_gate, cur);
+
+            if (il < n_layer_sparsity) {
+                // apply activation sparsity
+                gate_proj = gaussian_topk(gate_proj);
+            }
+            gate_proj = ggml_gelu(ctx0, gate_proj);
+
+            cur = ggml_mul(ctx0, up_proj, gate_proj);
+            cur = build_lora_mm(model.layers[il].ffn_down, cur);
+            cb(cur, "ffn_out", il);
+        }
+        cur = build_norm(cur, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, -1);
+        cb(cur, "ffn_post_norm", il);
+
+        ggml_tensor * attn_ffw_laurel_gated = ggml_add(ctx0, cur, attn_laurel);  // [n_embd, n_tokens]
+        cb(attn_ffw_laurel_gated, "attn_ffw_laurel_gated", il);
+
+        ggml_tensor * corrected = altup_correct(predictions, attn_ffw_laurel_gated, il);  // [n_embd, n_tokens, n_altup]
+
+        ggml_tensor * first_prediction;                                                   // [n_embd, n_tokens]
+        {
+            first_prediction = view_2d_slice(corrected, i_altup_act);                     // [n_embd, n_tokens]
+            first_prediction = ggml_mul(ctx0, first_prediction, model.layers[il].altup_correct_scale);
+            first_prediction = build_lora_mm(model.layers[il].per_layer_inp_gate, first_prediction);
+            first_prediction = ggml_gelu(ctx0, first_prediction);                 // [n_embd_altup, n_tokens]
+            cb(first_prediction, "first_prediction_gated", il);
+            ggml_tensor * inp_this_layer = view_2d_slice(inp_per_layer, il);      // [n_embd_altup, n_tokens]
+            first_prediction = ggml_mul(ctx0, first_prediction, inp_this_layer);  // [n_embd_altup, n_tokens]
+            cb(first_prediction, "first_prediction_scaled", il);
+
+            first_prediction = build_lora_mm(model.layers[il].per_layer_proj, first_prediction);  // [n_embd, n_tokens]
+            first_prediction =
+                build_norm(first_prediction, model.layers[il].per_layer_post_norm, NULL, LLM_NORM_RMS, il);
+            cb(first_prediction, "first_prediction_out", il);
+        }
+        // equivalent to python code: corrected_predictions[1:] += first_prediction
+        {
+            ggml_tensor * slice_first = view_2d_slice(corrected, 0);
+            ggml_tensor * slice_rest  = ggml_view_3d(
+                ctx0, corrected, n_embd, n_tokens, n_altup - 1, ggml_row_size(corrected->type, n_embd),
+                ggml_row_size(corrected->type, n_embd * n_tokens), n_embd * n_tokens * ggml_element_size(corrected));
+            ggml_tensor * tmp = ggml_add(ctx0, slice_rest, first_prediction);  // [n_embd, n_tokens, n_altup - 1]
+            corrected         = ggml_concat(ctx0, slice_first, tmp, 2);        // [n_embd, n_tokens, n_altup]
+        }
+        cur = corrected;                                                       // [n_embd, n_tokens, n_altup]
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;  // [n_embd, n_tokens, n_altup]
+
+    // cur now has multiple altup(s), we want to merge them back to 1 altup
+    {
+        ggml_tensor * target_magnitude = calc_magnitude(view_2d_slice(cur, i_altup_act));  // [n_embd, n_tokens]
+        // do a view to skip the first slice (active altup)
+        ggml_tensor * alt_slice =
+            ggml_view_3d(ctx0, cur, n_embd, n_tokens, n_altup - 1, ggml_row_size(cur->type, n_embd),
+                         ggml_row_size(cur->type, n_embd * n_tokens), n_embd * n_tokens * ggml_element_size(cur));
+        ggml_tensor * altup_unembd =
+            ggml_mul_mat(ctx0, model.altup_unembd_proj, alt_slice);  // shape: [n_embd, n_tokens, n_altup - 1]
+        ggml_tensor * new_magnitude = calc_magnitude(altup_unembd);
+        altup_unembd                = ggml_div(ctx0, ggml_mul(ctx0, altup_unembd, target_magnitude), new_magnitude);
+        cb(altup_unembd, "altup_unembd", -1);
+
+        // equivalent to torch.mean(hidden_states, dim=0)
+        cur = view_2d_slice(cur, 0);  // [n_embd, n_tokens]
+        for (int i = 0; i < n_altup - 1; ++i) {
+            cur = ggml_add(ctx0, cur, view_2d_slice(altup_unembd, i));
+        }
+        cur = ggml_scale(ctx0, cur, 1.0f / float(n_altup));  // [n_embd, n_tokens]
+        cb(cur, "unembd_merged", -1);
+    }
+    // cur now has shape: [n_embd, n_tokens]
+
+    // TODO: move this to right after the last KV layer
+    {
+        // skip computing output for unused tokens
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+        cur                       = ggml_get_rows(ctx0, cur, inp_out_ids);
+    }
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+
+    {
+        // final logit soft-capping
+        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);
+}
+
+ggml_tensor * llm_build_gemma3n_iswa::calc_magnitude(ggml_tensor * x) {
+    return ggml_sqrt(ctx0, ggml_sum_rows(ctx0, ggml_sqr(ctx0, x)));
+}
+
+// get 2D slice view from a 3D tensor, the idx corresponds to the 3rd dim
+ggml_tensor * llm_build_gemma3n_iswa::view_2d_slice(ggml_tensor * x, int idx) {
+    GGML_ASSERT(idx < (int) x->ne[2]);
+    return ggml_view_2d(ctx0, x, x->ne[0], x->ne[1], ggml_row_size(x->type, x->ne[0]),
+                        idx * x->ne[0] * x->ne[1] * ggml_element_size(x));
+}
+
+// equivalent to get_per_layer_inputs() in python code
+// output shape: [n_embd_altup, n_layer, n_tokens]
+ggml_tensor * llm_build_gemma3n_iswa::get_per_layer_inputs() {
+    auto          inp = std::make_unique<llm_graph_input_embd>();
+    ggml_tensor * inp_per_layer;
+    if (ubatch.token) {
+        inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
+        ggml_set_input(inp->tokens);
+        res->t_tokens = inp->tokens;
+        inp_per_layer = ggml_get_rows(ctx0, model.tok_embd_per_layer, inp->tokens);
+        inp_per_layer = ggml_reshape_3d(ctx0, inp_per_layer, n_embd_altup, n_layer, n_tokens);
+        inp_per_layer = ggml_scale(ctx0, inp_per_layer, sqrtf((float) n_embd_altup));
+        cb(inp_per_layer, "inp_per_layer_selected", -1);
+    } else {
+        GGML_ABORT("TODO: support embd input");
+    }
+    res->add_input(std::move(inp));
+    return inp_per_layer;
+}
+
+// equivalent to project_per_layer_inputs() in python code
+// this calculates the per-layer inputs, so the final tensor shape will have n_layer as the last dim
+// output shape: [n_embd_altup, n_tokens, n_layer]
+ggml_tensor * llm_build_gemma3n_iswa::project_per_layer_inputs(ggml_tensor * inputs_embeds, ggml_tensor * inp_per_layer) {
+    const float per_layer_projection_scale = 1.0f / sqrtf((float) n_embd);
+    const float per_layer_input_scale      = 1.0f / sqrtf(2.0f);
+
+    ggml_tensor * per_layer_proj = ggml_mul_mat(ctx0, model.per_layer_model_proj, inputs_embeds);
+    per_layer_proj               = ggml_scale(ctx0, per_layer_proj, per_layer_projection_scale);
+    per_layer_proj               = ggml_reshape_3d(ctx0, per_layer_proj, n_embd_altup, n_layer, n_tokens);
+    per_layer_proj               = build_norm(per_layer_proj, model.per_layer_proj_norm, NULL, LLM_NORM_RMS,
+                                              -1);  // [n_embd_altup, n_layer, n_tokens]
+    cb(per_layer_proj, "per_layer_proj", -1);
+
+    inp_per_layer = ggml_add(ctx0, inp_per_layer, per_layer_proj);
+    inp_per_layer = ggml_scale(ctx0, inp_per_layer, per_layer_input_scale);
+    cb(inp_per_layer, "inp_per_layer", -1);
+
+    // permute to shape: [n_embd_altup, n_tokens, n_layer]
+    inp_per_layer = ggml_cont(ctx0, ggml_permute(ctx0, inp_per_layer, 0, 2, 1, 3));
+    return inp_per_layer;
+}
+
+// input cur shape: [n_altup, n_tokens]
+// output    shape: [n_altup, n_tokens]
+ggml_tensor * llm_build_gemma3n_iswa::laurel(ggml_tensor * cur, int il) {
+    ggml_tensor * tmp = cur;
+    tmp               = build_lora_mm(model.layers[il].laurel_l, tmp);
+    tmp               = build_lora_mm(model.layers[il].laurel_r, tmp);
+    tmp               = build_norm(tmp, model.layers[il].laurel_post_norm, NULL, LLM_NORM_RMS, il);
+    tmp               = ggml_add(ctx0, tmp, cur);
+    cb(tmp, "laurel_out", il);
+    return tmp;
+}
+
+// input x shape: [n_embd, n_tokens]
+// output  shape: [n_embd, n_tokens]
+ggml_tensor * llm_build_gemma3n_iswa::gaussian_topk(ggml_tensor * x) {
+    ggml_tensor * mean = ggml_mean(ctx0, x);
+    ggml_tensor * std  = ggml_sqrt(ctx0, ggml_scale(ctx0, ggml_sum_rows(ctx0, ggml_sqr(ctx0, ggml_sub(ctx0, x, mean))),
+                                                    1.0f / (float) (x->ne[0] - 1)));
+    ggml_tensor * cutoff_x = ggml_add(ctx0, mean, ggml_scale(ctx0, std, f_sparsity_std_mul));
+    return ggml_relu(ctx0, ggml_sub(ctx0, x, cutoff_x));
+}
+
+//
+// altup functions
+//
+
+// equivalent to compute_router_modalities() in python code
+// input x shape: [n_embd,  n_tokens]
+// output  shape: [n_altup, n_tokens]
+ggml_tensor * llm_build_gemma3n_iswa::altup_compute_router_modalities(ggml_tensor * x, int il) {
+    ggml_tensor * router_inputs = build_norm(x, model.layers[il].altup_router_norm, NULL, LLM_NORM_RMS, il);
+
+    // router_input_scale
+    router_inputs = ggml_scale(ctx0, router_inputs, 1.0f / (float) n_embd);
+
+    ggml_tensor * output = ggml_mul_mat(ctx0, model.layers[il].altup_router, router_inputs);
+    return ggml_tanh(ctx0, output);  // [n_altup, n_tokens]
+}
+
+// input cur shape: [n_embd, n_tokens, n_altup]
+// output    shape: [n_embd, n_tokens, n_altup]
+ggml_tensor * llm_build_gemma3n_iswa::altup_predict(ggml_tensor * cur, int il) {
+    ggml_tensor * activated  = view_2d_slice(cur, i_altup_act);                 // [n_embd, n_tokens]
+    ggml_tensor * modalities = altup_compute_router_modalities(activated, il);  // [n_altup, n_tokens]
+    cb(modalities, "modalities", il);
+
+    ggml_tensor * all_coefs = build_lora_mm(model.layers[il].altup_predict_coef, modalities);
+    cb(all_coefs, "all_coefs", il);
+    // first dim now having n_altup^2 elements, we reshape it to 2D (so we end up with 3D tensor)
+    all_coefs = ggml_reshape_3d(ctx0, all_coefs, n_altup, n_altup, n_tokens);
+
+    // permute to [n_altup, n_embd, n_tokens]
+    ggml_tensor * cur_permuted = ggml_cont(ctx0, ggml_permute(ctx0, cur, 1, 2, 0, 3));
+    ggml_tensor * predictions  = ggml_mul_mat(ctx0, cur_permuted, all_coefs);  // [n_altup, n_embd, n_tokens]
+
+    // final shape must be the same as cur: [n_embd, n_tokens, n_altup]
+    predictions = ggml_cont(ctx0, ggml_permute(ctx0, predictions, 0, 2, 1, 3));
+    predictions = ggml_add(ctx0, predictions, cur);
+    cb(predictions, "predictions", il);
+
+    return predictions;
+}
+
+// input predictions       shape: [n_embd, n_tokens, n_altup]
+// input activated         shape: [n_embd, n_tokens]
+// output                  shape: [n_embd, n_tokens, n_altup]
+ggml_tensor * llm_build_gemma3n_iswa::altup_correct(ggml_tensor * predictions, ggml_tensor * activated, int il) {
+    ggml_tensor * modalities = altup_compute_router_modalities(activated, il);  // [n_altup, n_tokens]
+    cb(modalities, "modalities", il);
+
+    ggml_tensor * active_prediction = view_2d_slice(predictions, i_altup_act);
+    ggml_tensor * innovation        = ggml_sub(ctx0, activated, active_prediction);  // [n_embd, n_tokens]
+    cb(innovation, "innovation", il);
+
+    ggml_tensor * all_coefs = build_lora_mm(model.layers[il].altup_correct_coef, modalities);  // [n_altup, n_tokens]
+    all_coefs               = ggml_scale_bias(ctx0, all_coefs, 1.0f, 1.0f);                    // + 1.0
+    cb(all_coefs, "all_coefs", il);
+    all_coefs = ggml_transpose(ctx0, all_coefs);                                               // [n_tokens, n_altup]
+    all_coefs = ggml_cont_3d(ctx0, all_coefs, 1, n_tokens, n_altup);                           // [1, n_tokens, n_altup]
+
+    innovation              = ggml_repeat_4d(ctx0, innovation, n_embd, n_tokens, n_altup, 1);
+    ggml_tensor * corrected = ggml_mul(ctx0, innovation, all_coefs);   // [n_embd, n_tokens, n_altup]
+    corrected               = ggml_add(ctx0, corrected, predictions);  // [n_embd, n_tokens, n_altup]
+    cb(corrected, "corrected", il);
+
+    return corrected;
+}
diff --git a/src/models/glm4-moe.cpp b/src/models/glm4-moe.cpp
new file mode 100644
index 0000000000..036625dc34
--- /dev/null
+++ b/src/models/glm4-moe.cpp
@@ -0,0 +1,153 @@
+#include "models.h"
+
+llm_build_glm4_moe::llm_build_glm4_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        // Only process up to last layer (skip final NextN layer)
+        // Final layer tensors are loaded but not processed in forward pass
+        const int n_transformer_layers = n_layer - hparams.nextn_predict_layers;
+        for (int il = 0; il < n_transformer_layers; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // Pre-attention norm
+            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                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);
+                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);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                // Apply Q/K norm if available (GLM-4.5 355B variant)
+                if (model.layers[il].attn_q_norm) {
+                    Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+                    cb(Qcur, "Qcur_normed", il);
+                }
+                if (model.layers[il].attn_k_norm) {
+                    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
+                        );
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_transformer_layers - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // Post-attention norm
+            cur = build_norm(ffn_inp, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "post_attn_norm", il);
+
+            // Check if this is a dense layer (n_layer_dense_lead=1, so layer 0 is dense)
+            if (static_cast<uint32_t>(il) < hparams.n_layer_dense_lead) {
+                // Dense FFN layer
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+            } else {
+                // Process routed experts using existing MoE infrastructure
+                ggml_tensor * routed_out = build_moe_ffn(cur,
+                        model.layers[il].ffn_gate_inp,
+                        model.layers[il].ffn_up_exps,
+                        model.layers[il].ffn_gate_exps,
+                        model.layers[il].ffn_down_exps,
+                        model.layers[il].ffn_exp_probs_b,
+                        n_expert, n_expert_used,
+                        LLM_FFN_SILU, hparams.expert_weights_norm,
+                        true, hparams.expert_weights_scale,
+                        (llama_expert_gating_func_type) hparams.expert_gating_func,
+                        il);
+                cb(routed_out, "ffn_moe_out", il);
+
+                // Process shared expert on original input
+                ggml_tensor * shared_out = build_ffn(cur,
+                        model.layers[il].ffn_up_shexp,   NULL, NULL,
+                        model.layers[il].ffn_gate_shexp, NULL, NULL,
+                        model.layers[il].ffn_down_shexp, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(shared_out, "ffn_shexp_out", il);
+
+                // Final output: routed_output + shared_output
+                cur = ggml_add(ctx0, routed_out, shared_out);
+                cb(cur, "ffn_out", il);
+            }
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+        cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/glm4.cpp b/src/models/glm4.cpp
new file mode 100644
index 0000000000..f789b28248
--- /dev/null
+++ b/src/models/glm4.cpp
@@ -0,0 +1,127 @@
+#include "models.h"
+
+
+
+llm_build_glm4::llm_build_glm4(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // Pre-attention norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            ggml_tensor * Qcur = nullptr;
+            ggml_tensor * Kcur = nullptr;
+            ggml_tensor * Vcur = nullptr;
+
+            if (model.layers[il].wqkv == nullptr) {
+                Qcur = build_lora_mm(model.layers[il].wq, cur);
+                if (model.layers[il].bq) {
+                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
+                }
+                Kcur = build_lora_mm(model.layers[il].wk, cur);
+                if (model.layers[il].bk) {
+                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
+                }
+                Vcur = build_lora_mm(model.layers[il].wv, cur);
+                if (model.layers[il].bv) {
+                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
+                }
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+            } else {
+                cur = build_lora_mm(model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
+                if (model.layers[il].bqkv) {
+                    cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                    cb(cur, "bqkv", il);
+                }
+                Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, n_embd_head * sizeof(float), cur->nb[1],
+                                    0 * sizeof(float) * (n_embd));
+                Kcur = 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));
+                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);
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, NULL,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+        }
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        // Post-attention norm (new!)
+        cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "post_attn_norm", il);
+
+        // Add the input (residual connection after post-attention norm)
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // FF
+        {
+            // Pre-MLP norm
+            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            // MLP
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up, NULL, NULL,
+                    NULL, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL, LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
+            cb(cur, "ffn_out", il);
+
+            // Post-MLP norm
+            cur = build_norm(cur, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "post_mlp_norm", il);
+        }
+        // Add residual connection after post-MLP norm
+        inpL = ggml_add(ctx0, cur, ffn_inp);
+        cb(inpL, "l_out", il);
+    }
+    // Final norm
+    cur = build_norm(inpL, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // Output projection
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/gpt2.cpp b/src/models/gpt2.cpp
new file mode 100644
index 0000000000..60761c8e76
--- /dev/null
+++ b/src/models/gpt2.cpp
@@ -0,0 +1,105 @@
+#include "models.h"
+
+llm_build_gpt2::llm_build_gpt2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * cur;
+    ggml_tensor * pos;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
+    cb(pos, "pos_embd", -1);
+
+    inpL = ggml_add(ctx0, inpL, pos);
+    cb(inpL, "inpL", -1);
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm,
+                model.layers[il].attn_norm_b,
+                LLM_NORM, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            cur = build_lora_mm(model.layers[il].wqkv, cur);
+            cb(cur, "wqkv", il);
+
+            cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+            cb(cur, "bqkv", il);
+
+            ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
+            ggml_tensor * Kcur = 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));
+            ggml_tensor * 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));
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+        }
+
+        // add the input
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // FF
+        {
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm,
+                    model.layers[il].ffn_norm_b,
+                    LLM_NORM, il);
+            cb(cur, "ffn_norm", 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_GELU, LLM_FFN_SEQ, il);
+            cb(cur, "ffn_out", il);
+        }
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = build_norm(inpL,
+            model.output_norm,
+            model.output_norm_b,
+            LLM_NORM, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/gptneox.cpp b/src/models/gptneox.cpp
new file mode 100644
index 0000000000..2151b14e93
--- /dev/null
+++ b/src/models/gptneox.cpp
@@ -0,0 +1,144 @@
+#include "models.h"
+
+
+llm_build_gptneox::llm_build_gptneox(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm,
+                model.layers[il].attn_norm_b,
+                LLM_NORM, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            cur = build_lora_mm(model.layers[il].wqkv, cur);
+            cb(cur, "wqkv", il);
+
+            cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+            cb(cur, "bqkv", il);
+
+            ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
+            ggml_tensor * Kcur = 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));
+            ggml_tensor * 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
+                    );
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+        }
+
+        // ffn
+        if (hparams.use_par_res) {
+            // attention and ffn are computed in parallel
+            // x = x + attn(ln1(x)) + ffn(ln2(x))
+
+            ggml_tensor * attn_out = cur;
+
+            cur = build_norm(inpL,
+                    model.layers[il].ffn_norm,
+                    model.layers[il].ffn_norm_b,
+                    LLM_NORM, il);
+            cb(cur, "ffn_norm", 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_GELU, LLM_FFN_SEQ, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, inpL);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, attn_out);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        } else {
+            // attention and ffn are computed sequentially
+            // x = x + attn(ln1(x))
+            // x = x + ffn(ln2(x))
+
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+            cb(ffn_inp, "ffn_inp", il);
+
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm,
+                    model.layers[il].ffn_norm_b,
+                    LLM_NORM, il);
+            cb(cur, "ffn_norm", 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_GELU, LLM_FFN_SEQ, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+    }
+
+    cur = build_norm(inpL,
+            model.output_norm,
+            model.output_norm_b,
+            LLM_NORM, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/granite-hybrid.cpp b/src/models/granite-hybrid.cpp
new file mode 100644
index 0000000000..f6ca4c17a2
--- /dev/null
+++ b/src/models/granite-hybrid.cpp
@@ -0,0 +1,196 @@
+#include "models.h"
+
+
+llm_build_granite_hybrid::llm_build_granite_hybrid(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context_mamba(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    auto * inp = build_inp_mem_hybrid();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    // Positional embeddings populated if rope enabled
+    ggml_tensor * inp_pos = nullptr;
+    if (hparams.rope_finetuned) {
+        inp_pos = build_inp_pos();
+    }
+
+    for (int il = 0; il < n_layer; ++il) {
+        struct ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        if (hparams.is_recurrent(il)) {
+            // ssm layer //
+            cur = build_mamba2_layer(inp->get_recr(), cur, model, ubatch, il);
+        } else {
+            // attention layer //
+            cur = build_attention_layer(cur, inp_pos, inp->get_attn(), model, n_embd_head, il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        // ffn
+        cur = build_layer_ffn(cur, inpSA, model, il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    // For Granite architectures - scale logits
+    if (hparams.f_logit_scale) {
+        cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
+    }
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+ggml_tensor * llm_build_granite_hybrid::build_attention_layer(ggml_tensor *             cur,
+                                                              ggml_tensor *             inp_pos,
+                                                              llm_graph_input_attn_kv * inp_attn,
+                                                              const llama_model &       model,
+                                                              const int64_t             n_embd_head,
+                                                              const int                 il) {
+    // compute Q and K and (optionally) RoPE them
+    ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+    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);
+    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);
+    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, hparams.n_head(il), n_tokens);
+    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
+    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
+
+    const bool use_rope = hparams.rope_finetuned;
+    if (use_rope) {
+        ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+        Qcur = ggml_rope_ext(ctx0, Qcur, inp_pos, rope_factors, 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, rope_factors, 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);
+    cb(Vcur, "Vcur", il);
+
+    const float kq_scale =
+        hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+    cur = build_attn(inp_attn,
+            model.layers[il].wo, model.layers[il].bo,
+            Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+    cb(cur, "attn_out", il);
+    return cur;
+}
+
+ggml_tensor * llm_build_granite_hybrid::build_layer_ffn(ggml_tensor *       cur,
+                                                        ggml_tensor *       inpSA,
+                                                        const llama_model & model,
+                                                        const int           il) {
+    // For Granite architectures - scale residual
+    if (hparams.f_residual_scale) {
+        cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
+    }
+    ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+    cb(ffn_inp, "ffn_inp", il);
+
+    // feed-forward network (non-MoE)
+    if (model.layers[il].ffn_gate_inp == nullptr) {
+        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
+                model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+
+    } else {
+        // MoE branch
+        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        ggml_tensor * moe_out =
+            build_moe_ffn(cur,
+                model.layers[il].ffn_gate_inp,
+                model.layers[il].ffn_up_exps,
+                model.layers[il].ffn_gate_exps,
+                model.layers[il].ffn_down_exps,
+                nullptr,
+                n_expert, n_expert_used,
+                LLM_FFN_SILU, true,
+                false, 0.0,
+                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                il);
+        cb(moe_out, "ffn_moe_out", il);
+
+        // For Granite MoE Shared
+        if (hparams.n_ff_shexp > 0) {
+            ggml_tensor * ffn_shexp =
+                build_ffn(cur,
+                    model.layers[il].ffn_up_shexp, NULL, NULL,
+                    model.layers[il].ffn_gate_shexp, NULL, NULL,
+                    model.layers[il].ffn_down_shexp, NULL, NULL,
+                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(ffn_shexp, "ffn_shexp", il);
+
+            cur = ggml_add(ctx0, moe_out, ffn_shexp);
+            cb(cur, "ffn_out", il);
+        } else {
+            cur = moe_out;
+        }
+    }
+
+    // For Granite architectures - scale residual
+    if (hparams.f_residual_scale) {
+        cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
+    }
+    cur = ggml_add(ctx0, cur, ffn_inp);
+    cb(cur, "ffn_out", il);
+
+    cur = build_cvec(cur, il);
+    cb(cur, "l_out", il);
+
+    return cur;
+}
diff --git a/src/models/granite.cpp b/src/models/granite.cpp
new file mode 100644
index 0000000000..18748e9c26
--- /dev/null
+++ b/src/models/granite.cpp
@@ -0,0 +1,211 @@
+#include "models.h"
+
+
+llm_build_granite::llm_build_granite(
+    const llama_model & model,
+    const llm_graph_params & params)
+    : llm_graph_context(params) {
+
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - built only if rope enabled
+    ggml_tensor * inp_pos = nullptr;
+    if (hparams.rope_finetuned) {
+        inp_pos = build_inp_pos();
+    }
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        cur = build_attention_layer(
+            cur, inp_pos, inp_attn,
+            model, n_embd_head, il);
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        // ffn
+        cur = build_layer_ffn(cur, inpSA, model, il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    // For Granite architectures - scale logits
+    cur = ggml_scale(ctx0, cur, 1.0f / hparams.f_logit_scale);
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+ggml_tensor * llm_build_granite::build_attention_layer(
+          ggml_tensor             * cur,
+          ggml_tensor             * inp_pos,
+          llm_graph_input_attn_kv * inp_attn,
+    const llama_model             & model,
+    const int64_t                 n_embd_head,
+    const int                     il) {
+
+    // compute Q and K and (optionally) RoPE them
+    ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+    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);
+    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);
+    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, hparams.n_head(il),    n_tokens);
+    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
+    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
+
+    const bool use_rope = hparams.rope_finetuned;
+    if (use_rope) {
+        ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+        Qcur = ggml_rope_ext(
+                ctx0, Qcur, inp_pos, rope_factors,
+                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, rope_factors,
+                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);
+    cb(Vcur, "Vcur", il);
+
+    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+    cur = build_attn(inp_attn,
+            model.layers[il].wo, model.layers[il].bo,
+            Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+            cb(cur, "attn_out", il);
+    return cur;
+}
+
+ggml_tensor * llm_build_granite::build_layer_ffn(
+          ggml_tensor       * cur,
+          ggml_tensor       * inpSA,
+    const llama_model       & model,
+    const int                 il) {
+
+    // For Granite architectures - scale residual
+    if (hparams.f_residual_scale) {
+        cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
+    }
+    ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+    cb(ffn_inp, "ffn_inp", il);
+
+    // feed-forward network (non-MoE)
+    if (model.layers[il].ffn_gate_inp == nullptr) {
+
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                NULL,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+
+    } else {
+        // MoE branch
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+        ggml_tensor * moe_out = build_moe_ffn(cur,
+                model.layers[il].ffn_gate_inp,
+                model.layers[il].ffn_up_exps,
+                model.layers[il].ffn_gate_exps,
+                model.layers[il].ffn_down_exps,
+                nullptr,
+                n_expert, n_expert_used,
+                LLM_FFN_SILU, true,
+                false, 0.0,
+                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                il);
+        cb(moe_out, "ffn_moe_out", il);
+
+        // For Granite MoE Shared
+        if (hparams.n_ff_shexp > 0) {
+            ggml_tensor * ffn_shexp = build_ffn(cur,
+                model.layers[il].ffn_up_shexp,   NULL, NULL,
+                model.layers[il].ffn_gate_shexp, NULL, NULL,
+                model.layers[il].ffn_down_shexp, NULL, NULL,
+                NULL,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(ffn_shexp, "ffn_shexp", il);
+
+            cur = ggml_add(ctx0, moe_out, ffn_shexp);
+            cb(cur, "ffn_out", il);
+        } else {
+            cur = moe_out;
+        }
+    }
+
+    // For Granite architectures - scale residual
+    if (hparams.f_residual_scale) {
+        cur = ggml_scale(ctx0, cur, hparams.f_residual_scale);
+    }
+    cur = ggml_add(ctx0, cur, ffn_inp);
+    cb(cur, "ffn_out", il);
+
+    cur = build_cvec(cur, il);
+    cb(cur, "l_out", il);
+
+    return cur;
+}
diff --git a/src/models/graph-context-mamba.cpp b/src/models/graph-context-mamba.cpp
new file mode 100644
index 0000000000..b9a363b32b
--- /dev/null
+++ b/src/models/graph-context-mamba.cpp
@@ -0,0 +1,283 @@
+#include "models.h"
+
+llm_graph_context_mamba::llm_graph_context_mamba(const llm_graph_params & params) : llm_graph_context(params) {}
+
+ggml_tensor * llm_graph_context_mamba::build_mamba_layer(llm_graph_input_rs * inp,
+                                                         ggml_tensor *        cur,
+                                                         const llama_model &  model,
+                                                         const llama_ubatch & ubatch,
+                                                         int                  il) {
+    const auto * mctx_cur = inp->mctx;
+
+    const auto kv_head = mctx_cur->get_head();
+
+    const auto & layer = model.layers[il];
+
+    const int64_t d_conv         = hparams.ssm_d_conv;
+    const int64_t d_inner        = hparams.ssm_d_inner;
+    const int64_t d_state        = hparams.ssm_d_state;
+    const int64_t dt_rank        = hparams.ssm_dt_rank;
+    const int64_t n_head         = d_inner;
+    const int64_t head_dim       = 1;
+    const int64_t n_seqs         = ubatch.n_seqs;
+    // Some variants of Mamba arch (e.g. FalconMamba do apply layer norm on B and Dt layers)
+    const bool    ssm_dt_b_c_rms = hparams.ssm_dt_b_c_rms;
+
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    GGML_ASSERT(n_seqs != 0);
+    GGML_ASSERT(ubatch.equal_seqs());
+    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+    ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
+    ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
+
+    ggml_tensor * conv = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
+    conv               = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner, n_seqs);
+
+    // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs}
+    cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
+
+    // {n_embd, 2*d_inner} @ {n_embd, n_seq_tokens, n_seqs} => {2*d_inner, n_seq_tokens, n_seqs}
+    ggml_tensor * xz = build_lora_mm(layer.ssm_in, cur);
+    // split the above in two
+    // => {d_inner, n_seq_tokens, n_seqs}
+    ggml_tensor * x  = ggml_view_3d(ctx0, xz, d_inner, xz->ne[1], xz->ne[2], xz->nb[1], xz->nb[2], 0);
+    ggml_tensor * z =
+        ggml_view_3d(ctx0, xz, d_inner, xz->ne[1], xz->ne[2], xz->nb[1], xz->nb[2], d_inner * ggml_element_size(xz));
+
+    // conv
+    {
+        // => {d_conv - 1 + n_seq_tokens, d_inner, n_seqs}
+        ggml_tensor * conv_x = ggml_concat(ctx0, conv, ggml_transpose(ctx0, x), 0);
+
+        // copy last (d_conv - 1) columns back into the state cache
+        ggml_tensor * last_conv = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner, n_seqs, conv_x->nb[1], conv_x->nb[2],
+                                               n_seq_tokens * (conv_x->nb[0]));
+
+        ggml_build_forward_expand(
+            gf, ggml_cpy(ctx0, last_conv,
+                         ggml_view_1d(ctx0, conv_states_all, (d_conv - 1) * (d_inner) * (n_seqs),
+                                      kv_head * (d_conv - 1) * (d_inner) *ggml_element_size(conv_states_all))));
+
+        // 1D convolution
+        // The equivalent is to make a self-overlapping view of conv_x
+        // over d_conv columns at each stride in the 3rd dimension,
+        // then element-wise multiply that with the conv1d weight,
+        // then sum the elements of each row,
+        // (the last two steps are a dot product over rows (also doable with mul_mat))
+        // then permute away the ne[0] dimension,
+        // and then you're left with the resulting x tensor.
+        // For simultaneous sequences, all sequences need to have the same length.
+        x = ggml_ssm_conv(ctx0, conv_x, layer.ssm_conv1d);
+
+        // bias
+        x = ggml_add(ctx0, x, layer.ssm_conv1d_b);
+
+        x = ggml_silu(ctx0, x);
+    }
+
+    // ssm
+    {
+        // {d_inner, dt_rank + 2*d_state} @ {d_inner, n_seq_tokens, n_seqs} => {dt_rank + 2*d_state, n_seq_tokens, n_seqs}
+        ggml_tensor * x_db = build_lora_mm(layer.ssm_x, x);
+        // split
+        ggml_tensor * dt   = ggml_view_3d(ctx0, x_db, dt_rank, n_seq_tokens, n_seqs, x_db->nb[1], x_db->nb[2], 0);
+        ggml_tensor * B =
+            ggml_view_4d(ctx0, x_db, d_state, /* n_group */ 1, n_seq_tokens, n_seqs, d_state * x_db->nb[0], x_db->nb[1],
+                         x_db->nb[2], ggml_element_size(x_db) * dt_rank);
+        ggml_tensor * C =
+            ggml_view_4d(ctx0, x_db, d_state, /* n_group */ 1, n_seq_tokens, n_seqs, d_state * x_db->nb[0], x_db->nb[1],
+                         x_db->nb[2], ggml_element_size(x_db) * (dt_rank + d_state));
+
+        // Some Mamba variants (e.g. FalconMamba, Jamba) apply RMS norm in B, C & Dt layers
+        if (ssm_dt_b_c_rms || (layer.ssm_dt_norm && layer.ssm_b_norm && layer.ssm_c_norm)) {
+            dt = build_norm(dt, layer.ssm_dt_norm, NULL, LLM_NORM_RMS, il);
+            B  = build_norm(B, layer.ssm_b_norm, NULL, LLM_NORM_RMS, il);
+            C  = build_norm(C, layer.ssm_c_norm, NULL, LLM_NORM_RMS, il);
+        }
+
+        // {dt_rank, d_inner} @ {dt_rank, n_seq_tokens, n_seqs} => {d_inner, n_seq_tokens, n_seqs}
+        dt = build_lora_mm(layer.ssm_dt, dt);
+        dt = ggml_add(ctx0, dt, layer.ssm_dt_b);
+
+        cur = x;
+        x   = ggml_reshape_4d(ctx0, x, head_dim, n_head, n_seq_tokens, n_seqs);
+
+        ggml_tensor * A = layer.ssm_a;
+
+        // use the states and the indices provided by build_recurrent_state
+        // (this is necessary in order to properly use the states before they are overwritten,
+        //  while avoiding to make unnecessary copies of the states)
+        auto get_ssm_rows = [&](ggml_context * ctx, ggml_tensor * states, ggml_tensor * ids) {
+            ggml_tensor * ssm = ggml_reshape_4d(ctx, states, d_state, head_dim, n_head, mctx_cur->get_size());
+
+            // Custom operator to optimize the parallel associative scan
+            // as described in the Annex D of the Mamba paper.
+            // => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs}
+            return ggml_ssm_scan(ctx, ssm, x, dt, A, B, C, ids);
+        };
+
+        ggml_tensor * y_ssm = build_rs(inp, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows);
+
+        // store last states
+        ggml_build_forward_expand(
+            gf, ggml_cpy(ctx0, ggml_view_1d(ctx0, y_ssm, d_state * d_inner * n_seqs, x->nb[3] * x->ne[3]),
+                         ggml_view_1d(ctx0, ssm_states_all, d_state * d_inner * n_seqs,
+                                      kv_head * d_state * d_inner * ggml_element_size(ssm_states_all))));
+
+        ggml_tensor * y = ggml_view_3d(ctx0, y_ssm, d_inner, n_seq_tokens, n_seqs, x->nb[2], x->nb[3], 0);
+
+        // TODO: skip computing output earlier for unused tokens
+
+        y = ggml_add(ctx0, y, ggml_mul(ctx0, cur, layer.ssm_d));
+        y = ggml_swiglu_split(ctx0, ggml_cont(ctx0, z), y);
+
+        // {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs}
+        cur = build_lora_mm(layer.ssm_out, y);
+    }
+
+    // {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
+    cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs);
+
+    return cur;
+}
+
+ggml_tensor * llm_graph_context_mamba::build_mamba2_layer(llm_graph_input_rs * inp,
+                                                          ggml_tensor *        cur,
+                                                          const llama_model &  model,
+                                                          const llama_ubatch & ubatch,
+                                                          int                  il) const {
+    const auto * mctx_cur = inp->mctx;
+
+    const auto kv_head = mctx_cur->get_head();
+
+    const int64_t d_conv   = hparams.ssm_d_conv;
+    const int64_t d_inner  = hparams.ssm_d_inner;
+    const int64_t d_state  = hparams.ssm_d_state;
+    const int64_t n_head   = hparams.ssm_dt_rank;
+    const int64_t head_dim = d_inner / n_head;
+    const int64_t n_group  = hparams.ssm_n_group;
+    const int64_t n_seqs   = ubatch.n_seqs;
+
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    GGML_ASSERT(n_seqs != 0);
+    GGML_ASSERT(ubatch.equal_seqs());
+    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+    ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
+    ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
+
+    ggml_tensor * conv = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
+    conv               = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner + 2 * n_group * d_state, n_seqs);
+
+    // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs}
+    cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
+
+    // d_in_proj = 2 * self.d_inner + 2 * self.ngroups * self.d_state + self.nheads
+
+    // {n_embd, d_in_proj} @ {n_embd, n_seq_tokens, n_seqs} => {d_in_proj, n_seq_tokens, n_seqs}
+    ggml_tensor * zxBCdt = build_lora_mm(model.layers[il].ssm_in, cur);
+
+    // split the above in three
+    ggml_tensor * z   = ggml_view_4d(ctx0, zxBCdt, head_dim, n_head, n_seq_tokens, n_seqs, head_dim * zxBCdt->nb[0],
+                                     zxBCdt->nb[1], zxBCdt->nb[2], 0);
+    ggml_tensor * xBC = ggml_view_3d(ctx0, zxBCdt, d_inner + 2 * n_group * d_state, n_seq_tokens, n_seqs, zxBCdt->nb[1],
+                                     zxBCdt->nb[2], d_inner * ggml_element_size(zxBCdt));
+    ggml_tensor * dt  = ggml_view_3d(ctx0, zxBCdt, n_head, n_seq_tokens, n_seqs, zxBCdt->nb[1], zxBCdt->nb[2],
+                                     (2 * d_inner + 2 * n_group * d_state) * ggml_element_size(zxBCdt));
+
+    // conv
+    {
+        // => {d_conv - 1 + n_seq_tokens, d_inner + 2*n_group*d_state, n_seqs}
+        ggml_tensor * conv_x = ggml_concat(ctx0, conv, ggml_transpose(ctx0, xBC), 0);
+
+        // copy last (d_conv - 1) columns back into the state cache
+        ggml_tensor * last_conv = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner + 2 * n_group * d_state, n_seqs,
+                                               conv_x->nb[1], conv_x->nb[2], n_seq_tokens * (conv_x->nb[0]));
+
+        ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv,
+                                               ggml_view_1d(ctx0, conv_states_all,
+                                                            (d_conv - 1) * (d_inner + 2 * n_group * d_state) * (n_seqs),
+                                                            kv_head * (d_conv - 1) * (d_inner + 2 * n_group * d_state) *
+                                                                ggml_element_size(conv_states_all))));
+
+        // 1D convolution
+        // The equivalent is to make a self-overlapping view of conv_x
+        // over d_conv columns at each stride in the 3rd dimension,
+        // then element-wise multiply that with the conv1d weight,
+        // then sum the elements of each row,
+        // (the last two steps are a dot product over rows (also doable with mul_mat))
+        // then permute away the ne[0] dimension,
+        // and then you're left with the resulting x tensor.
+        // For simultaneous sequences, all sequences need to have the same length.
+        xBC = ggml_ssm_conv(ctx0, conv_x, model.layers[il].ssm_conv1d);
+
+        // bias
+        xBC = ggml_add(ctx0, xBC, model.layers[il].ssm_conv1d_b);
+
+        xBC = ggml_silu(ctx0, xBC);
+    }
+
+    // ssm
+    {
+        // These correspond to V K Q in SSM/attention duality
+        ggml_tensor * x = ggml_view_4d(ctx0, xBC, head_dim, n_head, n_seq_tokens, n_seqs, head_dim * xBC->nb[0],
+                                       xBC->nb[1], xBC->nb[2], 0);
+        ggml_tensor * B = ggml_view_4d(ctx0, xBC, d_state, n_group, n_seq_tokens, n_seqs, d_state * xBC->nb[0],
+                                       xBC->nb[1], xBC->nb[2], d_inner * ggml_element_size(xBC));
+        ggml_tensor * C = ggml_view_4d(ctx0, xBC, d_state, n_group, n_seq_tokens, n_seqs, d_state * xBC->nb[0],
+                                       xBC->nb[1], xBC->nb[2], (d_inner + n_group * d_state) * ggml_element_size(xBC));
+
+        // {n_head, n_seq_tokens, n_seqs}
+        dt = ggml_add(ctx0, ggml_cont(ctx0, dt), model.layers[il].ssm_dt_b);
+
+        ggml_tensor * A = model.layers[il].ssm_a;
+
+        // use the states and the indices provided by build_recurrent_state
+        // (this is necessary in order to properly use the states before they are overwritten,
+        //  while avoiding to make unnecessary copies of the states)
+        auto get_ssm_rows = [&](ggml_context * ctx, ggml_tensor * states, ggml_tensor * ids) {
+            ggml_tensor * ssm = ggml_reshape_4d(ctx, states, d_state, head_dim, n_head, mctx_cur->get_size());
+
+            // TODO: use semistructured matrices to implement state-space duality
+            // => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs}
+            return ggml_ssm_scan(ctx, ssm, x, dt, A, B, C, ids);
+        };
+
+        ggml_tensor * y_ssm = build_rs(inp, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows);
+
+        // store last states
+        ggml_build_forward_expand(
+            gf, ggml_cpy(ctx0, ggml_view_1d(ctx0, y_ssm, d_state * d_inner * n_seqs, ggml_nelements(x) * x->nb[0]),
+                         ggml_view_1d(ctx0, ssm_states_all, d_state * d_inner * n_seqs,
+                                      kv_head * d_state * d_inner * ggml_element_size(ssm_states_all))));
+
+        ggml_tensor * y = ggml_view_4d(ctx0, y_ssm, head_dim, n_head, n_seq_tokens, n_seqs, x->nb[1], n_head * x->nb[1],
+                                       n_seq_tokens * n_head * x->nb[1], 0);
+
+        // TODO: skip computing output earlier for unused tokens
+
+        y = ggml_add(ctx0, y, ggml_mul(ctx0, x, model.layers[il].ssm_d));
+        cb(y, "mamba2_y_add_d", il);
+        y = ggml_swiglu_split(ctx0, ggml_cont(ctx0, z), y);
+
+        // grouped RMS norm
+        if (model.layers[il].ssm_norm) {
+            y = ggml_reshape_4d(ctx0, y, d_inner / n_group, n_group, n_seq_tokens, n_seqs);
+            y = build_norm(y, model.layers[il].ssm_norm, NULL, LLM_NORM_RMS, il);
+        }
+
+        y = ggml_reshape_3d(ctx0, y, d_inner, n_seq_tokens, n_seqs);
+
+        // {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs}
+        cur = build_lora_mm(model.layers[il].ssm_out, y);
+    }
+
+    // {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
+    cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs);
+    cb(cur, "mamba_out", il);
+
+    return cur;
+}
diff --git a/src/models/grok.cpp b/src/models/grok.cpp
new file mode 100644
index 0000000000..6781a0e924
--- /dev/null
+++ b/src/models/grok.cpp
@@ -0,0 +1,160 @@
+#include "models.h"
+
+llm_build_grok::llm_build_grok(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                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);
+                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);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            cur = build_norm(cur,
+                    model.layers[il].attn_out_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_out_norm", il);
+
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            // MoE branch
+            ggml_tensor * moe_out = build_moe_ffn(cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    nullptr,
+                    n_expert, n_expert_used,
+                    LLM_FFN_GELU, true,
+                    false, 0.0,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                    il);
+            cb(moe_out, "ffn_moe_out", il);
+
+            if (model.layers[il].ffn_up) {
+                ggml_tensor * ffn_out = build_ffn(cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_GELU, LLM_FFN_PAR, il);
+                cb(ffn_out, "ffn_out", il);
+
+                cur = ggml_scale(ctx0, ggml_add(ctx0, ffn_out, moe_out), std::sqrt(2) / 2);
+                cb(cur, "ffn_out", il);
+            } else {
+                cur = moe_out;
+            }
+            cur = build_norm(cur,
+                    model.layers[il].ffn_post_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_post_norm", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cur = ggml_scale(ctx0, cur, hparams.f_logit_scale);
+
+        // final logit soft-capping
+        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);
+}
diff --git a/src/models/grovemoe.cpp b/src/models/grovemoe.cpp
new file mode 100644
index 0000000000..56b6db9a3d
--- /dev/null
+++ b/src/models/grovemoe.cpp
@@ -0,0 +1,141 @@
+#include "models.h"
+
+
+
+llm_build_grovemoe::llm_build_grovemoe(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params) {
+    const int64_t n_embd_head    = hparams.n_embd_head_v;
+    const int64_t n_chunk_expert = n_expert / hparams.n_group_experts;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self_attention
+        {
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+            cb(Qcur, "Qcur_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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+            cb(Kcur, "Kcur_normed", il);
+
+            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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // MoE branch
+        cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        ggml_tensor * probs = build_lora_mm(model.layers[il].ffn_gate_inp, cur);  // [n_expert, n_tokens]
+        cb(probs, "ffn_moe_logits", il);
+
+        ggml_tensor * moe_out =
+            build_moe_ffn(cur,
+                nullptr,
+                model.layers[il].ffn_up_exps,
+                model.layers[il].ffn_gate_exps,
+                model.layers[il].ffn_down_exps,
+                nullptr,
+                n_expert, n_expert_used,
+                LLM_FFN_SILU, true,
+                false, 0.0,
+                LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                il,
+                probs);
+        cb(moe_out, "ffn_moe_out", il);
+        cur = moe_out;
+
+        // TODO: Only do the expert selection and weights once
+        moe_out = build_moe_ffn(cur,
+                    nullptr,
+                    model.layers[il].ffn_up_chexps,
+                    model.layers[il].ffn_gate_chexps,
+                    model.layers[il].ffn_down_chexps,
+                    nullptr,
+                    n_chunk_expert, n_expert_used > n_chunk_expert ? n_chunk_expert : n_expert_used,
+                    LLM_FFN_SILU, true,
+                    false, 0.0,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                    il,
+                    probs);
+        cb(moe_out, "ffn_adj_moe_out", il);
+
+        cur = ggml_add(ctx0, cur, ggml_scale(ctx0, moe_out, hparams.expert_group_scale));
+        cb(cur, "ffn_final_moe_out", il);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/hunyuan-dense.cpp b/src/models/hunyuan-dense.cpp
new file mode 100644
index 0000000000..cb30a6a33a
--- /dev/null
+++ b/src/models/hunyuan-dense.cpp
@@ -0,0 +1,132 @@
+#include "models.h"
+
+llm_build_hunyuan_dense::llm_build_hunyuan_dense(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+            // self-attention
+            {
+                // rope freq factors for llama3; may return nullptr for llama2 and other models
+                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                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);
+                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);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                Qcur = ggml_rope_ext(
+                         ctx0, Qcur, inp_pos, rope_factors,
+                         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);
+                cb(Vcur, "Vcur", il);
+
+                Kcur = ggml_rope_ext(
+                         ctx0, Kcur, inp_pos, rope_factors,
+                         n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                         ext_factor, attn_factor, beta_fast, beta_slow
+                         );
+
+                Kcur = build_norm(Kcur,
+                         model.layers[il].attn_k_norm, nullptr,
+                         LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur_norm", il);
+
+                Qcur = build_norm(Qcur,
+                         model.layers[il].attn_q_norm, nullptr,
+                         LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_norm", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+                cb(cur, "attn_out", il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+            // feed-forward network (non-MoE)
+            ggml_tensor * cur_mlp = build_ffn(cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur_mlp, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur_mlp, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/hunyuan-moe.cpp b/src/models/hunyuan-moe.cpp
new file mode 100644
index 0000000000..a9940b04af
--- /dev/null
+++ b/src/models/hunyuan-moe.cpp
@@ -0,0 +1,154 @@
+#include "models.h"
+
+llm_build_hunyuan_moe::llm_build_hunyuan_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // rope freq factors for llama3; may return nullptr for llama2 and other models
+                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                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);
+                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);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, rope_factors,
+                        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);
+                cb(Vcur, "Vcur", il);
+
+                Kcur = ggml_rope_ext(
+                        ctx0, Kcur, inp_pos, rope_factors,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow
+                        );
+
+                Kcur = build_norm(Kcur,
+                        model.layers[il].attn_k_norm, nullptr,
+                        LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur_norm", il);
+
+                Qcur = build_norm(Qcur,
+                        model.layers[il].attn_q_norm, nullptr,
+                        LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_norm", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+                cb(cur, "attn_out", il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            // feed-forward network (non-MoE)
+            ggml_tensor * cur_mlp = build_ffn(cur,
+                    model.layers[il].ffn_up_shexp,   NULL, NULL,
+                    model.layers[il].ffn_gate_shexp, NULL, NULL,
+                    model.layers[il].ffn_down_shexp, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur_mlp, "ffn_mlp", il);
+
+            // MoE branch
+            ggml_tensor * cur_moe = build_moe_ffn(cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    nullptr,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU,
+                    true, // norm_topk_prob
+                    false,
+                    0.0,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                    il);
+            cb(cur_moe, "ffn_moe_out", il);
+
+            ggml_tensor * ffn_out = ggml_add(ctx0, cur_moe, cur_mlp);
+            cb(ffn_out, "ffn_out", il);
+
+            cur = ggml_add(ctx0, ffn_out, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/internlm2.cpp b/src/models/internlm2.cpp
new file mode 100644
index 0000000000..e97c82198d
--- /dev/null
+++ b/src/models/internlm2.cpp
@@ -0,0 +1,121 @@
+#include "models.h"
+
+
+llm_build_internlm2::llm_build_internlm2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                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);
+                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);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/jais.cpp b/src/models/jais.cpp
new file mode 100644
index 0000000000..a1c43065bb
--- /dev/null
+++ b/src/models/jais.cpp
@@ -0,0 +1,86 @@
+#include "models.h"
+
+llm_build_jais::llm_build_jais(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm,
+                    model.layers[il].attn_norm_b,
+                    LLM_NORM, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                cur = build_lora_mm(model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
+
+                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                cb(cur, "bqkv", il);
+
+                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*cur->nb[0]*(n_embd));
+                ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*cur->nb[0]*(n_embd));
+                ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 1*cur->nb[0]*(n_embd + n_embd_gqa));
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/float(n_embd_head), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            }
+            // add the input
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // FF
+            {
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm,
+                        model.layers[il].ffn_norm_b,
+                        LLM_NORM, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+            }
+            inpL = ggml_add(ctx0, cur, ffn_inp);
+            cb(inpL, "l_out", il);
+        }
+        cur = build_norm(inpL,
+                model.output_norm,
+                model.output_norm_b,
+                LLM_NORM, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/jamba.cpp b/src/models/jamba.cpp
new file mode 100644
index 0000000000..0c8c1361d4
--- /dev/null
+++ b/src/models/jamba.cpp
@@ -0,0 +1,107 @@
+#include "models.h"
+
+
+llm_build_jamba::llm_build_jamba(const llama_model & model, const llm_graph_params & params) : llm_graph_context_mamba(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        // {n_embd, n_tokens}
+        inpL = build_inp_embd(model.tok_embd);
+
+        auto * inp_hybrid = build_inp_mem_hybrid();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            const int64_t n_head_kv = hparams.n_head_kv(il);
+
+            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            if (n_head_kv == 0) {
+                cur = build_mamba_layer(inp_hybrid->get_recr(), cur, model, ubatch, il);
+            } else {
+                // Attention
+
+                struct ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                struct ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                struct ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                // No RoPE :)
+                cur = build_attn(inp_hybrid->get_attn(),
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, NULL, NULL, NULL, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            }
+            // residual
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, inpL, cur);
+            cb(cur, "ffn_inp", il);
+
+            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            // feed-forward network
+            if (model.layers[il].ffn_gate_inp == nullptr) {
+                // FFN
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+            } else {
+                // MoE branch
+                cur = build_moe_ffn(cur,
+                        model.layers[il].ffn_gate_inp,
+                        model.layers[il].ffn_up_exps,
+                        model.layers[il].ffn_gate_exps,
+                        model.layers[il].ffn_down_exps,
+                        nullptr,
+                        n_expert, n_expert_used,
+                        LLM_FFN_SILU, false,
+                        false, 0.0,
+                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                        il);
+                cb(cur, "ffn_moe_out", il);
+            }
+            // residual
+            cur = ggml_add(ctx0, ffn_inp, cur);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        // final rmsnorm
+        cur = build_norm(inpL, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/lfm2.cpp b/src/models/lfm2.cpp
new file mode 100644
index 0000000000..ca06bacd7b
--- /dev/null
+++ b/src/models/lfm2.cpp
@@ -0,0 +1,173 @@
+#include "models.h"
+
+#include "../llama-memory-hybrid.h"
+
+
+llm_build_lfm2::llm_build_lfm2(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params),
+    model(model) {
+    ggml_tensor * cur = build_inp_embd(model.tok_embd);
+    cb(cur, "model.embed_tokens", -1);
+
+    ggml_tensor * inp_pos     = build_inp_pos();
+    auto *        inp_hybrid  = build_inp_mem_hybrid();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        const bool is_moe_layer = il >= static_cast<int>(hparams.n_layer_dense_lead);
+
+        auto * prev_cur = cur;
+        cur             = build_norm(cur, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "model.layers.{}.operator_norm", il);
+
+        cur = hparams.is_recurrent(il) ? build_shortconv_block(cur, inp_hybrid->get_recr(), il) :
+                                         build_attn_block(cur, inp_pos, inp_hybrid->get_attn(), il);
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur      = ggml_get_rows(ctx0, cur, inp_out_ids);
+            prev_cur = ggml_get_rows(ctx0, prev_cur, inp_out_ids);
+        }
+
+        cur = ggml_add(ctx0, prev_cur, cur);
+
+        auto * ffn_norm_out = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(ffn_norm_out, "model.layers.{}.ffn_norm", il);
+
+        ggml_tensor * ffn_out =
+            is_moe_layer ? build_moe_feed_forward(ffn_norm_out, il) : build_dense_feed_forward(ffn_norm_out, il);
+        cb(ffn_norm_out, "model.layers.{}.ffn_out", il);
+
+        cur = ggml_add(ctx0, cur, ffn_out);
+    }
+
+    cur = build_norm(cur, model.tok_norm, NULL, LLM_NORM_RMS, -1);
+    cb(cur, "model.embedding_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+    cb(cur, "lm_head", -1);
+
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+ggml_tensor * llm_build_lfm2::build_moe_feed_forward(ggml_tensor * cur, int il) const {
+    return build_moe_ffn(cur,
+                        model.layers[il].ffn_gate_inp, model.layers[il].ffn_up_exps,
+                        model.layers[il].ffn_gate_exps, model.layers[il].ffn_down_exps,
+                        model.layers[il].ffn_exp_probs_b, n_expert, n_expert_used, LLM_FFN_SILU, true, false, 0.0,
+                        static_cast<llama_expert_gating_func_type>(hparams.expert_gating_func), il);
+}
+
+ggml_tensor * llm_build_lfm2::build_dense_feed_forward(ggml_tensor * cur, int il) const {
+    GGML_ASSERT(!model.layers[il].ffn_up_b);
+    GGML_ASSERT(!model.layers[il].ffn_gate_b);
+    GGML_ASSERT(!model.layers[il].ffn_down_b);
+    return build_ffn(cur,
+        model.layers[il].ffn_up, NULL, NULL,
+        model.layers[il].ffn_gate, NULL, NULL,
+        model.layers[il].ffn_down, NULL, NULL,
+        NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+}
+
+ggml_tensor * llm_build_lfm2::build_attn_block(ggml_tensor *             cur,
+                                               ggml_tensor *             inp_pos,
+                                               llm_graph_input_attn_kv * inp_attn,
+                                               int                       il) const {
+    GGML_ASSERT(hparams.n_embd_v_gqa(il) == hparams.n_embd_k_gqa(il));
+    const auto n_embd_head = hparams.n_embd_head_v;
+    const auto n_head_kv   = hparams.n_head_kv(il);
+
+    auto * q = build_lora_mm(model.layers[il].wq, cur);
+    cb(q, "model.layers.{}.self_attn.q_proj", il);
+    auto * k = build_lora_mm(model.layers[il].wk, cur);
+    cb(k, "model.layers.{}.self_attn.k_proj", il);
+    auto * v = build_lora_mm(model.layers[il].wv, cur);
+    cb(v, "model.layers.{}.self_attn.v_proj", il);
+
+    q = ggml_reshape_3d(ctx0, q, n_embd_head, n_head, n_tokens);
+    k = ggml_reshape_3d(ctx0, k, n_embd_head, n_head_kv, n_tokens);
+    v = ggml_reshape_3d(ctx0, v, n_embd_head, n_head_kv, n_tokens);
+
+    // qk norm
+    q = build_norm(q, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+    cb(q, "model.layers.{}.self_attn.q_layernorm", il);
+    k = build_norm(k, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+    cb(k, "model.layers.{}.self_attn.k_layernorm", il);
+
+    // RoPE
+    q = ggml_rope_ext(ctx0, q, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor,
+                      attn_factor, beta_fast, beta_slow);
+    k = ggml_rope_ext(ctx0, k, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale, ext_factor,
+                      attn_factor, beta_fast, beta_slow);
+
+    cur = build_attn(inp_attn,
+            model.layers[il].wo, NULL,
+            q, k, v, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+
+    cb(cur, "model.layers.{}.self_attn.out_proj", il);
+
+    return cur;
+}
+
+ggml_tensor * llm_build_lfm2::build_shortconv_block(ggml_tensor * cur, llm_graph_input_rs * inp_recr, int il) {
+    const auto *   mctx_cur     = static_cast<const llama_memory_hybrid_context *>(mctx)->get_recr();
+    const uint32_t kv_head      = mctx_cur->get_head();
+    const int64_t  n_seq_tokens = ubatch.n_seq_tokens;
+    const int64_t  n_seqs       = ubatch.n_seqs;
+    GGML_ASSERT(n_seqs != 0);
+    GGML_ASSERT(ubatch.equal_seqs());
+    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+    GGML_ASSERT(hparams.n_shortconv_l_cache > 1);
+    const uint32_t d_conv = hparams.n_shortconv_l_cache - 1;
+
+    // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs}
+    cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
+
+    auto * bcx = build_lora_mm(model.layers[il].shortconv.in_proj, cur);
+    cb(bcx, "model.layers.{}.conv.in_proj", il);
+
+    constexpr auto n_chunks = 3;
+    GGML_ASSERT(bcx->ne[0] % n_chunks == 0);
+    const auto chunk_size = bcx->ne[0] / n_chunks;
+    auto *     b          = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2],
+                                         0 * chunk_size * ggml_element_size(bcx));
+    auto *     c          = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2],
+                                         1 * chunk_size * ggml_element_size(bcx));
+    auto *     x          = ggml_view_3d(ctx0, bcx, chunk_size, bcx->ne[1], bcx->ne[2], bcx->nb[1], bcx->nb[2],
+                                         2 * chunk_size * ggml_element_size(bcx));
+
+    auto * bx = ggml_transpose(ctx0, ggml_mul(ctx0, b, x));
+
+    // read conv state
+    auto * conv_state = mctx_cur->get_r_l(il);
+    auto * conv_rs    = build_rs(inp_recr, conv_state, hparams.n_embd_r(), n_seqs);
+    auto * conv       = ggml_reshape_3d(ctx0, conv_rs, d_conv, hparams.n_embd, n_seqs);
+
+    bx = ggml_concat(ctx0, conv, bx, 0);
+    GGML_ASSERT(bx->ne[0] > conv->ne[0]);
+
+    // last d_conv columns is a new conv state
+    auto * new_conv = ggml_view_3d(ctx0, bx, conv->ne[0], bx->ne[1], bx->ne[2], bx->nb[1], bx->nb[2],
+                                   (bx->ne[0] - conv->ne[0]) * ggml_element_size(bx));
+    GGML_ASSERT(ggml_are_same_shape(conv, new_conv));
+
+    // write new conv conv state
+    ggml_build_forward_expand(gf, ggml_cpy(ctx0, new_conv,
+                                           ggml_view_1d(ctx0, conv_state, ggml_nelements(new_conv),
+                                                        kv_head * d_conv * n_embd * ggml_element_size(new_conv))));
+
+    auto * conv_kernel = model.layers[il].shortconv.conv;
+    auto * conv_out    = ggml_ssm_conv(ctx0, bx, conv_kernel);
+    cb(conv_out, "model.layers.{}.conv.conv", il);
+
+    auto * y = ggml_mul(ctx0, c, conv_out);
+    y        = build_lora_mm(model.layers[il].shortconv.out_proj, y);
+    cb(y, "model.layers.{}.conv.out_proj", il);
+    // {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
+    y = ggml_reshape_2d(ctx0, y, y->ne[0], n_seq_tokens * n_seqs);
+
+    return y;
+}
diff --git a/src/models/llada-moe.cpp b/src/models/llada-moe.cpp
new file mode 100644
index 0000000000..2dcef4cacc
--- /dev/null
+++ b/src/models/llada-moe.cpp
@@ -0,0 +1,123 @@
+#include "models.h"
+
+llm_build_llada_moe::llm_build_llada_moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_no_cache();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self_attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_normed", il);
+
+                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
+                        );
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // MoE branch
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_moe_ffn(cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    nullptr,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU, false,
+                    false, 0.0,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                    il);
+            cb(cur, "ffn_moe_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
+
diff --git a/src/models/llada.cpp b/src/models/llada.cpp
new file mode 100644
index 0000000000..b10b89b1f6
--- /dev/null
+++ b/src/models/llada.cpp
@@ -0,0 +1,101 @@
+#include "models.h"
+
+
+llm_build_llada::llm_build_llada(const llama_model & model, const llm_graph_params & params) :
+        llm_graph_context(params) {
