Merge branch 'upstream' into concedo_experimental

# Conflicts:
#	.devops/cuda.Dockerfile
#	.github/workflows/build-sycl.yml
#	.github/workflows/release.yml
#	ggml/CMakeLists.txt
#	ggml/src/ggml-hexagon/ggml-hexagon.cpp
#	ggml/src/ggml-hexagon/htp/gated-delta-net-ops.c
#	ggml/src/ggml-opencl/CMakeLists.txt
#	ggml/src/ggml-opencl/ggml-opencl.cpp
#	ggml/src/ggml-opencl/kernels/cvt.cl
#	ggml/src/ggml-opencl/kernels/gated_delta_net.cl
#	ggml/src/ggml-sycl/gated_delta_net.cpp
#	ggml/src/ggml-webgpu/ggml-webgpu.cpp
#	ggml/src/ggml-webgpu/wgsl-shaders/gated_delta_net.wgsl
#	scripts/sync-ggml.last
#	tests/test-backend-ops.cpp
#	tests/test-llama-archs.cpp

common/speculative.cpp

@@ -375,31 +375,437 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
     }
 };
 
+
+// EAGLE3 speculative decoding state
+//
+// Input of draft decoder: (This is different compared to MTP)
+//   At "pos P", the decoder takes input pair (t_{P+1}, g_P), with RoPE at P.
+//     - t_{P+1} = token at sequence pos P+1 (the *next* token after P)
+//     - g_P     = encoder output = projection of target's extracted hidden states at P
+//
+// Deferred boundary (MTP doesn't have this issue):
+//   Within a single process() call with n_tokens, we can only write decoder KV for
+//   training pos 0..n_tokens-2. The last training pos (n_tokens-1) needs t_{n_tokens}
+//   which lies *outside* this batch — it is the token target will sample next or the first token from next ubatch.
+//   So the last training pos of each process() call is *deferred* to whichever next call has
+//   the missing token in hand:
+//     - multi-ubatch prefill: the next process()'s first token completes the pair
+//                              (handled by the per-seq "cross-ubatch bridge")
+//     - single-ubatch prefill / after verify: draft()'s seed step uses "dp.id_last"
+//                              (target's freshest sample) to complete the pair
+//
+// Per-seq carry-over state:
+//   pending_g_last    [n_embd_dec]  ┐  the deferred boundary's (g, pos). Set by
+//   pending_pos_last  llama_pos     ┘  process() at end of ubatch (= last row);
+//                                       rebased by accept() to first-non-accepted pos.
+//   verify_g          [N × n_embd_dec] snapshot of process()'s encoder output;
+//   verify_pos_first  llama_pos         consumed by accept() to recover the right
+//   verify_g_rows     int32_t           pending_g_last row for any n_accepted value.
+//
+// Performance is overall good but there is waste in verify cycle:
+//   process() runs encoder + decoder on the *full* verify batch including rows for
+//   rejected drafts. The KV at those positions is then dropped.
+//
+// TODO: Not sure if we need optimization for this waste?
+// If so we may need hybrid stash:
+//      in verify mode, have process() only stash features and let draft() seed run
+//      encoder+decoder on n_accepted+1 rows).
 struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
-    //common_params_speculative_eagle3 params;
+    common_params_speculative_draft params;
+    llama_batch batch;
+
+    std::vector<common_sampler_ptr> smpls;
+
+    int32_t n_embd_dec = 0;       // draft hidden size
+    int32_t n_embd_enc = 0;       // target_layer_ids_n * target_hidden_size
+    int32_t n_embd_tgt = 0;       // target model hidden size
+
+    const int32_t * target_layer_ids   = nullptr; // model_dft's extract layer indices
+    uint32_t        target_layer_ids_n = 0;
+
+    // [per-seq] deferred boundary state
+    std::vector<std::vector<float>> pending_g_last;
+    std::vector<llama_pos>          pending_pos_last;
+
+    // [per-seq] snapshot of the most recent process()'s encoder output
+    std::vector<std::vector<float>> verify_g;         // [n_seq][n_rows * n_embd_dec]
+    std::vector<llama_pos>          verify_pos_first; // [n_seq] — pos of verify_g[seq][0]
+    std::vector<int32_t>            verify_g_rows;    // [n_seq] — number of rows
+
+    // scratch buffer for concatenated target features [n_tokens, n_embd_enc]
+    std::vector<float> features_buf;
+    std::vector<float> g_embd_buf;
 
