server: add "verbose" field to schema (#24864)

* server: real-time model load progress tracking via /models/sse

* update docs

* add mutex for notify_to_router

* correct docs

common/speculative.cpp

@@ -905,7 +905,13 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
 
     int32_t n_embd = 0;
 
-    bool is_mem_shared = false;
+    // One MTP draft driver, three modes (set once in the ctor):
+    //   is_mem_shared (gemma4): shares the target KV, runs all heads in one graph.
+    //   chain_heads (step35): n_mtp_layers trained heads, one per draft step.
+    //   neither (qwen35 / qwen35moe): a single trained MTP head.
+    int32_t n_mtp_layers  = 1;
+    bool    is_mem_shared = false;   // gemma4
+    bool    chain_heads   = false;   // derived in the ctor: n_mtp_layers > 1 && !is_mem_shared
 
     // Per-sequence cross-batch carryover: pair (h_p, x_{p+1}) at MTP pos p+1.
     // The last h-row of one process() call needs the first token of the NEXT
@@ -920,10 +926,8 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
     std::vector<std::vector<float>> verify_h;
     std::vector<int32_t> verify_h_rows;
 
-    // Per-seq draft length from the last draft() call, used in accept() to
-    // roll back ctx_dft's recurrent state past the AR draft's redundant
-    // pre-advancement before process() mirrored the verify batch.
-    std::vector<uint16_t> last_n_drafted;
+    std::vector<int>                i_last;
+    std::vector<std::vector<float>> chain_h;
 
     common_speculative_impl_draft_mtp(const common_params_speculative & params, uint32_t n_seq)
         : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, n_seq)
@@ -936,6 +940,7 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
         n_embd = llama_model_n_embd_out(llama_get_model(ctx_dft));
         GGML_ASSERT(n_embd == llama_model_n_embd(llama_get_model(ctx_tgt)) &&
                 "MTP input row width must match the target h_nextn width");
+        n_mtp_layers = std::max(1, (int) llama_model_n_layer_nextn(llama_get_model(ctx_dft)));
 
         LOG_INF("%s: adding speculative implementation 'draft-mtp'\n", __func__);
         LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%.2f, n_embd=%d, backend_sampling=%d\n", __func__, this->params.n_max, this->params.n_min, this->params.p_min, n_embd, (int) this->params.backend_sampling);
@@ -982,16 +987,25 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
         llama_set_embeddings_nextn(ctx_dft, true, /*masked*/ true);
 
         is_mem_shared = llama_get_ctx_other(ctx_dft) == ctx_tgt;
+        chain_heads   = n_mtp_layers > 1 && !is_mem_shared;
+
+        if (chain_heads) {
+            this->params.n_max = std::min(this->params.n_max, n_mtp_layers);
+
+            chain_h.assign(n_seq, {});
+            for (auto & c : chain_h) {
+                c.reserve((size_t) (this->params.n_max + 1) * n_embd);
+            }
+        }
 
         pending_h.assign(n_seq, std::vector<float>(n_embd, 0.0f));
 
+        i_last.assign(n_seq, -1);
         i_batch_beg.assign(n_seq, -1);
         i_batch_end.assign(n_seq, -1);
 
         verify_h.assign(n_seq, {});
         verify_h_rows.assign(n_seq, 0);
-
-        last_n_drafted.assign(n_seq, 0);
     }
 
     ~common_speculative_impl_draft_mtp() override {
@@ -1097,9 +1111,34 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
                 set_h(i_batch_beg[seq_id], pending_h[seq_id].data());
             }
 