+        // LLaDA is similar to LLaMA but uses non-causal attention for diffusion
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        // Non-causal attention for diffusion
+        auto * inp_attn = build_attn_inp_no_cache();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute separate Q, K, V projections without bias, matching LLaDALlamaBlock
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up, NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL, LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/llama-iswa.cpp b/src/models/llama-iswa.cpp
new file mode 100644
index 0000000000..03f8061682
--- /dev/null
+++ b/src/models/llama-iswa.cpp
@@ -0,0 +1,174 @@
+#include "models.h"
+
+llm_build_llama_iswa::llm_build_llama_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    // temperature tuning
+    ggml_tensor * inp_attn_scale = nullptr;
+    inp_attn_scale = build_inp_attn_scale();
+
+    auto * inp_attn = build_attn_inp_kv_iswa();
+
+    const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        const bool use_rope = hparams.n_no_rope_layer_step > 0 &&
+                              (il + 1) % hparams.n_no_rope_layer_step != 0;
+
+        // norm
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            // rope freq factors for llama3; may return nullptr for llama2 and other models
+            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            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);
+            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);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            if (use_rope) {
+                Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, rope_factors,
+                        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, rope_factors,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow
+                        );
+            } else if (inp_attn_scale) {
+                Qcur = ggml_mul(ctx0, Qcur, inp_attn_scale);
+            }
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            if (use_rope && hparams.use_kq_norm) {
+                // Llama4TextL2Norm
+                Qcur = ggml_rms_norm(ctx0, Qcur, hparams.f_norm_rms_eps);
+                Kcur = ggml_rms_norm(ctx0, Kcur, hparams.f_norm_rms_eps);
+                cb(Qcur, "Qcur_normed", il);
+                cb(Kcur, "Kcur_normed", il);
+            }
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+            cb(cur, "attn_out", il);
+        }
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network (non-MoE)
+        if (model.layers[il].ffn_gate_inp == nullptr) {
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                    model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        } else {
+            ggml_tensor * ffn_inp_normed = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            ggml_tensor * moe_out = build_moe_ffn(ffn_inp_normed,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    nullptr,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU, false,
+                    false, 0.0,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SIGMOID,
+                    il);
+
+            // Shared experts
+            ggml_tensor * shexp_out = build_ffn(ffn_inp_normed,
+                model.layers[il].ffn_up_shexp,   NULL, NULL,
+                model.layers[il].ffn_gate_shexp, NULL, NULL,
+                model.layers[il].ffn_down_shexp, NULL, NULL,
+                NULL,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(shexp_out, "ffn_moe_shexp", il);
+
+            cur = ggml_add(ctx0, moe_out, shexp_out);
+            cb(cur, "ffn_moe_out_merged", il);
+        }
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "ffn_out", il);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/llama.cpp b/src/models/llama.cpp
new file mode 100644
index 0000000000..289028959f
--- /dev/null
+++ b/src/models/llama.cpp
@@ -0,0 +1,156 @@
+#include "models.h"
+
+
+llm_build_llama::llm_build_llama(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // rope freq factors for llama3; may return nullptr for llama2 and other models
+                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                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);
+                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);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, rope_factors,
+                        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, rope_factors,
+                        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);
+                cb(Vcur, "Vcur", il);
+
+                if (hparams.use_kq_norm) {
+                    // Llama4TextL2Norm
+                    Qcur = ggml_rms_norm(ctx0, Qcur, hparams.f_norm_rms_eps);
+                    Kcur = ggml_rms_norm(ctx0, Kcur, hparams.f_norm_rms_eps);
+                    cb(Qcur, "Qcur_normed", il);
+                    cb(Kcur, "Kcur_normed", il);
+                }
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+                cb(cur, "attn_out", il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network (non-MoE)
+            if (model.layers[il].ffn_gate_inp == nullptr) {
+
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+            } else {
+                // MoE branch
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = build_moe_ffn(cur,
+                        model.layers[il].ffn_gate_inp,
+                        model.layers[il].ffn_up_exps,
+                        model.layers[il].ffn_gate_exps,
+                        model.layers[il].ffn_down_exps,
+                        nullptr,
+                        n_expert, n_expert_used,
+                        LLM_FFN_SILU, true,
+                        false, 0.0,
+                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                        il);
+                cb(cur, "ffn_moe_out", il);
+            }
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
diff --git a/src/models/mamba.cpp b/src/models/mamba.cpp
new file mode 100644
index 0000000000..46819613c2
--- /dev/null
+++ b/src/models/mamba.cpp
@@ -0,0 +1,55 @@
+#include "models.h"
+
+
+llm_build_mamba::llm_build_mamba(const llama_model & model, const llm_graph_params & params) : llm_graph_context_mamba(params) {
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    // {n_embd, n_tokens}
+    inpL = build_inp_embd(model.tok_embd);
+
+    auto * rs_inp = build_rs_inp();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        // norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        if (model.arch == LLM_ARCH_MAMBA2) {
+            cur = build_mamba2_layer(rs_inp, cur, model, ubatch, il);
+        } else {
+            cur = build_mamba_layer(rs_inp, cur, model, ubatch, il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur  = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+        }
+
+        // residual
+        cur = ggml_add(ctx0, cur, inpL);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    // final rmsnorm
+    cur = build_norm(inpL, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
diff --git a/src/models/minicpm3.cpp b/src/models/minicpm3.cpp
new file mode 100644
index 0000000000..02ce21ce65
--- /dev/null
+++ b/src/models/minicpm3.cpp
@@ -0,0 +1,200 @@
+#include "models.h"
+
+
+llm_build_minicpm3::llm_build_minicpm3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        //TODO: if the model varies, these parameters need to be read from the model
+        const int64_t n_embd_base = 256;
+        const float scale_embd  = 12.0f;
+        const float scale_depth = 1.4f;
+        const float kq_scale = 1.0f / sqrtf(float(hparams.n_embd_head_k));
+
+        const uint32_t n_embd_head_qk_rope = hparams.n_rot;
+        const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
+        const uint32_t kv_lora_rank = hparams.n_lora_kv;
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // scale the input embeddings
+        inpL = ggml_scale(ctx0, inpL, scale_embd);
+        cb(inpL, "inp_scaled", -1);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self_attention
+            {
+                ggml_tensor * q = NULL;
+                // {n_embd, q_lora_rank} * {n_embd, n_tokens} -> {q_lora_rank, n_tokens}
+                q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
+                cb(q, "q", il);
+
+                q = build_norm(q,
+                        model.layers[il].attn_q_a_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(q, "q", il);
+
+                // {q_lora_rank, n_head * hparams.n_embd_head_k} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k, n_tokens}
+                q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
+                cb(q, "q", il);
+
+                // split into {n_head * n_embd_head_qk_nope, n_tokens}
+                ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
+                        ggml_row_size(q->type, hparams.n_embd_head_k),
+                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
+                        0);
+                cb(q_nope, "q_nope", il);
+
+                // and {n_head * n_embd_head_qk_rope, n_tokens}
+                ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
+                        ggml_row_size(q->type, hparams.n_embd_head_k),
+                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
+                        ggml_row_size(q->type, n_embd_head_qk_nope));
+                cb(q_pe, "q_pe", il);
+
+                // {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens}
+                ggml_tensor * kv_pe_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
+                cb(kv_pe_compresseed, "kv_pe_compresseed", il);
+
+                // split into {kv_lora_rank, n_tokens}
+                ggml_tensor * kv_compressed = ggml_view_2d(ctx0, kv_pe_compresseed, kv_lora_rank, n_tokens,
+                        kv_pe_compresseed->nb[1],
+                        0);
+                cb(kv_compressed, "kv_compressed", il);
+
+                // and {n_embd_head_qk_rope, n_tokens}
+                ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_pe_compresseed, n_embd_head_qk_rope, 1, n_tokens,
+                        kv_pe_compresseed->nb[1],
+                        kv_pe_compresseed->nb[1],
+                        ggml_row_size(kv_pe_compresseed->type, kv_lora_rank));
+                cb(k_pe, "k_pe", il);
+
+                kv_compressed = build_norm(kv_compressed,
+                        model.layers[il].attn_kv_a_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(kv_compressed, "kv_compressed", il);
+
+                // {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)} * {kv_lora_rank, n_tokens} -> {n_head * (n_embd_head_qk_nope + n_embd_head_v), n_tokens}
+                ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_compressed);
+                cb(kv, "kv", il);
+
+                // split into {n_head * n_embd_head_qk_nope, n_tokens}
+                ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
+                        ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v),
+                        ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
+                        0);
+                cb(k_nope, "k_nope", il);
+
+                // and {n_head * n_embd_head_v, n_tokens}
+                ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens,
+                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)),
+                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head),
+                        ggml_row_size(kv->type, (n_embd_head_qk_nope)));
+                cb(v_states, "v_states", il);
+
+                v_states = ggml_cont(ctx0, v_states);
+                cb(v_states, "v_states", il);
+
+                q_pe = ggml_rope_ext(
+                        ctx0, q_pe, inp_pos, rope_factors,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow
+                        );
+                cb(q_pe, "q_pe", il);
+
+                // shared RoPE key
+                k_pe = ggml_rope_ext(
+                        ctx0, k_pe, inp_pos, rope_factors,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow
+                        );
+                cb(k_pe, "k_pe", il);
+
+                ggml_tensor * q_states = ggml_concat(ctx0, q_nope, q_pe, 0);
+                cb(q_states, "q_states", il);
+
+                ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);
+                cb(k_states, "k_states", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, NULL,
+                        q_states, k_states, v_states, nullptr, nullptr, nullptr, kq_scale, il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            // scale_res - scale the hidden states for residual connection
+            const float scale_res = scale_depth/sqrtf(float(n_layer)); // TODO: is this correct?
+            cur = ggml_scale(ctx0, cur, scale_res);
+            cb(cur, "hidden_scaled", il);
+
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            {
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+            }
+            // scale the hidden states for residual connection
+            cur = ggml_scale(ctx0, cur, scale_res);
+            cb(cur, "hidden_scaled_ffn", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head scaling
+        const float scale_lmhead = float(n_embd_base)/float(n_embd);
+        cur = ggml_scale(ctx0, cur, scale_lmhead);
+        cb(cur, "lmhead_scaling", -1);
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/minimax-m2.cpp b/src/models/minimax-m2.cpp
new file mode 100644
index 0000000000..f7001badf7
--- /dev/null
+++ b/src/models/minimax-m2.cpp
@@ -0,0 +1,124 @@
+
+#include "models.h"
+
+llm_build_minimax_m2::llm_build_minimax_m2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    // GGML_ASSERT(n_embd_head == hparams.n_rot); this is wrong in case of minimax, head_dim = 128, n_rot = 64
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    ggml_tensor * inp_pos = build_inp_pos();
+    auto inp_attn = build_attn_inp_kv();
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        cur = inpL;
+
+        // self_attention
+        {
+            cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            cb(Vcur, "Vcur", il);
+
+            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(Qcur, "Qcur_normed", il);
+
+            Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(Kcur, "Kcur_normed", 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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, NULL,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // MoE branch
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_moe_ffn(cur,
+                model.layers[il].ffn_gate_inp,
+                model.layers[il].ffn_up_exps,
+                model.layers[il].ffn_gate_exps,
+                model.layers[il].ffn_down_exps,
+                model.layers[il].ffn_exp_probs_b,
+                n_expert, n_expert_used,
+                LLM_FFN_SILU, true,
+                false, 0.0,
+                (llama_expert_gating_func_type) hparams.expert_gating_func,
+                il);
+        cb(cur, "ffn_moe_out", il);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/models.h b/src/models/models.h
new file mode 100644
index 0000000000..af203343a4
--- /dev/null
+++ b/src/models/models.h
@@ -0,0 +1,477 @@
+#pragma once
+
+#include "../llama-model.h"
+#include "../llama-graph.h"
+#include "../llama-memory-recurrent.h"
+
+#include <cmath>
+
+struct llm_graph_context_mamba : public llm_graph_context {
+    llm_graph_context_mamba(const llm_graph_params & params);
+
+    virtual ~llm_graph_context_mamba() = default;
+
+    ggml_tensor * build_mamba_layer(llm_graph_input_rs * inp, ggml_tensor * cur, const llama_model & model, const llama_ubatch & ubatch, int il);
+    ggml_tensor * build_mamba2_layer(llm_graph_input_rs * inp, ggml_tensor * cur, const llama_model & model, const llama_ubatch & ubatch, int il) const;
+
+};
+
+// Base class for RWKV-related models
+struct llm_build_rwkv6_base : public llm_graph_context {
+    const llama_model & model;
+
+    llm_build_rwkv6_base(const llama_model & model, const llm_graph_params & params);
+
+    virtual ~llm_build_rwkv6_base() = default;
+
+    ggml_tensor * build_rwkv6_channel_mix(const llama_layer * layer,
+                                          ggml_tensor *       cur,
+                                          ggml_tensor *       x_prev,
+                                          llm_arch            arch) const;
+
+    ggml_tensor * build_rwkv6_time_mix(llm_graph_input_rs * inp,
+                                       ggml_tensor *        cur,
+                                       ggml_tensor *        x_prev,
+                                       const llama_ubatch & ubatch,
+                                       int                  il) const;
+};
+
+// Base class for RWKV7-related models
+struct llm_build_rwkv7_base : public llm_graph_context {
+    const llama_model & model;
+
+    llm_build_rwkv7_base(const llama_model & model, const llm_graph_params & params);
+
+    virtual ~llm_build_rwkv7_base() = default;
+
+    // RWKV7-specific graph building methods
+    ggml_tensor * build_rwkv7_channel_mix(const llama_layer * layer,
+                                          ggml_tensor *       cur,
+                                          ggml_tensor *       x_prev,
+                                          llm_arch            arch) const;
+    ggml_tensor * build_rwkv7_time_mix(llm_graph_input_rs * inp,
+                                       ggml_tensor *        cur,
+                                       ggml_tensor *        x_prev,
+                                       ggml_tensor *&       first_layer_value,
+                                       const llama_ubatch & ubatch,
+                                       int                  il) const;
+};
+
+struct llm_build_apertus : public llm_graph_context {
+    llm_build_apertus(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_arcee : public llm_graph_context {
+    llm_build_arcee(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_arctic : public llm_graph_context {
+    llm_build_arctic(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_arwkv7 : public llm_build_rwkv7_base {
+    llm_build_arwkv7(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_baichuan : public llm_graph_context {
+    llm_build_baichuan(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_bailingmoe2 : public llm_graph_context {
+    llm_build_bailingmoe2(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_bailingmoe : public llm_graph_context {
+    llm_build_bailingmoe(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_bert : public llm_graph_context {
+    llm_build_bert(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_bitnet : public llm_graph_context {
+    llm_build_bitnet(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_bloom : public llm_graph_context {
+    llm_build_bloom(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_chameleon : public llm_graph_context {
+    llm_build_chameleon(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_chatglm : public llm_graph_context {
+    llm_build_chatglm(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_codeshell : public llm_graph_context {
+    llm_build_codeshell(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_cogvlm : public llm_graph_context {
+    llm_build_cogvlm(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_cohere2_iswa : public llm_graph_context {
+    llm_build_cohere2_iswa(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_command_r : public llm_graph_context {
+    llm_build_command_r(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_dbrx : public llm_graph_context {
+    llm_build_dbrx(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_deci : public llm_graph_context {
+    llm_build_deci(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_deepseek2 : public llm_graph_context {
+    llm_build_deepseek2(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_deepseek : public llm_graph_context {
+    llm_build_deepseek(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_dots1 : public llm_graph_context {
+    llm_build_dots1(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_dream : public llm_graph_context {
+    llm_build_dream(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_ernie4_5 : public llm_graph_context {
+    llm_build_ernie4_5(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_ernie4_5_moe : public llm_graph_context {
+    llm_build_ernie4_5_moe(const llama_model & model, const llm_graph_params & params);
+};
+
+template <bool iswa>
+struct llm_build_exaone4 : public llm_graph_context {
+    llm_build_exaone4(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_exaone : public llm_graph_context {
+    llm_build_exaone(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_falcon : public llm_graph_context {
+    llm_build_falcon(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_falcon_h1 : public llm_graph_context_mamba {
+    llm_build_falcon_h1(const llama_model & model, const llm_graph_params & params);
+};
+
+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);
+};
+
+struct llm_build_gemma3n_iswa : public llm_graph_context {
+    const llama_model & model;
+
+    const int64_t n_embd_head;
+    const int64_t n_embd_altup;
+    const int64_t n_altup;
+    const int     i_altup_act;
+    const int     n_layer_sparsity = 10; // number of layers using activation sparsity
+    const float   f_sparsity_std_mul = 1.6448533535003662f; // std_multiplier = normal_dist.icdf(0.95)
+
+    llm_build_gemma3n_iswa(const llama_model & model, const llm_graph_params & params);
+    ggml_tensor * calc_magnitude(ggml_tensor * x);
+    ggml_tensor * view_2d_slice(ggml_tensor * x, int idx);
+    ggml_tensor * get_per_layer_inputs();
+    ggml_tensor * project_per_layer_inputs(ggml_tensor * inputs_embeds, ggml_tensor * inp_per_layer);
+    ggml_tensor * gaussian_topk(ggml_tensor * x);
+    ggml_tensor * altup_compute_router_modalities(ggml_tensor * x, int il);
+    ggml_tensor * altup_predict(ggml_tensor * cur, int il);
+    ggml_tensor * laurel(ggml_tensor * cur, int il);
+    ggml_tensor * altup_correct(ggml_tensor * predictions, ggml_tensor * activated, int il);
+};
+
+struct llm_build_gemma_embedding : public llm_graph_context {
+    llm_build_gemma_embedding(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_gemma : public llm_graph_context {
+    llm_build_gemma(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_glm4 : public llm_graph_context {
+    llm_build_glm4(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_glm4_moe : public llm_graph_context {
+    llm_build_glm4_moe(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_gpt2 : public llm_graph_context {
+    llm_build_gpt2(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_gptneox : public llm_graph_context {
+    llm_build_gptneox(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_granite : public llm_graph_context {
+    llm_build_granite(const llama_model & model, const llm_graph_params & params);
+
+private:
+    ggml_tensor * build_attention_layer(
+              ggml_tensor             * cur,
+              ggml_tensor             * inp_pos,
+              llm_graph_input_attn_kv * inp_attn,
+        const llama_model             & model,
+        const int64_t                 n_embd_head,
+        const int                     il);
+
+    ggml_tensor * build_layer_ffn(
+              ggml_tensor       * cur,
+              ggml_tensor       * inpSA,
+        const llama_model       & model,
+        const int                 il);
+};
+
+struct llm_build_granite_hybrid : public llm_graph_context_mamba {
+    llm_build_granite_hybrid(const llama_model & model, const llm_graph_params & params);
+    ggml_tensor * build_layer_ffn(ggml_tensor * cur, ggml_tensor * inpSA, const llama_model & model, const int il);
+    ggml_tensor * build_attention_layer(ggml_tensor * cur, ggml_tensor * inp_pos, llm_graph_input_attn_kv * inp_attn,
+        const llama_model & model,const int64_t n_embd_head, const int il);
+};
+
+struct llm_build_grok : public llm_graph_context {
+    llm_build_grok(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_grovemoe : public llm_graph_context {
+    llm_build_grovemoe(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_hunyuan_dense : public llm_graph_context {
+    llm_build_hunyuan_dense(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_hunyuan_moe : public llm_graph_context {
+    llm_build_hunyuan_moe(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_internlm2 : public llm_graph_context {
+    llm_build_internlm2(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_jais : public llm_graph_context {
+    llm_build_jais(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_jamba : public llm_graph_context_mamba {
+    llm_build_jamba(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_lfm2 : public llm_graph_context {
+    const llama_model & model;
+
+    llm_build_lfm2(const llama_model & model, const llm_graph_params & params);
+    ggml_tensor * build_moe_feed_forward(ggml_tensor * cur, int il) const;
+    ggml_tensor * build_dense_feed_forward(ggml_tensor * cur, int il) const;
+    ggml_tensor * build_attn_block(ggml_tensor * cur, ggml_tensor * inp_pos, llm_graph_input_attn_kv * inp_attn, int il) const;
+    ggml_tensor * build_shortconv_block(ggml_tensor * cur, llm_graph_input_rs * inp_recr, int il);
+
+};
+
+struct llm_build_llada : public llm_graph_context {
+    llm_build_llada(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_llada_moe : public llm_graph_context {
+    llm_build_llada_moe(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_llama : public llm_graph_context {
+    llm_build_llama(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_llama_iswa : public llm_graph_context {
+    llm_build_llama_iswa(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_mamba : public llm_graph_context_mamba {
+    llm_build_mamba(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_minicpm3 : public llm_graph_context {
+    llm_build_minicpm3(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_minimax_m2 : public llm_graph_context {
+    llm_build_minimax_m2(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_mpt : public llm_graph_context {
+    llm_build_mpt(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_nemotron : public llm_graph_context {
+    llm_build_nemotron(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_nemotron_h : public llm_graph_context_mamba {
+    llm_build_nemotron_h(const llama_model & model, const llm_graph_params & params);
+    ggml_tensor * build_ffn_layer(ggml_tensor * cur, const llama_model & model, const int il);
+    ggml_tensor * build_attention_layer(ggml_tensor * cur, llm_graph_input_attn_kv * inp_attn,
+        const llama_model & model, const int64_t n_embd_head, const int il);
+};
+
+struct llm_build_neo_bert : public llm_graph_context {
+    llm_build_neo_bert(const llama_model & model, const llm_graph_params & params);
+};
+
+template <bool iswa>
+struct llm_build_olmo2 : public llm_graph_context {
+    llm_build_olmo2(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_olmoe : public llm_graph_context {
+    llm_build_olmoe(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_olmo : public llm_graph_context {
+    llm_build_olmo(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_openai_moe_iswa : public llm_graph_context {
+    llm_build_openai_moe_iswa(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_openelm : public llm_graph_context {
+    llm_build_openelm(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_orion : public llm_graph_context {
+    llm_build_orion(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_phi2 : public llm_graph_context {
+    llm_build_phi2(const llama_model & model, const llm_graph_params & params);
+};
+
+template<bool iswa>
+struct llm_build_phi3 : public llm_graph_context {
+    llm_build_phi3(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_plamo2 : public llm_graph_context_mamba {
+    llm_build_plamo2(const llama_model & model, const llm_graph_params & params);
+    private:
+        ggml_tensor * build_plamo2_mamba_layer(llm_graph_input_rs * inp, ggml_tensor * cur, const llama_model & model, const llama_ubatch & ubatch, int il);
+        ggml_tensor * build_plamo2_attn_layer(llm_graph_input_attn_kv * inp, ggml_tensor * inp_pos, ggml_tensor * cur,
+                                                const llama_model & model, int il);
+};
+
+struct llm_build_plamo : public llm_graph_context {
+    llm_build_plamo(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_plm : public llm_graph_context {
+    llm_build_plm(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_qwen2 : public llm_graph_context {
+    llm_build_qwen2(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_qwen2moe : public llm_graph_context {
+    llm_build_qwen2moe(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_qwen2vl : public llm_graph_context {
+    llm_build_qwen2vl(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_qwen3 : public llm_graph_context {
+    llm_build_qwen3(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_qwen3moe : public llm_graph_context {
+    llm_build_qwen3moe(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_qwen3vl : public llm_graph_context {
+    llm_build_qwen3vl(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_qwen3vlmoe : public llm_graph_context {
+    llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params);
+};
+
+
+struct llm_build_qwen : public llm_graph_context {
+    llm_build_qwen(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_refact : public llm_graph_context {
+    llm_build_refact(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_rwkv6 : public llm_build_rwkv6_base {
+    llm_build_rwkv6(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_rwkv6qwen2 : public llm_build_rwkv6_base {
+    llm_build_rwkv6qwen2(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_rwkv7 : public llm_build_rwkv7_base {
+    llm_build_rwkv7(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_seed_oss : public llm_graph_context {
+    llm_build_seed_oss(const llama_model & model, const llm_graph_params & params);
+};
+
+template <bool iswa>
+struct llm_build_smallthinker : public llm_graph_context {
+    llm_build_smallthinker(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_smollm3 : public llm_graph_context {
+    llm_build_smollm3(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_stablelm : public llm_graph_context {
+    llm_build_stablelm(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_starcoder2 : public llm_graph_context {
+    llm_build_starcoder2(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_starcoder : public llm_graph_context {
+    llm_build_starcoder(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_t5_dec : public llm_graph_context {
+    llm_build_t5_dec(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_t5_enc : public llm_graph_context {
+    llm_build_t5_enc(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_wavtokenizer_dec : public llm_graph_context {
+    llm_build_wavtokenizer_dec(const llama_model & model, const llm_graph_params & params);
+};
+
+struct llm_build_xverse : public llm_graph_context {
+    llm_build_xverse(const llama_model & model, const llm_graph_params & params);
+};
diff --git a/src/models/mpt.cpp b/src/models/mpt.cpp
new file mode 100644
index 0000000000..2328e027a7
--- /dev/null
+++ b/src/models/mpt.cpp
@@ -0,0 +1,126 @@
+#include "models.h"
+
+
+
+llm_build_mpt::llm_build_mpt(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * cur;
+    ggml_tensor * pos;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    if (model.pos_embd) {
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+        pos                   = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
+        cb(pos, "pos_embd", -1);
+
+        inpL = ggml_add(ctx0, inpL, pos);
+        cb(inpL, "inpL", -1);
+    }
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * attn_norm;
+
+        attn_norm = build_norm(inpL, model.layers[il].attn_norm, model.layers[il].attn_norm_b, LLM_NORM, il);
+        cb(attn_norm, "attn_norm", il);
+
+        // self-attention
+        {
+            cur = attn_norm;
+
+            cur = build_lora_mm(model.layers[il].wqkv, cur);
+            cb(cur, "wqkv", il);
+
+            if (model.layers[il].bqkv) {
+                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                cb(cur, "bqkv", il);
+            }
+
+            if (hparams.f_clamp_kqv > 0.0f) {
+                cur = ggml_clamp(ctx0, cur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+                cb(cur, "wqkv_clamped", il);
+            }
+
+            ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, n_embd_head * sizeof(float),
+                                              cur->nb[1], 0 * sizeof(float) * (n_embd));
+            ggml_tensor * Kcur = 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));
+            ggml_tensor * 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));
+
+            // Q/K Layernorm
+            if (model.layers[il].attn_q_norm) {
+                Qcur = ggml_reshape_2d(ctx0, Qcur, n_embd_head * n_head, n_tokens);
+                Kcur = ggml_reshape_2d(ctx0, Kcur, n_embd_head * n_head_kv, n_tokens);
+
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, model.layers[il].attn_q_norm_b, LLM_NORM, il);
+
+                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, model.layers[il].attn_k_norm_b, LLM_NORM, 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);
+            }
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur  = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+        }
+
+        // Add the input
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed forward
+        {
+            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, LLM_NORM, il);
+            cb(cur, "ffn_norm", 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,
+                model.layers[il].ffn_act, LLM_FFN_GELU, LLM_FFN_SEQ, il);
+            cb(cur, "ffn_out", il);
+        }
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/nemotron-h.cpp b/src/models/nemotron-h.cpp
new file mode 100644
index 0000000000..5414348888
--- /dev/null
+++ b/src/models/nemotron-h.cpp
@@ -0,0 +1,121 @@
+#include "models.h"
+
+
+
+llm_build_nemotron_h::llm_build_nemotron_h(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context_mamba(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+    ggml_build_forward_expand(gf, inpL);
+
+    auto * inp = build_inp_mem_hybrid();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        struct ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        if (hparams.is_recurrent(il)) {
+            // ssm layer //
+            cur = build_mamba2_layer(inp->get_recr(), cur, model, ubatch, il);
+        } else if (hparams.n_ff(il) == 0) {
+            // attention layer //
+            cur = build_attention_layer(cur, inp->get_attn(), model, n_embd_head, il);
+        } else {
+            cur = build_ffn_layer(cur, model, il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        // add residual
+        cur = ggml_add(ctx0, cur, inpSA);
+        cb(cur, "nemotron_h_block_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+ggml_tensor * llm_build_nemotron_h::build_attention_layer(ggml_tensor *             cur,
+                                                          llm_graph_input_attn_kv * inp_attn,
+                                                          const llama_model &       model,
+                                                          const int64_t             n_embd_head,
+                                                          const int                 il) {
+    // compute Q and K and (optionally) RoPE them
+    ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+    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);
+    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);
+    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, hparams.n_head(il), n_tokens);
+    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
+    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, hparams.n_head_kv(il), n_tokens);
+
+    cb(Qcur, "Qcur", il);
+    cb(Kcur, "Kcur", il);
+    cb(Vcur, "Vcur", il);
+
+    const float kq_scale =
+        hparams.f_attention_scale == 0.0f ? 1.0f / sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+    cur = build_attn(inp_attn,
+            model.layers[il].wo, model.layers[il].bo,
+            Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+    cb(cur, "attn_out", il);
+    return cur;
+}
+
+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);
+
+    cur = build_cvec(cur, il);
+    cb(cur, "l_out", il);
+
+    return cur;
+}
diff --git a/src/models/nemotron.cpp b/src/models/nemotron.cpp
new file mode 100644
index 0000000000..781aa71939
--- /dev/null
+++ b/src/models/nemotron.cpp
@@ -0,0 +1,122 @@
+#include "models.h"
+
+llm_build_nemotron::llm_build_nemotron(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        //GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm,
+                    model.layers[il].attn_norm_b,
+                    LLM_NORM, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                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);
+                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);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm,
+                    model.layers[il].ffn_norm_b,
+                    LLM_NORM, il);
+            cb(cur, "ffn_norm", 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_SEQ, il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, model.output_norm_b,
+                LLM_NORM, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/neo-bert.cpp b/src/models/neo-bert.cpp
new file mode 100644
index 0000000000..b05c79025b
--- /dev/null
+++ b/src/models/neo-bert.cpp
@@ -0,0 +1,104 @@
+#include "models.h"
+
+llm_build_neo_bert::llm_build_neo_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        // construct input embeddings (token, type, position)
+        inpL = build_inp_embd(model.tok_embd);
+        cb(inpL, "inp_embd", -1);
+
+        auto * inp_attn = build_attn_inp_no_cache();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * cur = inpL;
+
+            // pre-norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+
+            {
+                ggml_tensor * Qcur;
+                ggml_tensor * Kcur;
+                ggml_tensor * Vcur;
+
+                // self-attention
+                cur = build_lora_mm(model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
+
+                Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
+                Kcur = 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));
+                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));
+
+                // 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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, nullptr,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+                cb(cur, "kqv_out", il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            }
+            // re-add the layer input
+            cur = ggml_add(ctx0, cur, inpL);
+
+            ggml_tensor * ffn_inp = cur;
+            cb(ffn_inp, "ffn_inp", il);
+
+            // pre-norm
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            // feed-forward network
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,
+                    NULL, NULL, NULL, NULL, NULL,
+                    model.layers[il].ffn_down,
+                    NULL, NULL, NULL,
+                    LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
+
+            // attentions bypass the intermediate layer
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm_enc, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_embd", -1);
+        res->t_embd = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/olmo.cpp b/src/models/olmo.cpp
new file mode 100644
index 0000000000..e15d716536
--- /dev/null
+++ b/src/models/olmo.cpp
@@ -0,0 +1,121 @@
+#include "models.h"
+
+llm_build_olmo::llm_build_olmo(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    NULL, NULL,
+                    LLM_NORM, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+                if (hparams.f_clamp_kqv > 0.0f) {
+                    Qcur = ggml_clamp(ctx0, Qcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+                    cb(Qcur, "Qcur", il);
+                }
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+                if (hparams.f_clamp_kqv > 0.0f) {
+                    Kcur = ggml_clamp(ctx0, Kcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+                    cb(Kcur, "Kcur", il);
+                }
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+                if (hparams.f_clamp_kqv > 0.0f) {
+                    Vcur = ggml_clamp(ctx0, Vcur, -hparams.f_clamp_kqv, hparams.f_clamp_kqv);
+                    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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, nullptr,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = build_norm(ffn_inp,
+                    NULL, NULL,
+                    LLM_NORM, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                NULL, NULL,
+                LLM_NORM, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/olmo2.cpp b/src/models/olmo2.cpp
new file mode 100644
index 0000000000..b05a3f9b4b
--- /dev/null
+++ b/src/models/olmo2.cpp
@@ -0,0 +1,151 @@
+#include "models.h"
+
+
+template <bool iswa>
+llm_build_olmo2<iswa>::llm_build_olmo2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        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) {
+            ggml_tensor * inpSA = inpL;
+
+            cur = inpL;
+
+            // self_attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_normed", il);
+
+                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur_normed", 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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                const bool is_swa = hparams.is_swa(il);
+
+                if (is_swa) {
+                    // For sliding window layers, Olmo3 use regular rope with no yarn rope scaling.
+                    // This is achieved here by setting freq_scale and attn_factor to 1.
+                    // We also set ext_factor to 0 to avoid a few unnecessary computations.
+                    Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base, 1.0,
+                        0.0, 1.0, beta_fast, beta_slow
+                        );
+
+                    Kcur = ggml_rope_ext(
+                        ctx0, Kcur, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base, 1.0,
+                        0.0, 1.0, beta_fast, beta_slow
+                        );
+                } else {
+                    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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            cur = build_norm(cur,
+                    model.layers[il].attn_post_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_post_norm", il);
+
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = build_ffn(ffn_inp,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+
+            cur = build_norm(cur,
+                    model.layers[il].ffn_post_norm, NULL,
+                    LLM_NORM_RMS, -1);
+            cb(cur, "ffn_post_norm", -1);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+
+// Explicit template instantiations
+template struct llm_build_olmo2<false>;
+template struct llm_build_olmo2<true>;
diff --git a/src/models/olmoe.cpp b/src/models/olmoe.cpp
new file mode 100644
index 0000000000..49f51f9724
--- /dev/null
+++ b/src/models/olmoe.cpp
@@ -0,0 +1,124 @@
+#include "models.h"
+
+llm_build_olmoe::llm_build_olmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self_attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_normed", il);
+
+                Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur_normed", 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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // MoE branch
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_moe_ffn(cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    nullptr,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU, false,
+                    false, 0.0,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                    il);
+            cb(cur, "ffn_moe_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/openai-moe-iswa.cpp b/src/models/openai-moe-iswa.cpp
new file mode 100644
index 0000000000..14e55eeb7a
--- /dev/null
+++ b/src/models/openai-moe-iswa.cpp
@@ -0,0 +1,123 @@
+#include "models.h"
+
+llm_build_openai_moe_iswa::llm_build_openai_moe_iswa(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv_iswa();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, nullptr,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                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);
+                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);
+                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_rot, n_head,    n_tokens);
+                Kcur = ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_rot, n_head_kv, n_tokens);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, model.layers[il].attn_sinks, nullptr, 1.0f/sqrtf(float(n_rot)), il);
+
+                cb(cur, "attn_out", il);
+            }
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            cur = ffn_inp;
+            cur = build_norm(cur,
+                    model.layers[il].attn_post_norm, nullptr,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_post_norm", il);
+
+            // MoE branch
+            cur = build_moe_ffn(cur,
+                    model.layers[il].ffn_gate_inp,  model.layers[il].ffn_gate_inp_b,
+                    model.layers[il].ffn_up_exps,   model.layers[il].ffn_up_exps_b,
+                    model.layers[il].ffn_gate_exps, model.layers[il].ffn_gate_exps_b,
+                    model.layers[il].ffn_down_exps, model.layers[il].ffn_down_exps_b,
+                    nullptr,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SWIGLU_OAI_MOE, false,
+                    false, 0.0,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX_WEIGHT,
+                    il);
+            cb(cur, "ffn_moe_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/openelm.cpp b/src/models/openelm.cpp
new file mode 100644
index 0000000000..a16a459f3f
--- /dev/null
+++ b/src/models/openelm.cpp
@@ -0,0 +1,124 @@
+#include "models.h"
+
+llm_build_openelm::llm_build_openelm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            const int64_t n_head    = hparams.n_head(il);
+            const int64_t n_head_kv = hparams.n_head_kv(il);
+            const int64_t n_head_qkv = 2*n_head_kv + n_head;
+
+            cur = inpL;
+            ggml_tensor * residual = cur;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                cur = build_lora_mm(model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
+
+                cur = ggml_reshape_3d(ctx0, cur, n_embd_head_k, n_head_qkv, n_tokens);
+
+                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, cur->nb[1], cur->nb[2], 0);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*n_head);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*(n_head+n_head_kv)));
+                cb(Vcur, "Vcur", il);
+
+                Qcur = build_norm(Qcur,
+                        model.layers[il].attn_q_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur", il);
+
+                Kcur = build_norm(Kcur,
+                        model.layers[il].attn_k_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur", il);
+
+                Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, NULL,
+                        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, NULL,
+                        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);
+                cb(Qcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                residual = ggml_get_rows(ctx0, residual, inp_out_ids);
+                cur      = ggml_get_rows(ctx0, cur,      inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            {
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+            }
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            inpL = cur;
+        }
+        cur = inpL;
+
+        // norm
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/orion.cpp b/src/models/orion.cpp
new file mode 100644
index 0000000000..8c20c003ce
--- /dev/null
+++ b/src/models/orion.cpp
@@ -0,0 +1,123 @@
+#include "models.h"
+
+llm_build_orion::llm_build_orion(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, model.layers[il].attn_norm_b,
+                    LLM_NORM, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                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);
+                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);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
+                    LLM_NORM, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, model.output_norm_b,
+                LLM_NORM, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/phi2.cpp b/src/models/phi2.cpp
new file mode 100644
index 0000000000..22dbf61076
--- /dev/null
+++ b/src/models/phi2.cpp
@@ -0,0 +1,121 @@
+#include "models.h"
+
+
+llm_build_phi2::llm_build_phi2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+    const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * cur;
+    ggml_tensor * attn_norm_output;
+    ggml_tensor * ffn_output;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        attn_norm_output = build_norm(inpL,
+                model.layers[il].attn_norm,
+                model.layers[il].attn_norm_b,
+                LLM_NORM, il);
+        cb(attn_norm_output, "attn_norm", il);
+
+        // self-attention
+        {
+            ggml_tensor * Qcur = nullptr;
+            ggml_tensor * Kcur = nullptr;
+            ggml_tensor * Vcur = nullptr;
+
+            if (model.layers[il].wqkv) {
+                cur = build_lora_mm(model.layers[il].wqkv, attn_norm_output);
+                cb(cur, "wqkv", il);
+
+                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                cb(cur, "bqkv", il);
+
+                Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
+                Kcur = 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));
+                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));
+            } else {
+                Qcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wq, attn_norm_output), model.layers[il].bq);
+                Kcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wk, attn_norm_output), model.layers[il].bk);
+                Vcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wv, attn_norm_output), model.layers[il].bv);
+
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+            }
+            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);
+            cb(Vcur, "Vcur", il);
+
+            // with phi2, we scale the Q to avoid precision issues
+            // ref: https://github.com/ml-explore/mlx-examples/blob/08e862336ade809bc37d1035f94b359e7d1a5152/phi2/phi2.py#L64-L66
+            Qcur = ggml_scale(ctx0, Qcur, 1.0f/sqrtf(float(n_embd_head)));
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
+        }
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur              = ggml_get_rows(ctx0,              cur, inp_out_ids);
+            inpL             = ggml_get_rows(ctx0,             inpL, inp_out_ids);
+            attn_norm_output = ggml_get_rows(ctx0, attn_norm_output, inp_out_ids);
+        }
+        // FF
+        {
+            ffn_output = build_ffn(attn_norm_output,
+                    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_GELU, LLM_FFN_SEQ, il);
+            cb(ffn_output, "ffn_out", il);
+        }
+        cur = ggml_add(ctx0, cur, ffn_output);
+        cur = ggml_add(ctx0, cur, inpL);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = build_norm(inpL,
+            model.output_norm,
+            model.output_norm_b,
+            LLM_NORM, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+    cb(cur, "result_output_no_bias", -1);
+
+    cur = ggml_add(ctx0, cur, model.output_b);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/phi3.cpp b/src/models/phi3.cpp
new file mode 100644
index 0000000000..63907e3d4e
--- /dev/null
+++ b/src/models/phi3.cpp
@@ -0,0 +1,153 @@
+#include "models.h"
+
+
+template<bool iswa>
+llm_build_phi3<iswa>::llm_build_phi3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        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) {
+            auto * residual = inpL;
+
+            // self-attention
+            {
+                // rope freq factors for 128k context
+                ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+                ggml_tensor* attn_norm_output = build_norm(inpL,
+                        model.layers[il].attn_norm,
+                        model.layers[il].attn_norm_b,
+                        LLM_NORM_RMS, il);
+                cb(attn_norm_output, "attn_norm", il);
+
+                ggml_tensor * Qcur = nullptr;
+                ggml_tensor * Kcur = nullptr;
+                ggml_tensor * Vcur = nullptr;
+
+                if (model.layers[il].wqkv) {
+                    cur = build_lora_mm(model.layers[il].wqkv, attn_norm_output);
+                    cb(cur, "wqkv", il);
+
+                    Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head * sizeof(float), cur->nb[1], 0 * sizeof(float) * (n_embd));
+                    Kcur = 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));
+                    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));
+                    }
+                    else {
+                    Qcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wq, attn_norm_output), model.layers[il].bq);
+                    Kcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wk, attn_norm_output), model.layers[il].bk);
+                    Vcur = ggml_add(ctx0, build_lora_mm(model.layers[il].wv, attn_norm_output), model.layers[il].bv);
+
+                    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);
+                    Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+                }
+                Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, rope_factors,
+                        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, rope_factors,
+                        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);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head)));
+                cb(Qcur, "Qcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur      = ggml_get_rows(ctx0, cur,      inp_out_ids);
+                residual = ggml_get_rows(ctx0, residual, inp_out_ids);
+            }
+            cur = ggml_add(ctx0, cur, residual);
+            residual = cur;
+
+            cur = build_norm(cur,
+                    model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            // feed-forward network
+            if (model.layers[il].ffn_gate_inp == nullptr) {
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        NULL,                      NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
+                cb(cur, "ffn_out", il);
+            } else {
+                // MoE branch
+                cur = build_moe_ffn(cur,
+                        model.layers[il].ffn_gate_inp,
+                        model.layers[il].ffn_up_exps,
+                        model.layers[il].ffn_gate_exps,
+                        model.layers[il].ffn_down_exps,
+                        nullptr,
+                        n_expert, n_expert_used,
+                        LLM_FFN_SILU, true,
+                        false, 0.0,
+                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                        il);
+                cb(cur, "ffn_moe_out", il);
+            }
+            cur = ggml_add(ctx0, residual, cur);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = build_norm(inpL,
+                model.output_norm,
+                model.output_norm_b,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        cur = build_lora_mm(model.output, cur);
+
+        if (model.output_b != nullptr) {
+            cb(cur, "result_output_no_bias", -1);
+            cur = ggml_add(ctx0, cur, model.output_b);
+        }
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+
+// Explicit template instantiations
+template struct llm_build_phi3<false>;
+template struct llm_build_phi3<true>;
diff --git a/src/models/plamo.cpp b/src/models/plamo.cpp
new file mode 100644
index 0000000000..73b4473fca
--- /dev/null
+++ b/src/models/plamo.cpp
@@ -0,0 +1,110 @@
+#include "models.h"
+
+llm_build_plamo::llm_build_plamo(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            ggml_tensor * sa_inp = cur;
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                Qcur = ggml_rope_ext(
+                        ctx0, Qcur, inp_pos, nullptr,
+                        n_embd_head, 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_embd_head, 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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur    = ggml_get_rows(ctx0,    cur, inp_out_ids);
+                sa_inp = ggml_get_rows(ctx0, sa_inp, inp_out_ids);
+                inpL   = ggml_get_rows(ctx0,   inpL, inp_out_ids);
+            }
+            ggml_tensor * sa_out = cur;
+
+            cur = sa_inp;
+
+            // feed-forward network
+            {
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+            }
+            cur = ggml_add(ctx0, cur, sa_out);
+            cur = ggml_add(ctx0, cur, inpL);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/plamo2.cpp b/src/models/plamo2.cpp
new file mode 100644
index 0000000000..31115a08f9
--- /dev/null
+++ b/src/models/plamo2.cpp
@@ -0,0 +1,316 @@
+#include "models.h"
+
+llm_build_plamo2::llm_build_plamo2(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context_mamba(params) {
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    // {n_embd, n_tokens}
+    inpL = build_inp_embd(model.tok_embd);
+    cb(inpL, "embedding_output", -1);
+
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_hybrid = build_inp_mem_hybrid();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * residual = inpL;
+
+        // ggml_graph_add_node(gf, model.layers[il].attn_norm);
+        // cb(model.layers[il].attn_norm, "attn_norm", il);
+
+        // pre_mixer_norm
+        cur = build_norm(inpL, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+
+        // check if this layer is Mamba or Attention
+        bool is_mamba_layer = hparams.is_recurrent(il);
+
+        if (is_mamba_layer) {
+            // PLaMo-2 Mamba layer
+            cur = build_plamo2_mamba_layer(inp_hybrid->get_recr(), cur, model, ubatch, il);
+        } else {
+            // PLaMo-2 Attention layer
+            cur = build_plamo2_attn_layer(inp_hybrid->get_attn(), inp_pos, cur, model, il);
+        }
+
+        // post_mixer_norm
+        cur = build_norm(cur, model.layers[il].attn_post_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "attn_post_norm", il);
+
+        // residual connection
+        cur = ggml_add(ctx0, cur, residual);
+        cb(cur, "attn_residual", il);
+        residual = cur;
+
+        // pre-ffn norm
+        cur = build_norm(cur, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_pre_norm", il);
+
+        // feed-forward network
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up, NULL, NULL,
+                NULL, NULL, NULL,
+                model.layers[il].ffn_down, NULL, NULL,
+                NULL, LLM_FFN_SWIGLU, LLM_FFN_SEQ, il);
+        cb(cur, "ffn_out", il);
+
+        // post ffn norm
+        cur = build_norm(cur, model.layers[il].ffn_post_norm, NULL, LLM_NORM_RMS, il);
+        cb(cur, "ffn_post_norm", il);
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur      = ggml_get_rows(ctx0, cur, inp_out_ids);
+            residual = ggml_get_rows(ctx0, residual, inp_out_ids);
+        }
+
+        // residual connection
+        cur = ggml_add(ctx0, cur, residual);
+        cb(cur, "ffn_residual", il);
+
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    // final norm
+    cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+    cb(cur, "result_norm", -1);
+
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+    cb(cur, "result_output", -1);
+
+    // Explicitly mark as output tensor to ensure proper backend assignment
+    ggml_set_output(cur);
+
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+ggml_tensor * llm_build_plamo2::build_plamo2_attn_layer(llm_graph_input_attn_kv * inp,
+                                                        ggml_tensor *             inp_pos,
+                                                        ggml_tensor *             cur,
+                                                        const llama_model &       model,
+                                                        int                       il) {
+    // self-attention
+    {
+        // PLaMo-2 uses combined QKV tensor
+        ggml_tensor * qkv = build_lora_mm(model.layers[il].wqkv, cur);
+        cb(qkv, "wqkv", il);
+
+        // split QKV tensor into Q, K, V
+        const int64_t n_embd_head_q = hparams.n_embd_head_k;
+        const int64_t n_embd_head_k = hparams.n_embd_head_k;
+        const int64_t n_embd_head_v = hparams.n_embd_head_v;
+        int32_t       n_head        = hparams.n_head(il);
+        int32_t       n_head_kv     = hparams.n_head_kv(il);
+
+        const int64_t q_offset = 0;
+        const int64_t k_offset = n_embd_head_q * n_head;
+        const int64_t v_offset = k_offset + n_embd_head_k * n_head_kv;
+
+        ggml_tensor * Qcur = ggml_view_3d(ctx0, qkv, n_embd_head_q, n_head, n_tokens, n_embd_head_q * sizeof(float),
+                                          qkv->nb[1], q_offset * ggml_element_size(qkv));
+        ggml_tensor * Kcur = ggml_view_3d(ctx0, qkv, n_embd_head_k, n_head_kv, n_tokens, n_embd_head_k * sizeof(float),
+                                          qkv->nb[1], k_offset * ggml_element_size(qkv));
+        ggml_tensor * Vcur = ggml_view_3d(ctx0, qkv, n_embd_head_v, n_head_kv, n_tokens, n_embd_head_v * sizeof(float),
+                                          qkv->nb[1], v_offset * ggml_element_size(qkv));
+
+        cb(Qcur, "Qcur", il);
+        cb(Kcur, "Kcur", il);
+        cb(Vcur, "Vcur", il);
+
+        Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+        cb(Qcur, "Qcur_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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+        cb(Kcur, "Kcur_normed", il);
+
+        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);
+
+        cur = build_attn(inp,
+            model.layers[il].wo, NULL,
+            Qcur, Kcur, Vcur, NULL, NULL, NULL, 1.0f / sqrtf(float(n_embd_head_v)), il);
+    }
+
+    cb(cur, "attn_out", il);
+
+    return cur;
+}
+
+ggml_tensor * llm_build_plamo2::build_plamo2_mamba_layer(llm_graph_input_rs * inp,
+                                                         ggml_tensor *        cur,
+                                                         const llama_model &  model,
+                                                         const llama_ubatch & ubatch,
+                                                         int                  il) {
+    const auto * mctx_cur = inp->mctx;
+
+    const auto kv_head = mctx_cur->get_head();
+
+    const int64_t d_conv   = hparams.ssm_d_conv;
+    const int64_t d_inner  = hparams.ssm_d_inner;
+    const int64_t d_state  = hparams.ssm_d_state;
+    const int64_t n_heads  = hparams.ssm_dt_rank;
+    const int64_t head_dim = d_inner / n_heads;
+    const int64_t n_group  = hparams.ssm_n_group;
+    const int64_t n_seqs   = ubatch.n_seqs;
+
+    const int64_t n_seq_tokens = ubatch.n_seq_tokens;
+
+    GGML_ASSERT(n_seqs != 0);
+    GGML_ASSERT(ubatch.equal_seqs());
+    GGML_ASSERT(ubatch.n_tokens == n_seq_tokens * n_seqs);
+
+    ggml_tensor * conv_states_all = mctx_cur->get_r_l(il);
+    ggml_tensor * ssm_states_all  = mctx_cur->get_s_l(il);
+
+    ggml_tensor * conv = build_rs(inp, conv_states_all, hparams.n_embd_r(), n_seqs);
+    conv               = ggml_reshape_3d(ctx0, conv, d_conv - 1, d_inner + 2 * n_group * d_state, n_seqs);
+
+    // {n_embd, n_tokens} => {n_embd, n_seq_tokens, n_seqs}
+    cur = ggml_reshape_3d(ctx0, cur, cur->ne[0], n_seq_tokens, n_seqs);
+
+    // in_proj: {n_embd, 2*d_inner} @ {n_embd, n_seq_tokens, n_seqs} => {2*d_inner, n_seq_tokens, n_seqs}
+    ggml_tensor * zx = build_lora_mm(model.layers[il].ssm_in, cur);
+    cb(zx, "mamba_in_proj", il);
+    // {8192, 5, 1, 1} -> {8192, 1, 5, 1}
+    zx = ggml_permute(ctx0, zx, 0, 2, 1, 3);
+    zx = ggml_cont_4d(ctx0, zx, head_dim * 2, n_heads, n_seq_tokens, n_seqs);
+    cb(zx, "mamba_in_proj_out", il);
+
+    // split into z and x
+    // => {head_dim * n_heads, n_seq_tokens, n_seqs}
+    ggml_tensor * x = ggml_view_4d(ctx0, zx, head_dim, n_heads, n_seq_tokens, n_seqs, zx->nb[1], zx->nb[2], zx->nb[3],
+                                   head_dim * ggml_element_size(zx));
+    x               = ggml_cont_3d(ctx0, x, head_dim * n_heads, n_seq_tokens, n_seqs);
+    // x = ggml_permute(ctx0, x, 0, 2, 1, 3);
+    cb(x, "mamba_x_split", il);
+
+    ggml_tensor * z =
+        ggml_view_4d(ctx0, zx, head_dim, n_heads, n_seq_tokens, n_seqs, zx->nb[1], zx->nb[2], zx->nb[3], 0);
+    cb(z, "mamba_z_split", il);
+
+    // conv1d
+    {
+        // => {d_conv - 1 + n_seq_tokens, d_inner, n_seqs}
+        ggml_tensor * conv_x = ggml_concat(ctx0, conv, ggml_transpose(ctx0, x), 0);
+        cb(conv_x, "mamba_conv1d_input", il);
+
+        // copy last (d_conv - 1) columns back into the state cache
+        ggml_tensor * last_conv = ggml_view_3d(ctx0, conv_x, d_conv - 1, d_inner, n_seqs, conv_x->nb[1], conv_x->nb[2],
+                                               n_seq_tokens * (conv_x->nb[0]));
+
+        ggml_build_forward_expand(gf, ggml_cpy(ctx0, last_conv,
+                                               ggml_view_1d(ctx0, conv_states_all,
+                                                            (d_conv - 1) * (d_inner + 2 * n_group * d_state) * (n_seqs),
+                                                            kv_head * (d_conv - 1) * (d_inner + 2 * n_group * d_state) *
+                                                                ggml_element_size(conv_states_all))));
+        cb(conv_states_all, "mamba_conv1d_state", il);
+
+        // 1D convolution
+        x = ggml_ssm_conv(ctx0, conv_x, model.layers[il].ssm_conv1d);
+        cb(x, "mamba_conv1d", il);
+
+        x = ggml_silu(ctx0, x);
+        cb(x, "mamba_conv1d_silu", il);
+    }
+
+    // SSM
+    {
+        // bcdt_proj: {d_inner, dt_rank + 2*d_state} @ {d_inner, n_seq_tokens, n_seqs} => {dt_rank + 2*d_state, n_seq_tokens, n_seqs}
+        ggml_tensor * x_bcdt = build_lora_mm(model.layers[il].ssm_x, x);
+        cb(x_bcdt, "mamba_bcdt_proj", il);
+
+        // split into dt, B, C
+        const int64_t dt_dim = std::max(64, int(hparams.n_embd / 16));
+        ggml_tensor * B  = ggml_view_3d(ctx0, x_bcdt, d_state, n_seq_tokens, n_seqs, x_bcdt->nb[1], x_bcdt->nb[2], 0);
+        ggml_tensor * C  = ggml_view_3d(ctx0, x_bcdt, d_state, n_seq_tokens, n_seqs, x_bcdt->nb[1], x_bcdt->nb[2],
+                                        ggml_element_size(x_bcdt) * d_state);
+        ggml_tensor * dt = ggml_view_3d(ctx0, x_bcdt, dt_dim, n_seq_tokens, n_seqs, x_bcdt->nb[1], x_bcdt->nb[2],
+                                        ggml_element_size(x_bcdt) * (2 * d_state));
+        cb(B, "mamba_B_raw", il);
+        cb(C, "mamba_C_raw", il);
+        cb(dt, "mamba_dt_raw", il);
+
+        // Apply RMS norm to dt, B, C (PLaMo-2 specific)
+        B  = build_norm(B, model.layers[il].ssm_b_norm, NULL, LLM_NORM_RMS, il);
+        C  = build_norm(C, model.layers[il].ssm_c_norm, NULL, LLM_NORM_RMS, il);
+        dt = build_norm(dt, model.layers[il].ssm_dt_norm, NULL, LLM_NORM_RMS, il);
+        cb(B, "mamba_B_normed", il);
+        cb(C, "mamba_C_normed", il);
+        cb(dt, "mamba_dt_normed", il);
+
+        // dt_proj: {dt_rank, d_inner} @ {dt_rank, n_seq_tokens, n_seqs} => {d_inner, n_seq_tokens, n_seqs}
+        dt = build_lora_mm(model.layers[il].ssm_dt, dt);
+        dt = ggml_add(ctx0, dt, model.layers[il].ssm_dt_b);
+        cb(dt, "mamba_dt_proj", il);
+
+        ggml_tensor * A = ggml_reshape_2d(ctx0, model.layers[il].ssm_a, 1, n_heads);
+        cb(A, "mamba_A", il);
+
+        x = ggml_view_4d(ctx0, x, head_dim, n_heads, n_seq_tokens, n_seqs, head_dim * ggml_element_size(x),
+                         head_dim * n_heads * ggml_element_size(x),
+                         head_dim * n_heads * n_seq_tokens * ggml_element_size(x), 0);
+        B = ggml_view_4d(ctx0, B, d_state, 1, n_seq_tokens, n_seqs, d_state * B->nb[0], B->nb[1], B->nb[2], 0);