     common_speculative_impl_draft_eagle3(const common_params_speculative & params, uint32_t n_seq)
         : common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, n_seq)
+        , params(params.draft)
     {
         LOG_INF("%s: adding speculative implementation 'draft-eagle3'\n", __func__);
         LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%f\n", __func__, params.draft.n_max, params.draft.n_min, params.draft.p_min);
+
+        auto * ctx_tgt = this->params.ctx_tgt;
+        auto * ctx_dft = this->params.ctx_dft;
+        GGML_ASSERT(ctx_tgt && ctx_dft && "EAGLE3 requires ctx_tgt and ctx_dft to be set");
+
+        const llama_model * model_dft = llama_get_model(ctx_dft);
+        const llama_model * model_tgt = llama_get_model(ctx_tgt);
+
+        target_layer_ids   = llama_model_target_layer_ids  (model_dft);
+        target_layer_ids_n = llama_model_target_layer_ids_n(model_dft);
+        if (target_layer_ids_n != 3) {
+            throw std::runtime_error("draft model is not eagle3 (expected 3 extract layers, got " +
+                                     std::to_string(target_layer_ids_n) + ")");
+        }
+
+        n_embd_tgt = llama_model_n_embd(model_tgt);
+        n_embd_dec = llama_model_n_embd(model_dft);
+        n_embd_enc = (int32_t) target_layer_ids_n * n_embd_tgt;
+
+        const int32_t n_b = (int32_t) llama_n_batch(ctx_dft);
+        batch = llama_batch_init(/*n_tokens=*/ n_b, /*embd=*/ n_embd_dec, /*n_seq_max=*/ 1);
+        // llama_batch_init allocates only one of token/embd; eagle3 decoder needs both.
+        // TODO: fix, how to call without malloc
+        batch.token = (llama_token *) malloc(sizeof(llama_token) * n_b);
+
+        smpls.resize(n_seq);
+        for (auto & s : smpls) {
+            common_params_sampling sparams;
+            sparams.no_perf  = false;
+            sparams.top_k    = 10;
+            sparams.samplers = { COMMON_SAMPLER_TYPE_TOP_K };
+            s.reset(common_sampler_init(llama_get_model(ctx_dft), sparams));
+        }
+
+        // turn on extraction of the target layers' input embeddings
+        for (uint32_t k = 0; k < target_layer_ids_n; ++k) {
+            llama_set_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k], true);
+        }
+
+        // turn on extraction of the draft model's pre-norm hidden state
+        // (used both for the encoder output g_embd and the decoder pre-norm output).
+        llama_set_embeddings_nextn(ctx_dft, true, /*masked*/ true);
+
+        pending_g_last.assign(n_seq, std::vector<float>(n_embd_dec, 0.0f));
+        pending_pos_last.assign(n_seq, -1);
+
+        verify_g.assign(n_seq, std::vector<float>());
+        verify_pos_first.assign(n_seq, -1);
+        verify_g_rows.assign(n_seq, 0);
     }
 
-    void begin(llama_seq_id /*seq_id*/, const llama_tokens & /*prompt*/) override {
-        // noop
+    ~common_speculative_impl_draft_eagle3() override {
+        if (batch.token != nullptr) {
+            free(batch.token);
+            batch.token = nullptr;
+        }
+        llama_batch_free(batch);
     }
 