-            const int32_t rc = llama_decode(ctx_dft, batch);
-            if (rc != 0) {
-                LOG_ERR("%s: llama_decode(ctx_dft) failed rc=%d (pos=%d)\n", __func__, (int) rc, (int) batch_in.pos[0]);
+            auto * mem_dft = llama_get_memory(ctx_dft);
+
+            bool ok = true;
+            for (int head = 0; head < n_mtp_layers; ++head) {
+                if (chain_heads) {
+                    // ref: https://github.com/ggml-org/llama.cpp/pull/24340/changes#r3413498544
+                    for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+                        if (i_batch_beg[seq_id] < 0) {
+                            continue;
+                        }
+                        llama_memory_seq_rm(mem_dft, seq_id, batch_in.pos[i_batch_beg[seq_id]], -1);
+                    }
+                    llama_set_nextn_layer_offset(ctx_dft, head);
+                }
+
+                const int32_t rc = llama_decode(ctx_dft, batch);
+                if (rc != 0) {
+                    LOG_ERR("%s: llama_decode(ctx_dft) head=%d failed rc=%d (pos=%d)\n",
+                            __func__, head, (int) rc, (int) batch_in.pos[0]);
+                    ok = false;
+                    break;
+                }
+            }
+
+            if (chain_heads) {
+                llama_set_nextn_layer_offset(ctx_dft, 0); // restore default for non-draft decodes
+            }
+            if (!ok) {
                 return false;
             }
         }
@@ -1134,7 +1173,6 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
         int n_drafting = 0;
         std::vector<bool> drafting(n_seq);
 
-        const float * h_row = nullptr;
         const size_t row_bytes = (size_t) n_embd * sizeof(float);
 
         for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
@@ -1149,22 +1187,43 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
             common_sampler_reset(smpls[seq_id].get());
 
             common_batch_add(batch, dp.id_last, dp.n_past, { seq_id }, true);
+            std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd, pending_h[seq_id].data(), row_bytes);
 
-            h_row = pending_h[seq_id].data();
-            std::memcpy(batch.embd + n_embd*(batch.n_tokens - 1), h_row, row_bytes);
-        }
+            i_last[seq_id] = batch.n_tokens - 1;
 
-        int ret = llama_decode(ctx_dft, batch);
-        if (ret != 0) {
-            LOG_WRN("%s: llama_decode returned %d\n", __func__, ret);
-            return;
+            if (chain_heads) {
+                chain_h[seq_id].assign(pending_h[seq_id].begin(), pending_h[seq_id].end());
+            }
         }
 
         int i = 0;
 
         while (n_drafting > 0) {
-            int i_batch = 0;
+            // each step decodes under a different head, i.e. a different decoder layer, and
+            // KV is per layer. process() filled this layer's KV only for positions < n_past
+            // (prompt + accepted prefix) — nothing in the draft region yet. so reset the
+            // draft region (the seq_rm lower bound is n_past, leaving the prompt KV intact)
+            // and select head i so it rebuilds its own layer's KV there; decoding just the
+            // latest token would leave its attention reading cells only another head wrote.
+            if (chain_heads) {
+                auto * mem_dft = llama_get_memory(ctx_dft);
+                for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+                    if (drafting[seq_id]) {
+                        llama_memory_seq_rm(mem_dft, seq_id, dparams[seq_id].n_past, -1);
+                    }
+                }
+                llama_set_nextn_layer_offset(ctx_dft, i);
+            }
 
+            int ret = llama_decode(ctx_dft, batch);
+            if (ret != 0) {
+                LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
+                break;
+            }
+
+            // rebuild the batch for the next step: the growing-KV paths re-add only the
+            // new token (the KV already holds the prefix), while chained heads re-add the
+            // whole prefix at the next head. dropped sequences are simply not re-added.
             common_batch_clear(batch);
 
             for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
@@ -1174,9 +1233,8 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
 
                 auto * smpl = smpls[seq_id].get();
 
-                common_sampler_sample(smpl, ctx_dft, i_batch, true);
-                h_row = llama_get_embeddings_nextn_ith(ctx_dft, i_batch);
-                ++i_batch;
+                common_sampler_sample(smpl, ctx_dft, i_last[seq_id], true);
+                const float * h_row = llama_get_embeddings_nextn_ith(ctx_dft, i_last[seq_id]);
 
                 const auto * cur_p = common_sampler_get_candidates(smpl, true);
 
@@ -1210,30 +1268,41 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
                     continue;
                 }
 