+        C = ggml_view_4d(ctx0, C, d_state, 1, n_seq_tokens, n_seqs, d_state * C->nb[0], C->nb[1], C->nb[2], 0);
+
+        // use the states and the indices provided by build_recurrent_state
+        // (this is necessary in order to properly use the states before they are overwritten,
+        //  while avoiding to make unnecessary copies of the states)
+        auto get_ssm_rows = [&](ggml_context * ctx, ggml_tensor * states, ggml_tensor * ids) {
+            ggml_tensor * ssm = ggml_reshape_4d(ctx, states, d_state, head_dim, n_heads, mctx_cur->get_size());
+
+            // Custom operator to optimize the parallel associative scan
+            // as described in the Annex D of the Mamba paper.
+            // => {d_inner, n_seq_tokens, n_seqs} and {d_state, d_inner, n_seqs}
+            return ggml_ssm_scan(ctx, ssm, x, dt, A, B, C, ids);
+        };
+
+        ggml_tensor * y_ssm = build_rs(inp, ssm_states_all, hparams.n_embd_s(), ubatch.n_seqs, get_ssm_rows);
+        cb(y_ssm, "mamba_ssm_scan", il);
+
+        // store last states
+        ggml_build_forward_expand(
+            gf, ggml_cpy(
+                    ctx0,
+                    ggml_view_1d(ctx0, y_ssm, n_heads * head_dim * d_state * n_seqs,
+                                 n_heads * head_dim * n_seq_tokens * n_seqs * ggml_element_size(y_ssm)),
+                    ggml_view_1d(ctx0, ssm_states_all, n_heads * head_dim * d_state * n_seqs,
+                                 kv_head * n_seqs * n_heads * head_dim * d_state * ggml_element_size(ssm_states_all))));
+        cb(ssm_states_all, "mamba_ssm_states", il);
+
+        ggml_tensor * y = ggml_view_4d(ctx0, y_ssm, head_dim, n_heads, n_seq_tokens, n_seqs,
+                                       head_dim * ggml_element_size(x), head_dim * n_heads * ggml_element_size(x),
+                                       head_dim * n_heads * n_seq_tokens * ggml_element_size(x), 0);
+        cb(y, "mamba_y_view", il);
+
+        // Add D parameter and apply gating with z
+        // {d_inner, n_seq_tokens, n_seqs} * {d_inner} => {d_inner, n_seq_tokens, n_seqs}
+        ggml_tensor * D = ggml_reshape_2d(ctx0, model.layers[il].ssm_d, 1, n_heads);
+        y               = ggml_add(ctx0, y, ggml_mul(ctx0, x, D));
+        cb(y, "mamba_y_add_d", il);
+
+        y = ggml_swiglu_split(ctx0, ggml_cont(ctx0, z), y);
+        cb(y, "mamba_y_swiglu_z", il);
+
+        // out_proj: {d_inner, n_embd} @ {d_inner, n_seq_tokens, n_seqs} => {n_embd, n_seq_tokens, n_seqs}
+        y   = ggml_view_3d(ctx0, y, head_dim * n_heads, n_seq_tokens, n_seqs, y->nb[2], y->nb[3], 0);
+        cur = build_lora_mm(model.layers[il].ssm_out, y);
+        cb(cur, "mamba_out_proj", il);
+    }
+
+    // {n_embd, n_seq_tokens, n_seqs} => {n_embd, n_tokens}
+    cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], n_seq_tokens * n_seqs);
+    cb(cur, "mamba_out", il);
+
+    return cur;
+}
diff --git a/src/models/plm.cpp b/src/models/plm.cpp
new file mode 100644
index 0000000000..ddd52162b2
--- /dev/null
+++ b/src/models/plm.cpp
@@ -0,0 +1,168 @@
+#include "models.h"
+
+llm_build_plm::llm_build_plm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const float kq_scale = 1.0f/sqrtf(float(hparams.n_embd_head_k));
+
+        const uint32_t n_embd_head_qk_rope = hparams.n_rot;
+        const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
+        const uint32_t kv_lora_rank = hparams.n_lora_kv;
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        // {n_embd, n_tokens}
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self_attention
+            {
+                ggml_tensor * q = NULL;
+                q = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                cb(q, "q", il);
+
+                // split into {n_head * n_embd_head_qk_nope, n_tokens}
+                ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
+                        ggml_row_size(q->type, hparams.n_embd_head_k),
+                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
+                        0);
+                cb(q_nope, "q_nope", il);
+
+                // and {n_head * n_embd_head_qk_rope, n_tokens}
+                ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
+                        ggml_row_size(q->type, hparams.n_embd_head_k),
+                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
+                        ggml_row_size(q->type, n_embd_head_qk_nope));
+                cb(q_pe, "q_pe", il);
+
+                // {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens}
+                ggml_tensor * kv_pe_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
+                cb(kv_pe_compresseed, "kv_pe_compresseed", il);
+
+                // split into {kv_lora_rank, n_tokens}
+                ggml_tensor * kv_compressed = ggml_view_2d(ctx0, kv_pe_compresseed, kv_lora_rank, n_tokens,
+                        kv_pe_compresseed->nb[1],
+                        0);
+                cb(kv_compressed, "kv_compressed", il);
+
+                // and {n_embd_head_qk_rope, n_tokens}
+                ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_pe_compresseed, n_embd_head_qk_rope, 1, n_tokens,
+                        kv_pe_compresseed->nb[1],
+                        kv_pe_compresseed->nb[1],
+                        ggml_row_size(kv_pe_compresseed->type, kv_lora_rank));
+                cb(k_pe, "k_pe", il);
+
+                kv_compressed = build_norm(kv_compressed,
+                        model.layers[il].attn_kv_a_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(kv_compressed, "kv_compressed", il);
+
+                // {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)} * {kv_lora_rank, n_tokens} -> {n_head * (n_embd_head_qk_nope + n_embd_head_v), n_tokens}
+                ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_compressed);
+                cb(kv, "kv", il);
+
+                // split into {n_head * n_embd_head_qk_nope, n_tokens}
+                ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
+                        ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v),
+                        ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
+                        0);
+                cb(k_nope, "k_nope", il);
+
+                // and {n_head * n_embd_head_v, n_tokens}
+                ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens,
+                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)),
+                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head),
+                        ggml_row_size(kv->type, (n_embd_head_qk_nope)));
+                cb(v_states, "v_states", il);
+
+                v_states = ggml_cont(ctx0, v_states);
+                cb(v_states, "v_states", il);
+
+                v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v * n_head, n_tokens,
+                        ggml_row_size(kv->type, hparams.n_embd_head_v * n_head),
+                        0);
+                cb(v_states, "v_states", il);
+
+                q_pe = ggml_rope_ext(
+                        ctx0, q_pe, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow
+                        );
+                cb(q_pe, "q_pe", il);
+
+                // shared RoPE key
+                k_pe = ggml_rope_ext(
+                        ctx0, k_pe, inp_pos, nullptr,
+                        n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                        ext_factor, attn_factor, beta_fast, beta_slow
+                        );
+                cb(k_pe, "k_pe", il);
+
+                ggml_tensor * q_states = ggml_concat(ctx0, q_nope, q_pe, 0);
+                cb(q_states, "q_states", il);
+
+                ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);
+                cb(k_states, "k_states", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, NULL,
+                        q_states, k_states, v_states, nullptr, nullptr, nullptr, kq_scale, il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    NULL, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_RELU_SQR, LLM_FFN_SEQ, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/qwen.cpp b/src/models/qwen.cpp
new file mode 100644
index 0000000000..31fd9b7376
--- /dev/null
+++ b/src/models/qwen.cpp
@@ -0,0 +1,108 @@
+#include "models.h"
+
+
+llm_build_qwen::llm_build_qwen(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            cur = build_lora_mm(model.layers[il].wqkv, cur);
+            cb(cur, "wqkv", il);
+
+            cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+            cb(cur, "bqkv", il);
+
+            ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
+            ggml_tensor * Kcur = 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));
+            ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, n_embd_head*sizeof(float), cur->nb[1], 2*sizeof(float)*(n_embd));
+
+            // using mode = 2 for neox mode
+            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);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, NULL,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        }
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward forward
+        {
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        }
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/qwen2.cpp b/src/models/qwen2.cpp
new file mode 100644
index 0000000000..885cb46894
--- /dev/null
+++ b/src/models/qwen2.cpp
@@ -0,0 +1,118 @@
+#include "models.h"
+
+
+llm_build_qwen2::llm_build_qwen2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // 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);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                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);
+
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        if (model.output_b != nullptr) {
+            cur = ggml_add(ctx0, cur, model.output_b);
+        }
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/qwen2moe.cpp b/src/models/qwen2moe.cpp
new file mode 100644
index 0000000000..40623ea66f
--- /dev/null
+++ b/src/models/qwen2moe.cpp
@@ -0,0 +1,151 @@
+#include "models.h"
+
+llm_build_qwen2moe::llm_build_qwen2moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self_attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                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);
+                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);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // MoE branch
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            ggml_tensor * moe_out =
+                build_moe_ffn(cur,
+                        model.layers[il].ffn_gate_inp,
+                        model.layers[il].ffn_up_exps,
+                        model.layers[il].ffn_gate_exps,
+                        model.layers[il].ffn_down_exps,
+                        nullptr,
+                        n_expert, n_expert_used,
+                        LLM_FFN_SILU, false,
+                        false, 0.0,
+                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                        il);
+            cb(moe_out, "ffn_moe_out", il);
+
+            // FFN shared expert
+            {
+                ggml_tensor * cur_gate_inp = build_lora_mm(model.layers[il].ffn_gate_inp_shexp, cur);
+                cb(cur_gate_inp, "ffn_shexp_gate_inp", il);
+
+                // sigmoid
+                ggml_tensor * cur_gate = ggml_div(ctx0, ggml_silu(ctx0, cur_gate_inp), cur_gate_inp);
+                cb(cur_gate, "ffn_shexp_gate", il);
+
+                ggml_tensor * cur_ffn = build_ffn(cur,
+                        model.layers[il].ffn_up_shexp,   NULL, NULL,
+                        model.layers[il].ffn_gate_shexp, NULL, NULL,
+                        model.layers[il].ffn_down_shexp, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(cur_ffn, "ffn_shexp", il);
+
+                ggml_tensor * ffn_shexp_out = ggml_mul(ctx0, cur_ffn, cur_gate);
+                cb(ffn_shexp_out, "ffn_shexp_out", il);
+
+                moe_out = ggml_add(ctx0, moe_out, ffn_shexp_out);
+                cb(moe_out, "ffn_out", il);
+
+                cur = moe_out;
+            }
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/qwen2vl.cpp b/src/models/qwen2vl.cpp
new file mode 100644
index 0000000000..addc37f9a8
--- /dev/null
+++ b/src/models/qwen2vl.cpp
@@ -0,0 +1,117 @@
+#include "models.h"
+
+llm_build_qwen2vl::llm_build_qwen2vl(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        int sections[4];
+        std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // 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);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                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);
+
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                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
+                        );
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/qwen3.cpp b/src/models/qwen3.cpp
new file mode 100644
index 0000000000..782d32107a
--- /dev/null
+++ b/src/models/qwen3.cpp
@@ -0,0 +1,117 @@
+#include "models.h"
+
+llm_build_qwen3::llm_build_qwen3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur_normed", il);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/qwen3moe.cpp b/src/models/qwen3moe.cpp
new file mode 100644
index 0000000000..f5087cdb06
--- /dev/null
+++ b/src/models/qwen3moe.cpp
@@ -0,0 +1,124 @@
+#include "models.h"
+
+llm_build_qwen3moe::llm_build_qwen3moe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self_attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+                cb(Qcur, "Qcur_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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+                cb(Kcur, "Kcur_normed", il);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // MoE branch
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            ggml_tensor * moe_out =
+                build_moe_ffn(cur,
+                        model.layers[il].ffn_gate_inp,
+                        model.layers[il].ffn_up_exps,
+                        model.layers[il].ffn_gate_exps,
+                        model.layers[il].ffn_down_exps,
+                        nullptr,
+                        n_expert, n_expert_used,
+                        LLM_FFN_SILU, true,
+                        false, 0.0,
+                        LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                        il);
+            cb(moe_out, "ffn_moe_out", il);
+            cur = moe_out;
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/qwen3vl-moe.cpp b/src/models/qwen3vl-moe.cpp
new file mode 100644
index 0000000000..c48643c0cd
--- /dev/null
+++ b/src/models/qwen3vl-moe.cpp
@@ -0,0 +1,150 @@
+#include "models.h"
+
+llm_build_qwen3vlmoe::llm_build_qwen3vlmoe(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_full = hparams.n_embd; // main embd + deepstack embds
+    const size_t n_deepstack_layers = hparams.n_deepstack_layers;
+    const int64_t n_embd = n_embd_full / (n_deepstack_layers + 1);
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    int sections[4];
+    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
+    std::vector<ggml_tensor *> deepstack_features(n_deepstack_layers, nullptr);
+
+    if (ubatch.embd) {
+        // Image input: split main embd and deepstack embds
+        ggml_tensor * inpL_main = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], 0);
+        for (size_t i = 0; i < n_deepstack_layers; i++) {
+            deepstack_features[i] = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], (i + 1) * n_embd * sizeof(float));
+        }
+        inpL = inpL_main;
+    }
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self_attention
+        {
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+            cb(Qcur, "Qcur_normed", il);
+
+            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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+            cb(Kcur, "Kcur_normed", il);
+
+            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
+                    );
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // MoE branch
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        ggml_tensor * moe_out =
+            build_moe_ffn(cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    nullptr,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU, true,
+                    false, 0.0,
+                    LLAMA_EXPERT_GATING_FUNC_TYPE_SOFTMAX,
+                    il);
+        cb(moe_out, "ffn_moe_out", il);
+        cur = moe_out;
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        if (ubatch.embd && (size_t)il < n_deepstack_layers) {
+            cur = ggml_add(ctx0, cur, deepstack_features[il]);
+            cb(cur, "deepstack_out", il);
+        }
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
diff --git a/src/models/qwen3vl.cpp b/src/models/qwen3vl.cpp
new file mode 100644
index 0000000000..10b36c1f65
--- /dev/null
+++ b/src/models/qwen3vl.cpp
@@ -0,0 +1,144 @@
+#include "models.h"
+
+llm_build_qwen3vl::llm_build_qwen3vl(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+
+    const int64_t n_embd_full = hparams.n_embd; // main embd + deepstack embds
+    const size_t n_deepstack_layers = hparams.n_deepstack_layers;
+    const int64_t n_embd = n_embd_full / (n_deepstack_layers + 1);
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+    GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    int sections[4];
+    std::copy(std::begin(hparams.rope_sections), std::begin(hparams.rope_sections) + 4, sections);
+
+    std::vector<ggml_tensor *> deepstack_features(n_deepstack_layers, nullptr);
+
+    if (ubatch.embd) {
+        // Image input: split main embd and deepstack embds
+        ggml_tensor * inpL_main = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], 0);
+        for (size_t i = 0; i < n_deepstack_layers; i++) {
+            deepstack_features[i] = ggml_view_2d(ctx0, inpL, n_embd, n_tokens, inpL->nb[1], (i + 1) * n_embd * sizeof(float));
+        }
+        inpL = inpL_main;
+    }
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        ggml_tensor * inpSA = inpL;
+
+        // norm
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "attn_norm", il);
+
+        // self-attention
+        {
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            cb(Qcur, "Qcur", il);
+
+            ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+            cb(Kcur, "Kcur", il);
+
+            ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
+            cb(Qcur, "Qcur_normed", il);
+
+            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 = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
+            cb(Kcur, "Kcur_normed", il);
+
+            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
+                    );
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, model.layers[il].bo,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        }
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up,   NULL, NULL,
+                model.layers[il].ffn_gate, NULL, NULL,
+                model.layers[il].ffn_down, NULL, NULL,
+                NULL,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        if (ubatch.embd && (size_t)il < n_deepstack_layers) {
+            cur = ggml_add(ctx0, cur, deepstack_features[il]);
+            cb(cur, "deepstack_out", il);
+        }
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/refact.cpp b/src/models/refact.cpp
new file mode 100644
index 0000000000..951844f640
--- /dev/null
+++ b/src/models/refact.cpp
@@ -0,0 +1,94 @@
+#include "models.h"
+
+llm_build_refact::llm_build_refact(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            {
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+            }
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/rwkv6-base.cpp b/src/models/rwkv6-base.cpp
new file mode 100644
index 0000000000..7beed2daff
--- /dev/null
+++ b/src/models/rwkv6-base.cpp
@@ -0,0 +1,162 @@
+#include "models.h"
+
+llm_build_rwkv6_base::llm_build_rwkv6_base(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params),
+    model(model) {}
+
+ggml_tensor * llm_build_rwkv6_base::build_rwkv6_channel_mix(const llama_layer * layer,
+                                                            ggml_tensor *       cur,
+                                                            ggml_tensor *       x_prev,
+                                                            llm_arch            arch) const {
+    ggml_tensor * sx = ggml_sub(ctx0, x_prev, cur);
+    switch (arch) {
+        case LLM_ARCH_RWKV6:
+            {
+                ggml_tensor * xk = ggml_add(ctx0, ggml_mul(ctx0, sx, layer->channel_mix_lerp_k), cur);
+                ggml_tensor * xr = ggml_add(ctx0, ggml_mul(ctx0, sx, layer->channel_mix_lerp_r), cur);
+
+                ggml_tensor * r = ggml_sigmoid(ctx0, build_lora_mm(layer->channel_mix_receptance, xr));
+                ggml_tensor * k = ggml_sqr(ctx0, ggml_relu(ctx0, build_lora_mm(layer->channel_mix_key, xk)));
+                cur             = ggml_mul(ctx0, r, build_lora_mm(layer->channel_mix_value, k));
+            }
+            break;
+        default:
+            GGML_ABORT("fatal error");
+    }
+    return cur;
+}
+
+ggml_tensor * llm_build_rwkv6_base::build_rwkv6_time_mix(llm_graph_input_rs * inp,
+                                                         ggml_tensor *        cur,
+                                                         ggml_tensor *        x_prev,
+                                                         const llama_ubatch & ubatch,
+                                                         int                  il) const {
+    const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
+
+    const auto n_tokens     = ubatch.n_tokens;
+    const auto n_seqs       = ubatch.n_seqs;
+    const auto n_seq_tokens = ubatch.n_seq_tokens;
+    const auto n_embd       = hparams.n_embd;
+    const auto head_size    = hparams.wkv_head_size;
+    const auto n_head       = n_embd / head_size;
+    const auto n_head_kv    = hparams.n_head_kv(il);
+
+    const auto kv_head = mctx_cur->get_head();
+
+    const auto & layer = model.layers[il];
+
+    bool is_qrwkv = layer.time_mix_first == nullptr;
+
+    ggml_tensor * sx = ggml_sub(ctx0, x_prev, cur);
+
+    sx  = ggml_reshape_2d(ctx0, sx, n_embd, n_tokens);
+    cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
+
+    ggml_tensor * xxx = ggml_add(ctx0, ggml_mul(ctx0, sx, layer.time_mix_lerp_x), cur);
+
+    xxx = ggml_reshape_4d(ctx0, ggml_tanh(ctx0, ggml_mul_mat(ctx0, layer.time_mix_w1, xxx)),
+                          layer.time_mix_w1->ne[1] / 5, 1, 5, n_tokens);
+
+    xxx = ggml_cont(ctx0, ggml_permute(ctx0, xxx, 0, 1, 3, 2));
+
+    xxx = ggml_mul_mat(
+        ctx0, ggml_reshape_4d(ctx0, layer.time_mix_w2, layer.time_mix_w2->ne[0], layer.time_mix_w2->ne[1], 1, 5), xxx);
+
+    ggml_tensor *xw, *xk, *xv, *xr, *xg;
+    if (layer.time_mix_lerp_fused) {
+        // fusing these weights makes some performance improvement
+        sx  = ggml_reshape_3d(ctx0, sx, n_embd, 1, n_tokens);
+        cur = ggml_reshape_3d(ctx0, cur, n_embd, 1, n_tokens);
+        xxx = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xxx, layer.time_mix_lerp_fused), sx), cur);
+        xw  = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], 0);
+        xk  = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * sizeof(float));
+        xv  = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 2 * sizeof(float));
+        xr  = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 3 * sizeof(float));
+        xg  = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 4 * sizeof(float));
+    } else {
+        // for backward compatibility
+        xw = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], 0);
+        xk = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * sizeof(float));
+        xv = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 2 * sizeof(float));
+        xr = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 3 * sizeof(float));
+        xg = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 4 * sizeof(float));
+
+        xw = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xw, layer.time_mix_lerp_w), sx), cur);
+        xk = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xk, layer.time_mix_lerp_k), sx), cur);
+        xv = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xv, layer.time_mix_lerp_v), sx), cur);
+        xr = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xr, layer.time_mix_lerp_r), sx), cur);
+        xg = ggml_add(ctx0, ggml_mul(ctx0, ggml_add(ctx0, xg, layer.time_mix_lerp_g), sx), cur);
+    }
+    ggml_tensor * r = build_lora_mm(layer.time_mix_receptance, xr);
+    ggml_tensor * k = build_lora_mm(layer.time_mix_key, xk);
+    ggml_tensor * v = build_lora_mm(layer.time_mix_value, xv);
+    if (layer.time_mix_receptance_b) {
+        r = ggml_add(ctx0, r, layer.time_mix_receptance_b);
+    }
+    if (layer.time_mix_key_b) {
+        k = ggml_add(ctx0, k, layer.time_mix_key_b);
+    }
+    if (layer.time_mix_value_b) {
+        v = ggml_add(ctx0, v, layer.time_mix_value_b);
+    }
+    ggml_tensor * g = build_lora_mm(layer.time_mix_gate, xg);
+    if (is_qrwkv) {
+        g = ggml_sigmoid(ctx0, g);
+    } else {
+        g = ggml_silu(ctx0, g);
+    }
+    if (n_head_kv != 0 && n_head_kv != n_head) {
+        GGML_ASSERT(n_head % n_head_kv == 0);
+        k                 = ggml_reshape_4d(ctx0, k, head_size, 1, n_head_kv, n_tokens);
+        v                 = ggml_reshape_4d(ctx0, v, head_size, 1, n_head_kv, n_tokens);
+        ggml_tensor * tmp = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, head_size, n_head / n_head_kv, n_head_kv, n_tokens);
+        k                 = ggml_repeat(ctx0, k, tmp);
+        v                 = ggml_repeat(ctx0, v, tmp);
+    }
+    k = ggml_reshape_3d(ctx0, k, head_size, n_head, n_tokens);
+    v = ggml_reshape_3d(ctx0, v, head_size, n_head, n_tokens);
+    r = ggml_reshape_3d(ctx0, r, head_size, n_head, n_tokens);
+
+    ggml_tensor * w =
+        ggml_mul_mat(ctx0, layer.time_mix_decay_w2, ggml_tanh(ctx0, ggml_mul_mat(ctx0, layer.time_mix_decay_w1, xw)));
+
+    w = ggml_add(ctx0, w, layer.time_mix_decay);
+    w = ggml_exp(ctx0, ggml_neg(ctx0, ggml_exp(ctx0, w)));
+    w = ggml_reshape_3d(ctx0, w, head_size, n_head, n_tokens);
+
+    if (is_qrwkv) {
+        // k = k * (1 - w)
+        k = ggml_sub(ctx0, k, ggml_mul(ctx0, k, w));
+    }
+    ggml_tensor * wkv_state = build_rs(inp, mctx_cur->get_s_l(il), hparams.n_embd_s(), n_seqs);
+
+    ggml_tensor * wkv_output;
+    if (is_qrwkv) {
+        wkv_output = ggml_gated_linear_attn(ctx0, k, v, r, w, wkv_state, pow(head_size, -0.5f));
+    } else {
+        wkv_output = ggml_rwkv_wkv6(ctx0, k, v, r, layer.time_mix_first, w, wkv_state);
+    }
+    cur       = ggml_view_1d(ctx0, wkv_output, n_embd * n_tokens, 0);
+    wkv_state = ggml_view_1d(ctx0, wkv_output, n_embd * head_size * n_seqs, n_embd * n_tokens * sizeof(float));
+
+    ggml_build_forward_expand(
+        gf, ggml_cpy(ctx0, wkv_state,
+                     ggml_view_1d(ctx0, mctx_cur->get_s_l(il), hparams.n_embd_s() * n_seqs,
+                                  hparams.n_embd_s() * kv_head * ggml_element_size(mctx_cur->get_s_l(il)))));
+
+    if (!is_qrwkv) {
+        // group norm with head_count groups
+        cur = ggml_reshape_3d(ctx0, cur, n_embd / n_head, n_head, n_tokens);
+        cur = ggml_norm(ctx0, cur, 64e-5f);
+
+        // Convert back to regular vectors.
+        cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
+        cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.time_mix_ln), layer.time_mix_ln_b);
+    } else {
+        cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
+    }
+    cur = ggml_mul(ctx0, cur, g);
+    cur = build_lora_mm(layer.time_mix_output, cur);
+
+    return ggml_reshape_3d(ctx0, cur, n_embd, n_seq_tokens, n_seqs);
+}
diff --git a/src/models/rwkv6.cpp b/src/models/rwkv6.cpp
new file mode 100644
index 0000000000..15453fbf50
--- /dev/null
+++ b/src/models/rwkv6.cpp
@@ -0,0 +1,94 @@
+#include "models.h"
+
+llm_build_rwkv6::llm_build_rwkv6(const llama_model & model, const llm_graph_params & params) :
+    llm_build_rwkv6_base(model, params) {
+    GGML_ASSERT(hparams.token_shift_count == 2);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+    inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
+
+    auto * rs_inp = build_rs_inp();
+
+    const auto n_embd       = hparams.n_embd;
+    const auto n_seq_tokens = ubatch.n_seq_tokens;
+    const auto n_seqs       = ubatch.n_seqs;
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        const llama_layer * layer = &model.layers[il];
+        inpL                      = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
+
+        ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il);
+
+        ggml_tensor * att_shift =
+            ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], 0);
+        ggml_tensor * ffn_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1],
+                                               token_shift->nb[2], n_embd * ggml_element_size(token_shift));
+
+        ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM, il);
+        cb(att_norm, "attn_norm", il);
+
+        ggml_tensor * x_prev = ggml_concat(
+            ctx0, att_shift,
+            ggml_view_3d(ctx0, att_norm, n_embd, n_seq_tokens - 1, n_seqs, att_norm->nb[1], att_norm->nb[2], 0), 1);
+
+        cur = build_rwkv6_time_mix(rs_inp, att_norm, x_prev, ubatch, il);
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+        cb(ffn_inp, "ffn_inp", il);
+
+        ggml_tensor * ffn_norm = build_norm(ffn_inp, layer->attn_norm_2, layer->attn_norm_2_b, LLM_NORM, il);
+        cb(ffn_norm, "ffn_norm", il);
+
+        x_prev = ggml_concat(
+            ctx0, ffn_shift,
+            ggml_view_3d(ctx0, ffn_norm, n_embd, n_seq_tokens - 1, n_seqs, ffn_norm->nb[1], ffn_norm->nb[2], 0), 1);
+
+        token_shift = ggml_concat(ctx0,
+                                  ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2],
+                                               (n_seq_tokens - 1) * n_embd * ggml_element_size(att_norm)),
+                                  ggml_view_3d(ctx0, ffn_norm, n_embd, 1, n_seqs, ffn_norm->nb[1], ffn_norm->nb[2],
+                                               (n_seq_tokens - 1) * n_embd * ggml_element_size(ffn_norm)),
+                                  1);
+        ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));
+
+        ffn_inp  = ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens);
+        ffn_norm = ggml_reshape_2d(ctx0, ffn_norm, n_embd, n_tokens);
+        x_prev   = ggml_reshape_2d(ctx0, x_prev, n_embd, n_tokens);
+        cur      = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            ffn_inp  = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
+            ffn_norm = ggml_get_rows(ctx0, ffn_norm, inp_out_ids);
+            x_prev   = ggml_get_rows(ctx0, x_prev, inp_out_ids);
+            cur      = ggml_get_rows(ctx0, cur, inp_out_ids);
+        }
+        cur = build_rwkv6_channel_mix(layer, ffn_norm, x_prev, LLM_ARCH_RWKV6);
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        if (hparams.rescale_every_n_layers != 0 && (il + 1) % hparams.rescale_every_n_layers == 0) {
+            cur = ggml_scale(ctx0, cur, 0.5F);
+        }
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+    cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/rwkv6qwen2.cpp b/src/models/rwkv6qwen2.cpp
new file mode 100644
index 0000000000..e84e597382
--- /dev/null
+++ b/src/models/rwkv6qwen2.cpp
@@ -0,0 +1,86 @@
+#include "models.h"
+
+llm_build_rwkv6qwen2::llm_build_rwkv6qwen2(const llama_model & model, const llm_graph_params & params) : llm_build_rwkv6_base(model, params) {
+    GGML_ASSERT(n_embd == hparams.n_embd_r());
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    auto * rs_inp = build_rs_inp();
+
+    const auto n_embd = hparams.n_embd;
+    const auto n_seq_tokens = ubatch.n_seq_tokens;
+    const auto n_seqs = ubatch.n_seqs;
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        const llama_layer * layer = &model.layers[il];
+        inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
+
+        ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il);
+
+        ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM_RMS, il);
+        cb(att_norm, "attn_norm", il);
+
+        ggml_tensor * x_prev = ggml_concat(
+                ctx0,
+                token_shift,
+                ggml_view_3d(ctx0, att_norm, n_embd, n_seq_tokens - 1, n_seqs, att_norm->nb[1], att_norm->nb[2], 0),
+                1
+                );
+
+        cur = build_rwkv6_time_mix(rs_inp, att_norm, x_prev, ubatch, il);
+
+        token_shift = ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2], (n_seq_tokens-1)*n_embd*ggml_element_size(att_norm));
+        ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+        cb(ffn_inp, "ffn_inp", il);
+
+        cur     = ggml_reshape_2d(ctx0, cur,     n_embd, n_tokens);
+        ffn_inp = ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens);
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur     = ggml_get_rows(ctx0, cur,     inp_out_ids);
+            ffn_inp = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
+        }
+
+        // feed-forward network
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "ffn_norm", il);
+
+        cur = build_ffn(cur,
+                model.layers[il].ffn_up,   NULL, NULL,
+                model.layers[il].ffn_gate, NULL, NULL,
+                model.layers[il].ffn_down, NULL, NULL,
+                NULL,
+                LLM_FFN_SILU, LLM_FFN_PAR, il);
+        cb(cur, "ffn_out", il);
+
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+
+    cur = inpL;
+    cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM_RMS, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/rwkv7-base.cpp b/src/models/rwkv7-base.cpp
new file mode 100644
index 0000000000..cda4465384
--- /dev/null
+++ b/src/models/rwkv7-base.cpp
@@ -0,0 +1,135 @@
+#include "models.h"
+
+llm_build_rwkv7_base::llm_build_rwkv7_base(const llama_model & model, const llm_graph_params & params) :
+    llm_graph_context(params),
+    model(model) {}
+
+ggml_tensor * llm_build_rwkv7_base::build_rwkv7_channel_mix(const llama_layer * layer,
+                                                            ggml_tensor *       cur,
+                                                            ggml_tensor *       x_prev,
+                                                            llm_arch            arch) const {
+    ggml_tensor * sx = ggml_sub(ctx0, x_prev, cur);
+    switch (arch) {
+        case LLM_ARCH_RWKV7:
+            {
+                ggml_tensor * xk = ggml_add(ctx0, ggml_mul(ctx0, sx, layer->channel_mix_lerp_k), cur);
+
+                ggml_tensor * k = ggml_sqr(ctx0, ggml_relu(ctx0, build_lora_mm(layer->channel_mix_key, xk)));
+
+                cur = build_lora_mm(layer->channel_mix_value, k);
+            }
+            break;
+        default:
+            GGML_ABORT("fatal error");
+    }
+    return cur;
+}
+
+ggml_tensor * llm_build_rwkv7_base::build_rwkv7_time_mix(llm_graph_input_rs * inp,
+                                                         ggml_tensor *        cur,
+                                                         ggml_tensor *        x_prev,
+                                                         ggml_tensor *&       first_layer_value,
+                                                         const llama_ubatch & ubatch,
+                                                         int                  il) const {
+    const auto * mctx_cur = static_cast<const llama_memory_recurrent_context *>(mctx);
+
+    const auto n_tokens     = ubatch.n_tokens;
+    const auto n_seqs       = ubatch.n_seqs;
+    const auto n_embd       = hparams.n_embd;
+    const auto head_size    = hparams.wkv_head_size;
+    const auto head_count   = n_embd / head_size;
+    const auto n_seq_tokens = ubatch.n_seq_tokens;
+
+    const auto kv_head = mctx_cur->get_head();
+
+    const auto & layer = model.layers[il];
+
+    bool has_gating = layer.time_mix_g1 && layer.time_mix_g2;
+
+    ggml_tensor * sx    = ggml_sub(ctx0, x_prev, cur);
+    ggml_tensor * dummy = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, n_embd, n_seq_tokens, n_seqs, has_gating ? 6 : 5);
+    sx                  = ggml_repeat(ctx0, sx, dummy);
+
+    ggml_tensor * xxx = ggml_add(ctx0, ggml_mul(ctx0, sx, layer.time_mix_lerp_fused), cur);
+
+    ggml_tensor * xr = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], 0);
+    ggml_tensor * xw = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * sizeof(float));
+    ggml_tensor * xk = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 2 * sizeof(float));
+    ggml_tensor * xv = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 3 * sizeof(float));
+    ggml_tensor * xa = ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 4 * sizeof(float));
+    ggml_tensor * xg =
+        has_gating ? ggml_view_2d(ctx0, xxx, n_embd, n_tokens, xxx->nb[1], n_embd * n_tokens * 5 * sizeof(float)) :
+                     nullptr;
+
+    ggml_tensor * r = build_lora_mm(layer.time_mix_receptance, xr);
+    ggml_tensor * w = ggml_add(
+        ctx0, ggml_mul_mat(ctx0, layer.time_mix_w2, ggml_tanh(ctx0, ggml_mul_mat(ctx0, layer.time_mix_w1, xw))),
+        layer.time_mix_w0);
+    w = ggml_exp(ctx0, ggml_scale(ctx0, ggml_sigmoid(ctx0, w), -0.606531));
+
+    ggml_tensor * k = build_lora_mm(layer.time_mix_key, xk);
+    ggml_tensor * v = build_lora_mm(layer.time_mix_value, xv);
+    if (first_layer_value == nullptr) {
+        first_layer_value = v;
+    } else {
+        // Add the first layer value as a residual connection.
+        v = ggml_add(ctx0, v,
+                     ggml_mul(ctx0, ggml_sub(ctx0, first_layer_value, v),
+                              ggml_sigmoid(ctx0, ggml_add(ctx0,
+                                                          ggml_mul_mat(ctx0, layer.time_mix_v2,
+                                                                       ggml_mul_mat(ctx0, layer.time_mix_v1, xv)),
+                                                          layer.time_mix_v0))));
+    }
+    ggml_tensor * g = nullptr;
+    if (layer.time_mix_g1 && layer.time_mix_g2) {
+        g = ggml_mul_mat(ctx0, layer.time_mix_g2, ggml_sigmoid(ctx0, ggml_mul_mat(ctx0, layer.time_mix_g1, xg)));
+    }
+    ggml_tensor * a = ggml_sigmoid(
+        ctx0, ggml_add(ctx0, ggml_mul_mat(ctx0, layer.time_mix_a2, ggml_mul_mat(ctx0, layer.time_mix_a1, xa)),
+                       layer.time_mix_a0));
+
+    ggml_tensor * kk = ggml_reshape_3d(ctx0, ggml_mul(ctx0, k, layer.time_mix_k_k), head_size, head_count, n_tokens);
+    kk               = ggml_l2_norm(ctx0, kk, 1e-12);
+
+    ggml_tensor * ka = ggml_mul(ctx0, k, layer.time_mix_k_a);
+    k                = ggml_add(ctx0, k, ggml_sub(ctx0, ggml_mul(ctx0, a, ka), ka));
+
+    r = ggml_reshape_3d(ctx0, r, head_size, head_count, n_tokens);
+    w = ggml_reshape_3d(ctx0, w, head_size, head_count, n_tokens);
+    k = ggml_reshape_3d(ctx0, k, head_size, head_count, n_tokens);
+    v = ggml_reshape_3d(ctx0, v, head_size, head_count, n_tokens);
+    a = ggml_reshape_3d(ctx0, a, head_size, head_count, n_tokens);
+
+    ggml_tensor * wkv_state = build_rs(inp, mctx_cur->get_s_l(il), hparams.n_embd_s(), n_seqs);
+
+    ggml_tensor * wkv_output = ggml_rwkv_wkv7(ctx0, r, w, k, v, ggml_neg(ctx0, kk), ggml_mul(ctx0, kk, a), wkv_state);
+    cur                      = ggml_view_1d(ctx0, wkv_output, n_embd * n_tokens, 0);
+    wkv_state = ggml_view_1d(ctx0, wkv_output, n_embd * head_size * n_seqs, n_embd * n_tokens * sizeof(float));
+
+    ggml_build_forward_expand(
+        gf, ggml_cpy(ctx0, wkv_state,
+                     ggml_view_1d(ctx0, mctx_cur->get_s_l(il), hparams.n_embd_s() * n_seqs,
+                                  hparams.n_embd_s() * kv_head * ggml_element_size(mctx_cur->get_s_l(il)))));
+
+    if (layer.time_mix_ln && layer.time_mix_ln_b) {
+        // group norm with head_count groups
+        cur = ggml_reshape_3d(ctx0, cur, n_embd / head_count, head_count, n_tokens);
+        cur = ggml_norm(ctx0, cur, 64e-5f);
+
+        // Convert back to regular vectors.
+        cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
+        cur = ggml_add(ctx0, ggml_mul(ctx0, cur, layer.time_mix_ln), layer.time_mix_ln_b);
+    } else {
+        cur = ggml_reshape_2d(ctx0, cur, n_embd, n_tokens);
+    }
+    ggml_tensor * rk = ggml_sum_rows(
+        ctx0, ggml_mul(ctx0, ggml_mul(ctx0, k, r), ggml_reshape_2d(ctx0, layer.time_mix_r_k, head_size, head_count)));
+    cur = ggml_add(ctx0, cur, ggml_reshape_2d(ctx0, ggml_mul(ctx0, v, rk), n_embd, n_tokens));
+
+    if (has_gating) {
+        cur = ggml_mul(ctx0, cur, g);
+    }
+    cur = build_lora_mm(layer.time_mix_output, cur);
+
+    return ggml_reshape_3d(ctx0, cur, n_embd, n_seq_tokens, n_seqs);
+}
diff --git a/src/models/rwkv7.cpp b/src/models/rwkv7.cpp
new file mode 100644
index 0000000000..5caf6553df
--- /dev/null
+++ b/src/models/rwkv7.cpp
@@ -0,0 +1,90 @@
+#include "models.h"
+
+llm_build_rwkv7::llm_build_rwkv7(const llama_model & model, const llm_graph_params & params) :
+    llm_build_rwkv7_base(model, params) {
+    GGML_ASSERT(hparams.token_shift_count == 2);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+    ggml_tensor * v_first = nullptr;
+
+    inpL = build_inp_embd(model.tok_embd);
+    inpL = build_norm(inpL, model.tok_norm, model.tok_norm_b, LLM_NORM, -1);
+
+    auto * rs_inp = build_rs_inp();
+
+    const auto n_embd       = hparams.n_embd;
+    const auto n_seq_tokens = ubatch.n_seq_tokens;
+    const auto n_seqs       = ubatch.n_seqs;
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        const llama_layer * layer = &model.layers[il];
+        inpL                      = ggml_reshape_3d(ctx0, inpL, n_embd, n_seq_tokens, n_seqs);
+
+        ggml_tensor * token_shift = build_rwkv_token_shift_load(rs_inp, ubatch, il);
+
+        ggml_tensor * att_shift =
+            ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1], token_shift->nb[2], 0);
+        ggml_tensor * ffn_shift = ggml_view_3d(ctx0, token_shift, n_embd, 1, n_seqs, token_shift->nb[1],
+                                               token_shift->nb[2], n_embd * ggml_element_size(token_shift));
+
+        ggml_tensor * att_norm = build_norm(inpL, layer->attn_norm, layer->attn_norm_b, LLM_NORM, il);
+        cb(att_norm, "attn_norm", il);
+
+        ggml_tensor * x_prev = ggml_concat(
+            ctx0, att_shift,
+            ggml_view_3d(ctx0, att_norm, n_embd, n_seq_tokens - 1, n_seqs, att_norm->nb[1], att_norm->nb[2], 0), 1);
+
+        cur = build_rwkv7_time_mix(rs_inp, att_norm, x_prev, v_first, ubatch, il);
+
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+        cb(ffn_inp, "ffn_inp", il);
+
+        ggml_tensor * ffn_norm = build_norm(ffn_inp, layer->attn_norm_2, layer->attn_norm_2_b, LLM_NORM, il);
+        cb(ffn_norm, "ffn_norm", il);
+
+        x_prev = ggml_concat(
+            ctx0, ffn_shift,
+            ggml_view_3d(ctx0, ffn_norm, n_embd, n_seq_tokens - 1, n_seqs, ffn_norm->nb[1], ffn_norm->nb[2], 0), 1);
+
+        token_shift = ggml_concat(ctx0,
+                                  ggml_view_3d(ctx0, att_norm, n_embd, 1, n_seqs, att_norm->nb[1], att_norm->nb[2],
+                                               (n_seq_tokens - 1) * n_embd * ggml_element_size(att_norm)),
+                                  ggml_view_3d(ctx0, ffn_norm, n_embd, 1, n_seqs, ffn_norm->nb[1], ffn_norm->nb[2],
+                                               (n_seq_tokens - 1) * n_embd * ggml_element_size(ffn_norm)),
+                                  1);
+        ggml_build_forward_expand(gf, build_rwkv_token_shift_store(token_shift, ubatch, il));
+
+        ffn_inp  = ggml_reshape_2d(ctx0, ffn_inp, n_embd, n_tokens);
+        ffn_norm = ggml_reshape_2d(ctx0, ffn_norm, n_embd, n_tokens);
+        x_prev   = ggml_reshape_2d(ctx0, x_prev, n_embd, n_tokens);
+
+        if (il == n_layer - 1 && inp_out_ids) {
+            ffn_inp  = ggml_get_rows(ctx0, ffn_inp, inp_out_ids);
+            ffn_norm = ggml_get_rows(ctx0, ffn_norm, inp_out_ids);
+            x_prev   = ggml_get_rows(ctx0, x_prev, inp_out_ids);
+        }
+        cur = build_rwkv7_channel_mix(layer, ffn_norm, x_prev, LLM_ARCH_RWKV7);
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+    cur = build_norm(cur, model.output_norm, model.output_norm_b, LLM_NORM, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/seed-oss.cpp b/src/models/seed-oss.cpp
new file mode 100644
index 0000000000..94ce163362
--- /dev/null
+++ b/src/models/seed-oss.cpp
@@ -0,0 +1,124 @@
+#include "models.h"
+
+llm_build_seed_oss::llm_build_seed_oss(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                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);
+                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);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+                cb(cur, "attn_out", il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = build_norm(ffn_inp,
+                    model.layers[il].attn_post_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_post_norm", il);
+
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/smallthinker.cpp b/src/models/smallthinker.cpp
new file mode 100644
index 0000000000..2fcd87a8a0
--- /dev/null
+++ b/src/models/smallthinker.cpp
@@ -0,0 +1,120 @@
+#include "models.h"
+
+template <bool iswa>
+llm_build_smallthinker<iswa>::llm_build_smallthinker(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params){
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        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) {
+            ggml_tensor * inpSA  = inpL;
+            ggml_tensor * probs  = nullptr;
+
+            probs = build_lora_mm(model.layers[il].ffn_gate_inp, inpL);  // [n_expert, n_tokens]
+            cb(probs, "ffn_moe_logits", il);
+
+            // norm
+            cur = build_norm(inpL,model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self_attention
+            {
+                // compute Q and K and RoPE them
+                struct ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                struct ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                struct ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                if (hparams.n_no_rope_layer_step == n_layer || il % hparams.n_no_rope_layer_step != 0) {
+                    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);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f / sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+                probs = ggml_get_rows(ctx0, probs, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // MoE branch
+            cur = build_norm(ffn_inp, model.layers[il].ffn_norm, NULL, LLM_NORM_RMS, il);
+            cb(cur, "ffn_norm", il);
+
+            ggml_tensor * ffn_out =
+                build_moe_ffn(cur,
+                        nullptr,
+                        model.layers[il].ffn_up_exps,
+                        model.layers[il].ffn_gate_exps,
+                        model.layers[il].ffn_down_exps,
+                        nullptr,
+                        n_expert, n_expert_used,
+                        LLM_FFN_RELU, true,
+                        false, 0.0,
+                        static_cast<llama_expert_gating_func_type>(hparams.expert_gating_func),
+                        il, probs);
+
+            cb(ffn_out, "ffn_out", il);
+            cur = ffn_out;
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+    }
+
+// Explicit template instantiations
+template struct llm_build_smallthinker<false>;
+template struct llm_build_smallthinker<true>;
diff --git a/src/models/smollm3.cpp b/src/models/smollm3.cpp
new file mode 100644
index 0000000000..830aa35415
--- /dev/null
+++ b/src/models/smollm3.cpp
@@ -0,0 +1,128 @@
+#include "models.h"
+
+llm_build_smollm3::llm_build_smollm3(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        const float kq_scale = hparams.f_attention_scale == 0.0f ? 1.0f/sqrtf(float(n_embd_head)) : hparams.f_attention_scale;
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            const bool use_rope = (il + 1) % hparams.n_no_rope_layer_step != 0;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                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);
+                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);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                if (use_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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+                cb(cur, "attn_out", il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            {
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+            }
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/stablelm.cpp b/src/models/stablelm.cpp
new file mode 100644
index 0000000000..bed1915c00
--- /dev/null
+++ b/src/models/stablelm.cpp
@@ -0,0 +1,146 @@
+#include "models.h"
+
+llm_build_stablelm::llm_build_stablelm(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    const int64_t n_embd_head = hparams.n_embd_head_v;
+
+    GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    inpL = build_inp_embd(model.tok_embd);
+
+    // inp_pos - contains the positions
+    ggml_tensor * inp_pos = build_inp_pos();
+
+    auto * inp_attn = build_attn_inp_kv();
+
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+    for (int il = 0; il < n_layer; ++il) {
+        // norm
+        cur = build_norm(inpL,
+                model.layers[il].attn_norm,
+                model.layers[il].attn_norm_b,
+                LLM_NORM, il);
+        cb(cur, "attn_norm", il);
+
+        ggml_tensor * inpSA = cur;
+
+        // self-attention
+        {
+            // compute Q and K and RoPE them
+            ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+            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);
+            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);
+            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);
+            Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+            if (model.layers[il].attn_q_norm) {
+                Qcur = build_norm(Qcur,
+                        model.layers[il].attn_q_norm,
+                        NULL,
+                        LLM_NORM, il);
+                cb(Qcur, "Qcur", il);
+            }
+            if (model.layers[il].attn_k_norm) {
+                Kcur = build_norm(Kcur,
+                        model.layers[il].attn_k_norm,
+                        NULL,
+                        LLM_NORM, il);
+                cb(Kcur, "Kcur", 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
+                    );
+
+            cb(Qcur, "Qcur", il);
+            cb(Kcur, "Kcur", il);
+            cb(Vcur, "Vcur", il);
+
+            cur = build_attn(inp_attn,
+                    model.layers[il].wo, NULL,
+                    Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+        }
+        if (il == n_layer - 1 && inp_out_ids) {
+            cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+            inpL  = ggml_get_rows(ctx0,  inpL, inp_out_ids);
+            inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+        }
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // feed-forward network
+        {
+            if (model.layers[il].ffn_norm) {
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm,
+                        model.layers[il].ffn_norm_b,
+                        LLM_NORM, il);
+                cb(cur, "ffn_norm", il);
+            } else {
+                // parallel residual
+                cur = inpSA;
+            }
+            cur = build_ffn(cur,
+                    model.layers[il].ffn_up,   NULL, NULL,
+                    model.layers[il].ffn_gate, NULL, NULL,
+                    model.layers[il].ffn_down, NULL, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, il);
+            cb(cur, "ffn_out", il);
+        }
+        cur = ggml_add(ctx0, cur, ffn_inp);
+
+        cur = build_cvec(cur, il);
+        cb(cur, "l_out", il);
+
+        // input for next layer
+        inpL = cur;
+    }
+    cur = inpL;
+
+    cur = build_norm(cur,
+            model.output_norm,
+            model.output_norm_b,
+            LLM_NORM, -1);
+
+    cb(cur, "result_norm", -1);
+    res->t_embd = cur;
+
+    // lm_head
+    cur = build_lora_mm(model.output, cur);
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/starcoder.cpp b/src/models/starcoder.cpp
new file mode 100644
index 0000000000..0b9e58982a
--- /dev/null
+++ b/src/models/starcoder.cpp
@@ -0,0 +1,100 @@
+#include "models.h"
+
+llm_build_starcoder::llm_build_starcoder(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
+        cb(pos, "pos_embd", -1);
+
+        inpL = ggml_add(ctx0, inpL, pos);
+        cb(inpL, "inpL", -1);
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm,
+                    model.layers[il].attn_norm_b,
+                    LLM_NORM, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                cur = build_lora_mm(model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
+
+                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                cb(cur, "bqkv", il);
+
+                ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, n_embd_head, n_head,    n_tokens, n_embd_head*sizeof(float), cur->nb[1], 0*sizeof(float)*(n_embd));
+                ggml_tensor * Kcur = 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));
+                ggml_tensor * 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));
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            }
+            // add the input
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // FF
+            {
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm,
+                        model.layers[il].ffn_norm_b,
+                        LLM_NORM, il);
+                cb(cur, "ffn_norm", 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_GELU, LLM_FFN_SEQ, il);
+                cb(cur, "ffn_out", il);
+            }
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = build_norm(inpL,
+                model.output_norm,
+                model.output_norm_b,
+                LLM_NORM, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/starcoder2.cpp b/src/models/starcoder2.cpp
new file mode 100644
index 0000000000..67c26149e3
--- /dev/null
+++ b/src/models/starcoder2.cpp
@@ -0,0 +1,121 @@
+#include "models.h"
+
+llm_build_starcoder2::llm_build_starcoder2(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, model.layers[il].attn_norm_b,
+                    LLM_NORM, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                // compute Q and K and RoPE them
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                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);
+                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);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+
+            cur = build_norm(ffn_inp,
+                    model.layers[il].ffn_norm, model.layers[il].ffn_norm_b,
+                    LLM_NORM, il);
+            cb(cur, "ffn_norm", 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_GELU, LLM_FFN_SEQ, il);
+            cb(cur, "ffn_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur,
+                model.output_norm, model.output_norm_b,
+                LLM_NORM, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/t5-dec.cpp b/src/models/t5-dec.cpp
new file mode 100644
index 0000000000..c1974e7821
--- /dev/null
+++ b/src/models/t5-dec.cpp
@@ -0,0 +1,166 @@
+#include "models.h"
+
+llm_build_t5_dec::llm_build_t5_dec(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        //const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        ggml_tensor * embd_enc       = build_inp_cross_embd();
+        ggml_tensor * pos_bucket_dec = build_inp_pos_bucket_dec();
+
+        const int64_t n_outputs_enc = embd_enc->ne[1];
+
+        auto * inp_attn_self  = build_attn_inp_kv();
+        auto * inp_attn_cross = build_attn_inp_cross();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        const int64_t dec_n_layer = hparams.dec_n_layer;
+
+        for (int il = 0; il < dec_n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b ? model.layers[il].attn_rel_b : model.layers[0].attn_rel_b;
+                ggml_tensor * kq_b = build_pos_bias(pos_bucket_dec, attn_rel_b);
+
+                cur = build_attn(inp_attn_self,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Qcur, Kcur, Vcur, kq_b, nullptr, nullptr, 1.0f, il);
+                cb(cur, "kqv_out", il);
+            }
+            cur = ggml_add(ctx0, cur, inpSA);
+            cb(cur, "cross_inp", il);
+
+            ggml_tensor * inpCA = cur;
+
+            // norm
+            cur = build_norm(cur,
+                    model.layers[il].attn_norm_cross, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm_cross", il);
+
+            // cross-attention
+            {
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq_cross, cur);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk_cross, embd_enc);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv_cross, embd_enc);
+                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_outputs_enc);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_outputs_enc);
+
+                cur = build_attn(inp_attn_cross,
+                        model.layers[il].wo_cross, nullptr,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f, il);
+                cb(cur, "kqv_out", il);
+
+                //ggml_tensor * q =                 ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+                //ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
+
+                //ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+                //cb(kq, "kq", il);
+
+                //kq = ggml_soft_max_ext(ctx0, kq, KQ_mask_cross, 1.0f, hparams.f_max_alibi_bias);
+                //cb(kq, "kq_soft_max_ext", il);
+
+                //ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_outputs_enc)));
+                //cb(v, "v", il);
+
+                //ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_outputs_enc, n_embd_head, n_head_kv), kq);
+                //cb(kqv, "kqv", il);
+
+                //ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+                //cb(kqv_merged, "kqv_merged", il);
+
+                //cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
+                //cb(cur, "kqv_merged_cont", il);
+
+                //ggml_build_forward_expand(gf, cur);
+
+                //cur = build_lora_mm(model.layers[il].wo_cross, cur);
+                //cb(cur, "kqv_out", il);
+            }
+            if (il == dec_n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpCA = ggml_get_rows(ctx0, inpCA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpCA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            {
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+                // T5 uses relu, flan-T5 uses gelu-gated
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        model.layers[il].ffn_gate ? LLM_FFN_GELU : LLM_FFN_RELU,
+                        model.layers[il].ffn_gate ? LLM_FFN_PAR : LLM_FFN_SEQ,
+                        il);
+                cb(cur, "ffn_out", il);
+            }
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+        cb(cur, "result_embd", -1);
+
+        cur = build_norm(cur,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/t5-enc.cpp b/src/models/t5-enc.cpp
new file mode 100644
index 0000000000..6b29355d20
--- /dev/null
+++ b/src/models/t5-enc.cpp
@@ -0,0 +1,96 @@
+#include "models.h"
+
+llm_build_t5_enc::llm_build_t5_enc(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        ggml_tensor * pos_bucket_enc = build_inp_pos_bucket_enc();
+
+        auto * inp_attn = build_attn_inp_no_cache();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm_enc, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq_enc, cur);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk_enc, cur);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv_enc, cur);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b_enc ? model.layers[il].attn_rel_b_enc : model.layers[0].attn_rel_b_enc;
+                ggml_tensor * kq_b = build_pos_bias(pos_bucket_enc, attn_rel_b);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo_enc, nullptr,
+                        Qcur, Kcur, Vcur, kq_b, nullptr, nullptr, 1.0f, il);
+                cb(cur, "kqv_out", il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            {
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm_enc, NULL,
+                        LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+                // T5 uses relu, flan-T5 uses gelu-gated
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up_enc,   NULL, NULL,
+                        model.layers[il].ffn_gate_enc, NULL, NULL,
+                        model.layers[il].ffn_down_enc, NULL, NULL,
+                        NULL,
+                        model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
+                        model.layers[il].ffn_gate_enc ? LLM_FFN_PAR  : LLM_FFN_SEQ,
+                        il);
+                cb(cur, "ffn_out", il);
+            }
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cb(cur, "ffn_out", il);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+        cb(cur, "result_embd", -1);
+
+        cur = build_norm(cur,
+                model.output_norm_enc, NULL,
+                LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/wavtokenizer-dec.cpp b/src/models/wavtokenizer-dec.cpp
new file mode 100644
index 0000000000..81a3c5cd62
--- /dev/null
+++ b/src/models/wavtokenizer-dec.cpp
@@ -0,0 +1,149 @@
+#include "models.h"
+
+llm_build_wavtokenizer_dec::llm_build_wavtokenizer_dec(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        cur = ggml_cont(ctx0, ggml_transpose(ctx0, inpL));
+
+        cur = ggml_conv_1d_ph(ctx0, model.conv1d, cur, 1, 1);
+        cur = ggml_add(ctx0, cur, model.conv1d_b);
+
+        // posnet
+        for (uint32_t il = 0; il < hparams.posnet.n_layer; ++il) {
+            const auto & layer = model.layers[il].posnet;
+
+            inpL = cur;
+
+            switch (il) {
+                case 0:
+                case 1:
+                case 3:
+                case 4:
+                    {
+                        cur = build_norm(cur,
+                                layer.norm1,
+                                layer.norm1_b,
+                                LLM_NORM_GROUP, 0);
+
+                        cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur);
+
+                        cur = ggml_conv_1d_ph(ctx0, layer.conv1, cur, 1, 1);
+                        cur = ggml_add(ctx0, cur, layer.conv1_b);
+
+                        cur = build_norm(cur,
+                                layer.norm2,
+                                layer.norm2_b,
+                                LLM_NORM_GROUP, 0);
+
+                        cur = ggml_mul(ctx0, ggml_sigmoid(ctx0, cur), cur);
+
+                        cur = ggml_conv_1d_ph(ctx0, layer.conv2, cur, 1, 1);
+                        cur = ggml_add(ctx0, cur, layer.conv2_b);
+
+                        cur = ggml_add(ctx0, cur, inpL);
+                    } break;
+                case 2:
+                    {
+                        cur = build_norm(cur,
+                                layer.attn_norm,
+                                layer.attn_norm_b,
+                                LLM_NORM_GROUP, 0);
+
+                        ggml_tensor * q;
+                        ggml_tensor * k;
+                        ggml_tensor * v;
+
+                        q = ggml_conv_1d_ph(ctx0, layer.attn_q, cur, 1, 1);
+                        k = ggml_conv_1d_ph(ctx0, layer.attn_k, cur, 1, 1);
+                        v = ggml_conv_1d_ph(ctx0, layer.attn_v, cur, 1, 1);
+
+                        q = ggml_add(ctx0, q, layer.attn_q_b);
+                        k = ggml_add(ctx0, k, layer.attn_k_b);
+                        v = ggml_add(ctx0, v, layer.attn_v_b);
+
+                        q = ggml_cont(ctx0, ggml_transpose(ctx0, q));
+                        k = ggml_cont(ctx0, ggml_transpose(ctx0, k));
+
+                        ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+
+                        kq = ggml_soft_max_ext(ctx0, kq, nullptr, 1.0f/sqrtf(float(hparams.posnet.n_embd)), 0.0f);
+
+                        cur = ggml_mul_mat(ctx0, kq, v);
+
+                        cur = ggml_conv_1d_ph(ctx0, layer.attn_o, cur, 1, 1);
+                        cur = ggml_add(ctx0, cur, layer.attn_o_b);
+
+                        cur = ggml_add(ctx0, cur, inpL);
+                    } break;
+                case 5:
+                    {
+                        cur = build_norm(cur,
+                                layer.norm,
+                                layer.norm_b,
+                                LLM_NORM_GROUP, 0);
+                    } break;
+                default: GGML_ABORT("unknown posnet layer");
+            };
+        }
+        cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
+
+        cur = build_norm(cur,
+                model.tok_norm,
+                model.tok_norm_b,
+                LLM_NORM, -1);
+
+        cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
+
+        inpL = cur;
+
+        // convnext
+        for (uint32_t il = 0; il < hparams.convnext.n_layer; ++il) {
+            const auto & layer = model.layers[il].convnext;
+
+            cur = inpL;
+
+            cur = ggml_conv_1d_dw_ph(ctx0, layer.dw, cur, 1, 1);
+            cur = ggml_add(ctx0, cur, layer.dw_b);
+
+            cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
+
+            cur = build_norm(cur,
+                    layer.norm,
+                    layer.norm_b,
+                    LLM_NORM, -1);
+
+            cur = build_ffn(cur,
+                    layer.pw1, layer.pw1_b, NULL,
+                    NULL,      NULL,        NULL,
+                    layer.pw2, layer.pw2_b, NULL,
+                    NULL,
+                    LLM_FFN_GELU, LLM_FFN_SEQ, il);
+
+            cur = ggml_mul(ctx0, cur, layer.gamma);
+
+            cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
+
+            inpL = ggml_add(ctx0, cur, inpL);
+        }
+        cur = inpL;
+
+        cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur));
+
+        cur = build_norm(cur,
+                model.output_norm,
+                model.output_norm_b,
+                LLM_NORM, -1);
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cur = ggml_add(ctx0, cur, model.output_b);
+
+        cb(cur, "result_embd", -1);
+        res->t_embd = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}
diff --git a/src/models/xverse.cpp b/src/models/xverse.cpp
new file mode 100644
index 0000000000..95e2eafef3
--- /dev/null
+++ b/src/models/xverse.cpp
@@ -0,0 +1,108 @@
+#include "models.h"
+
+llm_build_xverse::llm_build_xverse(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+        GGML_ASSERT(n_embd_head == hparams.n_rot);
+
+        ggml_tensor * cur;
+        ggml_tensor * inpL;
+
+        inpL = build_inp_embd(model.tok_embd);
+
+        // inp_pos - contains the positions
+        ggml_tensor * inp_pos = build_inp_pos();
+
+        auto * inp_attn = build_attn_inp_kv();
+
+        ggml_tensor * inp_out_ids = build_inp_out_ids();
+
+        for (int il = 0; il < n_layer; ++il) {
+            ggml_tensor * inpSA = inpL;
+
+            cur = build_norm(inpL,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+                cb(Qcur, "Qcur", il);
+
+                ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+                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);
+                Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+                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);
+                cb(Vcur, "Vcur", il);
+
+                cur = build_attn(inp_attn,
+                        model.layers[il].wo, NULL,
+                        Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
+            }
+            if (il == n_layer - 1 && inp_out_ids) {
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+            ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            {
+                cur = build_norm(ffn_inp,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = build_ffn(cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, il);
+                cb(cur, "ffn_out", il);
+            }
+            cur = ggml_add(ctx0, cur, ffn_inp);
+
+            cur = build_cvec(cur, il);
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+        cur = inpL;
+
+        cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);
+
+        cb(cur, "result_norm", -1);
+        res->t_embd = cur;
+
+        // lm_head
+        cur = build_lora_mm(model.output, cur);
+
+        cb(cur, "result_output", -1);
+        res->t_logits = cur;
+
+        ggml_build_forward_expand(gf, cur);
+}