-    bool process(const llama_batch & /*batch*/) override {
-        // TODO: implement
+    void begin(llama_seq_id seq_id, const llama_tokens & prompt) override {
+        const int32_t N = (int32_t) prompt.size();
+        if (N <= 0) {
+            return;
+        }
+        // expected state after prefill: ctx_dft has pos 0..N-2 (last position is deferred to
+        // draft()'s seed step). Warn only if more than one position is missing.
+        auto * ctx_dft = this->params.ctx_dft;
+        const llama_pos pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx_dft), seq_id);
+        if (pos_max < N - 2) {
+            LOG_WRN("%s: ctx_dft pos_max=%d < N-2=%d — process() did not run on every prefill ubatch. "
+                    "Drafts may degrade.\n",
+                    __func__, (int) pos_max, N - 2);
+        }
+    }
+
+    bool process(const llama_batch & batch_in) override {
+        if (batch_in.n_tokens <= 0) {
+            return true;
+        }
+
+        if (batch_in.token == nullptr || batch_in.embd != nullptr) {
+            return true;
+        }
+
+        const int32_t n_tokens = batch_in.n_tokens;
+
+        // i_batch_beg[seq] / i_batch_end[seq]: inclusive batch indices of this seq's
+        // first/last token in batch_in. Assumes per-seq tokens are contiguous within
+        // the ubatch (server's default ordering).
+        std::vector<int32_t> i_batch_beg(n_seq, -1);
+        std::vector<int32_t> i_batch_end(n_seq, -1);
+        for (int k = 0; k < n_tokens; ++k) {
+            GGML_ASSERT(batch_in.n_seq_id[k] == 1);
+            const llama_seq_id seq_id = batch_in.seq_id[k][0];
+            if (seq_id < 0 || seq_id >= (llama_seq_id) n_seq) {
+                continue;
+            }
+            i_batch_end[seq_id] = k;
+            if (i_batch_beg[seq_id] < 0) {
+                i_batch_beg[seq_id] = k;
+            }
+        }
+
+        auto * ctx_tgt = this->params.ctx_tgt;
+        auto * ctx_dft = this->params.ctx_dft;
+
+        // Interleave each extract_layer's hidden state into a contiguous buffer of
+        // shape [n_tokens, target_layer_ids_n * n_embd_tgt]. Then run EAGLE3 encoder
+        // to get one g_embd row per token.
+        features_buf.resize((size_t) n_tokens * n_embd_enc, 0.0f);
+
+        for (uint32_t k = 0; k < target_layer_ids_n; ++k) {
+            const float * layer = llama_get_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k]);
+            if (!layer) {
+                GGML_ABORT("EAGLE3: target layer %d input not extracted.", target_layer_ids[k]);
+            }
+            for (int32_t i = 0; i < n_tokens; ++i) {
+                float * dst = features_buf.data() + (size_t) i * n_embd_enc + k * (size_t) n_embd_tgt;
+                const float * src = layer + (size_t) i * n_embd_tgt;
+                std::memcpy(dst, src, (size_t) n_embd_tgt * sizeof(float));
+            }
+        }
+
+        g_embd_buf.resize((size_t) n_tokens * n_embd_dec);
+
+        // llama_encode() requires the full encoder batch to fit in n_ubatch.
+        // Allow batch > ubatch: eagle3's per-token encoder can be chunked safely.
+        const int32_t n_ubatch_dft = (int32_t) llama_n_ubatch(ctx_dft);
+        for (int32_t i = 0; i < n_tokens; i += n_ubatch_dft) {
+            const int32_t n_chunk = std::min(n_ubatch_dft, n_tokens - i);
+
+            llama_batch enc_batch = {
+                /*.n_tokens =*/ n_chunk,
+                /*.token    =*/ nullptr,
+                /*.embd     =*/ features_buf.data() + (size_t) i * n_embd_enc,
+                /*.pos      =*/ nullptr,
+                /*.n_seq_id =*/ nullptr,
+                /*.seq_id   =*/ nullptr,
+                /*.logits   =*/ nullptr,
+            };
+            const int32_t rc = llama_encode(ctx_dft, enc_batch);
+            if (rc != 0) {
+                LOG_ERR("%s: llama_encode(ctx_dft) failed rc=%d (n_tokens=%d, offset=%d)\n",