-                if (is_mem_shared) {
+                if (chain_heads) {
+                    // ref: https://github.com/ggml-org/llama.cpp/pull/24340#discussion_r3448031546
+                    chain_h[seq_id].insert(chain_h[seq_id].end(), h_row, h_row + n_embd);
+
+                    const int n_rows = (int) result.size() + 1; // id_last + tokens drafted so far
+                    for (int t = 0; t < n_rows; ++t) {
+                        const llama_token tok = (t == 0) ? dp.id_last : result[t - 1];
+                        common_batch_add(batch, tok, dp.n_past + t, { seq_id }, t == n_rows - 1);
+                        std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd,
+                                    chain_h[seq_id].data() + (size_t) t * n_embd, row_bytes);
+                    }
+                } else if (is_mem_shared) {
                     // note: with shared memory (e.g. Gemma4 assistants) we use the same position for all draft tokens
                     // ref: https://github.com/huggingface/transformers/blob/effde20942e3f82a1b97449f60b3a48c5ff96145/docs/source/en/model_doc/gemma4_assistant.md?plain=1#L36-L37
                     common_batch_add(batch, id, dp.n_past, { seq_id }, true);
+                    std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd, h_row, row_bytes);
                 } else {
                     common_batch_add(batch, id, dp.n_past + i + 1, { seq_id }, true);
+                    std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd, h_row, row_bytes);
                 }
-                std::memcpy(batch.embd + n_embd*(batch.n_tokens - 1), h_row, row_bytes);
+
+                i_last[seq_id] = batch.n_tokens - 1;
             }
 
             if (batch.n_tokens == 0) {
                 break;
             }
 
-            // evaluate the drafted tokens on the draft model
-            ret = llama_decode(ctx_dft, batch);
-            if (ret != 0) {
-                LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
-                break;
-            }
-
             ++i;
         }
 
+        if (chain_heads) {
+            llama_set_nextn_layer_offset(ctx_dft, 0); // restore default for non-draft decodes
+        }
+
         for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
             auto & dp = dparams[seq_id];
             if (!dp.drafting) {
@@ -1243,8 +1312,6 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
             if (dp.result->size() < (size_t) params.n_min) {
                 dp.result->clear();
             }
-
-            last_n_drafted[seq_id] = (uint16_t) dp.result->size();
         }
     }
 
@@ -1857,7 +1924,7 @@ common_speculative * common_speculative_init(common_params_speculative & params,
 
         bool has_draft_simple = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE));
         bool has_draft_eagle3 = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3)) && params.draft.ctx_dft != nullptr;
-        bool has_mtp = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_MTP)) && params.draft.ctx_dft != nullptr;
+        bool has_draft_mtp    = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_MTP))    && params.draft.ctx_dft != nullptr;
 
 
 
@@ -1895,7 +1962,7 @@ common_speculative * common_speculative_init(common_params_speculative & params,
         if (has_draft_eagle3) {
             configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, params));
         }
-        if (has_mtp) {
+        if (has_draft_mtp) {
             configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, params));
         }
     }

include/llama.h

@@ -558,14 +558,15 @@ extern "C" {
     LLAMA_API const struct llama_vocab * llama_model_get_vocab(const struct llama_model * model);
     LLAMA_API enum llama_rope_type       llama_model_rope_type(const struct llama_model * model);
 
-    LLAMA_API int32_t llama_model_n_ctx_train(const struct llama_model * model);
-    LLAMA_API int32_t llama_model_n_embd     (const struct llama_model * model);
-    LLAMA_API int32_t llama_model_n_embd_inp (const struct llama_model * model);
-    LLAMA_API int32_t llama_model_n_embd_out (const struct llama_model * model);
-    LLAMA_API int32_t llama_model_n_layer    (const struct llama_model * model);
-    LLAMA_API int32_t llama_model_n_head     (const struct llama_model * model);
-    LLAMA_API int32_t llama_model_n_head_kv  (const struct llama_model * model);
-    LLAMA_API int32_t llama_model_n_swa      (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_ctx_train  (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_embd       (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_embd_inp   (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_embd_out   (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_layer      (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_layer_nextn(const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_head       (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_head_kv    (const struct llama_model * model);
+    LLAMA_API int32_t llama_model_n_swa        (const struct llama_model * model);
 
     // Get the model's RoPE frequency scaling factor
     LLAMA_API float llama_model_rope_freq_scale_train(const struct llama_model * model);

src/llama-context.cpp

@@ -1156,6 +1156,10 @@ void llama_context::set_embeddings_layer_inp(uint32_t lid, bool enable) {
     sched_need_reserve = true;
 }
 
+void llama_context::set_nextn_layer_offset(int32_t offset) {
+    cparams.nextn_layer_offset = offset;
+}
+
 void llama_context::set_causal_attn(bool value) {
     LLAMA_LOG_DEBUG("%s: value = %d\n", __func__, value);
 