From d3dc9dd898be805c23a408cc36daed5b3bf29221 Mon Sep 17 00:00:00 2001
From: Oliver Simons <osimons@nvidia.com>
Date: Sat, 1 Nov 2025 06:13:26 +0100
Subject: [PATCH 20/30] CUDA: Remove unneded bias/gate dims in fused mmvq
 (#16858)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* CUDA: Remove unneded bias/gate dims in fused mmvq

Pointed out
[here](https://github.com/ggml-org/llama.cpp/pull/16847#discussion_r2476798989)
that only a single value is needed per target col per thread

* Apply suggestions from code review

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

* Fix "Error 991-D: extra braces are nonstandard" during compilation

---------

Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---
 ggml/src/ggml-cuda/mmvq.cu | 14 ++++++++------
 1 file changed, 8 insertions(+), 6 deletions(-)

diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu
index 07645ad9e7..d671551c17 100644
--- a/ggml/src/ggml-cuda/mmvq.cu
+++ b/ggml/src/ggml-cuda/mmvq.cu
@@ -190,8 +190,8 @@ static __global__ void mul_mat_vec_q(
 
     const uint32_t channel_bias = ids ? channel_x : channel_dst;
 
-    float x_biases[ncols_dst][rows_per_cuda_block]    = { { 0.0f } };
-    float gate_biases[ncols_dst][rows_per_cuda_block] = { { 0.0f } };
+    float x_biases[ncols_dst]    = { 0.0f };
+    float gate_biases[ncols_dst] = { 0.0f };
     if constexpr (has_fusion) {
         if (use_bias) {
             x_bias = x_bias + sample_dst*stride_sample_dst + channel_bias*stride_channel_dst + row0;
@@ -199,8 +199,9 @@ static __global__ void mul_mat_vec_q(
             // 2. load only on threads that won't die after partial sum calculation
             if (threadIdx.x < rows_per_cuda_block && threadIdx.y == 0 &&
                 (rows_per_cuda_block == 1 || uint32_t(row0 + threadIdx.x) < stride_col_dst)) {
+#pragma unroll
                 for (int j = 0; j < ncols_dst; ++j) {
-                    x_biases[j][threadIdx.x] = x_bias[j * stride_col_dst + threadIdx.x];
+                    x_biases[j] = x_bias[j * stride_col_dst + threadIdx.x];
                 }
             }
         }
@@ -208,8 +209,9 @@ static __global__ void mul_mat_vec_q(
             gate_bias = gate_bias + sample_dst*stride_sample_dst + channel_bias*stride_channel_dst + row0;
             if (threadIdx.x < rows_per_cuda_block && threadIdx.y == 0 &&
                 (rows_per_cuda_block == 1 || uint32_t(row0 + threadIdx.x) < stride_col_dst)) {
+#pragma unroll
                 for (int j = 0; j < ncols_dst; ++j) {
-                    gate_biases[j][threadIdx.x] = gate_bias[j * stride_col_dst + threadIdx.x];
+                    gate_biases[j] = gate_bias[j * stride_col_dst + threadIdx.x];
                 }
             }
         }
@@ -299,12 +301,12 @@ static __global__ void mul_mat_vec_q(
             float result = tmp[j][threadIdx.x];
             if constexpr (has_fusion) {
                 if (use_bias) {
-                    result += x_biases[j][threadIdx.x];
+                    result += x_biases[j];
                 }
                 if (use_gate) {
                     float gate_value = tmp_gate[j][threadIdx.x];
                     if (use_gate_bias) {
-                        gate_value += gate_biases[j][threadIdx.x];
+                        gate_value += gate_biases[j];
                     }
                     switch (active_glu) {
                         case GGML_GLU_OP_SWIGLU:

From 2e76e013600cb0d51ccf158571ca1d0502952a07 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 1 Nov 2025 00:45:28 -0500
Subject: [PATCH 21/30] vulkan: fuse mul_mat+add and mul_mat_id+add_id (#16868)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* vulkan: fuse mul_mat+add and mul_mat_id+add_id

The fusion is only applied for the mat-vec mul paths.