+                        __func__, rc, (int) n_chunk, (int) i);
+                return false;
+            }
+
+            // g_embd has shape [n_chunk, n_embd_dec] in ctx_dft's pre-norm embeddings buffer.
+            const float * g_embd_chunk = llama_get_embeddings_nextn(ctx_dft);
+            GGML_ASSERT(g_embd_chunk && "EAGLE3 encoder produced no output.");
+            std::memcpy(g_embd_buf.data() + (size_t) i * n_embd_dec,
+                        g_embd_chunk,
+                        (size_t) n_chunk * n_embd_dec * sizeof(float));
+        }
+
+        const float * g_embd = g_embd_buf.data();
+
+        const size_t row_bytes = (size_t) n_embd_dec * sizeof(float);
+
+        // EAGLE3 decoder input convention: at memory pos P the input pair is
+        // (token[P+1], g_embd[P]). This shifts the token index "left by one" relative to g_embd.
+        //
+        // Per seq, in order:
+        //   (a) cross-ubatch bridge — when applicable, write the previously-deferred
+        //       pos using this ubatch's first token + pending_g_last.
+        //   (b) main write loop — for k in [beg, end-1], write (token[k+1], g_embd[k])
+        //       at pos[k]. The last training pos (k=end) is left unwritten = new
+        //       deferred boundary, completed by the next process() or draft() call.
+        //   (c) refresh deferred state — stash this ubatch's full g_embd into verify_g,
+        //       update pending_g_last / pending_pos_last to the last row.
+        common_batch_clear(batch);
+
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            const int32_t beg = i_batch_beg[seq_id];
+            const int32_t end = i_batch_end[seq_id];
+            if (beg < 0 || end < 0) {
+                continue;
+            }
+
+            // cross-ubatch bridge — complete the prior ubatch's deferred boundary.
+            // Fires iff all three preconditions hold:
+            //   1) pending_pos_last >= 0
+            //   2) pending_pos_last + 1 == pos[beg]
+            //   3) pending_pos_last > dft_pos_max // TODO: is this check needed?
+            const llama_pos pending_pos = pending_pos_last[seq_id];
+            if (pending_pos >= 0 && pending_pos + 1 == batch_in.pos[beg]) {
+                const llama_pos dft_pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx_dft), seq_id);
+                if (pending_pos > dft_pos_max) {
+                    common_batch_add(batch, batch_in.token[beg], pending_pos, { seq_id }, /*logits=*/ false);
+                    std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd_dec,
+                                pending_g_last[seq_id].data(), row_bytes);
+                }
+            }
+
+            for (int32_t k = beg; k < end; ++k) {
+                common_batch_add(batch, batch_in.token[k + 1], batch_in.pos[k], { seq_id }, /*logits=*/ false);
+                std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd_dec,
+                            g_embd + (size_t) k * n_embd_dec, row_bytes);
+            }
+
+            // refresh deferred state
+            const int32_t n_rows = end - beg + 1;
+            verify_pos_first[seq_id] = batch_in.pos[beg];
+            pending_pos_last[seq_id] = batch_in.pos[end];
+            verify_g_rows[seq_id]    = n_rows;
+            verify_g[seq_id].resize((size_t) n_rows * n_embd_dec, 0.0f);
+            std::memcpy(verify_g[seq_id].data(),       g_embd + (size_t) beg * n_embd_dec, row_bytes * n_rows);
+            std::memcpy(pending_g_last[seq_id].data(), g_embd + (size_t) end * n_embd_dec, row_bytes);
+        }
+
+        if (batch.n_tokens > 0) {
+            const int32_t rc = llama_decode(ctx_dft, batch);
+            if (rc != 0) {
+                LOG_ERR("%s: llama_decode(ctx_dft) failed rc=%d (n_tokens=%d, ubatch_pos[0]=%d)\n",
+                        __func__, rc, (int) batch.n_tokens, (int) batch_in.pos[0]);
+                return false;
+            }
+        }
+
         return true;
     }
 