@@ -3699,6 +3703,10 @@ void llama_set_embeddings_layer_inp(llama_context * ctx, uint32_t lid, bool valu
     ctx->set_embeddings_layer_inp(lid, value);
 }
 
+void llama_set_nextn_layer_offset(llama_context * ctx, int32_t offset) {
+    ctx->set_nextn_layer_offset(offset);
+}
+
 llama_memory_t llama_get_memory(const struct llama_context * ctx) {
     if (!ctx) {
         return nullptr;

src/llama-context.h

@@ -115,6 +115,7 @@ struct llama_context {
     void set_embeddings (bool value);
     void set_embeddings_nextn(bool value, bool masked);
     void set_embeddings_layer_inp(uint32_t lid, bool enable);
+    void set_nextn_layer_offset(int32_t offset);
     void set_causal_attn(bool value);
     void set_warmup(bool value);
 

src/llama-cparams.h

@@ -18,6 +18,8 @@ struct llama_cparams {
     int32_t  n_threads;       // number of threads to use for generation
     int32_t  n_threads_batch; // number of threads to use for batch processing
 
+    int32_t  nextn_layer_offset = 0;
+
     float rope_freq_base;
     float rope_freq_scale;
 

src/llama-ext.h

@@ -95,6 +95,11 @@ LLAMA_API llama_memory_breakdown llama_get_memory_breakdown(const struct llama_c
 // If masked == false, output the embeddings for all tokens in the batch regardless of batch.logits
 LLAMA_API void llama_set_embeddings_nextn(struct llama_context * ctx, bool value, bool masked);
 
+// Select which appended NextN block the DECODER_MTP graph runs (offset past
+// the trunk: il = n_layer() + offset). Used by the speculative NextN driver to
+// chain multiple trained NextN heads. Default 0 (first head).
+LLAMA_API void llama_set_nextn_layer_offset(struct llama_context * ctx, int32_t offset);
+
 // mirrors:
 // LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
 LLAMA_API float * llama_get_embeddings_nextn(struct llama_context * ctx);

src/llama-graph.h

@@ -682,9 +682,16 @@ struct llm_graph_params {
             }
         }
 
+        // TODO: https://github.com/ggml-org/llama.cpp/pull/24340#discussion_r3448035248
+        if (cparams.nextn_layer_offset != other.cparams.nextn_layer_offset) {
+            return false;
+        }
+
         return
-            cparams.embeddings  == other.cparams.embeddings  &&
-            cparams.causal_attn == other.cparams.causal_attn &&
+            cparams.embeddings              == other.cparams.embeddings              &&
+            cparams.embeddings_nextn        == other.cparams.embeddings_nextn        &&
+            cparams.embeddings_nextn_masked == other.cparams.embeddings_nextn_masked &&
+            cparams.causal_attn             == other.cparams.causal_attn             &&
             arch  == other.arch  &&
             gtype == other.gtype &&
             cvec  == other.cvec  &&

src/llama-model.cpp

@@ -2312,6 +2312,10 @@ int32_t llama_model_n_layer(const llama_model * model) {
     return model->hparams.n_layer();
 }
 
+int32_t llama_model_n_layer_nextn(const llama_model * model) {
+    return model->hparams.n_layer_nextn;
+}
+
 int32_t llama_model_n_head(const llama_model * model) {
     return model->hparams.n_head();
 }

src/models/step35.cpp

@@ -112,7 +112,7 @@ void llama_model_step35::load_arch_tensors(llama_model_loader & ml) {
         layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, TENSOR_NOT_REQUIRED);
     };
 
-    auto load_block_mtp = [&](int i, bool is_first_mtp) {
+    auto load_block_mtp = [&](int i) {
         auto & layer = layers[i];
 
         const uint32_t n_head_l      = hparams.n_head(i);
@@ -121,15 +121,12 @@ void llama_model_step35::load_arch_tensors(llama_model_loader & ml) {
 
         // The MTP block is a full Step3p5 decoder layer (mtp_block) plus the
         // NextN-specific wiring (enorm/hnorm/eh_proj + optional shared head).
-        // `mtp_flags` becomes NOT_REQUIRED when the GGUF is trunk-only.
-        //
-        // Only the FIRST MTP block (i == n_main) is required for the
-        // single-block MTP runtime; trailing MTP blocks are always tolerated
-        // as missing so pruned GGUFs (block 0 only) load cleanly. Override
-        // mtp_flags to NOT_REQUIRED for those.
-        const int eff_mtp_flags = is_first_mtp ? mtp_flags : (mtp_flags | TENSOR_NOT_REQUIRED);
+        // Multi-block MTP: every declared MTP block is required (the draft chain
+        // runs all n_layer_nextn heads), so each block uses the captured
+        // `mtp_flags` directly — already NOT_REQUIRED for a trunk-only GGUF,
+        // which keeps that path correct.
 