* Apply suggestions from code review

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

* fix 32b build

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          | 513 +++++++++++++-----
 .../vulkan-shaders/mul_mat_vec_base.glsl      |  34 +-
 .../vulkan-shaders/mul_mat_vec_nc.comp        |   6 +
 .../vulkan-shaders/mul_mat_vec_p021.comp      |   6 +
 4 files changed, 436 insertions(+), 123 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index c3e5a9eccc..6a46d0889b 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -797,9 +797,18 @@ struct vk_mat_mat_push_constants {
     uint32_t padded_N;
 };
 struct vk_mat_vec_push_constants {
-    uint32_t ncols; uint32_t stride_a; uint32_t stride_b; uint32_t stride_d;
-    uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d;
-    uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3;
+    uint32_t ncols;
+    uint32_t stride_a;
+    uint32_t stride_b;
+    uint32_t stride_d;
+    uint32_t batch_stride_a;
+    uint32_t batch_stride_b;
+    uint32_t batch_stride_d;
+    uint32_t enable_bias;
+    uint32_t ne02;
+    uint32_t ne12;
+    uint32_t broadcast2;
+    uint32_t broadcast3;
 };
 
 struct vk_mat_mat_id_push_constants {
@@ -810,9 +819,16 @@ struct vk_mat_mat_id_push_constants {
     uint32_t padded_N;
 };
 struct vk_mat_vec_id_push_constants {
-    uint32_t ncols; uint32_t stride_a; uint32_t stride_b; uint32_t stride_d;
-    uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d;
-    uint32_t nei0; uint32_t ne11;
+    uint32_t ncols;
+    uint32_t stride_a;
+    uint32_t stride_b;
+    uint32_t stride_d;
+    uint32_t batch_stride_a;
+    uint32_t batch_stride_b;
+    uint32_t batch_stride_d;
+    uint32_t enable_bias;
+    uint32_t nei0;
+    uint32_t ne11;
 };
 
 struct vk_flash_attn_push_constants {
@@ -3347,92 +3363,92 @@ static void ggml_vk_load_shaders(vk_device& device) {
                                               SHADER_REDUCTION_MODE_SHMEM;
 
         for (uint32_t i = 0; i < mul_mat_vec_max_cols; ++i) {
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f32_f32",  arr_dmmv_f32_f32_f32_len[reduc],  arr_dmmv_f32_f32_f32_data[reduc],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f32_f32",  arr_dmmv_f16_f32_f32_len[reduc],  arr_dmmv_f16_f32_f32_data[reduc],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f32_f32", arr_dmmv_bf16_f32_f32_len[reduc], arr_dmmv_bf16_f32_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f32_f32", arr_dmmv_q4_0_f32_f32_len[reduc], arr_dmmv_q4_0_f32_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f32_f32", arr_dmmv_q4_1_f32_f32_len[reduc], arr_dmmv_q4_1_f32_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f32_f32", arr_dmmv_q5_0_f32_f32_len[reduc], arr_dmmv_q5_0_f32_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f32_f32", arr_dmmv_q5_1_f32_f32_len[reduc], arr_dmmv_q5_1_f32_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f32_f32", arr_dmmv_q8_0_f32_f32_len[reduc], arr_dmmv_q8_0_f32_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f32_f32", arr_dmmv_q2_k_f32_f32_len[reduc16], arr_dmmv_q2_k_f32_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f32_f32", arr_dmmv_q3_k_f32_f32_len[reduc16], arr_dmmv_q3_k_f32_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f32_f32", arr_dmmv_q4_k_f32_f32_len[reduc16], arr_dmmv_q4_k_f32_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f32_f32", arr_dmmv_q5_k_f32_f32_len[reduc16], arr_dmmv_q5_k_f32_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f32_f32", arr_dmmv_q6_k_f32_f32_len[reduc16], arr_dmmv_q6_k_f32_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f32_f32",   arr_dmmv_iq1_s_f32_f32_len[reduc16],   arr_dmmv_iq1_s_f32_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f32_f32",   arr_dmmv_iq1_m_f32_f32_len[reduc16],   arr_dmmv_iq1_m_f32_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f32_f32", arr_dmmv_iq2_xxs_f32_f32_len[reduc16], arr_dmmv_iq2_xxs_f32_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f32_f32",  arr_dmmv_iq2_xs_f32_f32_len[reduc16],  arr_dmmv_iq2_xs_f32_f32_data[reduc16],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f32_f32",   arr_dmmv_iq2_s_f32_f32_len[reduc16],   arr_dmmv_iq2_s_f32_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f32_f32", arr_dmmv_iq3_xxs_f32_f32_len[reduc16], arr_dmmv_iq3_xxs_f32_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f32_f32",   arr_dmmv_iq3_s_f32_f32_len[reduc16],   arr_dmmv_iq3_s_f32_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f32_f32",  arr_dmmv_iq4_xs_f32_f32_len[reduc16],  arr_dmmv_iq4_xs_f32_f32_data[reduc16],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f32_f32",  arr_dmmv_iq4_nl_f32_f32_len[reduc16],  arr_dmmv_iq4_nl_f32_f32_data[reduc16],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f32_f32",   arr_dmmv_mxfp4_f32_f32_len[reduc16],   arr_dmmv_mxfp4_f32_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f32_f32",  arr_dmmv_f32_f32_f32_len[reduc],  arr_dmmv_f32_f32_f32_data[reduc],  "main", 4, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f32_f32",  arr_dmmv_f16_f32_f32_len[reduc],  arr_dmmv_f16_f32_f32_data[reduc],  "main", 4, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f32_f32", arr_dmmv_bf16_f32_f32_len[reduc], arr_dmmv_bf16_f32_f32_data[reduc], "main", 4, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f32_f32", arr_dmmv_q4_0_f32_f32_len[reduc], arr_dmmv_q4_0_f32_f32_data[reduc], "main", 4, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f32_f32", arr_dmmv_q4_1_f32_f32_len[reduc], arr_dmmv_q4_1_f32_f32_data[reduc], "main", 4, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f32_f32", arr_dmmv_q5_0_f32_f32_len[reduc], arr_dmmv_q5_0_f32_f32_data[reduc], "main", 4, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f32_f32", arr_dmmv_q5_1_f32_f32_len[reduc], arr_dmmv_q5_1_f32_f32_data[reduc], "main", 4, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f32_f32", arr_dmmv_q8_0_f32_f32_len[reduc], arr_dmmv_q8_0_f32_f32_data[reduc], "main", 4, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f32_f32", arr_dmmv_q2_k_f32_f32_len[reduc16], arr_dmmv_q2_k_f32_f32_data[reduc16], "main", 4, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f32_f32", arr_dmmv_q3_k_f32_f32_len[reduc16], arr_dmmv_q3_k_f32_f32_data[reduc16], "main", 4, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f32_f32", arr_dmmv_q4_k_f32_f32_len[reduc16], arr_dmmv_q4_k_f32_f32_data[reduc16], "main", 4, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f32_f32", arr_dmmv_q5_k_f32_f32_len[reduc16], arr_dmmv_q5_k_f32_f32_data[reduc16], "main", 4, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f32_f32", arr_dmmv_q6_k_f32_f32_len[reduc16], arr_dmmv_q6_k_f32_f32_data[reduc16], "main", 4, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f32_f32",   arr_dmmv_iq1_s_f32_f32_len[reduc16],   arr_dmmv_iq1_s_f32_f32_data[reduc16],   "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f32_f32",   arr_dmmv_iq1_m_f32_f32_len[reduc16],   arr_dmmv_iq1_m_f32_f32_data[reduc16],   "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f32_f32", arr_dmmv_iq2_xxs_f32_f32_len[reduc16], arr_dmmv_iq2_xxs_f32_f32_data[reduc16], "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f32_f32",  arr_dmmv_iq2_xs_f32_f32_len[reduc16],  arr_dmmv_iq2_xs_f32_f32_data[reduc16],  "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f32_f32",   arr_dmmv_iq2_s_f32_f32_len[reduc16],   arr_dmmv_iq2_s_f32_f32_data[reduc16],   "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f32_f32", arr_dmmv_iq3_xxs_f32_f32_len[reduc16], arr_dmmv_iq3_xxs_f32_f32_data[reduc16], "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f32_f32",   arr_dmmv_iq3_s_f32_f32_len[reduc16],   arr_dmmv_iq3_s_f32_f32_data[reduc16],   "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f32_f32",  arr_dmmv_iq4_xs_f32_f32_len[reduc16],  arr_dmmv_iq4_xs_f32_f32_data[reduc16],  "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f32_f32",  arr_dmmv_iq4_nl_f32_f32_len[reduc16],  arr_dmmv_iq4_nl_f32_f32_data[reduc16],  "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f32_f32",   arr_dmmv_mxfp4_f32_f32_len[reduc16],   arr_dmmv_mxfp4_f32_f32_data[reduc16],   "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
 
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32",  arr_dmmv_f32_f16_f32_len[reduc],  arr_dmmv_f32_f16_f32_data[reduc],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32",  arr_dmmv_f16_f16_f32_len[reduc],  arr_dmmv_f16_f16_f32_data[reduc],  "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f16_f32", arr_dmmv_bf16_f16_f32_len[reduc], arr_dmmv_bf16_f16_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f16_f32", arr_dmmv_q4_0_f16_f32_len[reduc], arr_dmmv_q4_0_f16_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f16_f32", arr_dmmv_q4_1_f16_f32_len[reduc], arr_dmmv_q4_1_f16_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f16_f32", arr_dmmv_q5_0_f16_f32_len[reduc], arr_dmmv_q5_0_f16_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f16_f32", arr_dmmv_q5_1_f16_f32_len[reduc], arr_dmmv_q5_1_f16_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f16_f32", arr_dmmv_q8_0_f16_f32_len[reduc], arr_dmmv_q8_0_f16_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f16_f32", arr_dmmv_q2_k_f16_f32_len[reduc16], arr_dmmv_q2_k_f16_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f16_f32", arr_dmmv_q3_k_f16_f32_len[reduc16], arr_dmmv_q3_k_f16_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f16_f32", arr_dmmv_q4_k_f16_f32_len[reduc16], arr_dmmv_q4_k_f16_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f16_f32", arr_dmmv_q5_k_f16_f32_len[reduc16], arr_dmmv_q5_k_f16_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f16_f32", arr_dmmv_q6_k_f16_f32_len[reduc16], arr_dmmv_q6_k_f16_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f16_f32",   arr_dmmv_iq1_s_f16_f32_len[reduc16],   arr_dmmv_iq1_s_f16_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f16_f32",   arr_dmmv_iq1_m_f16_f32_len[reduc16],   arr_dmmv_iq1_m_f16_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f16_f32", arr_dmmv_iq2_xxs_f16_f32_len[reduc16], arr_dmmv_iq2_xxs_f16_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f16_f32",  arr_dmmv_iq2_xs_f16_f32_len[reduc16],  arr_dmmv_iq2_xs_f16_f32_data[reduc16],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f16_f32",   arr_dmmv_iq2_s_f16_f32_len[reduc16],   arr_dmmv_iq2_s_f16_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f16_f32", arr_dmmv_iq3_xxs_f16_f32_len[reduc16], arr_dmmv_iq3_xxs_f16_f32_data[reduc16], "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f16_f32",   arr_dmmv_iq3_s_f16_f32_len[reduc16],   arr_dmmv_iq3_s_f16_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f16_f32",  arr_dmmv_iq4_xs_f16_f32_len[reduc16],  arr_dmmv_iq4_xs_f16_f32_data[reduc16],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f16_f32",  arr_dmmv_iq4_nl_f16_f32_len[reduc16],  arr_dmmv_iq4_nl_f16_f32_data[reduc16],  "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
-            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f16_f32",   arr_dmmv_mxfp4_f16_f32_len[reduc16],   arr_dmmv_mxfp4_f16_f32_data[reduc16],   "main", 3, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32",  arr_dmmv_f32_f16_f32_len[reduc],  arr_dmmv_f32_f16_f32_data[reduc],  "main", 4, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32",  arr_dmmv_f16_f16_f32_len[reduc],  arr_dmmv_f16_f16_f32_data[reduc],  "main", 4, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f16_f32", arr_dmmv_bf16_f16_f32_len[reduc], arr_dmmv_bf16_f16_f32_data[reduc], "main", 4, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f16_f32", arr_dmmv_q4_0_f16_f32_len[reduc], arr_dmmv_q4_0_f16_f32_data[reduc], "main", 4, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_f16_f32", arr_dmmv_q4_1_f16_f32_len[reduc], arr_dmmv_q4_1_f16_f32_data[reduc], "main", 4, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_f16_f32", arr_dmmv_q5_0_f16_f32_len[reduc], arr_dmmv_q5_0_f16_f32_data[reduc], "main", 4, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_f16_f32", arr_dmmv_q5_1_f16_f32_len[reduc], arr_dmmv_q5_1_f16_f32_data[reduc], "main", 4, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_f16_f32", arr_dmmv_q8_0_f16_f32_len[reduc], arr_dmmv_q8_0_f16_f32_data[reduc], "main", 4, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup, 1*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q2_K][i], "mul_mat_vec_q2_k_f16_f32", arr_dmmv_q2_k_f16_f32_len[reduc16], arr_dmmv_q2_k_f16_f32_data[reduc16], "main", 4, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q3_K][i], "mul_mat_vec_q3_k_f16_f32", arr_dmmv_q3_k_f16_f32_len[reduc16], arr_dmmv_q3_k_f16_f32_data[reduc16], "main", 4, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_f16_f32", arr_dmmv_q4_k_f16_f32_len[reduc16], arr_dmmv_q4_k_f16_f32_data[reduc16], "main", 4, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_f16_f32", arr_dmmv_q5_k_f16_f32_len[reduc16], arr_dmmv_q5_k_f16_f32_data[reduc16], "main", 4, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_f16_f32", arr_dmmv_q6_k_f16_f32_len[reduc16], arr_dmmv_q6_k_f16_f32_data[reduc16], "main", 4, sizeof(vk_mat_vec_push_constants), {rm_kq, 1, 1}, {wg_size_subgroup16, rm_kq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ1_S][i],   "mul_mat_vec_iq1_s_f16_f32",   arr_dmmv_iq1_s_f16_f32_len[reduc16],   arr_dmmv_iq1_s_f16_f32_data[reduc16],   "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ1_M][i],   "mul_mat_vec_iq1_m_f16_f32",   arr_dmmv_iq1_m_f16_f32_len[reduc16],   arr_dmmv_iq1_m_f16_f32_data[reduc16],   "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_XXS][i], "mul_mat_vec_iq2_xxs_f16_f32", arr_dmmv_iq2_xxs_f16_f32_len[reduc16], arr_dmmv_iq2_xxs_f16_f32_data[reduc16], "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_XS][i],  "mul_mat_vec_iq2_xs_f16_f32",  arr_dmmv_iq2_xs_f16_f32_len[reduc16],  arr_dmmv_iq2_xs_f16_f32_data[reduc16],  "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ2_S][i],   "mul_mat_vec_iq2_s_f16_f32",   arr_dmmv_iq2_s_f16_f32_len[reduc16],   arr_dmmv_iq2_s_f16_f32_data[reduc16],   "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ3_XXS][i], "mul_mat_vec_iq3_xxs_f16_f32", arr_dmmv_iq3_xxs_f16_f32_len[reduc16], arr_dmmv_iq3_xxs_f16_f32_data[reduc16], "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ3_S][i],   "mul_mat_vec_iq3_s_f16_f32",   arr_dmmv_iq3_s_f16_f32_len[reduc16],   arr_dmmv_iq3_s_f16_f32_data[reduc16],   "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ4_XS][i],  "mul_mat_vec_iq4_xs_f16_f32",  arr_dmmv_iq4_xs_f16_f32_len[reduc16],  arr_dmmv_iq4_xs_f16_f32_data[reduc16],  "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_IQ4_NL][i],  "mul_mat_vec_iq4_nl_f16_f32",  arr_dmmv_iq4_nl_f16_f32_len[reduc16],  arr_dmmv_iq4_nl_f16_f32_data[reduc16],  "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
+            ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_MXFP4][i],   "mul_mat_vec_mxfp4_f16_f32",   arr_dmmv_mxfp4_f16_f32_len[reduc16],   arr_dmmv_mxfp4_f16_f32_data[reduc16],   "main", 4, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16);
 
 #if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
             if (device->integer_dot_product) {
                 const uint32_t subgroup_size_int = (device->vendor_id == VK_VENDOR_ID_INTEL && device->subgroup_size_control) ? device->subgroup_min_size : device->subgroup_size;
                 const uint32_t wg_size_subgroup_int = (w == DMMV_WG_SIZE_SUBGROUP) ? subgroup_size_int : (subgroup_size_int * 4);
 
-                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_q8_1_f32", arr_dmmv_q4_0_q8_1_f32_len[reduc], arr_dmmv_q4_0_q8_1_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
-                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_q8_1_f32", arr_dmmv_q4_1_q8_1_f32_len[reduc], arr_dmmv_q4_1_q8_1_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
-                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_q8_1_f32", arr_dmmv_q5_0_q8_1_f32_len[reduc], arr_dmmv_q5_0_q8_1_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
-                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_q8_1_f32", arr_dmmv_q5_1_q8_1_f32_len[reduc], arr_dmmv_q5_1_q8_1_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
-                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_q8_1_f32", arr_dmmv_q8_0_q8_1_f32_len[reduc], arr_dmmv_q8_0_q8_1_f32_data[reduc], "main", 3, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_q8_1_f32", arr_dmmv_q4_0_q8_1_f32_len[reduc], arr_dmmv_q4_0_q8_1_f32_data[reduc], "main", 4, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_1][i], "mul_mat_vec_q4_1_q8_1_f32", arr_dmmv_q4_1_q8_1_f32_len[reduc], arr_dmmv_q4_1_q8_1_f32_data[reduc], "main", 4, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_0][i], "mul_mat_vec_q5_0_q8_1_f32", arr_dmmv_q5_0_q8_1_f32_len[reduc], arr_dmmv_q5_0_q8_1_f32_data[reduc], "main", 4, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_1][i], "mul_mat_vec_q5_1_q8_1_f32", arr_dmmv_q5_1_q8_1_f32_len[reduc], arr_dmmv_q5_1_q8_1_f32_data[reduc], "main", 4, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup_int, 2*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
+                ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q8_0][i], "mul_mat_vec_q8_0_q8_1_f32", arr_dmmv_q8_0_q8_1_f32_len[reduc], arr_dmmv_q8_0_q8_1_f32_data[reduc], "main", 4, sizeof(vk_mat_vec_push_constants), {1*rm_stdq, 1, 1}, {wg_size_subgroup_int, 1*rm_stdq, i+1}, 1, true, use_subgroups, subgroup_size_int);
             }
 #endif // GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT
         }
     }
 
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32",  mul_mat_vec_id_f32_f32_len,  mul_mat_vec_id_f32_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32",  mul_mat_vec_id_f16_f32_len,  mul_mat_vec_id_f16_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_BF16], "mul_mat_vec_id_bf16_f32", mul_mat_vec_id_bf16_f32_len, mul_mat_vec_id_bf16_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", mul_mat_vec_id_q2_k_f32_len, mul_mat_vec_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ1_S],   "mul_mat_vec_id_iq1_s_f32",   mul_mat_vec_id_iq1_s_f32_len,   mul_mat_vec_id_iq1_s_f32_data,   "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ1_M],   "mul_mat_vec_id_iq1_m_f32",   mul_mat_vec_id_iq1_m_f32_len,   mul_mat_vec_id_iq1_m_f32_data,   "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ2_XXS], "mul_mat_vec_id_iq2_xxs_f32", mul_mat_vec_id_iq2_xxs_f32_len, mul_mat_vec_id_iq2_xxs_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ2_XS],  "mul_mat_vec_id_iq2_xs_f32",  mul_mat_vec_id_iq2_xs_f32_len,  mul_mat_vec_id_iq2_xs_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ2_S],   "mul_mat_vec_id_iq2_s_f32",   mul_mat_vec_id_iq2_s_f32_len,   mul_mat_vec_id_iq2_s_f32_data,   "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ3_XXS], "mul_mat_vec_id_iq3_xxs_f32", mul_mat_vec_id_iq3_xxs_f32_len, mul_mat_vec_id_iq3_xxs_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ3_S],   "mul_mat_vec_id_iq3_s_f32",   mul_mat_vec_id_iq3_s_f32_len,   mul_mat_vec_id_iq3_s_f32_data,   "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_XS],  "mul_mat_vec_id_iq4_xs_f32",  mul_mat_vec_id_iq4_xs_f32_len,  mul_mat_vec_id_iq4_xs_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL],  "mul_mat_vec_id_iq4_nl_f32",  mul_mat_vec_id_iq4_nl_f32_len,  mul_mat_vec_id_iq4_nl_f32_data,  "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
-    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_MXFP4],   "mul_mat_vec_id_mxfp4_f32",   mul_mat_vec_id_mxfp4_f32_len,   mul_mat_vec_id_mxfp4_f32_data,   "main", 4, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32",  mul_mat_vec_id_f32_f32_len,  mul_mat_vec_id_f32_f32_data,  "main", 5, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32",  mul_mat_vec_id_f16_f32_len,  mul_mat_vec_id_f16_f32_data,  "main", 5, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_BF16], "mul_mat_vec_id_bf16_f32", mul_mat_vec_id_bf16_f32_len, mul_mat_vec_id_bf16_f32_data, "main", 5, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {device->subgroup_size, 2}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 5, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 5, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 5, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 5, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {device->subgroup_size, 2*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 5, sizeof(vk_mat_vec_id_push_constants), {1*rm_stdq, 1, 1}, {device->subgroup_size, 1*rm_stdq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", mul_mat_vec_id_q2_k_f32_len, mul_mat_vec_id_q2_k_f32_data, "main", 5, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", 5, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 5, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 5, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 5, sizeof(vk_mat_vec_id_push_constants), {rm_kq, 1, 1}, {subgroup_size_16, rm_kq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ1_S],   "mul_mat_vec_id_iq1_s_f32",   mul_mat_vec_id_iq1_s_f32_len,   mul_mat_vec_id_iq1_s_f32_data,   "main", 5, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ1_M],   "mul_mat_vec_id_iq1_m_f32",   mul_mat_vec_id_iq1_m_f32_len,   mul_mat_vec_id_iq1_m_f32_data,   "main", 5, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ2_XXS], "mul_mat_vec_id_iq2_xxs_f32", mul_mat_vec_id_iq2_xxs_f32_len, mul_mat_vec_id_iq2_xxs_f32_data, "main", 5, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ2_XS],  "mul_mat_vec_id_iq2_xs_f32",  mul_mat_vec_id_iq2_xs_f32_len,  mul_mat_vec_id_iq2_xs_f32_data,  "main", 5, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ2_S],   "mul_mat_vec_id_iq2_s_f32",   mul_mat_vec_id_iq2_s_f32_len,   mul_mat_vec_id_iq2_s_f32_data,   "main", 5, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ3_XXS], "mul_mat_vec_id_iq3_xxs_f32", mul_mat_vec_id_iq3_xxs_f32_len, mul_mat_vec_id_iq3_xxs_f32_data, "main", 5, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ3_S],   "mul_mat_vec_id_iq3_s_f32",   mul_mat_vec_id_iq3_s_f32_len,   mul_mat_vec_id_iq3_s_f32_data,   "main", 5, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_XS],  "mul_mat_vec_id_iq4_xs_f32",  mul_mat_vec_id_iq4_xs_f32_len,  mul_mat_vec_id_iq4_xs_f32_data,  "main", 5, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_IQ4_NL],  "mul_mat_vec_id_iq4_nl_f32",  mul_mat_vec_id_iq4_nl_f32_len,  mul_mat_vec_id_iq4_nl_f32_data,  "main", 5, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
+    ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_MXFP4],   "mul_mat_vec_id_mxfp4_f32",   mul_mat_vec_id_mxfp4_f32_len,   mul_mat_vec_id_mxfp4_f32_data,   "main", 5, sizeof(vk_mat_vec_id_push_constants), {rm_iq, 1, 1}, {subgroup_size_16, rm_iq}, 1, true);
 
     // dequant shaders
     ggml_vk_create_pipeline(device, device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16",   dequant_f32_len,  dequant_f32_data,  "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1);
@@ -3519,12 +3535,12 @@ static void ggml_vk_load_shaders(vk_device& device) {
 
     for (uint32_t i = 0; i < p021_max_gqa_ratio; ++i) {
         if (device->subgroup_arithmetic && device->subgroup_require_full_support) {
-            ggml_vk_create_pipeline2(device, device->pipeline_mul_mat_vec_p021_f16_f32[i], "mul_mat_vec_p021_f16_f32"+std::to_string(i+1), mul_mat_vec_p021_f16_f32_subgroup_add_len, mul_mat_vec_p021_f16_f32_subgroup_add_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {device->subgroup_size, i + 1}, 1, true, true);
+            ggml_vk_create_pipeline2(device, device->pipeline_mul_mat_vec_p021_f16_f32[i], "mul_mat_vec_p021_f16_f32"+std::to_string(i+1), mul_mat_vec_p021_f16_f32_subgroup_add_len, mul_mat_vec_p021_f16_f32_subgroup_add_data, "main", 4, 7 * sizeof(uint32_t), {1, 1, 1}, {device->subgroup_size, i + 1}, 1, true, true);
         } else {
-            ggml_vk_create_pipeline2(device, device->pipeline_mul_mat_vec_p021_f16_f32[i], "mul_mat_vec_p021_f16_f32"+std::to_string(i+1), mul_mat_vec_p021_f16_f32_len,              mul_mat_vec_p021_f16_f32_data,              "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {device->subgroup_size, i + 1}, 1, true);
+            ggml_vk_create_pipeline2(device, device->pipeline_mul_mat_vec_p021_f16_f32[i], "mul_mat_vec_p021_f16_f32"+std::to_string(i+1), mul_mat_vec_p021_f16_f32_len,              mul_mat_vec_p021_f16_f32_data,              "main", 4, 7 * sizeof(uint32_t), {1, 1, 1}, {device->subgroup_size, i + 1}, 1, true);
         }
     }
-    ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 12 * sizeof(uint32_t), {1, 1, 1}, {}, 1);
+    ggml_vk_create_pipeline(device, device->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 4, 13 * sizeof(uint32_t), {1, 1, 1}, {}, 1);
 
     ggml_vk_create_pipeline(device, device->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
     ggml_vk_create_pipeline(device, device->pipeline_group_norm_f32, "group_norm_f32", group_norm_f32_len, group_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1);
@@ -6501,7 +6517,11 @@ static bool ggml_vk_should_use_mmvq(const vk_device& device, uint32_t m, uint32_
     GGML_UNUSED(k);
 }
 
-static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx, bool dryrun = false) {
+    ggml_tensor * dst = cgraph->nodes[node_idx];
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+
     VK_LOG_DEBUG("ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
     std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
     std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
@@ -6532,7 +6552,6 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
     GGML_ASSERT(ne11 == 1 || ne12 * ne13 == 1);
     bool batch_n = ne11 > 1;
 
-    ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
     ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context;
     ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context;
 
@@ -6634,8 +6653,20 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
         return;
     }
 
-    vk_buffer d_D = dst_buf_ctx->dev_buffer;
-    const uint64_t d_buf_offset = vk_tensor_offset(dst) + dst->view_offs;
+    vk_buffer d_D;
+    uint64_t d_buf_offset = 0;
+
+    if (ctx->num_additional_fused_ops > 0) {
+        const ggml_tensor * add = cgraph->nodes[node_idx + 1];
+        ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)add->buffer->context;
+        d_D = dst_buf_ctx->dev_buffer;
+        d_buf_offset = vk_tensor_offset(add) + add->view_offs;
+    } else {
+        ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
+        d_D = dst_buf_ctx->dev_buffer;
+        d_buf_offset = vk_tensor_offset(dst) + dst->view_offs;
+    }
+
     GGML_ASSERT(d_D != nullptr);
     vk_buffer d_X;
     uint64_t x_buf_offset = 0;
@@ -6730,14 +6761,43 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
         y_sz_total = CEIL_DIV(y_sz_total, 144) * 144;
     }
 
+    uint32_t enable_bias = ctx->num_additional_fused_ops > 0;
+
+    vk_buffer d_B = d_D;
+    size_t b_buf_offset = 0;
+    uint64_t b_sz = 0;
+
+    if (enable_bias) {
+        const ggml_tensor * add = cgraph->nodes[node_idx + 1];
+        const ggml_tensor * bias = add->src[0] == dst ? add->src[1] : add->src[0];
+
+        bool b_uma = false;
+        if (ctx->device->uma) {
+            ggml_vk_host_get(ctx->device, bias->data, d_B, b_buf_offset);
+            b_uma = d_B != nullptr;
+        }
+        if(!b_uma) {
+            ggml_backend_vk_buffer_context * bias_buf_ctx = (ggml_backend_vk_buffer_context *)bias->buffer->context;
+            d_B = bias_buf_ctx->dev_buffer;
+            b_buf_offset = vk_tensor_offset(bias) + bias->view_offs;
+            GGML_ASSERT(d_B != nullptr);
+            b_sz = ggml_nbytes(bias);
+        }
+    }
+
     // compute
     const vk_mat_vec_push_constants pc = {
         (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01,
-        stride_batch_x, stride_batch_y, stride_batch_d,
+        stride_batch_x, stride_batch_y, stride_batch_d, enable_bias,
         (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3,
     };
     ggml_vk_dispatch_pipeline(ctx, subctx, dmmv,
-                              { vk_subbuffer{ d_X, x_buf_offset, x_sz * ne02 * ne03 }, vk_subbuffer{ d_Y, y_buf_offset, y_sz_total }, vk_subbuffer{ d_D, d_buf_offset, d_sz * ne22 * ne23} },
+                              {
+                                vk_subbuffer{ d_X, x_buf_offset, x_sz * ne02 * ne03 },
+                                vk_subbuffer{ d_Y, y_buf_offset, y_sz_total },
+                                vk_subbuffer{ d_D, d_buf_offset, d_sz * ne22 * ne23},
+                                vk_subbuffer{ d_B, b_buf_offset, b_sz },
+                              },
                               pc, { groups_x, (uint32_t)(ne12 * ne13), groups_z });
 
     if (x_non_contig) {
@@ -6748,7 +6808,10 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context&
     }
 }
 
-static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx, bool dryrun = false) {
+    ggml_tensor * dst = cgraph->nodes[node_idx];
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
     VK_LOG_DEBUG("ggml_vk_mul_mat_p021_f16_f32(" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
     std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
     std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
@@ -6771,7 +6834,6 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
 
     GGML_ASSERT(ne11 == 1);
 
-    ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
     ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context;
     ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context;
 
@@ -6805,8 +6867,19 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
         return;
     }
 
-    vk_buffer d_D = dst_buf_ctx->dev_buffer;
-    const uint64_t d_buf_offset = vk_tensor_offset(dst) + dst->view_offs;
+    vk_buffer d_D;
+    uint64_t d_buf_offset = 0;
+
+    if (ctx->num_additional_fused_ops > 0) {
+        const ggml_tensor * add = cgraph->nodes[node_idx + 1];
+        ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)add->buffer->context;
+        d_D = dst_buf_ctx->dev_buffer;
+        d_buf_offset = vk_tensor_offset(add) + add->view_offs;
+    } else {
+        ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
+        d_D = dst_buf_ctx->dev_buffer;
+        d_buf_offset = vk_tensor_offset(dst) + dst->view_offs;
+    }
     GGML_ASSERT(d_D != nullptr);
     vk_buffer d_Qx = src0_buf_ctx->dev_buffer;
     const uint64_t qx_buf_offset = vk_tensor_offset(src0) + src0->view_offs;
@@ -6823,8 +6896,32 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
     const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
     const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset;
 
+    uint32_t enable_bias = ctx->num_additional_fused_ops > 0;
+
+    vk_buffer d_B = d_D;
+    size_t b_buf_offset = 0;
+    uint64_t b_sz = 0;
+
+    if (enable_bias) {
+        const ggml_tensor * add = cgraph->nodes[node_idx + 1];
+        const ggml_tensor * bias = add->src[0] == dst ? add->src[1] : add->src[0];
+
+        bool b_uma = false;
+        if (ctx->device->uma) {
+            ggml_vk_host_get(ctx->device, bias->data, d_B, b_buf_offset);
+            b_uma = d_B != nullptr;
+        }
+        if(!b_uma) {
+            ggml_backend_vk_buffer_context * bias_buf_ctx = (ggml_backend_vk_buffer_context *)bias->buffer->context;
+            d_B = bias_buf_ctx->dev_buffer;
+            b_buf_offset = vk_tensor_offset(bias) + bias->view_offs;
+            GGML_ASSERT(d_B != nullptr);
+            b_sz = ggml_nbytes(bias);
+        }
+    }
+
     // compute
-    const std::array<uint32_t, 6> pc = { (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) };
+    const std::array<uint32_t, 7> pc = { (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)), enable_bias };
 
     uint32_t workgroups_z = (uint32_t)ne12;
     // When gqa_ratio > 1, each invocation does multiple rows and we can launch fewer workgroups
@@ -6832,10 +6929,19 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c
         workgroups_z /= gqa_ratio;
     }
 
-    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32[gqa_ratio - 1], { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset } }, pc, { 1, (uint32_t)ne01, workgroups_z });
+    ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_p021_f16_f32[gqa_ratio - 1],
+        {
+            vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz },
+            vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset },
+            vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset },
+            vk_subbuffer{ d_B, b_buf_offset, b_sz },
+        }, pc, { 1, (uint32_t)ne01, workgroups_z });
 }
 
-static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx, bool dryrun = false) {
+    ggml_tensor * dst = cgraph->nodes[node_idx];
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
     VK_LOG_DEBUG("ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
     std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
     std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
@@ -6868,7 +6974,6 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
     GGML_ASSERT(ne11 == 1);
     GGML_ASSERT(src0->ne[3] == src1->ne[3]); // checked in supports_op
 
-    ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
     ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context;
     ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context;
 
@@ -6898,8 +7003,20 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
         return;
     }
 
-    vk_buffer d_D = dst_buf_ctx->dev_buffer;
-    const uint64_t d_buf_offset = vk_tensor_offset(dst) + dst->view_offs;
+    vk_buffer d_D;
+    uint64_t d_buf_offset = 0;
+
+    if (ctx->num_additional_fused_ops > 0) {
+        const ggml_tensor * add = cgraph->nodes[node_idx + 1];
+        ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)add->buffer->context;
+        d_D = dst_buf_ctx->dev_buffer;
+        d_buf_offset = vk_tensor_offset(add) + add->view_offs;
+    } else {
+        ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
+        d_D = dst_buf_ctx->dev_buffer;
+        d_buf_offset = vk_tensor_offset(dst) + dst->view_offs;
+    }
+
     GGML_ASSERT(d_D != nullptr);
     vk_buffer d_Qx = src0_buf_ctx->dev_buffer;
     const uint64_t qx_buf_offset = vk_tensor_offset(src0) + src0->view_offs;
@@ -6916,13 +7033,45 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con
     const uint64_t d_buffer_offset = (d_buf_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment;
     const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset;
 
+    uint32_t enable_bias = ctx->num_additional_fused_ops > 0;
+
+    vk_buffer d_B = d_D;
+    size_t b_buf_offset = 0;
+    uint64_t b_sz = 0;
+
+    if (enable_bias) {
+        const ggml_tensor * add = cgraph->nodes[node_idx + 1];
+        const ggml_tensor * bias = add->src[0] == dst ? add->src[1] : add->src[0];
+
+        bool b_uma = false;
+        if (ctx->device->uma) {
+            ggml_vk_host_get(ctx->device, bias->data, d_B, b_buf_offset);
+            b_uma = d_B != nullptr;
+        }
+        if(!b_uma) {
+            ggml_backend_vk_buffer_context * bias_buf_ctx = (ggml_backend_vk_buffer_context *)bias->buffer->context;
+            d_B = bias_buf_ctx->dev_buffer;
+            b_buf_offset = vk_tensor_offset(bias) + bias->view_offs;
+            GGML_ASSERT(d_B != nullptr);
+            b_sz = ggml_nbytes(bias);
+        }
+    }
+
     // compute
-    const std::array<uint32_t, 12> pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, channel_stride_y, (uint32_t)(ne12 / ne02), (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)), nb03, nb13, nb23 };
+    const std::array<uint32_t, 13> pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, channel_stride_y, (uint32_t)(ne12 / ne02), (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)), nb03, nb13, nb23, enable_bias };
     ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_mul_mat_vec_nc_f16_f32,
-        { vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz }, vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset } }, pc, { (uint32_t)ne03, (uint32_t)ne01, (uint32_t)ne12 });
+        {
+            vk_subbuffer{ d_Qx, qx_buf_offset, qx_sz },
+            vk_subbuffer{ d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset },
+            vk_subbuffer{ d_D, d_buffer_offset, d_sz + d_shader_offset },
+            vk_subbuffer{ d_B, b_buf_offset, b_sz },
+        }, pc, { (uint32_t)ne03, (uint32_t)ne01, (uint32_t)ne12 });
 }
 
-static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
+static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx, bool dryrun = false) {
+    ggml_tensor * dst = cgraph->nodes[node_idx];
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src1 = dst->src[1];
     VK_LOG_DEBUG("ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")");
 
     // Handle huge A matrix by splitting the M dimensions. This works well for convolution use cases
@@ -6961,15 +7110,15 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context& subctx, g
         src1->nb[1] <= src1->nb[3] &&
         src0->ne[3] == 1 &&
         src1->ne[3] == 1) {
-        ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, src0, src1, dst, dryrun);
+        ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, cgraph, node_idx, dryrun);
     } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && dst->ne[1] == 1 &&
                !ggml_is_permuted(src0) && !ggml_is_permuted(src1)) {
-        ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, src0, src1, dst, dryrun);
+        ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, cgraph, node_idx, dryrun);
     // mul_mat_vec supports batching ne12*ne13 when ne11==1, or treating ne11 as the batch size (up to four)
     // when ne12 and ne13 are one.
     } else if ((dst->ne[1] == 1 || (dst->ne[1] <= mul_mat_vec_max_cols && src1->ne[2] * src1->ne[3] == 1)) &&
                (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_BF16 || ggml_is_quantized(src0->type))) {
-        ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst, dryrun);
+        ggml_vk_mul_mat_vec_q_f16(ctx, subctx, cgraph, node_idx, dryrun);
     } else {
         ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, dst, false, dryrun);
     }
@@ -7249,7 +7398,11 @@ static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context&
     }
 }
 
-static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst, bool dryrun = false) {
+static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx, bool dryrun = false) {
+    ggml_tensor * dst = cgraph->nodes[node_idx];
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src1 = dst->src[1];
+    ggml_tensor * ids = dst->src[2];
     VK_LOG_DEBUG("ggml_vk_mul_mat_vec_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3];
     std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3];
     std::cerr << "), (" << ids << ", name=" << ids->name << ", type=" << ids->type << ", ne0=" << ids->ne[0] << ", ne1=" << ids->ne[1] << ", ne2=" << ids->ne[2] << ", ne3=" << ids->ne[3] << ", nb0=" << ids->nb[0] << ", nb1=" << ids->nb[1] << ", nb2=" << ids->nb[2] << ", nb3=" << ids->nb[3];
@@ -7281,7 +7434,6 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
     const uint64_t ne22 = dst->ne[2];
     const uint64_t ne23 = dst->ne[3];
 