-    void draft(common_speculative_draft_params_vec & /*dparams*/) override {
-        // TODO: implement
+    void draft(common_speculative_draft_params_vec & dparams) override {
+        auto & ctx_dft = params.ctx_dft;
+
+        common_batch_clear(batch);
+
+        // keep track of which sequences are still drafting
+        int n_drafting = 0;
+        std::vector<bool> drafting(n_seq);
+
+        const size_t row_bytes = (size_t) n_embd_dec * sizeof(float);
+
+        // Complete the deferred boundary pair (dp.id_last, pending_g_last) at memory
+        // pos pending_pos_last. dp.id_last is target's freshest sample (= corrected
+        // token after verify, or first generated token after prefill), matching the
+        // EAGLE3 input convention (token[P+1], g_embd[P]) at pos P.
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            auto & dp = dparams[seq_id];
+
+            if (!dp.drafting) {
+                continue;
+            }
+            if (pending_pos_last[seq_id] < 0) {
+                continue;
+            }
+
+            n_drafting++;
+            drafting[seq_id] = true;
+            common_sampler_reset(smpls[seq_id].get());
+
+            llama_memory_seq_rm(llama_get_memory(ctx_dft), seq_id, pending_pos_last[seq_id], -1);
+
+            common_batch_add(batch, dp.id_last, pending_pos_last[seq_id], { seq_id }, true);
+            std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd_dec,
+                        pending_g_last[seq_id].data(),
+                        row_bytes);
+        }
+
+        if (batch.n_tokens == 0) {
+            return;
+        }
+
+        int ret = llama_decode(ctx_dft, batch);
+        if (ret != 0) {
+            LOG_WRN("%s: llama_decode returned %d\n", __func__, ret);
+            return;
+        }
+
+        int i = 0;
+
+        while (n_drafting > 0) {
+            int i_batch = 0;
+
+            common_batch_clear(batch);
+
+            for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+                if (!drafting[seq_id]) {
+                    continue;
+                }
+
+                auto * smpl = smpls[seq_id].get();
+
+                common_sampler_sample(smpl, ctx_dft, i_batch, true);
+                // pre-norm hidden state of this position becomes g_embd for the next step
+                const float * prenorm = llama_get_embeddings_nextn_ith(ctx_dft, i_batch);
+                ++i_batch;
+
+                const auto * cur_p = common_sampler_get_candidates(smpl, true);
+
+                for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
+                    LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
+                            seq_id, k, i, cur_p->data[k].id, cur_p->data[k].p,
+                            common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
+                }
+
+                const llama_token id = cur_p->data[0].id;
+
+                // only collect very high-confidence draft tokens
+                // (configurable via --spec-draft-p-min, set to 0.0 to disable early-stop)
+                if (cur_p->data[0].p < params.p_min) {
+                    drafting[seq_id] = false;
+                    n_drafting--;
+
+                    continue;
+                }
+
+                common_sampler_accept(smpl, id, true);
+
+                auto & dp = dparams.at(seq_id);
+                auto & result = *dp.result;
+
+                result.push_back(id);
+
+                if (params.n_max <= (int) result.size()) {
+                    drafting[seq_id] = false;
+                    n_drafting--;
+                    continue;
+                }
+
+                common_batch_add(batch, id, pending_pos_last[seq_id] + (i + 1), { seq_id }, true);
+                std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd_dec, prenorm, row_bytes);
+            }
+
+            if (batch.n_tokens == 0) {
+                break;
+            }
+
+            ret = llama_decode(ctx_dft, batch);
+            if (ret != 0) {
+                LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
+                break;
+            }
+
+            ++i;
+        }
+
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+            auto & dp = dparams[seq_id];
+            if (!dp.drafting) {
+                continue;
+            }
+
+            if (dp.result->size() < (size_t) params.n_min) {
+                dp.result->clear();
+            }
+        }
     }
 