-        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, eff_mtp_flags);
+        layer.attn_norm   = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, mtp_flags);
         layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
         layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, TENSOR_NOT_REQUIRED);
 
@@ -140,12 +137,12 @@ void llama_model_step35::load_arch_tensors(llama_model_loader & ml) {
             layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot_max/2}, TENSOR_NOT_REQUIRED | TENSOR_DUPLICATED);
         }
 
-        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head_l, n_embd_k_gqa, n_embd_v_gqa, eff_mtp_flags);
-        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v * n_head_l, n_embd}, eff_mtp_flags);
+        create_tensor_qkv(layer, i, n_embd, n_embd_head_k * n_head_l, n_embd_k_gqa, n_embd_v_gqa, mtp_flags);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_v * n_head_l, n_embd}, mtp_flags);
 
         layer.wqkv_gate = create_tensor(tn(LLM_TENSOR_ATTN_GATE, "weight", i), {n_embd, n_head_l}, TENSOR_NOT_REQUIRED);
 
-        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, eff_mtp_flags);
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, mtp_flags);
 
         // dense MLP (leading dense blocks) — present if the MTP block isn't MoE
         layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, TENSOR_NOT_REQUIRED);
@@ -165,9 +162,9 @@ void llama_model_step35::load_arch_tensors(llama_model_loader & ml) {
         layer.ffn_down_shexp = create_tensor(tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {hparams.n_ff_shexp, n_embd}, TENSOR_NOT_REQUIRED);
 
         // NextN-specific tensors that define the MTP block.
-        layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ,          "weight", i), { 2 * n_embd, n_embd }, eff_mtp_flags);
-        layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,            "weight", i), { n_embd },              eff_mtp_flags);
-        layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,            "weight", i), { n_embd },              eff_mtp_flags);
+        layer.nextn.eh_proj          = create_tensor(tn(LLM_TENSOR_NEXTN_EH_PROJ,          "weight", i), { 2 * n_embd, n_embd }, mtp_flags);
+        layer.nextn.enorm            = create_tensor(tn(LLM_TENSOR_NEXTN_ENORM,            "weight", i), { n_embd },              mtp_flags);
+        layer.nextn.hnorm            = create_tensor(tn(LLM_TENSOR_NEXTN_HNORM,            "weight", i), { n_embd },              mtp_flags);
         layer.nextn.embed_tokens     = create_tensor(tn(LLM_TENSOR_NEXTN_EMBED_TOKENS,     "weight", i), { n_embd, n_vocab },     TENSOR_NOT_REQUIRED);
         layer.nextn.shared_head_head = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_HEAD, "weight", i), { n_embd, n_vocab },     TENSOR_NOT_REQUIRED);
         layer.nextn.shared_head_norm = create_tensor(tn(LLM_TENSOR_NEXTN_SHARED_HEAD_NORM, "weight", i), { n_embd },              TENSOR_NOT_REQUIRED);
@@ -176,13 +173,11 @@ void llama_model_step35::load_arch_tensors(llama_model_loader & ml) {
     for (int i = 0; i < n_layer; ++i) {
         load_block_trunk(i, trunk_flags);
     }
-    // Only the first MTP block (i == n_main) is required at runtime — the
-    // single-block-MTP graph in build_arch_graph always uses that one.
-    // Trailing MTP blocks are loaded if present (so an un-pruned GGUF with
-    // all MTP layers still works) but tolerated when absent via the pruning
-    // path. See scripts/prune_step35_extra_mtp.py for the pruner.
+    // All n_layer_nextn MTP blocks are required — the multi-block draft chain
+    // runs every head (head k at offset k). The GGUF declares the count via
+    // step35.nextn_predict_layers.
     for (int i = n_layer; i < n_layer_all; ++i) {
-        load_block_mtp(i, /*is_first_mtp=*/ i == n_layer);
+        load_block_mtp(i);
     }
 }
 