-    ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
     ggml_backend_vk_buffer_context * src0_buf_ctx = (ggml_backend_vk_buffer_context *)src0->buffer->context;
     ggml_backend_vk_buffer_context * src1_buf_ctx = (ggml_backend_vk_buffer_context *)src1->buffer->context;
     ggml_backend_vk_buffer_context * ids_buf_ctx = (ggml_backend_vk_buffer_context *)ids->buffer->context;
@@ -7369,8 +7521,20 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
         return;
     }
 
-    vk_buffer d_D = dst_buf_ctx->dev_buffer;
-    const uint64_t d_buf_offset = vk_tensor_offset(dst) + dst->view_offs;
+    vk_buffer d_D;
+    uint64_t d_buf_offset = 0;
+
+    if (ctx->num_additional_fused_ops > 0) {
+        const ggml_tensor * add = cgraph->nodes[node_idx + 1];
+        ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)add->buffer->context;
+        d_D = dst_buf_ctx->dev_buffer;
+        d_buf_offset = vk_tensor_offset(add) + add->view_offs;
+    } else {
+        ggml_backend_vk_buffer_context * dst_buf_ctx = (ggml_backend_vk_buffer_context *)dst->buffer->context;
+        d_D = dst_buf_ctx->dev_buffer;
+        d_buf_offset = vk_tensor_offset(dst) + dst->view_offs;
+    }
+
     GGML_ASSERT(d_D != nullptr);
     vk_buffer d_X;
     uint64_t x_buf_offset = 0;
@@ -7445,15 +7609,46 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
         groups_x = CEIL_DIV(groups_x, groups_z);
     }
 
+    uint32_t enable_bias = ctx->num_additional_fused_ops > 0;
+
+    vk_buffer d_B = d_D;
+    size_t b_buf_offset = 0;
+    uint64_t b_sz = 0;
+
+    if (enable_bias) {
+        const ggml_tensor * bias = cgraph->nodes[node_idx + 1]->src[1];
+
+        bool b_uma = false;
+        if (ctx->device->uma) {
+            ggml_vk_host_get(ctx->device, bias->data, d_B, b_buf_offset);
+            b_uma = d_B != nullptr;
+        }
+        if(!b_uma) {
+            ggml_backend_vk_buffer_context * bias_buf_ctx = (ggml_backend_vk_buffer_context *)bias->buffer->context;
+            d_B = bias_buf_ctx->dev_buffer;
+            b_buf_offset = vk_tensor_offset(bias) + bias->view_offs;
+            GGML_ASSERT(d_B != nullptr);
+            b_sz = ggml_nbytes(bias);
+        }
+    }
+
     // compute
     const vk_mat_vec_id_push_constants pc = {
         (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01,
         (uint32_t)x_ne, stride_batch_y, (uint32_t)(ne20*ne21),
+
+        enable_bias,
+
         (uint32_t)nei0, (uint32_t)ne11,
     };
     ggml_vk_dispatch_pipeline(ctx, subctx, dmmv,
-        { vk_subbuffer{ d_X, x_buf_offset, x_sz * ne02 * ne03 },
-        vk_subbuffer{ d_Y, y_buf_offset, y_sz * ne12 * ne13 }, vk_subbuffer{ d_D, d_buf_offset, d_sz * ne22 * ne23}, vk_subbuffer{ d_ids, ids_buf_offset, ids_sz } },
+        {
+            vk_subbuffer{ d_X, x_buf_offset, x_sz * ne02 * ne03 },
+            vk_subbuffer{ d_Y, y_buf_offset, y_sz * ne12 * ne13 },
+            vk_subbuffer{ d_D, d_buf_offset, d_sz * ne22 * ne23},
+            vk_subbuffer{ d_B, b_buf_offset, b_sz },
+            vk_subbuffer{ d_ids, ids_buf_offset, ids_sz },
+        },
         pc, { groups_x, (uint32_t)nei0, groups_z });
 
     if (x_non_contig) {
@@ -7464,10 +7659,21 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte
     }
 }
 
-static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, bool dryrun = false) {
+static bool ggml_vk_use_mul_mat_vec_id(const struct ggml_cgraph * cgraph, int node_idx) {
+    ggml_tensor * dst = cgraph->nodes[node_idx];
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src2 = dst->src[2];
+    return src2->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type));
+}
+
+static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context& subctx, const struct ggml_cgraph * cgraph, int node_idx, bool dryrun = false) {
+    ggml_tensor * dst = cgraph->nodes[node_idx];
+    ggml_tensor * src0 = dst->src[0];
+    ggml_tensor * src1 = dst->src[1];
+    ggml_tensor * src2 = dst->src[2];
     VK_LOG_DEBUG("ggml_vk_mul_mat_id(" << src0 << ", " << src1 << ", " << src2 << ", " << dst << ")");
-    if (src2->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) {
-        ggml_vk_mul_mat_vec_id_q_f16(ctx, subctx, src0, src1, src2, dst, dryrun);
+    if (ggml_vk_use_mul_mat_vec_id(cgraph, node_idx)) {
+        ggml_vk_mul_mat_vec_id_q_f16(ctx, subctx, cgraph, node_idx, dryrun);
     } else {
         ggml_vk_mul_mat_id_q_f16(ctx, subctx, src0, src1, src2, dst, dryrun);
     }
@@ -8433,7 +8639,7 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) {
     }
 }
 
-static uint32_t get_misalign_bytes(ggml_backend_vk_context * ctx, const ggml_tensor * t)
+static uint32_t get_misalign_bytes(const ggml_backend_vk_context * ctx, const ggml_tensor * t)
 {
     return ((vk_tensor_offset(t) + t->view_offs) & (ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1));;
 }
@@ -11793,11 +11999,11 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
 
         break;
     case GGML_OP_MUL_MAT:
-        ggml_vk_mul_mat(ctx, compute_ctx, src0, src1, node, dryrun);
+        ggml_vk_mul_mat(ctx, compute_ctx, cgraph, node_idx, dryrun);
 
         break;
     case GGML_OP_MUL_MAT_ID:
-        ggml_vk_mul_mat_id(ctx, compute_ctx, src0, src1, src2, node, dryrun);
+        ggml_vk_mul_mat_id(ctx, compute_ctx, cgraph, node_idx, dryrun);
 
         break;
 
@@ -12474,7 +12680,7 @@ static bool ggml_vk_is_empty(ggml_tensor * node) {
     return ggml_is_empty(node) || node->op == GGML_OP_NONE || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE;
 }
 
-static bool ggml_vk_can_fuse(const struct ggml_cgraph * cgraph, int node_idx, std::initializer_list<enum ggml_op> ops) {
+static bool ggml_vk_can_fuse(const ggml_backend_vk_context * ctx, const struct ggml_cgraph * cgraph, int node_idx, std::initializer_list<enum ggml_op> ops) {
     if (!ggml_can_fuse(cgraph, node_idx, ops)) {
         return false;
     }
@@ -12502,6 +12708,61 @@ static bool ggml_vk_can_fuse(const struct ggml_cgraph * cgraph, int node_idx, st
             return false;
         }
     }
+    if (ops.size() == 2 && ops.begin()[0] == GGML_OP_MUL_MAT && ops.begin()[1] == GGML_OP_ADD) {
+        // additional constraints specific to this fusion
+        const ggml_tensor *mul = cgraph->nodes[node_idx];
+        const ggml_tensor *add = cgraph->nodes[node_idx + 1];
+        const ggml_tensor *bias = add->src[0] == mul ? add->src[1] : add->src[0];
+
+        // mat-vec only
+        if (ggml_nrows(mul) != 1) {
+            return false;
+        }
+        // shaders assume the types match
+        if (mul->type != bias->type) {
+            return false;
+        }
+        // shaders reuse the D shape for bias
+        if (!ggml_are_same_shape(mul, bias) ||
+            !ggml_are_same_stride(mul, bias)) {
+            return false;
+        }
+        // unaligned bias isn't handled
+        if (get_misalign_bytes(ctx, bias) != 0) {
+            return false;
+        }
+    }
+    if (ops.size() == 2 && ops.begin()[0] == GGML_OP_MUL_MAT_ID && ops.begin()[1] == GGML_OP_ADD_ID) {
+        // additional constraints specific to this fusion
+        const ggml_tensor *mul = cgraph->nodes[node_idx];
+        const ggml_tensor *add = cgraph->nodes[node_idx + 1];
+        const ggml_tensor *bias = add->src[1];
+
+        if (mul != add->src[0]) {
+            return false;
+        }
+        // mat-vec only
+        if (!ggml_vk_use_mul_mat_vec_id(cgraph, node_idx)) {
+            return false;
+        }
+        // shaders assume the types match
+        if (mul->type != bias->type) {
+            return false;
+        }
+        // shaders assume the bias is contiguous
+        if (!ggml_is_contiguous(bias)) {
+            return false;
+        }
+        // the ID tensor must be the same for mul_mat_id and add_id
+        if (mul->src[2] != add->src[2]) {
+            return false;
+        }
+        // unaligned bias isn't handled
+        if (get_misalign_bytes(ctx, bias) != 0) {
+            return false;
+        }
+    }
+
     return true;
 }
 
@@ -12670,7 +12931,11 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
             uint32_t num_adds = ggml_vk_fuse_multi_add(ctx, cgraph, i);
             if (num_adds) {
                 ctx->num_additional_fused_ops = num_adds - 1;
-            } else if (ggml_vk_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
+            } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
+                ctx->num_additional_fused_ops = 1;
+            } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT, GGML_OP_ADD })) {
+                ctx->num_additional_fused_ops = 1;
+            } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT_ID, GGML_OP_ADD_ID })) {
                 ctx->num_additional_fused_ops = 1;
             } 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) &&
@@ -12783,7 +13048,11 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
             uint32_t num_adds = ggml_vk_fuse_multi_add(ctx, cgraph, i);
             if (num_adds) {
                 ctx->num_additional_fused_ops = num_adds - 1;
-            } else if (ggml_vk_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
+            } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
+                ctx->num_additional_fused_ops = 1;
+            } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT, GGML_OP_ADD })) {
+                ctx->num_additional_fused_ops = 1;
+            } else if (ggml_vk_can_fuse(ctx, cgraph, i, { GGML_OP_MUL_MAT_ID, GGML_OP_ADD_ID })) {
                 ctx->num_additional_fused_ops = 1;
             } 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) &&
@@ -13005,7 +13274,9 @@ static void ggml_vk_graph_optimize(ggml_backend_t backend, struct ggml_cgraph *
             for (int c = first_unused; c < j; ++c) {
                 if (!used[c] &&
                     is_src_of(graph->nodes[j], graph->nodes[c]) &&
-                    !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_RMS_NORM && graph->nodes[j]->op == GGML_OP_MUL)) {
+                    !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_RMS_NORM && graph->nodes[j]->op == GGML_OP_MUL) &&
+                    !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_MUL_MAT && graph->nodes[j]->op == GGML_OP_ADD) &&
+                    !(j == c+1 && c == current_set.back() && graph->nodes[c]->op == GGML_OP_MUL_MAT_ID && graph->nodes[j]->op == GGML_OP_ADD_ID)) {
                     ok = false;
                     break;
                 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.glsl
index 450dee0408..bbb4d1206b 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.glsl
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_base.glsl
@@ -28,8 +28,11 @@ layout (binding = 1) readonly buffer BV4 {B_TYPE_VEC4 data_b_v4[];};
 #endif
 
 layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
+
+layout (binding = 3) readonly buffer Bias {D_TYPE data_bias[];};
+
 #ifdef MUL_MAT_ID
-layout (binding = 3) readonly buffer IDS {int data_ids[];};
+layout (binding = 4) readonly buffer IDS {int data_ids[];};
 #endif
 
 #include "dequant_funcs.glsl"
@@ -45,6 +48,8 @@ layout (push_constant) uniform parameter
     uint batch_stride_b;
     uint batch_stride_d;
 
+    uint enable_bias;
+
 #ifdef MUL_MAT_ID
     uint nei0;
     uint ne11;
@@ -56,6 +61,10 @@ layout (push_constant) uniform parameter
 #endif
 } p;
 
+#ifdef MUL_MAT_ID
+uint expert_id;
+#endif
+
 void get_offsets(out uint a_offset, out uint b_offset, out uint d_offset) {
 #ifdef MUL_MAT_ID
     const uint expert_idx = gl_GlobalInvocationID.y;
@@ -75,7 +84,7 @@ void get_offsets(out uint a_offset, out uint b_offset, out uint d_offset) {
         batch_idx_a = i03 * p.ne02 + i02;
     }
 #else
-    const uint expert_id = data_ids[expert_idx];
+    expert_id = data_ids[expert_idx];
 #endif
 
     a_offset =
@@ -113,6 +122,13 @@ void reduce_result(inout FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t
     if (tid == 0) {
         [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
             [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+                if (p.enable_bias != 0) {
+#ifdef MUL_MAT_ID
+                    temp[j][n] += FLOAT_TYPE(data_bias[expert_id*p.stride_d + first_row + n]);
+#else
+                    temp[j][n] += FLOAT_TYPE(data_bias[j*p.batch_stride_d + d_offset + first_row + n]);
+#endif
+                }
                 data_d[j*p.batch_stride_d + d_offset + first_row + n] = D_TYPE(temp[j][n]);
             }
         }
@@ -148,6 +164,13 @@ void reduce_result(FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t d_offs
                 [[unroll]] for (uint s = 0; s < gl_NumSubgroups; ++s) {
                     temp[j][n] += tmpsh[j][n][s];
                 }
+                if (p.enable_bias != 0) {
+#ifdef MUL_MAT_ID
+                    temp[j][n] += FLOAT_TYPE(data_bias[expert_id*p.stride_d + first_row + n]);
+#else
+                    temp[j][n] += FLOAT_TYPE(data_bias[j*p.batch_stride_d + d_offset + first_row + n]);
+#endif
+                }
                 data_d[j*p.batch_stride_d + d_offset + first_row + n] = D_TYPE(temp[j][n]);
             }
         }
@@ -173,6 +196,13 @@ void reduce_result(FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const in uint32_t d_offs
     if (tid == 0) {
         [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
             [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+                if (p.enable_bias != 0) {
+#ifdef MUL_MAT_ID
+                    tmpsh[j][n][0] += FLOAT_TYPE(data_bias[expert_id*p.stride_d + first_row + n]);
+#else
+                    tmpsh[j][n][0] += FLOAT_TYPE(data_bias[j*p.batch_stride_d + d_offset + first_row + n]);
+#endif
+                }
                 data_d[j*p.batch_stride_d + d_offset + first_row + n] = D_TYPE(tmpsh[j][n][0]);
             }
         }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp
index 638878d94c..3f4584c984 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_nc.comp
@@ -15,6 +15,8 @@ layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
 layout (binding = 0) readonly buffer AV4 {A_TYPE_VEC4 data_a_v4[];};
 layout (binding = 1) readonly buffer BV4 {B_TYPE_VEC4 data_b_v4[];};
 
+layout (binding = 3) readonly buffer Bias {D_TYPE data_bias[];};
+
 layout (push_constant) uniform parameter
 {
     uint ncols_x;
@@ -29,6 +31,7 @@ layout (push_constant) uniform parameter
     uint nb03;
     uint nb13;
     uint nb23;
+    uint enable_bias;
 } p;
 
 shared FLOAT_TYPE tmp[BLOCK_SIZE];
@@ -117,6 +120,9 @@ void main() {
     }
 
     if (tid == 0) {
+        if (p.enable_bias != 0) {
+            tmp[0] += FLOAT_TYPE(data_bias[idst]);
+        }
         dst[idst] = tmp[0];
     }
 }
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_p021.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_p021.comp
index 7aa070eebd..d51424d417 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_p021.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_p021.comp
@@ -17,6 +17,8 @@ layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
 layout (binding = 0) readonly buffer AV4 {A_TYPE_VEC4 data_a_v4[];};
 layout (binding = 1) readonly buffer BV4 {B_TYPE_VEC4 data_b_v4[];};
 
+layout (binding = 3) readonly buffer Bias {D_TYPE data_bias[];};
+
 layout(constant_id = 0) const int BLOCK_SIZE = 32;
 // gqa_ratio is in the range [1,8]
 layout(constant_id = 1) const uint gqa_ratio = 1;
@@ -29,6 +31,7 @@ layout (push_constant) uniform parameter
     uint nchannels_y;
     uint b_offset;
     uint d_offset;
+    uint enable_bias;
 } p;
 
 #if !USE_SUBGROUP_ADD
@@ -148,6 +151,9 @@ void main() {
         [[unroll]] for (uint c = 0; c < gqa_ratio; ++c) {
             // dst is not transposed and not permuted
             const uint idst = (channel + c)*nrows_dst + row_dst;
+            if (p.enable_bias != 0) {
+                temp[c] += FLOAT_TYPE(data_bias[idst]);
+            }
             dst[idst] = temp[c];
         }
     }

From 5d8bb900bc7daa84bfa7bb1d25ab7e32394919f3 Mon Sep 17 00:00:00 2001
From: Jeff Bolz <jbolz@nvidia.com>
Date: Sat, 1 Nov 2025 00:52:14 -0500
Subject: [PATCH 22/30] vulkan: Fix multi_add invalid descriptor usage (#16899)

---
 ggml/src/ggml-vulkan/ggml-vulkan.cpp          |   2 -
 .../ggml-vulkan/vulkan-shaders/multi_add.comp | 104 ++++++++++++++++--
 2 files changed, 94 insertions(+), 12 deletions(-)

diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
index 6a46d0889b..8d1a85c969 100644
--- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp
+++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp
@@ -4274,8 +4274,6 @@ static vk_device ggml_vk_get_device(size_t idx) {
 
         device->multi_add = vk12_props.shaderRoundingModeRTEFloat16 &&
                             device->properties.limits.maxPushConstantsSize >= sizeof(vk_op_multi_add_push_constants) &&
-                            vk12_features.runtimeDescriptorArray &&
-                            device->vendor_id != VK_VENDOR_ID_INTEL &&
                             getenv("GGML_VK_DISABLE_MULTI_ADD") == nullptr;
 
         device->shader_int64 = device_features2.features.shaderInt64;
diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp b/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp
index 1e8f694a72..10cf5202a4 100644
--- a/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp
+++ b/ggml/src/ggml-vulkan/vulkan-shaders/multi_add.comp
@@ -23,16 +23,100 @@ layout (push_constant) uniform parameter2
     uint rms_partials;
 } p;
 
-// Workaround for MoltenVK Bug, see https://github.com/ggml-org/llama.cpp/issues/15498
-// layout (binding = 0) readonly buffer A {A_TYPE data_a[];} a[];
-// layout (binding = 0) writeonly buffer D {D_TYPE data_d[];} d[];
-layout (binding = 0) buffer A {A_TYPE data_a[];} a[];
-layout (binding = 0) buffer D {D_TYPE data_d[];} d[];
-
-layout (binding = 0, std430) buffer PartialBuf {float partial_sums[];} partials[];
+// No readonly/writeonly decorations. Workaround for MoltenVK Bug, see https://github.com/ggml-org/llama.cpp/issues/15498
+layout (binding = 0)  buffer A0 {A_TYPE data_a[];} a0;
+layout (binding = 1)  buffer A1 {A_TYPE data_a[];} a1;
+layout (binding = 2)  buffer A2 {A_TYPE data_a[];} a2;
+layout (binding = 3)  buffer A3 {A_TYPE data_a[];} a3;
+layout (binding = 4)  buffer A4 {A_TYPE data_a[];} a4;
+layout (binding = 5)  buffer A5 {A_TYPE data_a[];} a5;
+layout (binding = 6)  buffer A6 {A_TYPE data_a[];} a6;
+layout (binding = 7)  buffer A7 {A_TYPE data_a[];} a7;
+layout (binding = 8)  buffer A8 {A_TYPE data_a[];} a8;
+layout (binding = 9)  buffer A9 {A_TYPE data_a[];} a9;
+layout (binding = 10) buffer A10 {A_TYPE data_a[];} a10;
+layout (binding = 11) buffer A11 {A_TYPE data_a[];} a11;
+layout (binding = 0)  buffer D0 {D_TYPE data_d[];} d0;
+layout (binding = 1)  buffer D1 {D_TYPE data_d[];} d1;
+layout (binding = 2)  buffer D2 {D_TYPE data_d[];} d2;
+layout (binding = 3)  buffer D3 {D_TYPE data_d[];} d3;
+layout (binding = 4)  buffer D4 {D_TYPE data_d[];} d4;
+layout (binding = 5)  buffer D5 {D_TYPE data_d[];} d5;
+layout (binding = 6)  buffer D6 {D_TYPE data_d[];} d6;
+layout (binding = 7)  buffer D7 {D_TYPE data_d[];} d7;
+layout (binding = 8)  buffer D8 {D_TYPE data_d[];} d8;
+layout (binding = 9)  buffer D9 {D_TYPE data_d[];} d9;
+layout (binding = 10) buffer D10 {D_TYPE data_d[];} d10;
+layout (binding = 11) buffer D11 {D_TYPE data_d[];} d11;
+layout (binding = 0, std430)  buffer PartialBuf0 {float partial_sums[];} partials0;
+layout (binding = 1, std430)  buffer PartialBuf1 {float partial_sums[];} partials1;
+layout (binding = 2, std430)  buffer PartialBuf2 {float partial_sums[];} partials2;
+layout (binding = 3, std430)  buffer PartialBuf3 {float partial_sums[];} partials3;
+layout (binding = 4, std430)  buffer PartialBuf4 {float partial_sums[];} partials4;
+layout (binding = 5, std430)  buffer PartialBuf5 {float partial_sums[];} partials5;
+layout (binding = 6, std430)  buffer PartialBuf6 {float partial_sums[];} partials6;
+layout (binding = 7, std430)  buffer PartialBuf7 {float partial_sums[];} partials7;
+layout (binding = 8, std430)  buffer PartialBuf8 {float partial_sums[];} partials8;
+layout (binding = 9, std430)  buffer PartialBuf9 {float partial_sums[];} partials9;
+layout (binding = 10, std430) buffer PartialBuf10 {float partial_sums[];} partials10;
+layout (binding = 11, std430) buffer PartialBuf11 {float partial_sums[];} partials11;
 
 layout(constant_id = 0) const uint num_srcs = 2;
 
+FLOAT_TYPE load_a(uint b, uint i) {
+    switch (b) {
+    case 0:  return FLOAT_TYPE(a0.data_a[i]);
+    case 1:  return FLOAT_TYPE(a1.data_a[i]);
+    case 2:  return FLOAT_TYPE(a2.data_a[i]);
+    case 3:  return FLOAT_TYPE(a3.data_a[i]);
+    case 4:  return FLOAT_TYPE(a4.data_a[i]);
+    case 5:  return FLOAT_TYPE(a5.data_a[i]);
+    case 6:  return FLOAT_TYPE(a6.data_a[i]);
+    case 7:  return FLOAT_TYPE(a7.data_a[i]);
+    case 8:  return FLOAT_TYPE(a8.data_a[i]);
+    case 9:  return FLOAT_TYPE(a9.data_a[i]);
+    case 10: return FLOAT_TYPE(a10.data_a[i]);
+    case 11: return FLOAT_TYPE(a11.data_a[i]);
+    default: return FLOAT_TYPE(0);
+    }
+}
+
+void store_d(uint b, uint i, FLOAT_TYPE v) {
+    switch (b) {
+    case 0:  d0.data_d[i] = D_TYPE(v); break;
+    case 1:  d1.data_d[i] = D_TYPE(v); break;
+    case 2:  d2.data_d[i] = D_TYPE(v); break;
+    case 3:  d3.data_d[i] = D_TYPE(v); break;
+    case 4:  d4.data_d[i] = D_TYPE(v); break;
+    case 5:  d5.data_d[i] = D_TYPE(v); break;
+    case 6:  d6.data_d[i] = D_TYPE(v); break;
+    case 7:  d7.data_d[i] = D_TYPE(v); break;
+    case 8:  d8.data_d[i] = D_TYPE(v); break;
+    case 9:  d9.data_d[i] = D_TYPE(v); break;
+    case 10: d10.data_d[i] = D_TYPE(v); break;
+    case 11: d11.data_d[i] = D_TYPE(v); break;
+    default: break;
+    }
+}
+
+void store_partial(uint b, uint i, float v) {
+    switch (b) {
+    case 0:  partials0.partial_sums[i] = v; break;
+    case 1:  partials1.partial_sums[i] = v; break;
+    case 2:  partials2.partial_sums[i] = v; break;
+    case 3:  partials3.partial_sums[i] = v; break;
+    case 4:  partials4.partial_sums[i] = v; break;
+    case 5:  partials5.partial_sums[i] = v; break;
+    case 6:  partials6.partial_sums[i] = v; break;
+    case 7:  partials7.partial_sums[i] = v; break;
+    case 8:  partials8.partial_sums[i] = v; break;
+    case 9:  partials9.partial_sums[i] = v; break;
+    case 10: partials10.partial_sums[i] = v; break;
+    case 11: partials11.partial_sums[i] = v; break;
+    default: break;
+    }
+}
+
 uint src_idx(uint s, uint i00, uint i01, uint i02, uint i03) {
     return i03*p.nb[s][3] + i02*p.nb[s][2] + i01*p.nb[s][1] + i00*p.nb[s][0];
 }
@@ -78,10 +162,10 @@ void main() {
 
         FLOAT_TYPE sum = FLOAT_TYPE(0);
         [[unroll]] for (uint s = 0; s < num_srcs; ++s) {
-            sum += FLOAT_TYPE(a[s].data_a[src_idx(s, i00, i01, i02, i03)]);
+            sum += load_a(s, src_idx(s, i00, i01, i02, i03));
         }
         sum_sq += sum*sum;
-        d[num_srcs].data_d[dst_idx(i00, i01, i02, i03)] = D_TYPE(sum);
+        store_d(num_srcs, dst_idx(i00, i01, i02, i03), sum);
 
         idx += num_threads;
     }
@@ -104,7 +188,7 @@ void main() {
         }
 
         if (gl_SubgroupID == 0 && gl_SubgroupInvocationID == 0) {
-            partials[num_srcs + 1].partial_sums[orig_idx / (num_iter * num_threads)] = sum_sq;
+            store_partial(num_srcs + 1, orig_idx / (num_iter * num_threads), sum_sq);
         }
     }
 #endif

From 74fef4129fa58f6277c243777a4894371479dbad Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Sat, 1 Nov 2025 08:55:25 +0100
Subject: [PATCH 23/30] codeowners : update after refactor (#16905)

---
 CODEOWNERS | 1 +
 1 file changed, 1 insertion(+)

diff --git a/CODEOWNERS b/CODEOWNERS
index bacc86cbbd..908d13a35b 100644
--- a/CODEOWNERS
+++ b/CODEOWNERS
@@ -89,6 +89,7 @@
 /src/llama-model-loader.*               @slaren
 /src/llama-model.*                      @CISC
 /src/llama-vocab.*                      @CISC
+/src/models/                            @CISC
 /tests/                                 @ggerganov
 /tests/test-backend-ops.cpp             @slaren
 /tests/test-thread-safety.cpp           @slaren

From 961660b8c395afb8903f61ace69396a158ea0cb4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com>
Date: Sat, 1 Nov 2025 11:01:42 +0100
Subject: [PATCH 24/30] common : allow --system-prompt-file for diffusion-cli
 (#16903)

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

diff --git a/common/arg.cpp b/common/arg.cpp
index e8941cda41..d8f9bbd243 100644
--- a/common/arg.cpp
+++ b/common/arg.cpp
@@ -2030,7 +2030,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
                 params.system_prompt.pop_back();
             }
         }
-    ).set_examples({LLAMA_EXAMPLE_MAIN}));
+    ).set_examples({LLAMA_EXAMPLE_MAIN, LLAMA_EXAMPLE_DIFFUSION}));
     add_opt(common_arg(
         {"--in-file"}, "FNAME",
         "an input file (repeat to specify multiple files)",

From 1ae74882f8f6755e44dff8f23f3abdc5b53ab7c1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Jarom=C3=ADr=20Hrad=C3=ADlek?= <jhradilek@gmail.com>
Date: Sat, 1 Nov 2025 15:02:57 +0100
Subject: [PATCH 25/30] webui: recognize AsciiDoc files as valid text files
 (#16850)

* webui: recognize AsciiDoc files as valid text files

* webui: add an updated static webui build

* webui: add the updated dependency list

* webui: re-add an updated static webui build

This also reverts commit 742dbb837939c176a813868c268d28ebd3fafb7c.
---
 tools/server/public/index.html.gz             | Bin 859993 -> 860046 bytes
 .../src/lib/constants/supported-file-types.ts |   4 ++++
 tools/server/webui/src/lib/enums/files.ts     |   3 +++
 3 files changed, 7 insertions(+)

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index 026b53b28632fb82031333a4ef411444cdabddb2..29579e08c251298048f842eafac774166f23d516 100644
GIT binary patch
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diff --git a/tools/server/webui/src/lib/constants/supported-file-types.ts b/tools/server/webui/src/lib/constants/supported-file-types.ts
index f6c5d2dc18..1258c3a059 100644
--- a/tools/server/webui/src/lib/constants/supported-file-types.ts
+++ b/tools/server/webui/src/lib/constants/supported-file-types.ts
@@ -69,6 +69,10 @@ export const TEXT_FILE_TYPES = {
 		extensions: [FileExtensionText.MD],
 		mimeTypes: [MimeTypeText.MARKDOWN]
 	},
+	[FileTypeText.ASCIIDOC]: {
+		extensions: [FileExtensionText.ADOC],
+		mimeTypes: [MimeTypeText.ASCIIDOC]
+	},
 	[FileTypeText.JAVASCRIPT]: {
 		extensions: [FileExtensionText.JS],
 		mimeTypes: [MimeTypeText.JAVASCRIPT, MimeTypeText.JAVASCRIPT_APP]
diff --git a/tools/server/webui/src/lib/enums/files.ts b/tools/server/webui/src/lib/enums/files.ts
index 19b79d32de..3f725da227 100644
--- a/tools/server/webui/src/lib/enums/files.ts
+++ b/tools/server/webui/src/lib/enums/files.ts
@@ -33,6 +33,7 @@ export enum FileTypePdf {
 export enum FileTypeText {
 	PLAIN_TEXT = 'plainText',
 	MARKDOWN = 'markdown',
+	ASCIIDOC = 'asciidoc',
 	JAVASCRIPT = 'javascript',
 	TYPESCRIPT = 'typescript',
 	JSX = 'jsx',
@@ -86,6 +87,7 @@ export enum FileExtensionPdf {
 export enum FileExtensionText {
 	TXT = '.txt',
 	MD = '.md',
+	ADOC = '.adoc',
 	JS = '.js',
 	TS = '.ts',
 	JSX = '.jsx',
@@ -147,6 +149,7 @@ export enum MimeTypeImage {
 export enum MimeTypeText {
 	PLAIN = 'text/plain',
 	MARKDOWN = 'text/markdown',
+	ASCIIDOC = 'text/asciidoc',
 	JAVASCRIPT = 'text/javascript',
 	JAVASCRIPT_APP = 'application/javascript',
 	TYPESCRIPT = 'text/typescript',

From d8b860a219c2415faac8cc0e50b48b4aa11e3b64 Mon Sep 17 00:00:00 2001
From: Aleksander Grygier <aleksander.grygier@gmail.com>
Date: Sat, 1 Nov 2025 15:35:57 +0100
Subject: [PATCH 26/30] Add a setting to display message generation statistics
 (#16901)

* feat: Add setting to display message generation statistics

* chore: build static webui output
---
 tools/server/public/index.html.gz             | Bin 860046 -> 861835 bytes
 .../ChatMessages/ChatMessageAssistant.svelte  |  73 ++++++++++++++----
 .../ChatSettings/ChatSettingsDialog.svelte    |   5 ++
 .../src/lib/constants/settings-config.ts      |   3 +
 4 files changed, 67 insertions(+), 14 deletions(-)

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index 29579e08c251298048f842eafac774166f23d516..a81bae04d1983d992a4b46da93153d1cfa63c704 100644
GIT binary patch
delta 856927
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diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageAssistant.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageAssistant.svelte
index e878e7bf8a..41774c6f87 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageAssistant.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageAssistant.svelte
@@ -3,7 +3,16 @@
 	import { useProcessingState } from '$lib/hooks/use-processing-state.svelte';
 	import { isLoading } from '$lib/stores/chat.svelte';
 	import { fade } from 'svelte/transition';
-	import { Check, Copy, Package, X } from '@lucide/svelte';
+	import {
+		Check,
+		Copy,
+		Package,
+		X,
+		Gauge,
+		Clock,
+		WholeWord,
+		ChartNoAxesColumn
+	} from '@lucide/svelte';
 	import { Button } from '$lib/components/ui/button';
 	import { Checkbox } from '$lib/components/ui/checkbox';
 	import { INPUT_CLASSES } from '$lib/constants/input-classes';
@@ -160,22 +169,58 @@
 		</div>
 	{/if}
 
-	{#if displayedModel()}
-		<span class="mt-6 mb-4 inline-flex items-center gap-1 text-xs text-muted-foreground">
-			<Package class="h-3.5 w-3.5" />
+	<div class="info my-6 grid gap-4">
+		{#if displayedModel()}
+			<span class="inline-flex items-center gap-2 text-xs text-muted-foreground">
+				<span class="inline-flex items-center gap-1">
+					<Package class="h-3.5 w-3.5" />
 
-			<span>Model used:</span>
+					<span>Model used:</span>
+				</span>
 
-			<button
-				class="inline-flex cursor-pointer items-center gap-1 rounded-sm bg-muted-foreground/15 px-1.5 py-0.75"
-				onclick={handleCopyModel}
-			>
-				{displayedModel()}
+				<button
+					class="inline-flex cursor-pointer items-center gap-1 rounded-sm bg-muted-foreground/15 px-1.5 py-0.75"
+					onclick={handleCopyModel}
+				>
+					{displayedModel()}
 
-				<Copy class="ml-1 h-3 w-3 " />
-			</button>
-		</span>
-	{/if}
+					<Copy class="ml-1 h-3 w-3 " />
+				</button>
+			</span>
+		{/if}
+
+		{#if currentConfig.showMessageStats && message.timings && message.timings.predicted_n && message.timings.predicted_ms}
+			{@const tokensPerSecond = (message.timings.predicted_n / message.timings.predicted_ms) * 1000}
+			<span class="inline-flex items-center gap-2 text-xs text-muted-foreground">
+				<span class="inline-flex items-center gap-1">
+					<ChartNoAxesColumn class="h-3.5 w-3.5" />
+
+					<span>Statistics:</span>
+				</span>
+
+				<div class="inline-flex flex-wrap items-center gap-2 text-xs text-muted-foreground">
+					<span
+						class="inline-flex items-center gap-1 rounded-sm bg-muted-foreground/15 px-1.5 py-0.75"
+					>
+						<Gauge class="h-3 w-3" />
+						{tokensPerSecond.toFixed(2)} tokens/s
+					</span>
+					<span
+						class="inline-flex items-center gap-1 rounded-sm bg-muted-foreground/15 px-1.5 py-0.75"
+					>
+						<WholeWord class="h-3 w-3" />
+						{message.timings.predicted_n} tokens
+					</span>
+					<span
+						class="inline-flex items-center gap-1 rounded-sm bg-muted-foreground/15 px-1.5 py-0.75"
+					>
+						<Clock class="h-3 w-3" />
+						{(message.timings.predicted_ms / 1000).toFixed(2)}s
+					</span>
+				</div>
+			</span>
+		{/if}
+	</div>
 
 	{#if message.timestamp && !isEditing}
 		<ChatMessageActions
diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsDialog.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsDialog.svelte
index 20e4d3b332..e4672b787e 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsDialog.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsDialog.svelte
@@ -52,6 +52,11 @@
 						{ value: 'dark', label: 'Dark', icon: Moon }
 					]
 				},
+				{
+					key: 'showMessageStats',
+					label: 'Show message generation statistics',
+					type: 'checkbox'
+				},
 				{
 					key: 'showTokensPerSecond',
 					label: 'Show tokens per second',
diff --git a/tools/server/webui/src/lib/constants/settings-config.ts b/tools/server/webui/src/lib/constants/settings-config.ts
index 512dcc9699..c25f380846 100644
--- a/tools/server/webui/src/lib/constants/settings-config.ts
+++ b/tools/server/webui/src/lib/constants/settings-config.ts
@@ -8,6 +8,7 @@ export const SETTING_CONFIG_DEFAULT: Record<string, string | number | boolean> =
 	showThoughtInProgress: false,
 	disableReasoningFormat: false,
 	keepStatsVisible: false,
+	showMessageStats: true,
 	askForTitleConfirmation: false,
 	pasteLongTextToFileLen: 2500,
 	pdfAsImage: false,
@@ -82,6 +83,8 @@ export const SETTING_CONFIG_INFO: Record<string, string> = {
 	disableReasoningFormat:
 		'Show raw LLM output without backend parsing and frontend Markdown rendering to inspect streaming across different models.',
 	keepStatsVisible: 'Keep processing statistics visible after generation finishes.',
+	showMessageStats:
+		'Display generation statistics (tokens/second, token count, duration) below each assistant message.',
 	askForTitleConfirmation:
 		'Ask for confirmation before automatically changing conversation title when editing the first message.',
 	pdfAsImage: 'Parse PDF as image instead of text (requires vision-capable model).',

From cf659bbb8ef9eb048e5153a27cf787fd83c05560 Mon Sep 17 00:00:00 2001
From: Xuan-Son Nguyen <son@huggingface.co>
Date: Sat, 1 Nov 2025 15:51:36 +0100
Subject: [PATCH 27/30] mtmd: refactor preprocessing + support max/min pixels
 (#16878)

* mtmd: refactor preprocessing + support max/min pixels

* fix mlp type

* implement mix/max pixels

* improve hparams

* better image preproc for qwen

* fix

* fix out of bound composite

* fix (2)

* fix token calculation

* get_merge_kernel_size()

* fix llama4 and lfm2

* gonna fix them all

* use simple resize for qwen

* qwen: increase min tokens

* no resize if dst size == src size

* restore to initial min/max tokens value for qwen
---
 tools/mtmd/clip-impl.h |   2 +-
 tools/mtmd/clip.cpp    | 762 +++++++++++++++++++++++------------------
 2 files changed, 432 insertions(+), 332 deletions(-)

diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h
index 311a4c9086..c7e9498349 100644
--- a/tools/mtmd/clip-impl.h
+++ b/tools/mtmd/clip-impl.h
@@ -154,8 +154,8 @@ enum projector_type {
     PROJECTOR_TYPE_LFM2,
     PROJECTOR_TYPE_KIMIVL,
     PROJECTOR_TYPE_LIGHTONOCR,
-    PROJECTOR_TYPE_UNKNOWN,
     PROJECTOR_TYPE_COGVLM,
+    PROJECTOR_TYPE_UNKNOWN,
 };
 
 static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp
index b312fda637..dcfdb49600 100644
--- a/tools/mtmd/clip.cpp
+++ b/tools/mtmd/clip.cpp
@@ -171,8 +171,10 @@ struct clip_hparams {
     int32_t n_head;
     int32_t n_layer;
     // idefics3
-    int32_t preproc_image_size = 0; // aka max_dimension
-    int32_t proj_scale_factor = 0;
+    int32_t image_longest_edge = 0;
+    int32_t image_min_pixels = 0;
+    int32_t image_max_pixels = 0;
+    int32_t n_merge = 0; // number of patch merges **per-side**
 
     float image_mean[3];
     float image_std[3];
@@ -194,7 +196,6 @@ struct clip_hparams {
     std::unordered_set<int32_t> vision_feature_layer;
     int32_t attn_window_size = 0;
     int32_t n_wa_pattern = 0;
-    int32_t spatial_merge_size = 0;
 
     // audio
     int32_t n_mel_bins = 0; // whisper preprocessor
@@ -204,6 +205,21 @@ struct clip_hparams {
     bool has_llava_projector = false;
     int minicpmv_version = 0;
     int32_t minicpmv_query_num = 0;         // MiniCPM-V query number
+
+    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 = n_tokens_min * patch_area;
+        image_max_pixels = 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;
+    }
 };
 
 struct clip_layer {
@@ -532,7 +548,7 @@ struct clip_graph {
             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.proj_scale_factor;
+            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);
@@ -554,13 +570,13 @@ struct clip_graph {
         } 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.proj_scale_factor;
+            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.proj_scale_factor;
+            const int scale_factor = model.hparams.n_merge;
             cur = build_patch_merge_permute(cur, scale_factor);
 
             // projection
@@ -584,7 +600,7 @@ struct clip_graph {
     }
 
     ggml_cgraph * build_pixtral() {
-        const int n_merge = hparams.spatial_merge_size;
+        const int n_merge = hparams.n_merge;
 
         // 2D input positions
         ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches);
@@ -610,7 +626,7 @@ struct clip_graph {
         // 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.spatial_merge_size > 0);
+            GGML_ASSERT(hparams.n_merge > 0);
 
             cur = ggml_mul(ctx0, ggml_rms_norm(ctx0, cur, eps), model.mm_input_norm_w);
 
@@ -926,7 +942,7 @@ struct clip_graph {
 
         // 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.spatial_merge_size > 0 ? hparams.spatial_merge_size * hparams.spatial_merge_size : 4; // default 2x2=4 for qwen3vl
+        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++) {
@@ -1149,7 +1165,7 @@ struct clip_graph {
 
         // pixel shuffle
         {
-            const int scale_factor = model.hparams.proj_scale_factor;
+            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;
@@ -1239,7 +1255,7 @@ struct clip_graph {
         // based on Llama4VisionPixelShuffleMLP
         // https://github.com/huggingface/transformers/blob/2932f318a20d9e54cc7aea052e040164d85de7d6/src/transformers/models/llama4/modeling_llama4.py#L1151
         {
-            const int scale_factor = model.hparams.proj_scale_factor;
+            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
@@ -1311,7 +1327,7 @@ struct clip_graph {
 
         {
             // patch_merger
-            const int scale_factor = model.hparams.proj_scale_factor;
+            const int scale_factor = model.hparams.n_merge;
             cur = build_patch_merge_permute(cur, scale_factor);
 