-    void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/, bool /*is_other*/) override {
-        // noop
+    void accept(llama_seq_id seq_id, uint16_t n_accepted, bool /*is_other*/) override {
+        if (seq_id < 0 || seq_id >= (llama_seq_id) n_seq) {
+            return;
+        }
+
+        const int32_t n_rows = verify_g_rows[seq_id];
+        if (n_rows <= 0) {
+            return;
+        }
+
+        const int32_t i_g = std::min<int32_t>(n_accepted, n_rows - 1);
+        pending_pos_last[seq_id] = verify_pos_first[seq_id] + i_g;
+        std::memcpy(pending_g_last[seq_id].data(),
+                    verify_g[seq_id].data() + (size_t) i_g * n_embd_dec,
+                    (size_t) n_embd_dec * sizeof(float));
     }
 
     bool need_embd() const override {
@@ -1370,9 +1776,11 @@ common_speculative * common_speculative_init(common_params_speculative & params,
         uint32_t enabled_configs = common_get_enabled_speculative_configs(params.types);
 
         bool has_draft_simple = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE));
-        bool has_draft_eagle3 = false; // TODO PR-18039: if params.speculative.eagle3
+        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_ngram_cache   = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_CACHE));
         bool has_ngram_simple  = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE));
         bool has_ngram_map_k   = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K));

conversion/__init__.py

@@ -130,6 +130,9 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "LlamaBidirectionalModel": "llama",
     "LlamaForCausalLM": "llama",
     "LlamaModel": "llama",
+    "Eagle3DraftModel": "llama",
+    "Eagle3Speculator": "llama",
+    "LlamaForCausalLMEagle3": "llama",
     "LlavaForConditionalGeneration": "llama",
     "LlavaStableLMEpochForCausalLM": "stablelm",
     "MPTForCausalLM": "mpt",

conversion/base.py

@@ -94,6 +94,7 @@ class ModelBase:
     metadata: gguf.Metadata
     dir_model_card: Path
     remote_hf_model_id: str | None
+    target_model_dir: Path | None
 
     # subclasses should define this!
     model_arch: gguf.MODEL_ARCH
@@ -119,6 +120,7 @@ class ModelBase:
                  small_first_shard: bool = False, hparams: dict[str, Any] | None = None, remote_hf_model_id: str | None = None,
                  disable_mistral_community_chat_template: bool = False,
                  sentence_transformers_dense_modules: bool = False,
+                 target_model_dir: Path | None = None,
                  fuse_gate_up_exps: bool = False,
                  fp8_as_q8: bool = False):
         if type(self) is ModelBase or \
@@ -139,6 +141,7 @@ class ModelBase:
         self.dry_run = dry_run
         self.remote_hf_model_id = remote_hf_model_id
         self.sentence_transformers_dense_modules = sentence_transformers_dense_modules
+        self.target_model_dir = target_model_dir
         self.fuse_gate_up_exps = fuse_gate_up_exps
         self._gate_exp_buffer: dict[int, Tensor] = {}
         self._up_exp_buffer: dict[int, Tensor] = {}
@@ -2481,6 +2484,7 @@ class LazyTorchTensor(gguf.LazyBase):
         torch.float16: np.float16,
         torch.float32: np.float32,
         torch.uint8: np.uint8,
+        torch.int64: np.int64,
     }
 