@@ -372,13 +367,14 @@ llama_model_step35::graph_mtp::graph_mtp(const llama_model & model, const llm_gr
     : llm_graph_context(params) {
     GGML_ASSERT(hparams.n_layer_nextn > 0 && "STEP35 MTP requires n_layer_nextn > 0");
 
-    // Single-block MTP only: always run the first trained MTP block (Qwen
-    // MTP / vLLM single-MTP-layer style). Multi-block round-robin proved to
-    // be a much deeper refactor than this PR justifies; the trailing MTP
-    // blocks are loaded with TENSOR_NOT_REQUIRED so pruned GGUFs (with just
-    // block 0) also work — see load_arch_tensors below and
-    // scripts/prune_step35_extra_mtp.py.
-    const int il = hparams.n_layer();
+    // Multi-block MTP: the DECODER_MTP graph runs the MTP head selected by
+    // cparams.nextn_layer_offset (0 = first trained head). The speculative driver
+    // bumps the offset per draft step to chain heads 45->46->47. offset 0 keeps
+    // single-block behavior identical to before.
+    const int il = hparams.n_layer() + cparams.nextn_layer_offset;
+    GGML_ASSERT(cparams.nextn_layer_offset >= 0 &&
+                cparams.nextn_layer_offset < (int) hparams.n_layer_nextn &&
+                "nextn_layer_offset out of range [0, n_layer_nextn)");
     const auto & layer = model.layers[il];
 
     GGML_ASSERT(layer.nextn.eh_proj && "MTP block missing nextn.eh_proj");
@@ -536,6 +532,9 @@ llama_model_step35::graph_mtp::graph_mtp(const llama_model & model, const llm_gr
     cur = ggml_add(ctx0, cur, ffn_inp);
     cb(cur, "mtp_post_ffn", il);
 
+    ggml_tensor * inp_out_ids = build_inp_out_ids();
+    cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+
     // Pre-norm hidden state: used by the AR draft loop to seed the next MTP step.
     cb(cur, "h_nextn", -1);
     res->t_h_nextn = cur;

tools/server/README.md

@@ -1859,9 +1859,33 @@ Example events:
 
 {
   "model": "...",
-  "event": "download_finished",
+  "event": "model_status",
   "data": {
-    "status": "loading"
+    "status": "loading",
+    "progress": {
+      "stage": "fit_params",
+      "value": 0.5 // from 0.0 to 1.0 ; note: not all stages have this "value"
+    }
+  }
+}
+
+{
+  "model": "...",
+  "event": "model_status",
+  "data": {
+    "status": "loaded",
+    "info": {
+      // note: only include info on first load
+      // waking up from sleep doesn't have this
+    }
+  }
+}
+
+{
+  "model": "...",
+  "event": "model_status",
+  "data": {
+    "status": "sleeping"
   }
 }
 

tools/server/server-context.cpp

@@ -833,6 +833,8 @@ private:
 
     bool sleeping = false;
 
+    int64_t t_last_load_progress_ms = 0;
+
     void destroy() {
         spec.reset();
         ctx_dft.reset();
@@ -863,6 +865,30 @@ private:
         sleeping = new_state;
     }
 
+    static bool load_progress_callback(float progress, void * user_data) {
+        auto * ctx = static_cast<server_context_impl *>(user_data);
+        GGML_ASSERT(ctx);
+        // always emit the first and final sample; throttle the rest to one per 200ms
+        {
+            auto & t_last = ctx->t_last_load_progress_ms;
+            const int64_t t_now = ggml_time_ms();
+            const bool first = t_last == 0;
+            const bool done  = progress >= 1.0f;
+            const bool throttled = !first && !done && (t_now - t_last) < 200;
+            if (throttled) {
+                return true;
+            }
+            t_last = t_now;
+        }
+        if (ctx->callback_state) {
+            ctx->callback_state(SERVER_STATE_LOADING, {
+                {"stage", "text_model"},
+                {"value", progress},
+            });
+        }
+        return true;
+    }
+
     // load the model and initialize llama_context
     // this may also be called to resume from sleeping state
     bool load_model(common_params & params) {
@@ -916,6 +942,10 @@ private:
 