             // projection norm
@@ -2577,7 +2593,6 @@ struct clip_model_loader {
 
             if (is_vision) {
                 get_u32(KEY_IMAGE_SIZE, hparams.image_size);
-                get_u32(KEY_PREPROC_IMAGE_SIZE, hparams.preproc_image_size, false);
                 get_u32(KEY_PATCH_SIZE, hparams.patch_size);
                 get_u32(KEY_IMAGE_CROP_RESOLUTION, hparams.image_crop_resolution, false);
                 get_i32(KEY_MINICPMV_VERSION, hparams.minicpmv_version, false); // legacy
@@ -2686,65 +2701,68 @@ struct clip_model_loader {
                             hparams.minicpmv_version = 2; // default to 2 if not set
                         }
                     } break;
-                case PROJECTOR_TYPE_IDEFICS3:
-                case PROJECTOR_TYPE_LFM2:
                 case PROJECTOR_TYPE_INTERNVL:
                     {
-                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.proj_scale_factor, false);
+                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
+                    } break;
+                case PROJECTOR_TYPE_IDEFICS3:
+                    {
+                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
+                        get_u32(KEY_PREPROC_IMAGE_SIZE, hparams.image_longest_edge, false);
+                    } break;
+                case PROJECTOR_TYPE_LFM2:
+                    {
+                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
+                        // ref: https://huggingface.co/LiquidAI/LFM2-VL-3B/blob/main/preprocessor_config.json
+                        hparams.set_limit_image_tokens(64, 256);
                     } break;
                 case PROJECTOR_TYPE_PIXTRAL:
                 case PROJECTOR_TYPE_LIGHTONOCR:
                     {
+                        // ref: https://huggingface.co/mistral-community/pixtral-12b/blob/main/preprocessor_config.json
+                        // TODO: verify the image_min_tokens
                         hparams.rope_theta = 10000.0f;
-                        hparams.warmup_image_size = hparams.patch_size * 8;
-                        // Mistral Small 2506 needs 1024x1024 image size cap to prevent OOM
-                        // ref: https://github.com/ggml-org/llama.cpp/issues/14310
-                        hparams.image_size = 1024;
-                        get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.spatial_merge_size, false);
+                        get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false);
+                        hparams.set_limit_image_tokens(8, 1024);
+                        hparams.set_warmup_n_tokens(256); // avoid OOM on warmup
                     } break;
                 case PROJECTOR_TYPE_KIMIVL:
                     {
                         hparams.rope_theta = 10000.0f;
-                        hparams.warmup_image_size = hparams.patch_size * 8;
-                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.proj_scale_factor, false);
+                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
+                        // TODO: check kimivl preprocessor for exact values
+                        hparams.set_limit_image_tokens(8, 1024);
+                        hparams.set_warmup_n_tokens(256); // avoid OOM on warmup
                     } break;
                 case PROJECTOR_TYPE_GEMMA3:
                     {
                         // default value (used by all model sizes in gemma 3 family)
                         // number of patches for each **side** is reduced by a factor of 4
-                        hparams.proj_scale_factor = 4;
+                        hparams.n_merge = 4;
                         // test model (tinygemma3) has a different value, we optionally read it
-                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.proj_scale_factor, false);
+                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
                     } break;
                 case PROJECTOR_TYPE_QWEN2VL:
-                    {
-                        // max image size = sqrt(max_pixels) = 3584
-                        // ref: https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct/blob/main/preprocessor_config.json
-                        // however, the model use unreasonable memory past 1024 size, we force it to 1024 otherwise it's unusable
-                        // ref: https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct/discussions/10
-                        hparams.image_size = 1024;
-                        hparams.warmup_image_size = hparams.patch_size * 8;
-                    } break;
                 case PROJECTOR_TYPE_QWEN25VL:
-                    {
-                        // max image size = sqrt(max_pixels)
-                        // https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct/blob/main/preprocessor_config.json
-                        // however, the model use unreasonable memory past 1024 size, we force it to 1024 otherwise it's unusable
-                        // ref: https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct/discussions/10
-                        hparams.image_size = 1024;
-                        hparams.warmup_image_size = hparams.patch_size * 8;
-                        get_u32(KEY_WIN_ATTN_PATTERN, hparams.n_wa_pattern);
-                    } break;
                 case PROJECTOR_TYPE_QWEN3VL:
                     {
-                        hparams.image_size = 1024; // still need this?
-                        hparams.warmup_image_size = hparams.patch_size * 8;
-                        get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.spatial_merge_size, false);
+                        hparams.n_merge = 2; // default value for Qwen 2 and 2.5
+                        get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false);
+                        get_u32(KEY_WIN_ATTN_PATTERN, hparams.n_wa_pattern, model.proj_type == PROJECTOR_TYPE_QWEN25VL); // only 2.5 requires it
+                        // ref: https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct/blob/main/preprocessor_config.json
+                        // the actual max limit is 12845056/14/14/2/2/4 = 4096 tokens
+                        // but we set a lower value to avoid OOM
+                        // TODO: make it configurable by user
+                        // TODO (2): bbox coordinates become inaccurate with small number of tokens,
+                        //           therefore we need to increase the min_tokens
+                        //           see: https://github.com/ggml-org/llama.cpp/issues/16842#issuecomment-3475144858
+                        hparams.set_limit_image_tokens(8, 2048);
+                        hparams.set_warmup_n_tokens(256); // avoid OOM on warmup
                     } break;
                 case PROJECTOR_TYPE_LLAMA4:
                     {
                         hparams.rope_theta = 10000.0f;
-                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.proj_scale_factor);
+                        get_u32(KEY_PROJ_SCALE_FACTOR, hparams.n_merge, false);
                         set_llava_uhd_res_candidates(model, 3);
                     } break;
                 case PROJECTOR_TYPE_ULTRAVOX:
@@ -2777,10 +2795,13 @@ struct clip_model_loader {
                 LOG_INF("%s: patch_size:         %d\n", __func__, hparams.patch_size);
                 LOG_INF("%s: has_llava_proj:     %d\n", __func__, hparams.has_llava_projector);
                 LOG_INF("%s: minicpmv_version:   %d\n", __func__, hparams.minicpmv_version);
-                LOG_INF("%s: proj_scale_factor:  %d\n", __func__, hparams.proj_scale_factor);
+                LOG_INF("%s: n_merge:            %d\n", __func__, hparams.n_merge);
                 LOG_INF("%s: n_wa_pattern:       %d\n", __func__, hparams.n_wa_pattern);
-                if (hparams.spatial_merge_size > 0) {
-                    LOG_INF("%s: spatial_merge_size: %d\n", __func__, hparams.spatial_merge_size);
+                if (hparams.image_min_pixels > 0) {
+                    LOG_INF("%s: image_min_pixels:   %d\n", __func__, hparams.image_min_pixels);
+                }
+                if (hparams.image_max_pixels > 0) {
+                    LOG_INF("%s: image_max_pixels:   %d\n", __func__, hparams.image_max_pixels);
                 }
             } else if (is_audio) {
                 LOG_INF("\n--- audio hparams ---\n");
@@ -3181,9 +3202,11 @@ struct clip_model_loader {
         if (ctx_clip.model.modality == CLIP_MODALITY_VISION) {
             img->nx = hparams.warmup_image_size;
             img->ny = hparams.warmup_image_size;
+            LOG_INF("%s: warmup with image size = %d x %d\n", __func__, img->nx, img->ny);
         } else {
             img->nx = hparams.warmup_audio_size;
             img->ny = hparams.n_mel_bins;
+            LOG_INF("%s: warmup with audio size = %d\n", __func__, img->nx);
         }
         batch.entries.push_back(std::move(img));
 
@@ -3399,9 +3422,169 @@ static void normalize_image_u8_to_f32(const clip_image_u8 & src, clip_image_f32
 
 // set of tools to manupulate images
 // in the future, we can have HW acceleration by allowing this struct to access 3rd party lib like imagick or opencv
-struct image_manipulation {
+struct img_tool {
+    enum resize_algo {
+        RESIZE_ALGO_BILINEAR,
+        RESIZE_ALGO_BICUBIC,
+        // RESIZE_ALGO_LANCZOS, // TODO
+    };
+
+    static void resize(
+            const clip_image_u8 & src,
+            clip_image_u8 & dst,
+            const clip_image_size & target_resolution,
+            resize_algo algo,
+            bool add_padding = true, // TODO: define the behavior for add_padding = false
+            std::array<uint8_t, 3> pad_color = {0, 0, 0}) {
+        dst.nx = target_resolution.width;
+        dst.ny = target_resolution.height;
+        dst.buf.resize(3 * dst.nx * dst.ny);
+
+        if (dst.nx == src.nx && dst.ny == src.ny) {
+            // no resize needed, simple copy
+            dst.buf = src.buf;
+            return;
+        }
+
+        if (!add_padding) {
+            // direct resize
+            switch (algo) {
+                case RESIZE_ALGO_BILINEAR:
+                    resize_bilinear(src, dst, target_resolution.width, target_resolution.height);
+                    break;
+                case RESIZE_ALGO_BICUBIC:
+                    resize_bicubic(src, dst, target_resolution.width, target_resolution.height);
+                    break;
+                default:
+                    throw std::runtime_error("Unsupported resize algorithm");
+            }
+        } else {
+            // resize with padding
+            clip_image_u8 resized_image;
+            float scale_w = static_cast<float>(target_resolution.width) / src.nx;
+            float scale_h = static_cast<float>(target_resolution.height) / src.ny;
+            float scale = std::min(scale_w, scale_h);
+            int new_width  = std::min(static_cast<int>(std::ceil(src.nx * scale)), target_resolution.width);
+            int new_height = std::min(static_cast<int>(std::ceil(src.ny * scale)), target_resolution.height);
+
+            switch (algo) {
+                case RESIZE_ALGO_BILINEAR:
+                    resize_bilinear(src, resized_image, new_width, new_height);
+                    break;
+                case RESIZE_ALGO_BICUBIC:
+                    resize_bicubic(src, resized_image, new_width, new_height);
+                    break;
+                default:
+                    throw std::runtime_error("Unsupported resize algorithm");
+            }
+
+            // fill dst with pad_color
+            fill(dst, pad_color);
+
+            int offset_x = (target_resolution.width  - new_width)  / 2;
+            int offset_y = (target_resolution.height - new_height) / 2;
+
+            composite(dst, resized_image, offset_x, offset_y);
+        }
+    }
+
+    static void crop(const clip_image_u8 & image, clip_image_u8 & dst, int x, int y, int w, int h) {
+        dst.nx = w;
+        dst.ny = h;
+        dst.buf.resize(3 * w * h);
+
+        for (int i = 0; i < h; ++i) {
+            for (int j = 0; j < w; ++j) {
+                int src_idx = 3 * ((y + i)*image.nx + (x + j));
+                int dst_idx = 3 * (i*w + j);
+                dst.buf[dst_idx]     = image.buf[src_idx];
+                dst.buf[dst_idx + 1] = image.buf[src_idx + 1];
+                dst.buf[dst_idx + 2] = image.buf[src_idx + 2];
+            }
+        }
+    }
+
+    // calculate the size of the **resized** image, while preserving the aspect ratio
+    // the calculated size will be aligned to the nearest multiple of align_size
+    // if H or W size is larger than longest_edge, it will be resized to longest_edge
+    static clip_image_size calc_size_preserved_ratio(const clip_image_size & inp_size, const int align_size, const int longest_edge) {
+        GGML_ASSERT(align_size > 0);
+        if (inp_size.width <= 0 || inp_size.height <= 0 || longest_edge <= 0) {
+            return {0, 0};
+        }
+
+        float scale = std::min(static_cast<float>(longest_edge) / inp_size.width,
+                               static_cast<float>(longest_edge) / inp_size.height);
+
+        float target_width_f  = static_cast<float>(inp_size.width)  * scale;
+        float target_height_f = static_cast<float>(inp_size.height) * scale;
+
+        auto ceil_by_factor = [f = align_size](float x) { return static_cast<int>(std::ceil(x / static_cast<float>(f))) * f; };
+        int aligned_width  = ceil_by_factor(target_width_f);
+        int aligned_height = ceil_by_factor(target_height_f);
+
+        return {aligned_width, aligned_height};
+    }
+
+    // calculate the size of the **resized** image, while preserving the aspect ratio
+    // the calculated size will have min_pixels <= W*H <= max_pixels
+    // this is referred as "smart_resize" in transformers code
+    static clip_image_size calc_size_preserved_ratio(const clip_image_size & inp_size, const int align_size, const int min_pixels, const int max_pixels) {
+        GGML_ASSERT(align_size > 0);
+        const int width  = inp_size.width;
+        const int height = inp_size.height;
+
+        auto ceil_by_factor  = [f = align_size](float x) { return static_cast<int>(std::ceil(x / static_cast<float>(f))) * f; };
+        auto floor_by_factor = [f = align_size](float x) { return static_cast<int>(std::floor(x / static_cast<float>(f))) * f; };
+
+        // always align up first
+        int h_bar = std::max(align_size, ceil_by_factor(height));
+        int w_bar = std::max(align_size, ceil_by_factor(width));
+
+        if (h_bar * w_bar > max_pixels) {
+            const auto beta = std::sqrt(static_cast<float>(height * width) / max_pixels);
+            h_bar = std::max(align_size, floor_by_factor(height / beta));
+            w_bar = std::max(align_size, floor_by_factor(width  / beta));
+        } else if (h_bar * w_bar < min_pixels) {
+            const auto beta = std::sqrt(static_cast<float>(min_pixels) / (height * width));
+            h_bar = ceil_by_factor(height * beta);
+            w_bar = ceil_by_factor(width * beta);
+        }
+
+        return {w_bar, h_bar};
+    }
+
+    // draw src image into dst image at offset (offset_x, offset_y)
+    static void composite(clip_image_u8 & dst, const clip_image_u8 & src, int offset_x, int offset_y) {
+        for (int y = 0; y < src.ny; ++y) {
+            for (int x = 0; x < src.nx; ++x) {
+                int dx = x + offset_x;
+                int dy = y + offset_y;
+                // skip pixels that would be out of bounds in the destination
+                if (dx < 0 || dy < 0 || dx >= dst.nx || dy >= dst.ny) {
+                    continue;
+                }
+                size_t dst_idx = 3 * (static_cast<size_t>(dy) * dst.nx + static_cast<size_t>(dx));
+                size_t src_idx = 3 * (static_cast<size_t>(y) * src.nx + static_cast<size_t>(x));
+                dst.buf[dst_idx + 0] = src.buf[src_idx + 0];
+                dst.buf[dst_idx + 1] = src.buf[src_idx + 1];
+                dst.buf[dst_idx + 2] = src.buf[src_idx + 2];
+            }
+        }
+    }
+
+    // fill the image with a solid color
+    static void fill(clip_image_u8 & img, const std::array<uint8_t, 3> & color) {
+        for (size_t i = 0; i < img.buf.size(); i += 3) {
+            img.buf[i]     = color[0];
+            img.buf[i + 1] = color[1];
+            img.buf[i + 2] = color[2];
+        }
+    }
+
+private:
     // Bilinear resize function
-    static void bilinear_resize(const clip_image_u8& src, clip_image_u8& dst, int target_width, int target_height) {
+    static void resize_bilinear(const clip_image_u8 & src, clip_image_u8 & dst, int target_width, int target_height) {
         dst.nx = target_width;
         dst.ny = target_height;
         dst.buf.resize(3 * target_width * target_height);
@@ -3437,7 +3620,7 @@ struct image_manipulation {
 
     // Bicubic resize function
     // part of image will be cropped if the aspect ratio is different
-    static bool bicubic_resize(const clip_image_u8 & img, clip_image_u8 & dst, int target_width, int target_height) {
+    static bool resize_bicubic(const clip_image_u8 & img, clip_image_u8 & dst, int target_width, int target_height) {
         const int nx = img.nx;
         const int ny = img.ny;
 
@@ -3500,93 +3683,6 @@ struct image_manipulation {
         return true;
     }
 
-    // llava-1.6 type of resize_and_pad
-    // if the ratio is not 1:1, padding with pad_color will be applied
-    // pad_color is single channel, default is 0 (black)
-    static void resize_and_pad_image(const clip_image_u8 & image, clip_image_u8 & dst, const clip_image_size & target_resolution, std::array<uint8_t, 3> pad_color = {0, 0, 0}) {
-        int target_width  = target_resolution.width;
-        int target_height = target_resolution.height;
-
-        float scale_w = static_cast<float>(target_width) / image.nx;
-        float scale_h = static_cast<float>(target_height) / image.ny;
-
-        int new_width, new_height;
-
-        if (scale_w < scale_h) {
-            new_width  = target_width;
-            new_height = std::min(static_cast<int>(std::ceil(image.ny * scale_w)), target_height);
-        } else {
-            new_height = target_height;
-            new_width  = std::min(static_cast<int>(std::ceil(image.nx * scale_h)), target_width);
-        }
-
-        clip_image_u8 resized_image;
-        bicubic_resize(image, resized_image, new_width, new_height);
-
-        clip_image_u8 padded_image;
-        padded_image.nx = target_width;
-        padded_image.ny = target_height;
-        padded_image.buf.resize(3 * target_width * target_height);
-
-        // Fill the padded image with the fill color
-        for (size_t i = 0; i < padded_image.buf.size(); i += 3) {
-            padded_image.buf[i]     = pad_color[0];
-            padded_image.buf[i + 1] = pad_color[1];
-            padded_image.buf[i + 2] = pad_color[2];
-        }
-
-        // Calculate padding offsets
-        int pad_x = (target_width  - new_width)  / 2;
-        int pad_y = (target_height - new_height) / 2;
-
-        // Copy the resized image into the center of the padded buffer
-        for (int y = 0; y < new_height; ++y) {
-            for (int x = 0; x < new_width; ++x) {
-                for (int c = 0; c < 3; ++c) {
-                    padded_image.buf[3 * ((y + pad_y) * target_width + (x + pad_x)) + c] = resized_image.buf[3 * (y * new_width + x) + c];
-                }
-            }
-        }
-        dst = std::move(padded_image);
-    }
-
-    static void crop_image(const clip_image_u8 & image, clip_image_u8 & dst, int x, int y, int w, int h) {
-        dst.nx = w;
-        dst.ny = h;
-        dst.buf.resize(3 * w * h);
-
-        for (int i = 0; i < h; ++i) {
-            for (int j = 0; j < w; ++j) {
-                int src_idx = 3 * ((y + i)*image.nx + (x + j));
-                int dst_idx = 3 * (i*w + j);
-                dst.buf[dst_idx]     = image.buf[src_idx];
-                dst.buf[dst_idx + 1] = image.buf[src_idx + 1];
-                dst.buf[dst_idx + 2] = image.buf[src_idx + 2];
-            }
-        }
-    }
-
-    // calculate the size of the **resized** image, while preserving the aspect ratio
-    // the calculated size will be aligned to the nearest multiple of align_size
-    // if H or W size is larger than max_dimension, it will be resized to max_dimension
-    static clip_image_size calc_size_preserved_ratio(const clip_image_size & inp_size, const int align_size, const int max_dimension) {
-        if (inp_size.width <= 0 || inp_size.height <= 0 || align_size <= 0 || max_dimension <= 0) {
-            return {0, 0};
-        }
-
-        float scale = std::min(static_cast<float>(max_dimension) / inp_size.width,
-                               static_cast<float>(max_dimension) / inp_size.height);
-
-        float target_width_f  = static_cast<float>(inp_size.width)  * scale;
-        float target_height_f = static_cast<float>(inp_size.height) * scale;
-
-        int aligned_width  = CLIP_ALIGN((int)target_width_f,  align_size);
-        int aligned_height = CLIP_ALIGN((int)target_height_f, align_size);
-
-        return {aligned_width, aligned_height};
-    }
-
-private:
     static inline int clip(int x, int lower, int upper) {
         return std::max(lower, std::min(x, upper));
     }
@@ -3735,10 +3831,11 @@ 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());
-        image_manipulation::resize_and_pad_image(*img, *resized_img, inst.overview_size);
+        img_tool::resize(*img, *resized_img, inst.overview_size, interpolation);
         output.push_back(std::move(resized_img));
         if (inst.slices.empty()) {
             // no slices, just return the resized image
@@ -3748,9 +3845,11 @@ struct llava_uhd {
         // resize to refined size
         clip_image_u8_ptr refined_img(clip_image_u8_init());
         if (inst.padding_refined) {
-            image_manipulation::resize_and_pad_image(*img, *refined_img, inst.refined_size);
+            img_tool::resize(*img, *refined_img, inst.refined_size, interpolation);
         } else {
-            image_manipulation::bilinear_resize(*img, *refined_img, inst.refined_size.width, inst.refined_size.height);
+            // 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);
         }
 
         // create slices
@@ -3761,7 +3860,7 @@ struct llava_uhd {
             int h = slice.size.height;
 
             clip_image_u8_ptr img_slice(clip_image_u8_init());
-            image_manipulation::crop_image(*refined_img, *img_slice, x, y, w, h);
+            img_tool::crop(*refined_img, *img_slice, x, y, w, h);
             output.push_back(std::move(img_slice));
         }
 
@@ -3896,208 +3995,211 @@ private:
 // res_imgs memory is being allocated here, previous allocations will be freed if found
 bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, struct clip_image_f32_batch * res_imgs) {
     clip_image_size original_size{img->nx, img->ny};
-    bool pad_to_square = true;
     auto & params = ctx->model.hparams;
-    // The model config actually contains all we need to decide on how to preprocess, here we automatically switch to the new llava-1.6 preprocessing
-    if (params.mm_patch_merge_type == PATCH_MERGE_SPATIAL_UNPAD) {
-        pad_to_square = false;
-    }
 
-    if (clip_is_minicpmv(ctx)) {
-        auto const inst = llava_uhd::get_slice_instructions(ctx, original_size);
-        std::vector<clip_image_u8_ptr> imgs = llava_uhd::slice_image(img, inst);
+    switch (ctx->proj_type()) {
+        case PROJECTOR_TYPE_MINICPMV:
+            {
+                auto const inst = llava_uhd::get_slice_instructions(ctx, original_size);
+                std::vector<clip_image_u8_ptr> imgs = llava_uhd::slice_image(img, inst);
 
-        for (size_t i = 0; i < imgs.size(); ++i) {
-            // clip_image_save_to_bmp(*imgs[i], "slice_" + std::to_string(i) + ".bmp");
-            clip_image_f32_ptr res(clip_image_f32_init());
-            normalize_image_u8_to_f32(*imgs[i], *res, params.image_mean, params.image_std);
-            res_imgs->entries.push_back(std::move(res));
-        }
+                for (size_t i = 0; i < imgs.size(); ++i) {
+                    // clip_image_save_to_bmp(*imgs[i], "slice_" + std::to_string(i) + ".bmp");
+                    clip_image_f32_ptr res(clip_image_f32_init());
+                    normalize_image_u8_to_f32(*imgs[i], *res, params.image_mean, params.image_std);
+                    res_imgs->entries.push_back(std::move(res));
+                }
 
-        res_imgs->grid_x = inst.grid_size.width;
-        res_imgs->grid_y = inst.grid_size.height;
-        return true;
+                res_imgs->grid_x = inst.grid_size.width;
+                res_imgs->grid_y = inst.grid_size.height;
+            } break;
 
-    } else if (ctx->proj_type() == PROJECTOR_TYPE_QWEN2VL || ctx->proj_type() == PROJECTOR_TYPE_QWEN25VL || ctx->proj_type() == PROJECTOR_TYPE_QWEN3VL) {
-        clip_image_u8 resized;
-        auto patch_size = params.patch_size * 2;
-        auto new_size = image_manipulation::calc_size_preserved_ratio(original_size, patch_size, params.image_size);
-        image_manipulation::bicubic_resize(*img, resized, new_size.width, new_size.height);
+        case PROJECTOR_TYPE_QWEN2VL:
+        case PROJECTOR_TYPE_QWEN25VL:
+        case PROJECTOR_TYPE_QWEN3VL:
+            {
+                // step 1: make a blank canvas which aligns to the grid
+                clip_image_u8 resized;
+                const clip_image_size new_size = img_tool::calc_size_preserved_ratio(
+                    original_size,
+                    params.patch_size * 2,
+                    params.image_min_pixels,
+                    params.image_max_pixels);
+                img_tool::resize(*img, resized, new_size, img_tool::RESIZE_ALGO_BILINEAR, false);
+                // clip_image_save_to_bmp(resized, "preproc.bmp");
+                clip_image_f32_ptr img_f32(clip_image_f32_init());
+                // clip_image_f32_ptr res(clip_image_f32_init());
+                normalize_image_u8_to_f32(resized, *img_f32, params.image_mean, params.image_std);
+                // res_imgs->data[0] = *res;
+                res_imgs->entries.push_back(std::move(img_f32));
+            } break;
 
-        clip_image_f32_ptr img_f32(clip_image_f32_init());
-        // clip_image_f32_ptr res(clip_image_f32_init());
-        normalize_image_u8_to_f32(resized, *img_f32, params.image_mean, params.image_std);
-        // res_imgs->data[0] = *res;
-        res_imgs->entries.push_back(std::move(img_f32));
-        return true;
-    } else if (ctx->proj_type() == PROJECTOR_TYPE_IDEFICS3) {
-        // The refined size has two steps:
-        // 1. Resize w/ aspect-ratio preserving such that the longer side is
-        //      the preprocessor longest size
-        // 2. Resize w/out preserving aspect ratio such that both sides are
-        //      multiples of image_size (always rounding up)
-        //
-        // CITE: https://github.com/huggingface/transformers/blob/main/src/transformers/models/idefics3/image_processing_idefics3.py#L737
-        const clip_image_size refined_size = image_manipulation::calc_size_preserved_ratio(
-            original_size, params.image_size, params.preproc_image_size);
-        // LOG_INF("%s: original size: %d x %d, refined size: %d x %d\n",
-        //         __func__, original_size.width, original_size.height,
-        //         refined_size.width, refined_size.height);
+        case PROJECTOR_TYPE_IDEFICS3:
+            {
+                // The refined size has two steps:
+                // 1. Resize w/ aspect-ratio preserving such that the longer side is
+                //      the preprocessor longest size
+                // 2. Resize w/out preserving aspect ratio such that both sides are
+                //      multiples of image_size (always rounding up)
+                //
+                // CITE: https://github.com/huggingface/transformers/blob/main/src/transformers/models/idefics3/image_processing_idefics3.py#L737
+                const clip_image_size refined_size = img_tool::calc_size_preserved_ratio(
+                    original_size, params.image_size, params.image_longest_edge);
+                // LOG_INF("%s: original size: %d x %d, refined size: %d x %d\n",
+                //         __func__, original_size.width, original_size.height,
+                //         refined_size.width, refined_size.height);
 
-        llava_uhd::slice_instructions instructions;
-        instructions.overview_size = clip_image_size{params.image_size, params.image_size};
-        instructions.refined_size = refined_size;
-        instructions.grid_size = clip_image_size{
-            static_cast<int>(std::ceil(static_cast<float>(refined_size.width) / params.image_size)),
-            static_cast<int>(std::ceil(static_cast<float>(refined_size.height) / params.image_size)),
-        };
-        for (int y = 0; y < refined_size.height; y += params.image_size) {
-            for (int x = 0; x < refined_size.width; x += params.image_size) {
-                // LOG_INF("%s: adding slice at x=%d, y=%d\n", __func__, x, y);
-                instructions.slices.push_back(llava_uhd::slice_coordinates{
-                    /* x    */x,
-                    /* y    */y,
-                    /* size */clip_image_size{
-                        std::min(params.image_size, refined_size.width - x),
-                        std::min(params.image_size, refined_size.height - y)
+                llava_uhd::slice_instructions instructions;
+                instructions.overview_size = clip_image_size{params.image_size, params.image_size};
+                instructions.refined_size = refined_size;
+                instructions.grid_size = clip_image_size{
+                    static_cast<int>(std::ceil(static_cast<float>(refined_size.width) / params.image_size)),
+                    static_cast<int>(std::ceil(static_cast<float>(refined_size.height) / params.image_size)),
+                };
+                for (int y = 0; y < refined_size.height; y += params.image_size) {
+                    for (int x = 0; x < refined_size.width; x += params.image_size) {
+                        // LOG_INF("%s: adding slice at x=%d, y=%d\n", __func__, x, y);
+                        instructions.slices.push_back(llava_uhd::slice_coordinates{
+                            /* x    */x,
+                            /* y    */y,
+                            /* size */clip_image_size{
+                                std::min(params.image_size, refined_size.width - x),
+                                std::min(params.image_size, refined_size.height - y)
+                            }
+                        });
                     }
-                });
-            }
-        }
-        auto imgs = llava_uhd::slice_image(img, instructions);
+                }
+                auto imgs = llava_uhd::slice_image(img, instructions);
 
-        // cast and normalize to f32
-        for (size_t i = 0; i < imgs.size(); ++i) {
-            // clip_image_save_to_bmp(*imgs[i], "slice_" + std::to_string(i) + ".bmp");
-            clip_image_f32_ptr res(clip_image_f32_init());
-            normalize_image_u8_to_f32(*imgs[i], *res, params.image_mean, params.image_std);
-            res_imgs->entries.push_back(std::move(res));
-        }
+                // cast and normalize to f32
+                for (size_t i = 0; i < imgs.size(); ++i) {
+                    // clip_image_save_to_bmp(*imgs[i], "slice_" + std::to_string(i) + ".bmp");
+                    clip_image_f32_ptr res(clip_image_f32_init());
+                    normalize_image_u8_to_f32(*imgs[i], *res, params.image_mean, params.image_std);
+                    res_imgs->entries.push_back(std::move(res));
+                }
 
-        res_imgs->grid_x = instructions.grid_size.width;
-        res_imgs->grid_y = instructions.grid_size.height;
-        return true;
-    } else if (ctx->proj_type() == PROJECTOR_TYPE_GLM_EDGE
-            || ctx->proj_type() == PROJECTOR_TYPE_GEMMA3
-            || ctx->proj_type() == PROJECTOR_TYPE_INTERNVL // TODO @ngxson : support dynamic resolution
-    ) {
-        clip_image_u8 resized_image;
-        int sz = params.image_size;
-        image_manipulation::resize_and_pad_image(*img, resized_image, {sz, sz});
-        clip_image_f32_ptr img_f32(clip_image_f32_init());
-        //clip_image_save_to_bmp(resized_image, "resized.bmp");
-        normalize_image_u8_to_f32(resized_image, *img_f32, params.image_mean, params.image_std);
-        res_imgs->entries.push_back(std::move(img_f32));
-        return true;
+                res_imgs->grid_x = instructions.grid_size.width;
+                res_imgs->grid_y = instructions.grid_size.height;
+            } break;
 
-    } else if (ctx->proj_type() == PROJECTOR_TYPE_PIXTRAL
-            || ctx->proj_type() == PROJECTOR_TYPE_LIGHTONOCR
-    ) {
-        clip_image_u8 resized_image;
-        auto new_size = image_manipulation::calc_size_preserved_ratio(original_size, params.patch_size, params.image_size);
-        image_manipulation::bilinear_resize(*img, resized_image, new_size.width, new_size.height);
-        clip_image_f32_ptr img_f32(clip_image_f32_init());
-        normalize_image_u8_to_f32(resized_image, *img_f32, params.image_mean, params.image_std);
-        res_imgs->entries.push_back(std::move(img_f32));
-        return true;
+        case PROJECTOR_TYPE_GLM_EDGE:
+        case PROJECTOR_TYPE_GEMMA3:
+        case PROJECTOR_TYPE_INTERNVL: // TODO @ngxson : support dynamic resolution
+            {
+                clip_image_u8 resized_image;
+                int sz = params.image_size;
+                img_tool::resize(*img, resized_image, {sz, sz}, img_tool::RESIZE_ALGO_BILINEAR);
+                clip_image_f32_ptr img_f32(clip_image_f32_init());
+                //clip_image_save_to_bmp(resized_image, "resized.bmp");
+                normalize_image_u8_to_f32(resized_image, *img_f32, params.image_mean, params.image_std);
+                res_imgs->entries.push_back(std::move(img_f32));
+            } break;
 
-    } else if (ctx->proj_type() == PROJECTOR_TYPE_LLAMA4) {
-        GGML_ASSERT(!params.image_res_candidates.empty());
-        auto const inst = llava_uhd::get_slice_instructions(ctx, original_size);
-        std::vector<clip_image_u8_ptr> imgs = llava_uhd::slice_image(img, inst);
+        case PROJECTOR_TYPE_PIXTRAL:
+        case PROJECTOR_TYPE_LIGHTONOCR:
+            {
+                GGML_ASSERT(params.image_min_pixels && params.image_max_pixels);
+                clip_image_u8 resized_image;
+                // the original pixtral model doesn't have n_merge
+                const int cur_merge = params.n_merge == 0 ? 1 : params.n_merge;
+                const clip_image_size target_size = img_tool::calc_size_preserved_ratio(
+                    original_size,
+                    params.patch_size * cur_merge,
+                    params.image_min_pixels,
+                    params.image_max_pixels);
+                img_tool::resize(*img, resized_image, target_size, img_tool::RESIZE_ALGO_BILINEAR);
+                clip_image_f32_ptr img_f32(clip_image_f32_init());
+                normalize_image_u8_to_f32(resized_image, *img_f32, params.image_mean, params.image_std);
+                res_imgs->entries.push_back(std::move(img_f32));
+            } break;
 
-        for (size_t i = 0; i < imgs.size(); ++i) {
-            clip_image_f32_ptr res(clip_image_f32_init());
-            normalize_image_u8_to_f32(*imgs[i], *res, params.image_mean, params.image_std);
-            res_imgs->entries.push_back(std::move(res));
-        }
+        case PROJECTOR_TYPE_LLAMA4:
+            {
+                GGML_ASSERT(!params.image_res_candidates.empty());
+                auto const inst = llava_uhd::get_slice_instructions(ctx, original_size);
+                std::vector<clip_image_u8_ptr> imgs = llava_uhd::slice_image(img, inst);
 
-        res_imgs->grid_x = inst.grid_size.width;
-        res_imgs->grid_y = inst.grid_size.height;
-        return true;
+                for (size_t i = 0; i < imgs.size(); ++i) {
+                    clip_image_f32_ptr res(clip_image_f32_init());
+                    normalize_image_u8_to_f32(*imgs[i], *res, params.image_mean, params.image_std);
+                    res_imgs->entries.push_back(std::move(res));
+                }
 
-    } else if ( ctx->proj_type() == PROJECTOR_TYPE_LFM2
-             || ctx->proj_type() == PROJECTOR_TYPE_KIMIVL
-    ) {
-        GGML_ASSERT(params.proj_scale_factor);
+                res_imgs->grid_x = inst.grid_size.width;
+                res_imgs->grid_y = inst.grid_size.height;
+            } break;
 
-        // smart resize
-        const int width = img->nx;
-        const int height = img->ny;
-        const int total_factor = params.patch_size * params.proj_scale_factor;
-        constexpr int min_image_tokens = 64;
-        constexpr int max_image_tokens = 1024;
-        const float min_pixels = min_image_tokens * total_factor * total_factor;
-        const float max_pixels = max_image_tokens * total_factor * total_factor;
+        case PROJECTOR_TYPE_LFM2:
+        case PROJECTOR_TYPE_KIMIVL:
+            {
+                GGML_ASSERT(params.image_min_pixels && params.image_max_pixels);
+                const clip_image_size target_size = img_tool::calc_size_preserved_ratio(
+                    original_size,
+                    params.patch_size * params.n_merge,
+                    params.image_min_pixels,
+                    params.image_max_pixels);
+                const std::array<uint8_t, 3> pad_color = {122, 116, 104};
 
-        auto round_by_factor = [f = total_factor](float x) { return static_cast<int>(std::nearbyintf(x / static_cast<float>(f))) * f; };
-        auto ceil_by_factor  = [f = total_factor](float x) { return static_cast<int>(std::ceil(x / static_cast<float>(f))) * f; };
-        auto floor_by_factor = [f = total_factor](float x) { return static_cast<int>(std::floor(x / static_cast<float>(f))) * f; };
+                clip_image_u8 resized_img;
+                img_tool::resize(*img, resized_img, target_size, img_tool::RESIZE_ALGO_BILINEAR, true, pad_color);
+                clip_image_f32_ptr res(clip_image_f32_init());
+                normalize_image_u8_to_f32(resized_img, *res, params.image_mean, params.image_std);
+                res_imgs->entries.push_back(std::move(res));
+            } break;
 
-        int h_bar = std::max(total_factor, round_by_factor(height));
-        int w_bar = std::max(total_factor, round_by_factor(width));
+        case PROJECTOR_TYPE_MLP:
+        case PROJECTOR_TYPE_MLP_NORM:
+        case PROJECTOR_TYPE_LDP:
+        case PROJECTOR_TYPE_LDPV2:
+        case PROJECTOR_TYPE_COGVLM: // TODO @ngxson : is this correct for cogvlm?
+            {
+                // TODO @ngxson : refactor the code below to avoid duplicated logic
 
-        if (h_bar * w_bar > max_pixels) {
-            const auto beta = std::sqrt((height * width) / max_pixels);
-            h_bar = std::max(total_factor, floor_by_factor(height / beta));
-            w_bar = std::max(total_factor, floor_by_factor(width / beta));
-        } else if (h_bar * w_bar < min_pixels) {
-            const auto beta = std::sqrt(min_pixels / (height * width));
-            h_bar = ceil_by_factor(height * beta);
-            w_bar = ceil_by_factor(width * beta);
-        }
+                // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104)
+                // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
 
-        const std::array<uint8_t, 3> pad_color = {122, 116, 104};
+                clip_image_u8_ptr temp(clip_image_u8_init()); // we will keep the input image data here temporarily
 
-        clip_image_u8 resized_img;
-        image_manipulation::resize_and_pad_image(*img, resized_img, clip_image_size{w_bar, h_bar}, pad_color);
-        clip_image_f32_ptr res(clip_image_f32_init());
-        normalize_image_u8_to_f32(resized_img, *res, params.image_mean, params.image_std);
-        res_imgs->entries.push_back(std::move(res));
-        return true;
-    }
-
-    // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104)
-    // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
-
-    clip_image_u8_ptr temp(clip_image_u8_init()); // we will keep the input image data here temporarily
-
-    if (pad_to_square) {
-        // for llava-1.5, we resize image to a square, and pad the shorter side with a background color
-        // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
-        const int longer_side = std::max(img->nx, img->ny);
-        temp->nx = longer_side;
-        temp->ny = longer_side;
-        temp->buf.resize(3 * longer_side * longer_side);
-
-        // background color in RGB from LLaVA (this is the mean rgb color * 255)
-        const std::array<uint8_t, 3> pad_color = {122, 116, 104};
-
-        // resize the image to the target_size
-        image_manipulation::resize_and_pad_image(*img, *temp, clip_image_size{params.image_size, params.image_size}, pad_color);
-
-        clip_image_f32_ptr res(clip_image_f32_init());
-        normalize_image_u8_to_f32(*temp, *res, params.image_mean, params.image_std);
-        res_imgs->entries.push_back(std::move(res));
-        return true;
-
-    } else if (!params.image_res_candidates.empty()) {
-        // "spatial_unpad" with "anyres" processing for llava-1.6
-        auto const inst = llava_uhd::get_slice_instructions(ctx, original_size);
-        std::vector<clip_image_u8_ptr> imgs = llava_uhd::slice_image(img, inst);
-
-        for (size_t i = 0; i < imgs.size(); ++i) {
-            // clip_image_save_to_bmp(*imgs[i], "slice_" + std::to_string(i) + ".bmp");
-            clip_image_f32_ptr res(clip_image_f32_init());
-            normalize_image_u8_to_f32(*imgs[i], *res, params.image_mean, params.image_std);
-            res_imgs->entries.push_back(std::move(res));
-        }
-
-        return true;
-    } else {
-        GGML_ABORT("Unknown image preprocessing type");
+                // The model config actually contains all we need to decide on how to preprocess, here we automatically switch to the new llava-1.6 preprocessing
+                if (params.image_res_candidates.empty()) { // pad_to_square
+                    // for llava-1.5, we resize image to a square, and pad the shorter side with a background color
+                    // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
+                    const int longer_side = std::max(img->nx, img->ny);
+                    temp->nx = longer_side;
+                    temp->ny = longer_side;
+                    temp->buf.resize(3 * longer_side * longer_side);
+
+                    // background color in RGB from LLaVA (this is the mean rgb color * 255)
+                    const std::array<uint8_t, 3> pad_color = {122, 116, 104};
+
+                    // resize the image to the target_size
+                    img_tool::resize(*img, *temp, clip_image_size{params.image_size, params.image_size}, img_tool::RESIZE_ALGO_BILINEAR, true, pad_color);
+
+                    clip_image_f32_ptr res(clip_image_f32_init());
+                    normalize_image_u8_to_f32(*temp, *res, params.image_mean, params.image_std);
+                    res_imgs->entries.push_back(std::move(res));
+
+                } else {
+                    // "spatial_unpad" with "anyres" processing for llava-1.6
+                    auto const inst = llava_uhd::get_slice_instructions(ctx, original_size);
+                    std::vector<clip_image_u8_ptr> imgs = llava_uhd::slice_image(img, inst);
+
+                    for (size_t i = 0; i < imgs.size(); ++i) {
+                        // clip_image_save_to_bmp(*imgs[i], "slice_" + std::to_string(i) + ".bmp");
+                        clip_image_f32_ptr res(clip_image_f32_init());
+                        normalize_image_u8_to_f32(*imgs[i], *res, params.image_mean, params.image_std);
+                        res_imgs->entries.push_back(std::move(res));
+                    }
+                }
+            } break;
+
+        default:
+            LOG_ERR("%s: unsupported projector type %d\n", __func__, ctx->proj_type());
+            return false;
     }
 
+    return true;
 }
 
 ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx) {
@@ -4145,7 +4247,7 @@ int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 *
     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) + (int)(img->nx % params.patch_size > 0);
+        return img->nx / (params.patch_size * 2);
     }
     return n_total;
 }
@@ -4153,7 +4255,7 @@ int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 *
 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) + (int)(img->ny % params.patch_size > 0);
+        return img->ny / (params.patch_size * 2);
     }
     return 1;
 }
@@ -4211,9 +4313,8 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
         case PROJECTOR_TYPE_QWEN3VL:
             {
                 // dynamic size (2 conv, so double patch size)
-                int patch_size = params.patch_size * 2;
-                int x_patch = img->nx / patch_size + (int)(img->nx % patch_size > 0);
-                int y_patch = img->ny / patch_size + (int)(img->ny % patch_size > 0);
+                int x_patch = img->nx / (params.patch_size * 2);
+                int y_patch = img->ny / (params.patch_size * 2);
                 n_patches = x_patch * y_patch;
             } break;
         case PROJECTOR_TYPE_GEMMA3:
@@ -4222,15 +4323,14 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
         case PROJECTOR_TYPE_LLAMA4:
             {
                 // both X and Y are downscaled by the scale factor
-                int scale_factor = ctx->model.hparams.proj_scale_factor;
+                int scale_factor = ctx->model.hparams.n_merge;
                 n_patches /= (scale_factor * scale_factor);
             } break;
         case PROJECTOR_TYPE_LFM2:
         case PROJECTOR_TYPE_KIMIVL:
             {
                 // dynamic size
-                int scale_factor = ctx->model.hparams.proj_scale_factor;
-                int out_patch_size = params.patch_size * scale_factor;
+                int out_patch_size = params.patch_size * ctx->model.hparams.n_merge;
                 int x_patch = CLIP_ALIGN(img->nx, out_patch_size) / out_patch_size;
                 int y_patch = CLIP_ALIGN(img->ny, out_patch_size) / out_patch_size;
                 n_patches = x_patch * y_patch;
@@ -4239,7 +4339,7 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
         case PROJECTOR_TYPE_LIGHTONOCR:
             {
                 // dynamic size
-                int n_merge = params.spatial_merge_size;
+                int n_merge = ctx->model.hparams.n_merge;
                 int n_patches_x = img->nx / patch_size / (n_merge > 0 ? n_merge : 1);
                 int n_patches_y = img->ny / patch_size / (n_merge > 0 ? n_merge : 1);
                 if (ctx->model.token_embd_img_break) {

From dd5e8cab512c7752392b6e51a6f118f348fb3f16 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Adrien=20Gallou=C3=ABt?= <angt@huggingface.co>
Date: Sat, 1 Nov 2025 16:52:17 +0100
Subject: [PATCH 28/30] vendor : update cpp-httplib to 0.27.0 (#16846)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Signed-off-by: Adrien Gallouët <angt@huggingface.co>
---
 vendor/cpp-httplib/httplib.h | 264 +++++++++++++++++++++++++++--------
 1 file changed, 209 insertions(+), 55 deletions(-)

diff --git a/vendor/cpp-httplib/httplib.h b/vendor/cpp-httplib/httplib.h
index db55d07e25..b76a17d07a 100644
--- a/vendor/cpp-httplib/httplib.h
+++ b/vendor/cpp-httplib/httplib.h
@@ -8,8 +8,8 @@
 #ifndef CPPHTTPLIB_HTTPLIB_H
 #define CPPHTTPLIB_HTTPLIB_H
 
-#define CPPHTTPLIB_VERSION "0.26.0"
-#define CPPHTTPLIB_VERSION_NUM "0x001A00"
+#define CPPHTTPLIB_VERSION "0.27.0"
+#define CPPHTTPLIB_VERSION_NUM "0x001B00"
 
 /*
  * Platform compatibility check
@@ -1052,6 +1052,9 @@ private:
 
 ssize_t write_headers(Stream &strm, const Headers &headers);
 
+std::string make_host_and_port_string(const std::string &host, int port,
+                                      bool is_ssl);
+
 } // namespace detail
 
 class Server {
@@ -1129,6 +1132,8 @@ public:
   Server &
   set_header_writer(std::function<ssize_t(Stream &, Headers &)> const &writer);
 
+  Server &set_trusted_proxies(const std::vector<std::string> &proxies);
+
   Server &set_keep_alive_max_count(size_t count);
   Server &set_keep_alive_timeout(time_t sec);
 
@@ -1167,6 +1172,9 @@ protected:
                        const std::function<void(Request &)> &setup_request);
 
   std::atomic<socket_t> svr_sock_{INVALID_SOCKET};
+
+  std::vector<std::string> trusted_proxies_;
+
   size_t keep_alive_max_count_ = CPPHTTPLIB_KEEPALIVE_MAX_COUNT;
   time_t keep_alive_timeout_sec_ = CPPHTTPLIB_KEEPALIVE_TIMEOUT_SECOND;
   time_t read_timeout_sec_ = CPPHTTPLIB_SERVER_READ_TIMEOUT_SECOND;
@@ -1719,8 +1727,6 @@ private:
       const std::string &boundary, const UploadFormDataItems &items,
       const FormDataProviderItems &provider_items) const;
 
-  std::string adjust_host_string(const std::string &host) const;
-
   virtual bool
   process_socket(const Socket &socket,
                  std::chrono::time_point<std::chrono::steady_clock> start_time,
@@ -1953,14 +1959,17 @@ public:
   void update_certs(X509 *cert, EVP_PKEY *private_key,
                     X509_STORE *client_ca_cert_store = nullptr);
 
+  int ssl_last_error() const { return last_ssl_error_; }
+
 private:
   bool process_and_close_socket(socket_t sock) override;
 
+  STACK_OF(X509_NAME) * extract_ca_names_from_x509_store(X509_STORE *store);
+
   SSL_CTX *ctx_;
   std::mutex ctx_mutex_;
-#ifdef CPPHTTPLIB_OPENSSL_SUPPORT
+
   int last_ssl_error_ = 0;
-#endif
 };
 
 class SSLClient final : public ClientImpl {
@@ -4596,13 +4605,35 @@ inline bool zstd_decompressor::decompress(const char *data, size_t data_length,
 }
 #endif
 