     # only used when byteswapping data. Only correct size is needed

conversion/llama.py

@@ -5,12 +5,13 @@ import math
 
 from typing import Callable, Iterable, TYPE_CHECKING
 
+import numpy as np
 import torch
 
 if TYPE_CHECKING:
     from torch import Tensor
 
-from .base import ModelBase, TextModel, gguf
+from .base import ModelBase, TextModel, gguf, logger
 
 
 @ModelBase.register(
@@ -21,6 +22,9 @@ from .base import ModelBase, TextModel, gguf
     "VLlama3ForCausalLM",
     "LlavaForConditionalGeneration",
     "VoxtralForConditionalGeneration",
+    "LlamaForCausalLMEagle3",
+    "Eagle3Speculator",
+    "Eagle3DraftModel",
     "IQuestCoderForCausalLM",
     "LlamaModel")
 class LlamaModel(TextModel):
@@ -39,7 +43,61 @@ class LlamaModel(TextModel):
             hparams = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
             self.origin_hf_arch = hparams.get('architectures', [None])[0]
 
+        # Detect eagle3 draft checkpoint by hparams (some models don't use a distinct HF arch name)
+        if "draft_vocab_size" in self.hparams and self.hparams["num_hidden_layers"] == 1:
+            self.is_eagle3 = True
+            self.model_arch = gguf.MODEL_ARCH.EAGLE3
+            logger.info("Detected EAGLE-3 draft model, switching to EAGLE3 architecture")
+            # Re-initialize tensor_map with eagle3 architecture
+            self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+            # Update gguf_writer architecture
+            self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[self.model_arch]
+            self.gguf_writer.add_architecture()
+            if self.target_model_dir is None:
+                raise ValueError(
+                    "EAGLE-3 model requires --target-model-dir to be specified. "
+                    "Please provide the path to the target model directory to read config.json"
+                )
+            # Read both eagle3 raw config and target model config
+            with open(self.dir_model / "config.json", 'r', encoding='utf-8') as f:
+                eagle3_raw_config = json.load(f)
+            with open(self.target_model_dir / "config.json", 'r', encoding='utf-8') as f:
+                target_config = json.load(f)
+
+            if "text_config" in target_config:
+                target_config = {**target_config, **target_config["text_config"]}
+            self.target_vocab_size = target_config["vocab_size"]
+
+            # target_layers: derived from target model layer count (low/mid/high)
+            target_num_layers = target_config["num_hidden_layers"]
+            target_layers = [2, target_num_layers // 2, target_num_layers - 3]
+            logger.info(f"EAGLE-3: target_layers = {target_layers} (target model has {target_num_layers} layers)")
+            self.gguf_writer.add_array(f"{self.gguf_writer.arch}.target_layers", target_layers)
+
+            # target_hidden_size: prefer eagle3 config, fallback to target config
+            if eagle3_raw_config.get("target_hidden_size") is not None:
+                target_hidden_size = eagle3_raw_config["target_hidden_size"]
+                src = "EAGLE-3 config"
+            else:
+                target_hidden_size = target_config["hidden_size"]
+                src = "target model config"
+            logger.info(f"EAGLE-3: target_hidden_size = {target_hidden_size} (from {src})")
+            self.gguf_writer.add_uint32(f"{self.gguf_writer.arch}.target_hidden_size", target_hidden_size)
+
+            # norm_before_residual (RedHat-style eagle3 specific)
+            norm_before_residual = eagle3_raw_config.get("norm_before_residual", False)
+            logger.info(f"EAGLE-3: norm_before_residual = {norm_before_residual}")
+            self.gguf_writer.add_bool(f"{self.gguf_writer.arch}.norm_before_residual", norm_before_residual)
+
     def set_vocab(self):
+        # eagle3: use tokenizer from target model if provided
+        original_dir_model = None
+        if getattr(self, 'is_eagle3', False):
+            assert self.target_model_dir is not None
+            logger.info(f"EAGLE-3: Using tokenizer from target model: {self.target_model_dir}")
+            original_dir_model = self.dir_model
+            self.dir_model = self.target_model_dir
+
         if self.origin_hf_arch == "GlmasrModel":
             return self._set_vocab_glmedge()
 