         // optionally reserve VRAM for the draft / MTP context before fitting the target model
         if (params_base.fit_params) {
+            if (callback_state) {
+                callback_state(SERVER_STATE_LOADING, {{"stage", "fit_params"}});
+            }
+
             const bool spec_mtp = std::find(params_base.speculative.types.begin(),
                                             params_base.speculative.types.end(),
                                             COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params_base.speculative.types.end();
@@ -991,6 +1021,13 @@ private:
             }
         }
 
+        // attach a progress callback
+        {
+            t_last_load_progress_ms = 0;
+            params_base.load_progress_callback = load_progress_callback;
+            params_base.load_progress_callback_user_data = this;
+        }
+
         llama_init = common_init_from_params(params_base);
 
         model_tgt = llama_init->model();
@@ -1008,6 +1045,10 @@ private:
         add_bos_token = llama_vocab_get_add_bos(vocab);
 
         if (params_base.speculative.has_dft()) {
+            if (callback_state) {
+                callback_state(SERVER_STATE_LOADING, {{"stage", "spec_model"}});
+            }
+
             // TODO speculative: move to common/speculative.cpp?
             const auto & params_spec = params_base.speculative.draft;
 
@@ -1079,6 +1120,10 @@ private:
         }
 
         if (has_mmproj) {
+            if (callback_state) {
+                callback_state(SERVER_STATE_LOADING, {{"stage", "mmproj_model"}});
+            }
+
             if (!is_resume) {
                 mtmd_helper_log_set(common_log_default_callback, nullptr);
             }
@@ -1259,6 +1304,10 @@ private:
             return init();
         }
 
+        if (callback_state) {
+            callback_state(SERVER_STATE_READY, {});
+        }
+
         return true;
     }
 
@@ -1335,6 +1384,9 @@ private:
             const bool enable_thinking = params_base.enable_reasoning != 0 && template_supports_thinking;
             SRV_INF("%s: chat template, thinking = %d\n", __func__, enable_thinking);
 
+            // IMPORTANT: chat_params is reused across sleeping / resuming states,
+            //            never store llama_context/llama_model pointers in chat_params,
+            //            as they may be invalidated after sleeping
             chat_params = {
                 /* use_jinja             */ params_base.use_jinja,
                 /* prefill_assistant     */ params_base.prefill_assistant,
@@ -3734,7 +3786,10 @@ struct server_res_generator : server_http_res {
 void server_context::set_state_callback(server_state_callback_t callback) {
     impl->callback_state = std::move(callback);
     impl->queue_tasks.on_sleeping_state([this](bool sleeping) {
-        impl->callback_state(sleeping ? SERVER_STATE_SLEEPING : SERVER_STATE_READY, {});
+        if (sleeping) {
+            impl->callback_state(SERVER_STATE_SLEEPING, {});
+        }
+        // for sleeping == false, event is emitted by load_model()
     });
 }
 