+inline bool is_prohibited_header_name(const std::string &name) {
+  using udl::operator""_t;
+
+  switch (str2tag(name)) {
+  case "REMOTE_ADDR"_t:
+  case "REMOTE_PORT"_t:
+  case "LOCAL_ADDR"_t:
+  case "LOCAL_PORT"_t: return true;
+  default: return false;
+  }
+}
+
 inline bool has_header(const Headers &headers, const std::string &key) {
+  if (is_prohibited_header_name(key)) { return false; }
   return headers.find(key) != headers.end();
 }
 
 inline const char *get_header_value(const Headers &headers,
                                     const std::string &key, const char *def,
                                     size_t id) {
+  if (is_prohibited_header_name(key)) {
+#ifndef CPPHTTPLIB_NO_EXCEPTIONS
+    std::string msg = "Prohibited header name '" + key + "' is specified.";
+    throw std::invalid_argument(msg);
+#else
+    return "";
+#endif
+  }
+
   auto rng = headers.equal_range(key);
   auto it = rng.first;
   std::advance(it, static_cast<ssize_t>(id));
@@ -7261,6 +7292,30 @@ inline bool RegexMatcher::match(Request &request) const {
   return std::regex_match(request.path, request.matches, regex_);
 }
 
+inline std::string make_host_and_port_string(const std::string &host, int port,
+                                             bool is_ssl) {
+  std::string result;
+
+  // Enclose IPv6 address in brackets (but not if already enclosed)
+  if (host.find(':') == std::string::npos ||
+      (!host.empty() && host[0] == '[')) {
+    // IPv4, hostname, or already bracketed IPv6
+    result = host;
+  } else {
+    // IPv6 address without brackets
+    result = "[" + host + "]";
+  }
+
+  // Append port if not default
+  if ((!is_ssl && port == 80) || (is_ssl && port == 443)) {
+    ; // do nothing
+  } else {
+    result += ":" + std::to_string(port);
+  }
+
+  return result;
+}
+
 } // namespace detail
 
 // HTTP server implementation
@@ -7473,6 +7528,12 @@ inline Server &Server::set_header_writer(
   return *this;
 }
 
+inline Server &
+Server::set_trusted_proxies(const std::vector<std::string> &proxies) {
+  trusted_proxies_ = proxies;
+  return *this;
+}
+
 inline Server &Server::set_keep_alive_max_count(size_t count) {
   keep_alive_max_count_ = count;
   return *this;
@@ -8261,6 +8322,40 @@ inline bool Server::dispatch_request_for_content_reader(
   return false;
 }
 
+inline std::string
+get_client_ip(const std::string &x_forwarded_for,
+              const std::vector<std::string> &trusted_proxies) {
+  // X-Forwarded-For is a comma-separated list per RFC 7239
+  std::vector<std::string> ip_list;
+  detail::split(x_forwarded_for.data(),
+                x_forwarded_for.data() + x_forwarded_for.size(), ',',
+                [&](const char *b, const char *e) {
+                  auto r = detail::trim(b, e, 0, static_cast<size_t>(e - b));
+                  ip_list.emplace_back(std::string(b + r.first, b + r.second));
+                });
+
+  for (size_t i = 0; i < ip_list.size(); ++i) {
+    auto ip = ip_list[i];
+
+    auto is_trusted_proxy =
+        std::any_of(trusted_proxies.begin(), trusted_proxies.end(),
+                    [&](const std::string &proxy) { return ip == proxy; });
+
+    if (is_trusted_proxy) {
+      if (i == 0) {
+        // If the trusted proxy is the first IP, there's no preceding client IP
+        return ip;
+      } else {
+        // Return the IP immediately before the trusted proxy
+        return ip_list[i - 1];
+      }
+    }
+  }
+
+  // If no trusted proxy is found, return the first IP in the list
+  return ip_list.front();
+}
+
 inline bool
 Server::process_request(Stream &strm, const std::string &remote_addr,
                         int remote_port, const std::string &local_addr,
@@ -8324,15 +8419,16 @@ Server::process_request(Stream &strm, const std::string &remote_addr,
     connection_closed = true;
   }
 
-  req.remote_addr = remote_addr;
+  if (!trusted_proxies_.empty() && req.has_header("X-Forwarded-For")) {
+    auto x_forwarded_for = req.get_header_value("X-Forwarded-For");
+    req.remote_addr = get_client_ip(x_forwarded_for, trusted_proxies_);
+  } else {
+    req.remote_addr = remote_addr;
+  }
   req.remote_port = remote_port;
-  req.set_header("REMOTE_ADDR", req.remote_addr);
-  req.set_header("REMOTE_PORT", std::to_string(req.remote_port));
 
   req.local_addr = local_addr;
   req.local_port = local_port;
-  req.set_header("LOCAL_ADDR", req.local_addr);
-  req.set_header("LOCAL_PORT", std::to_string(req.local_port));
 
   if (req.has_header("Accept")) {
     const auto &accept_header = req.get_header_value("Accept");
@@ -8522,7 +8618,7 @@ inline ClientImpl::ClientImpl(const std::string &host, int port,
                               const std::string &client_cert_path,
                               const std::string &client_key_path)
     : host_(detail::escape_abstract_namespace_unix_domain(host)), port_(port),
-      host_and_port_(adjust_host_string(host_) + ":" + std::to_string(port)),
+      host_and_port_(detail::make_host_and_port_string(host_, port, is_ssl())),
       client_cert_path_(client_cert_path), client_key_path_(client_key_path) {}
 
 inline ClientImpl::~ClientImpl() {
@@ -8703,8 +8799,9 @@ inline bool ClientImpl::send_(Request &req, Response &res, Error &error) {
   {
     std::lock_guard<std::mutex> guard(socket_mutex_);
 
-    // Set this to false immediately - if it ever gets set to true by the end of
-    // the request, we know another thread instructed us to close the socket.
+    // Set this to false immediately - if it ever gets set to true by the end
+    // of the request, we know another thread instructed us to close the
+    // socket.
     socket_should_be_closed_when_request_is_done_ = false;
 
     auto is_alive = false;
@@ -8720,10 +8817,10 @@ inline bool ClientImpl::send_(Request &req, Response &res, Error &error) {
 #endif
 
       if (!is_alive) {
-        // Attempt to avoid sigpipe by shutting down non-gracefully if it seems
-        // like the other side has already closed the connection Also, there
-        // cannot be any requests in flight from other threads since we locked
-        // request_mutex_, so safe to close everything immediately
+        // Attempt to avoid sigpipe by shutting down non-gracefully if it
+        // seems like the other side has already closed the connection Also,
+        // there cannot be any requests in flight from other threads since we
+        // locked request_mutex_, so safe to close everything immediately
         const bool shutdown_gracefully = false;
         shutdown_ssl(socket_, shutdown_gracefully);
         shutdown_socket(socket_);
@@ -9027,7 +9124,8 @@ inline bool ClientImpl::create_redirect_client(
   }
 }
 
-// New method for robust client setup (based on basic_manual_redirect.cpp logic)
+// New method for robust client setup (based on basic_manual_redirect.cpp
+// logic)
 template <typename ClientType>
 inline void ClientImpl::setup_redirect_client(ClientType &client) {
   // Copy basic settings first
@@ -9131,18 +9229,8 @@ inline bool ClientImpl::write_request(Stream &strm, Request &req,
     // curl behavior)
     if (address_family_ == AF_UNIX) {
       req.set_header("Host", "localhost");
-    } else if (is_ssl()) {
-      if (port_ == 443) {
-        req.set_header("Host", host_);
-      } else {
-        req.set_header("Host", host_and_port_);
-      }
     } else {
-      if (port_ == 80) {
-        req.set_header("Host", host_);
-      } else {
-        req.set_header("Host", host_and_port_);
-      }
+      req.set_header("Host", host_and_port_);
     }
   }
 
@@ -9409,12 +9497,6 @@ inline Result ClientImpl::send_with_content_provider(
 #endif
 }
 
-inline std::string
-ClientImpl::adjust_host_string(const std::string &host) const {
-  if (host.find(':') != std::string::npos) { return "[" + host + "]"; }
-  return host;
-}
-
 inline void ClientImpl::output_log(const Request &req,
                                    const Response &res) const {
   if (logger_) {
@@ -9538,8 +9620,8 @@ inline ContentProviderWithoutLength ClientImpl::get_multipart_content_provider(
     const FormDataProviderItems &provider_items) const {
   size_t cur_item = 0;
   size_t cur_start = 0;
-  // cur_item and cur_start are copied to within the std::function and maintain
-  // state between successive calls
+  // cur_item and cur_start are copied to within the std::function and
+  // maintain state between successive calls
   return [&, cur_item, cur_start](size_t offset,
                                   DataSink &sink) mutable -> bool {
     if (!offset && !items.empty()) {
@@ -10251,8 +10333,8 @@ inline void ClientImpl::stop() {
   // If there is anything ongoing right now, the ONLY thread-safe thing we can
   // do is to shutdown_socket, so that threads using this socket suddenly
   // discover they can't read/write any more and error out. Everything else
-  // (closing the socket, shutting ssl down) is unsafe because these actions are
-  // not thread-safe.
+  // (closing the socket, shutting ssl down) is unsafe because these actions
+  // are not thread-safe.
   if (socket_requests_in_flight_ > 0) {
     shutdown_socket(socket_);
 
@@ -10705,6 +10787,19 @@ inline SSLServer::SSLServer(const char *cert_path, const char *private_key_path,
       SSL_CTX_load_verify_locations(ctx_, client_ca_cert_file_path,
                                     client_ca_cert_dir_path);
 
+      // Set client CA list to be sent to clients during TLS handshake
+      if (client_ca_cert_file_path) {
+        auto ca_list = SSL_load_client_CA_file(client_ca_cert_file_path);
+        if (ca_list != nullptr) {
+          SSL_CTX_set_client_CA_list(ctx_, ca_list);
+        } else {
+          // Failed to load client CA list, but we continue since
+          // SSL_CTX_load_verify_locations already succeeded and
+          // certificate verification will still work
+          last_ssl_error_ = static_cast<int>(ERR_get_error());
+        }
+      }
+
       SSL_CTX_set_verify(
           ctx_, SSL_VERIFY_PEER | SSL_VERIFY_FAIL_IF_NO_PEER_CERT, nullptr);
     }
@@ -10729,6 +10824,15 @@ inline SSLServer::SSLServer(X509 *cert, EVP_PKEY *private_key,
     } else if (client_ca_cert_store) {
       SSL_CTX_set_cert_store(ctx_, client_ca_cert_store);
 
+      // Extract CA names from the store and set them as the client CA list
+      auto ca_list = extract_ca_names_from_x509_store(client_ca_cert_store);
+      if (ca_list) {
+        SSL_CTX_set_client_CA_list(ctx_, ca_list);
+      } else {
+        // Failed to extract CA names, record the error
+        last_ssl_error_ = static_cast<int>(ERR_get_error());
+      }
+
       SSL_CTX_set_verify(
           ctx_, SSL_VERIFY_PEER | SSL_VERIFY_FAIL_IF_NO_PEER_CERT, nullptr);
     }
@@ -10809,6 +10913,44 @@ inline bool SSLServer::process_and_close_socket(socket_t sock) {
   return ret;
 }
 
+inline STACK_OF(X509_NAME) * SSLServer::extract_ca_names_from_x509_store(
+                                 X509_STORE *store) {
+  if (!store) { return nullptr; }
+
+  auto ca_list = sk_X509_NAME_new_null();
+  if (!ca_list) { return nullptr; }
+
+  // Get all objects from the store
+  auto objs = X509_STORE_get0_objects(store);
+  if (!objs) {
+    sk_X509_NAME_free(ca_list);
+    return nullptr;
+  }
+
+  // Iterate through objects and extract certificate subject names
+  for (int i = 0; i < sk_X509_OBJECT_num(objs); i++) {
+    auto obj = sk_X509_OBJECT_value(objs, i);
+    if (X509_OBJECT_get_type(obj) == X509_LU_X509) {
+      auto cert = X509_OBJECT_get0_X509(obj);
+      if (cert) {
+        auto subject = X509_get_subject_name(cert);
+        if (subject) {
+          auto name_dup = X509_NAME_dup(subject);
+          if (name_dup) { sk_X509_NAME_push(ca_list, name_dup); }
+        }
+      }
+    }
+  }
+
+  // If no names were extracted, free the list and return nullptr
+  if (sk_X509_NAME_num(ca_list) == 0) {
+    sk_X509_NAME_free(ca_list);
+    return nullptr;
+  }
+
+  return ca_list;
+}
+
 // SSL HTTP client implementation
 inline SSLClient::SSLClient(const std::string &host)
     : SSLClient(host, 443, std::string(), std::string()) {}
@@ -10889,7 +11031,8 @@ inline void SSLClient::set_ca_cert_store(X509_STORE *ca_cert_store) {
   if (ca_cert_store) {
     if (ctx_) {
       if (SSL_CTX_get_cert_store(ctx_) != ca_cert_store) {
-        // Free memory allocated for old cert and use new store `ca_cert_store`
+        // Free memory allocated for old cert and use new store
+        // `ca_cert_store`
         SSL_CTX_set_cert_store(ctx_, ca_cert_store);
         ca_cert_store_ = ca_cert_store;
       }
@@ -10911,10 +11054,15 @@ inline long SSLClient::get_openssl_verify_result() const {
 inline SSL_CTX *SSLClient::ssl_context() const { return ctx_; }
 
 inline bool SSLClient::create_and_connect_socket(Socket &socket, Error &error) {
-  return is_valid() && ClientImpl::create_and_connect_socket(socket, error);
+  if (!is_valid()) {
+    error = Error::SSLConnection;
+    return false;
+  }
+  return ClientImpl::create_and_connect_socket(socket, error);
 }
 
-// Assumes that socket_mutex_ is locked and that there are no requests in flight
+// Assumes that socket_mutex_ is locked and that there are no requests in
+// flight
 inline bool SSLClient::connect_with_proxy(
     Socket &socket,
     std::chrono::time_point<std::chrono::steady_clock> start_time,
@@ -11128,6 +11276,11 @@ inline bool SSLClient::initialize_ssl(Socket &socket, Error &error) {
     return true;
   }
 
+  if (ctx_ == nullptr) {
+    error = Error::SSLConnection;
+    last_openssl_error_ = ERR_get_error();
+  }
+
   shutdown_socket(socket);
   close_socket(socket);
   return false;
@@ -11221,21 +11374,22 @@ SSLClient::verify_host_with_subject_alt_name(X509 *server_cert) const {
 
     for (decltype(count) i = 0; i < count && !dsn_matched; i++) {
       auto val = sk_GENERAL_NAME_value(alt_names, i);
-      if (val->type == type) {
-        auto name =
-            reinterpret_cast<const char *>(ASN1_STRING_get0_data(val->d.ia5));
-        auto name_len = static_cast<size_t>(ASN1_STRING_length(val->d.ia5));
+      if (!val || val->type != type) { continue; }
 
-        switch (type) {
-        case GEN_DNS: dsn_matched = check_host_name(name, name_len); break;
+      auto name =
+          reinterpret_cast<const char *>(ASN1_STRING_get0_data(val->d.ia5));
+      if (name == nullptr) { continue; }
 
-        case GEN_IPADD:
-          if (!memcmp(&addr6, name, addr_len) ||
-              !memcmp(&addr, name, addr_len)) {
-            ip_matched = true;
-          }
-          break;
+      auto name_len = static_cast<size_t>(ASN1_STRING_length(val->d.ia5));
+
+      switch (type) {
+      case GEN_DNS: dsn_matched = check_host_name(name, name_len); break;
+
+      case GEN_IPADD:
+        if (!memcmp(&addr6, name, addr_len) || !memcmp(&addr, name, addr_len)) {
+          ip_matched = true;
         }
+        break;
       }
     }
 

From e4a71599e5846110159955dec0008eb4aa24222b Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Sat, 1 Nov 2025 17:14:54 +0100
Subject: [PATCH 29/30] webui: add HTML/JS preview support to MarkdownContent
 with sandboxed iframe (#16757)

* webui: add HTML/JS preview support to MarkdownContent with sandboxed iframe dialog

Extended MarkdownContent to flag previewable code languages,
add a preview button alongside copy controls, manage preview
dialog state, and share styling for the new button group

Introduced CodePreviewDialog.svelte, a sandboxed iframe modal
for rendering HTML/JS previews with consistent dialog controls

* webui: fullscreen HTML preview dialog using bits-ui

* Update tools/server/webui/src/lib/components/app/misc/CodePreviewDialog.svelte

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

* Update tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte

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

* webui: pedantic style tweak for CodePreviewDialog close button

* webui: remove overengineered preview language logic

* chore: update webui static build

---------

Co-authored-by: Aleksander Grygier <aleksander.grygier@gmail.com>
---
 tools/server/public/index.html.gz             | Bin 861835 -> 861777 bytes
 .../app/misc/CodePreviewDialog.svelte         |  93 +++++++++
 .../app/misc/MarkdownContent.svelte           | 182 +++++++++++++-----
 3 files changed, 231 insertions(+), 44 deletions(-)
 create mode 100644 tools/server/webui/src/lib/components/app/misc/CodePreviewDialog.svelte

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index a81bae04d1983d992a4b46da93153d1cfa63c704..501fa455a24169b1c00bb2ebe056c3719b4ce1e8 100644
GIT binary patch
delta 822429
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diff --git a/tools/server/webui/src/lib/components/app/misc/CodePreviewDialog.svelte b/tools/server/webui/src/lib/components/app/misc/CodePreviewDialog.svelte
new file mode 100644
index 0000000000..702519f9ff
--- /dev/null
+++ b/tools/server/webui/src/lib/components/app/misc/CodePreviewDialog.svelte
@@ -0,0 +1,93 @@
+<script lang="ts">
+	import { Dialog as DialogPrimitive } from 'bits-ui';
+	import XIcon from '@lucide/svelte/icons/x';
+
+	interface Props {
+		open: boolean;
+		code: string;
+		language: string;
+		onOpenChange?: (open: boolean) => void;
+	}
+
+	let { open = $bindable(), code, language, onOpenChange }: Props = $props();
+
+	let iframeRef = $state<HTMLIFrameElement | null>(null);
+
+	$effect(() => {
+		if (!iframeRef) return;
+
+		if (open) {
+			iframeRef.srcdoc = code;
+		} else {
+			iframeRef.srcdoc = '';
+		}
+	});
+
+	function handleOpenChange(nextOpen: boolean) {
+		open = nextOpen;
+		onOpenChange?.(nextOpen);
+	}
+</script>
+
+<DialogPrimitive.Root {open} onOpenChange={handleOpenChange}>
+	<DialogPrimitive.Portal>
+		<DialogPrimitive.Overlay class="code-preview-overlay" />
+
+		<DialogPrimitive.Content class="code-preview-content">
+			<iframe
+				bind:this={iframeRef}
+				title="Preview {language}"
+				sandbox="allow-scripts"
+				class="code-preview-iframe"
+			></iframe>
+
+			<DialogPrimitive.Close
+				class="code-preview-close absolute top-4 right-4 border-none bg-transparent text-white opacity-70 mix-blend-difference transition-opacity hover:opacity-100 focus-visible:ring-0 focus-visible:ring-offset-0 focus-visible:outline-none disabled:pointer-events-none [&_svg]:pointer-events-none [&_svg]:shrink-0 [&_svg:not([class*='size-'])]:size-8"
+				aria-label="Close preview"
+			>
+				<XIcon />
+				<span class="sr-only">Close preview</span>
+			</DialogPrimitive.Close>
+		</DialogPrimitive.Content>
+	</DialogPrimitive.Portal>
+</DialogPrimitive.Root>
+
+<style lang="postcss">
+	:global(.code-preview-overlay) {
+		position: fixed;
+		inset: 0;
+		background-color: transparent;
+		z-index: 100000;
+	}
+
+	:global(.code-preview-content) {
+		position: fixed;
+		inset: 0;
+		top: 0 !important;
+		left: 0 !important;
+		width: 100dvw;
+		height: 100dvh;
+		margin: 0;
+		padding: 0;
+		border: none;
+		border-radius: 0;
+		background-color: transparent;
+		box-shadow: none;
+		display: block;
+		overflow: hidden;
+		transform: none !important;
+		z-index: 100001;
+	}
+
+	:global(.code-preview-iframe) {
+		display: block;
+		width: 100dvw;
+		height: 100dvh;
+		border: 0;
+	}
+
+	:global(.code-preview-close) {
+		position: absolute;
+		z-index: 100002;
+	}
+</style>
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 1f4caa9003..1c069db58d 100644
--- a/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte
+++ b/tools/server/webui/src/lib/components/app/misc/MarkdownContent.svelte
@@ -15,6 +15,7 @@
 	import githubLightCss from 'highlight.js/styles/github.css?inline';
 	import { mode } from 'mode-watcher';
 	import { remarkLiteralHtml } from '$lib/markdown/literal-html';
+	import CodePreviewDialog from './CodePreviewDialog.svelte';
 
 	interface Props {
 		content: string;
@@ -25,6 +26,9 @@
 
 	let containerRef = $state<HTMLDivElement>();
 	let processedHtml = $state('');
+	let previewDialogOpen = $state(false);
+	let previewCode = $state('');
+	let previewLanguage = $state('text');
 
 	function loadHighlightTheme(isDark: boolean) {
 		if (!browser) return;
@@ -117,7 +121,6 @@
 
 			const rawCode = codeElement.textContent || '';
 			const codeId = `code-${Date.now()}-${index}`;
-
 			codeElement.setAttribute('data-code-id', codeId);
 			codeElement.setAttribute('data-raw-code', rawCode);
 
@@ -138,11 +141,30 @@
 			copyButton.setAttribute('type', 'button');
 
 			copyButton.innerHTML = `
-				<svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-copy-icon lucide-copy"><rect width="14" height="14" x="8" y="8" rx="2" ry="2"/><path d="M4 16c-1.1 0-2-.9-2-2V4c0-1.1.9-2 2-2h10c1.1 0 2 .9 2 2"/></svg>
-			`;
+                                <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-copy-icon lucide-copy"><rect width="14" height="14" x="8" y="8" rx="2" ry="2"/><path d="M4 16c-1.1 0-2-.9-2-2V4c0-1.1.9-2 2-2h10c1.1 0 2 .9 2 2"/></svg>
+                        `;
+
+			const actions = document.createElement('div');
+			actions.className = 'code-block-actions';
+
+			actions.appendChild(copyButton);
+
+			if (language.toLowerCase() === 'html') {
+				const previewButton = document.createElement('button');
+				previewButton.className = 'preview-code-btn';
+				previewButton.setAttribute('data-code-id', codeId);
+				previewButton.setAttribute('title', 'Preview code');
+				previewButton.setAttribute('type', 'button');
+
+				previewButton.innerHTML = `
+                                        <svg xmlns="http://www.w3.org/2000/svg" width="16" height="16" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-eye lucide-eye-icon"><path d="M2.062 12.345a1 1 0 0 1 0-.69C3.5 7.73 7.36 5 12 5s8.5 2.73 9.938 6.655a1 1 0 0 1 0 .69C20.5 16.27 16.64 19 12 19s-8.5-2.73-9.938-6.655"/><circle cx="12" cy="12" r="3"/></svg>
+                                `;
+
+				actions.appendChild(previewButton);
+			}
 
 			header.appendChild(languageLabel);
-			header.appendChild(copyButton);
+			header.appendChild(actions);
 			wrapper.appendChild(header);
 
 			const clonedPre = pre.cloneNode(true) as HTMLElement;
@@ -180,49 +202,105 @@
 		}
 	}
 
-	function setupCopyButtons() {
+	function getCodeInfoFromTarget(target: HTMLElement) {
+		const wrapper = target.closest('.code-block-wrapper');
+
+		if (!wrapper) {
+			console.error('No wrapper found');
+			return null;
+		}
+
+		const codeElement = wrapper.querySelector<HTMLElement>('code[data-code-id]');
+
+		if (!codeElement) {
+			console.error('No code element found in wrapper');
+			return null;
+		}
+
+		const rawCode = codeElement.getAttribute('data-raw-code');
+
+		if (rawCode === null) {
+			console.error('No raw code found');
+			return null;
+		}
+
+		const languageLabel = wrapper.querySelector<HTMLElement>('.code-language');
+		const language = languageLabel?.textContent?.trim() || 'text';
+
+		return { rawCode, language };
+	}
+
+	async function handleCopyClick(event: Event) {
+		event.preventDefault();
+		event.stopPropagation();
+
+		const target = event.currentTarget as HTMLButtonElement | null;
+
+		if (!target) {
+			return;
+		}
+
+		const info = getCodeInfoFromTarget(target);
+
+		if (!info) {
+			return;
+		}
+
+		try {
+			await copyCodeToClipboard(info.rawCode);
+		} catch (error) {
+			console.error('Failed to copy code:', error);
+		}
+	}
+
+	function handlePreviewClick(event: Event) {
+		event.preventDefault();
+		event.stopPropagation();
+
+		const target = event.currentTarget as HTMLButtonElement | null;
+
+		if (!target) {
+			return;
+		}
+
+		const info = getCodeInfoFromTarget(target);
+
+		if (!info) {
+			return;
+		}
+
+		previewCode = info.rawCode;
+		previewLanguage = info.language;
+		previewDialogOpen = true;
+	}
+
+	function setupCodeBlockActions() {
 		if (!containerRef) return;
 
-		const copyButtons = containerRef.querySelectorAll('.copy-code-btn');
+		const wrappers = containerRef.querySelectorAll<HTMLElement>('.code-block-wrapper');
 
-		for (const button of copyButtons) {
-			button.addEventListener('click', async (e) => {
-				e.preventDefault();
-				e.stopPropagation();
+		for (const wrapper of wrappers) {
+			const copyButton = wrapper.querySelector<HTMLButtonElement>('.copy-code-btn');
+			const previewButton = wrapper.querySelector<HTMLButtonElement>('.preview-code-btn');
 
-				const target = e.currentTarget as HTMLButtonElement;
-				const codeId = target.getAttribute('data-code-id');
+			if (copyButton && copyButton.dataset.listenerBound !== 'true') {
+				copyButton.dataset.listenerBound = 'true';
+				copyButton.addEventListener('click', handleCopyClick);
+			}
 
-				if (!codeId) {
-					console.error('No code ID found on button');
-					return;
-				}
+			if (previewButton && previewButton.dataset.listenerBound !== 'true') {
+				previewButton.dataset.listenerBound = 'true';
+				previewButton.addEventListener('click', handlePreviewClick);
+			}
+		}
+	}
 
-				// Find the code element within the same wrapper
-				const wrapper = target.closest('.code-block-wrapper');
-				if (!wrapper) {
-					console.error('No wrapper found');
-					return;
-				}
+	function handlePreviewDialogOpenChange(open: boolean) {
+		previewDialogOpen = open;
 
-				const codeElement = wrapper.querySelector('code[data-code-id]');
-				if (!codeElement) {
-					console.error('No code element found in wrapper');
-					return;
-				}
-
-				const rawCode = codeElement.getAttribute('data-raw-code');
-				if (!rawCode) {
-					console.error('No raw code found');
-					return;
-				}
-
-				try {
-					await copyCodeToClipboard(rawCode);
-				} catch (error) {
-					console.error('Failed to copy code:', error);
-				}
-			});
+		if (!open) {
+			previewCode = '';
+			previewLanguage = 'text';
 		}
 	}
 
@@ -243,7 +321,7 @@
 
 	$effect(() => {
 		if (containerRef && processedHtml) {
-			setupCopyButtons();
+			setupCodeBlockActions();
 		}
 	});
 </script>
@@ -253,6 +331,13 @@
 	{@html processedHtml}
 </div>
 
+<CodePreviewDialog
+	open={previewDialogOpen}
+	code={previewCode}
+	language={previewLanguage}
+	onOpenChange={handlePreviewDialogOpenChange}
+/>
+
 <style>
 	/* Base typography styles */
 	div :global(p:not(:last-child)) {
@@ -472,7 +557,14 @@
 		letter-spacing: 0.05em;
 	}
 
-	div :global(.copy-code-btn) {
+	div :global(.code-block-actions) {
+		display: flex;
+		align-items: center;
+		gap: 0.5rem;
+	}
+
+	div :global(.copy-code-btn),
+	div :global(.preview-code-btn) {
 		display: flex;
 		align-items: center;
 		justify-content: center;
@@ -483,11 +575,13 @@
 		transition: all 0.2s ease;
 	}
 
-	div :global(.copy-code-btn:hover) {
+	div :global(.copy-code-btn:hover),
+	div :global(.preview-code-btn:hover) {
 		transform: scale(1.05);
 	}
 
-	div :global(.copy-code-btn:active) {
+	div :global(.copy-code-btn:active),
+	div :global(.preview-code-btn:active) {
 		transform: scale(0.95);
 	}
 

From 2f68ce7cfd20e9e7098514bf730e5389b7bba908 Mon Sep 17 00:00:00 2001
From: Pascal <admin@serveurperso.com>
Date: Sat, 1 Nov 2025 19:49:51 +0100
Subject: [PATCH 30/30] webui: auto-refresh /props on inference start to resync
 model metadata (#16784)

* webui: auto-refresh /props on inference start to resync model metadata

- Add no-cache headers to /props and /slots
- Throttle slot checks to 30s
- Prevent concurrent fetches with promise guard
- Trigger refresh from chat streaming for legacy and ModelSelector
- Show dynamic serverWarning when using cached data

* fix: restore proper legacy behavior in webui by using unified /props refresh

Updated assistant message bubbles to show each message's stored model when available,
falling back to the current server model only when the per-message value is missing

When the model selector is disabled, now fetches /props and prioritizes that model name
over chunk metadata, then persists it with the streamed message so legacy mode properly
reflects the backend configuration

* fix: detect first valid SSE chunk and refresh server props once

* fix: removed the slots availability throttle constant and state

* webui: purge ai-generated cruft

* chore: update webui static build
---
 tools/server/public/index.html.gz             | Bin 861777 -> 862214 bytes
 .../ChatMessages/ChatMessageAssistant.svelte  |   4 +-
 tools/server/webui/src/lib/services/chat.ts   |  20 ++-
 .../webui/src/lib/stores/chat.svelte.ts       |  54 +++++-
 .../webui/src/lib/stores/server.svelte.ts     | 170 +++++++++++-------
 tools/server/webui/src/lib/types/api.d.ts     |   1 +
 .../server/webui/src/lib/types/settings.d.ts  |   1 +
 7 files changed, 180 insertions(+), 70 deletions(-)

diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz
index 501fa455a24169b1c00bb2ebe056c3719b4ce1e8..b71690cc81e29b13069e5b548016b91ba074014e 100644
GIT binary patch
delta 821147
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diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageAssistant.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageAssistant.svelte
index 41774c6f87..d8f5630fd1 100644
--- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageAssistant.svelte
+++ b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageAssistant.svelte
@@ -85,8 +85,8 @@
 	let displayedModel = $derived((): string | null => {
 		if (!currentConfig.showModelInfo) return null;
 
-		if (currentConfig.modelSelectorEnabled) {
-			return message.model ?? null;
+		if (message.model) {
+			return message.model;
 		}
 
 		return serverModel;
diff --git a/tools/server/webui/src/lib/services/chat.ts b/tools/server/webui/src/lib/services/chat.ts
index df03b10251..414e060764 100644
--- a/tools/server/webui/src/lib/services/chat.ts
+++ b/tools/server/webui/src/lib/services/chat.ts
@@ -54,6 +54,7 @@ export class ChatService {
 			onError,
 			onReasoningChunk,
 			onModel,
+			onFirstValidChunk,
 			// Generation parameters
 			temperature,
 			max_tokens,
@@ -201,6 +202,7 @@ export class ChatService {
 					onError,
 					onReasoningChunk,
 					onModel,
+					onFirstValidChunk,
 					conversationId,
 					abortController.signal
 				);
@@ -267,6 +269,7 @@ export class ChatService {
 		onError?: (error: Error) => void,
 		onReasoningChunk?: (chunk: string) => void,
 		onModel?: (model: string) => void,
+		onFirstValidChunk?: () => void,
 		conversationId?: string,
 		abortSignal?: AbortSignal
 	): Promise<void> {
@@ -283,6 +286,7 @@ export class ChatService {
 		let lastTimings: ChatMessageTimings | undefined;
 		let streamFinished = false;
 		let modelEmitted = false;
+		let firstValidChunkEmitted = false;
 
 		try {
 			let chunk = '';
@@ -311,10 +315,12 @@ export class ChatService {
 						try {
 							const parsed: ApiChatCompletionStreamChunk = JSON.parse(data);
 
-							const chunkModel = this.extractModelName(parsed);
-							if (chunkModel && !modelEmitted) {
-								modelEmitted = true;
-								onModel?.(chunkModel);
+							if (!firstValidChunkEmitted && parsed.object === 'chat.completion.chunk') {
+								firstValidChunkEmitted = true;
+
+								if (!abortSignal?.aborted) {
+									onFirstValidChunk?.();
+								}
 							}
 
 							const content = parsed.choices[0]?.delta?.content;
@@ -322,6 +328,12 @@ export class ChatService {
 							const timings = parsed.timings;
 							const promptProgress = parsed.prompt_progress;
 
+							const chunkModel = this.extractModelName(parsed);
+							if (chunkModel && !modelEmitted) {
+								modelEmitted = true;
+								onModel?.(chunkModel);
+							}
+
 							if (timings || promptProgress) {
 								this.updateProcessingState(timings, promptProgress, conversationId);
 								if (timings) {
diff --git a/tools/server/webui/src/lib/stores/chat.svelte.ts b/tools/server/webui/src/lib/stores/chat.svelte.ts
index a2e74a2e10..3f97a89183 100644
--- a/tools/server/webui/src/lib/stores/chat.svelte.ts
+++ b/tools/server/webui/src/lib/stores/chat.svelte.ts
@@ -1,6 +1,7 @@
 import { DatabaseStore } from '$lib/stores/database';
 import { chatService, slotsService } from '$lib/services';
 import { config } from '$lib/stores/settings.svelte';
+import { serverStore } from '$lib/stores/server.svelte';
 import { normalizeModelName } from '$lib/utils/model-names';
 import { filterByLeafNodeId, findLeafNode, findDescendantMessages } from '$lib/utils/branching';
 import { browser } from '$app/environment';
@@ -362,9 +363,41 @@ class ChatStore {
 
 		let resolvedModel: string | null = null;
 		let modelPersisted = false;
+		const currentConfig = config();
+		const preferServerPropsModel = !currentConfig.modelSelectorEnabled;
+		let serverPropsRefreshed = false;
+		let updateModelFromServerProps: ((persistImmediately?: boolean) => void) | null = null;
 
-		const recordModel = (modelName: string, persistImmediately = true): void => {
-			const normalizedModel = normalizeModelName(modelName);
+		const refreshServerPropsOnce = () => {
+			if (serverPropsRefreshed) {
+				return;
+			}
+
+			serverPropsRefreshed = true;
+
+			const hasExistingProps = serverStore.serverProps !== null;
+
+			serverStore
+				.fetchServerProps({ silent: hasExistingProps })
+				.then(() => {
+					updateModelFromServerProps?.(true);
+				})
+				.catch((error) => {
+					console.warn('Failed to refresh server props after streaming started:', error);
+				});
+		};
+
+		const recordModel = (modelName: string | null | undefined, persistImmediately = true): void => {
+			const serverModelName = serverStore.modelName;
+			const preferredModelSource = preferServerPropsModel
+				? (serverModelName ?? modelName ?? null)
+				: (modelName ?? serverModelName ?? null);
+
+			if (!preferredModelSource) {
+				return;
+			}
+
+			const normalizedModel = normalizeModelName(preferredModelSource);
 
 			if (!normalizedModel || normalizedModel === resolvedModel) {
 				return;
@@ -388,6 +421,20 @@ class ChatStore {
 			}
 		};
 
+		if (preferServerPropsModel) {
+			updateModelFromServerProps = (persistImmediately = true) => {
+				const currentServerModel = serverStore.modelName;
+
+				if (!currentServerModel) {
+					return;
+				}
+
+				recordModel(currentServerModel, persistImmediately);
+			};
+
+			updateModelFromServerProps(false);
+		}
+
 		slotsService.startStreaming();
 		slotsService.setActiveConversation(assistantMessage.convId);
 
@@ -396,6 +443,9 @@ class ChatStore {
 			{
 				...this.getApiOptions(),
 
+				onFirstValidChunk: () => {
+					refreshServerPropsOnce();
+				},
 				onChunk: (chunk: string) => {
 					streamedContent += chunk;
 					this.setConversationStreaming(
diff --git a/tools/server/webui/src/lib/stores/server.svelte.ts b/tools/server/webui/src/lib/stores/server.svelte.ts
index 1fd4afb040..c7056cc120 100644
--- a/tools/server/webui/src/lib/stores/server.svelte.ts
+++ b/tools/server/webui/src/lib/stores/server.svelte.ts
@@ -52,6 +52,7 @@ class ServerStore {
 	private _error = $state<string | null>(null);
 	private _serverWarning = $state<string | null>(null);
 	private _slotsEndpointAvailable = $state<boolean | null>(null);
+	private fetchServerPropsPromise: Promise<void> | null = null;
 
 	private readCachedServerProps(): ApiLlamaCppServerProps | null {
 		if (!browser) return null;
@@ -171,73 +172,65 @@ class ServerStore {
 	/**
 	 * Fetches server properties from the server
 	 */
-	async fetchServerProps(): Promise<void> {
-		this._loading = true;
-		this._error = null;
-		this._serverWarning = null;
+	async fetchServerProps(options: { silent?: boolean } = {}): Promise<void> {
+		const { silent = false } = options;
+		const isSilent = silent && this._serverProps !== null;
 
-		try {
-			console.log('Fetching server properties...');
-			const props = await ChatService.getServerProps();
-			this._serverProps = props;
-			this.persistServerProps(props);
-			console.log('Server properties loaded:', props);
+		if (this.fetchServerPropsPromise) {
+			return this.fetchServerPropsPromise;
+		}
 
-			// Check slots endpoint availability after server props are loaded
-			await this.checkSlotsEndpointAvailability();
-		} catch (error) {
-			const hadCachedProps = this._serverProps !== null;
-			let errorMessage = 'Failed to connect to server';
-			let isOfflineLikeError = false;
-			let isServerSideError = false;
+		if (!isSilent) {
+			this._loading = true;
+			this._error = null;
+			this._serverWarning = null;
+		}
 
-			if (error instanceof Error) {
-				// Handle specific error types with user-friendly messages
-				if (error.name === 'TypeError' && error.message.includes('fetch')) {
-					errorMessage = 'Server is not running or unreachable';
-					isOfflineLikeError = true;
-				} else if (error.message.includes('ECONNREFUSED')) {
-					errorMessage = 'Connection refused - server may be offline';
-					isOfflineLikeError = true;
-				} else if (error.message.includes('ENOTFOUND')) {
-					errorMessage = 'Server not found - check server address';
-					isOfflineLikeError = true;
-				} else if (error.message.includes('ETIMEDOUT')) {
-					errorMessage = 'Request timed out - the server took too long to respond';
-					isOfflineLikeError = true;
-				} else if (error.message.includes('503')) {
-					errorMessage = 'Server temporarily unavailable - try again shortly';
-					isServerSideError = true;
-				} else if (error.message.includes('500')) {
-					errorMessage = 'Server error - check server logs';
-					isServerSideError = true;
-				} else if (error.message.includes('404')) {
-					errorMessage = 'Server endpoint not found';
-				} else if (error.message.includes('403') || error.message.includes('401')) {
-					errorMessage = 'Access denied';
+		const hadProps = this._serverProps !== null;
+
+		const fetchPromise = (async () => {
+			try {
+				const props = await ChatService.getServerProps();
+				this._serverProps = props;
+				this.persistServerProps(props);
+				this._error = null;
+				this._serverWarning = null;
+				await this.checkSlotsEndpointAvailability();
+			} catch (error) {
+				if (isSilent && hadProps) {
+					console.warn('Silent server props refresh failed, keeping cached data:', error);
+					return;
 				}
+
+				this.handleFetchServerPropsError(error, hadProps);
+			} finally {
+				if (!isSilent) {
+					this._loading = false;
+				}
+
+				this.fetchServerPropsPromise = null;
 			}
+		})();
 
-			let cachedProps: ApiLlamaCppServerProps | null = null;
+		this.fetchServerPropsPromise = fetchPromise;
 
-			if (!hadCachedProps) {
-				cachedProps = this.readCachedServerProps();
-				if (cachedProps) {
-					this._serverProps = cachedProps;
-					this._error = null;
+		await fetchPromise;
+	}
 
-					if (isOfflineLikeError || isServerSideError) {
-						this._serverWarning = errorMessage;
-					}
+	/**
+	 * Handles fetch failures by attempting to recover cached server props and
+	 * updating the user-facing error or warning state appropriately.
+	 */
+	private handleFetchServerPropsError(error: unknown, hadProps: boolean): void {
+		const { errorMessage, isOfflineLikeError, isServerSideError } = this.normalizeFetchError(error);
 
-					console.warn(
-						'Failed to refresh server properties, using cached values from localStorage:',
-						errorMessage
-					);
-				} else {
-					this._error = errorMessage;
-				}
-			} else {
+		let cachedProps: ApiLlamaCppServerProps | null = null;
+
+		if (!hadProps) {
+			cachedProps = this.readCachedServerProps();
+
+			if (cachedProps) {
+				this._serverProps = cachedProps;
 				this._error = null;
 
 				if (isOfflineLikeError || isServerSideError) {
@@ -245,14 +238,66 @@ class ServerStore {
 				}
 
 				console.warn(
-					'Failed to refresh server properties, continuing with cached values:',
+					'Failed to refresh server properties, using cached values from localStorage:',
 					errorMessage
 				);
+			} else {
+				this._error = errorMessage;
 			}
-			console.error('Error fetching server properties:', error);
-		} finally {
-			this._loading = false;
+		} else {
+			this._error = null;
+
+			if (isOfflineLikeError || isServerSideError) {
+				this._serverWarning = errorMessage;
+			}
+
+			console.warn(
+				'Failed to refresh server properties, continuing with cached values:',
+				errorMessage
+			);
 		}
+
+		console.error('Error fetching server properties:', error);
+	}
+
+	private normalizeFetchError(error: unknown): {
+		errorMessage: string;
+		isOfflineLikeError: boolean;
+		isServerSideError: boolean;
+	} {
+		let errorMessage = 'Failed to connect to server';
+		let isOfflineLikeError = false;
+		let isServerSideError = false;
+
+		if (error instanceof Error) {
+			const message = error.message || '';
+
+			if (error.name === 'TypeError' && message.includes('fetch')) {
+				errorMessage = 'Server is not running or unreachable';
+				isOfflineLikeError = true;
+			} else if (message.includes('ECONNREFUSED')) {
+				errorMessage = 'Connection refused - server may be offline';
+				isOfflineLikeError = true;
+			} else if (message.includes('ENOTFOUND')) {
+				errorMessage = 'Server not found - check server address';
+				isOfflineLikeError = true;
+			} else if (message.includes('ETIMEDOUT')) {
+				errorMessage = 'Request timed out - the server took too long to respond';
+				isOfflineLikeError = true;
+			} else if (message.includes('503')) {
+				errorMessage = 'Server temporarily unavailable - try again shortly';
+				isServerSideError = true;
+			} else if (message.includes('500')) {
+				errorMessage = 'Server error - check server logs';
+				isServerSideError = true;
+			} else if (message.includes('404')) {
+				errorMessage = 'Server endpoint not found';
+			} else if (message.includes('403') || message.includes('401')) {
+				errorMessage = 'Access denied';
+			}
+		}
+
+		return { errorMessage, isOfflineLikeError, isServerSideError };
 	}
 
 	/**
@@ -264,6 +309,7 @@ class ServerStore {
 		this._serverWarning = null;
 		this._loading = false;
 		this._slotsEndpointAvailable = null;
+		this.fetchServerPropsPromise = null;
 		this.persistServerProps(null);
 	}
 }
diff --git a/tools/server/webui/src/lib/types/api.d.ts b/tools/server/webui/src/lib/types/api.d.ts
index 6d76ab1f68..6ebc43db0e 100644
--- a/tools/server/webui/src/lib/types/api.d.ts
+++ b/tools/server/webui/src/lib/types/api.d.ts
@@ -186,6 +186,7 @@ export interface ApiChatCompletionRequest {
 }
 
 export interface ApiChatCompletionStreamChunk {
+	object?: string;
 	model?: string;
 	choices: Array<{
 		model?: string;
diff --git a/tools/server/webui/src/lib/types/settings.d.ts b/tools/server/webui/src/lib/types/settings.d.ts
index 659fb0c7d1..946ef015e9 100644
--- a/tools/server/webui/src/lib/types/settings.d.ts
+++ b/tools/server/webui/src/lib/types/settings.d.ts
@@ -42,6 +42,7 @@ export interface SettingsChatServiceOptions {
 	onChunk?: (chunk: string) => void;
 	onReasoningChunk?: (chunk: string) => void;
 	onModel?: (model: string) => void;
+	onFirstValidChunk?: () => void;
 	onComplete?: (response: string, reasoningContent?: string, timings?: ChatMessageTimings) => void;
 	onError?: (error: Error) => void;
 }