tools/server/server-models.cpp

@@ -442,6 +442,7 @@ void server_models::load_models() {
                 /* last_used     */ 0,
                 /* args          */ std::vector<std::string>(),
                 /* loaded_info   */ {},
+                /* progress      */ {},
                 /* exit_code     */ 0,
                 /* stop_timeout  */ DEFAULT_STOP_TIMEOUT,
                 /* multimodal    */ mtmd_caps{false, false},
@@ -608,6 +609,7 @@ void server_models::load_models() {
                     /* last_used     */ 0,
                     /* args          */ std::vector<std::string>(),
                     /* loaded_info   */ {},
+                    /* progress      */ {},
                     /* exit_code     */ 0,
                     /* stop_timeout  */ DEFAULT_STOP_TIMEOUT,
                     /* multimodal    */ mtmd_caps{false, false},
@@ -1140,6 +1142,9 @@ void server_models::update_status(const std::string & name, const update_status_
         if (!args.loaded_info.is_null()) {
             meta.loaded_info = args.loaded_info;
         }
+        if (!args.progress.is_null()) {
+            meta.progress = args.progress;
+        }
     }
     // broadcast status change to SSE
     {
@@ -1152,6 +1157,9 @@ void server_models::update_status(const std::string & name, const update_status_
         if (!args.loaded_info.is_null()) {
             data["info"] = args.loaded_info;
         }
+        if (!args.progress.is_null()) {
+            data["progress"] = args.progress;
+        }
         // note: notify_sse doesn't acquire the lock, so no deadlock here
         notify_sse("status_change", name, data);
     }
@@ -1322,8 +1330,12 @@ void server_models::handle_child_state(const std::string & name, const std::stri
     switch (state) {
         case SERVER_STATE_LOADING:
             {
-                // do nothing for now
-                // TODO: report loading progress for first load and wakeup from sleep
+                update_status(name, {
+                    SERVER_MODEL_STATUS_LOADING,
+                    0,
+                    nullptr, // no loaded_info yet
+                    payload,
+                });
             } break;
         case SERVER_STATE_READY:
             {
@@ -1331,7 +1343,8 @@ void server_models::handle_child_state(const std::string & name, const std::stri
                     SERVER_MODEL_STATUS_LOADED,
                     0,
                     // note: payload can be empty if this is a wakeup from sleep
-                    payload.size() > 0 ? payload : nullptr
+                    payload.size() > 0 ? payload : nullptr,
+                    {}, // reset progress info
                 });
             } break;
         case SERVER_STATE_SLEEPING:
@@ -1384,6 +1397,7 @@ void server_child::notify_to_router(const std::string & state, const json & payl
         {"state", state},
         {"payload", payload},
     };
+    std::lock_guard<std::mutex> lk(mtx_stdout);
     common_log_pause(common_log_main());
     fflush(stdout);
     fprintf(stdout, "%s%s\n", CMD_CHILD_TO_ROUTER_STATE, safe_json_to_str(data).c_str());

tools/server/server-models.h

@@ -72,6 +72,7 @@ struct server_model_meta {
     int64_t last_used = 0; // for LRU unloading
     std::vector<std::string> args; // args passed to the model instance, will be populated by render_args()
     json loaded_info; // info to be reflected via /v1/models endpoint ; if in DOWNLOADING state, it should contain download progress info
+    json progress; // reflect load or download progress info, if any
     int exit_code = 0; // exit code of the model instance process (only valid if status == FAILED)
     int stop_timeout = 0; // seconds to wait before force-killing the model instance during shutdown
     mtmd_caps multimodal; // multimodal capabilities
@@ -170,12 +171,14 @@ public:
     // to stop the download, call unload()
     void download(common_params_model && model, common_download_opts && opts);
 
-    // update the status of a model instance (thread-safe)
     struct update_status_args {
         server_model_status status;
         int exit_code = 0; // only valid if status == UNLOADED
         json loaded_info = nullptr;
+        json progress = nullptr;
     };
+    // update the status of a model instance (thread-safe)
+    // also send SSE notification to /models/sse endpoint
     void update_status(const std::string & name, const update_status_args & args);
     void update_download_progress(const std::string & name, const common_download_progress & progress, bool done, bool ok = true);
 
@@ -208,6 +211,9 @@ public:
 };
 
 struct server_child {
+    // serializes the notify_to_router writes
+    std::mutex mtx_stdout;
+
     // return true if the current process is a child server instance
     bool is_child();
 

tools/server/server-schema.cpp

@@ -14,6 +14,9 @@ std::vector<std::unique_ptr<field>> make_llama_cmpl_schema(const common_params &
         fields.emplace_back(f);
     };
 
+    add((new field_bool("verbose", params.verbose))
+        ->set_desc("Include __verbose field in the response with additional debug information"));
+
     add((new field_bool("timings_per_token", params.timings_per_token))
         ->set_desc("Include prompt processing and text generation speed information in each response"));
 

tools/server/tests/unit/test_chat_completion.py

@@ -603,3 +603,23 @@ def test_chat_completions_token_count():
         })
         assert res.status_code == 200
         assert res.body["input_tokens"] > 5
+
+
+def test_verbose_debug():
+    global server
+    server.start()
+    for verbose in [True, False]:
+        res = server.make_request("POST", "/chat/completions", data={
+            "max_tokens": 2,
+            "messages": [
+                {"role": "system", "content": "Book"},
+                {"role": "user", "content": "What is the best book"},
+            ],
+            "verbose": verbose,
+        })
+        assert res.status_code == 200
+        if verbose:
+            assert "__verbose" in res.body
+            assert "Book" in res.body["__verbose"]["prompt"]
+        else:
+            assert "__verbose" not in res.body