@@ -85,6 +143,10 @@ class LlamaModel(TextModel):
         if self.hparams.get("vocab_size", 32000) == 49152:
             self.gguf_writer.add_add_bos_token(False)
 
+        # eagle3: Restore original dir_model
+        if original_dir_model is not None:
+            self.dir_model = original_dir_model
+
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
         hparams = self.hparams
@@ -129,7 +191,49 @@ class LlamaModel(TextModel):
 
         return super().filter_tensors((name, gen))
 
+    def index_tensors(self, remote_hf_model_id: str | None = None) -> dict[str, Callable[[], Tensor]]:
+        tensors = super().index_tensors(remote_hf_model_id)
+
+        # Handle Eagle3Speculator nested config
+        if "transformer_layer_config" in self.hparams:
+            self.hparams = {**self.hparams, **self.hparams["transformer_layer_config"]}
+
+        # eagle3 detection
+        if "draft_vocab_size" in self.hparams and self.hparams["num_hidden_layers"] == 1:
+            logger.info("EAGLE-3: renaming midlayer.* / layers.0.* to model.layers.0.*")
+            new_tensors = {}
+            for name, gen in tensors.items():
+                if name.startswith("midlayer."):
+                    new_name = "model.layers.0." + name[len("midlayer."):]
+                    new_tensors[new_name] = gen
+                elif name.startswith("layers.0."):  # Eagle3Speculator format
+                    new_name = "model." + name
+                    new_tensors[new_name] = gen
+                else:
+                    new_tensors[name] = gen
+            return new_tensors
+
+        return tensors
+
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # eagle3: special tensors that bypass standard llama mapping
+        if getattr(self, 'is_eagle3', False):
+            if name == "fc.weight":
+                yield (name, data_torch)
+                return
+            if name == "d2t":
+                # store for manual int64 handling in prepare_tensors (avoid F32 conversion)
+                if not hasattr(self, '_eagle3_int_tensors'):
+                    self._eagle3_int_tensors = {}
+                self._eagle3_int_tensors[name] = data_torch
+                return
+            if name == "t2d":
+                # not used at runtime, skip
+                return
+            if name.endswith(".hidden_norm.weight"):
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_NORM_2, bid), data_torch)
+                return
+
         n_head = self.find_hparam(["n_heads", "num_attention_heads"])
         n_kv_head = self.find_hparam(["n_kv_heads", "num_key_value_heads"])
 
@@ -205,8 +309,33 @@ class LlamaModel(TextModel):
                 yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
 
     def prepare_tensors(self):
+        # eagle3: collect d2t original dtype before parent converts tensors to F32
+        eagle3_original_dtypes = {}
+        if getattr(self, 'is_eagle3', False):
+            for name, data_torch in self.get_tensors():
+                if name == "d2t":
+                    eagle3_original_dtypes[name] = data_torch.dtype
+
         super().prepare_tensors()
 
+        # eagle3: write d2t as absolute target token ids
+        if getattr(self, 'is_eagle3', False) and hasattr(self, '_eagle3_int_tensors'):
+            for name, data_torch in self._eagle3_int_tensors.items():
+                old_dtype = eagle3_original_dtypes.get(name, data_torch.dtype)
+                data = data_torch.to(torch.int64).cpu().numpy()
+                if name == "d2t":
+                    data = data.reshape(-1)
+                    data = data + np.arange(data.size, dtype=np.int64)
+                    if np.any((data < 0) | (data >= self.target_vocab_size)):
+                        raise ValueError(f"EAGLE-3 d2t target ids out of range for target vocab size {self.target_vocab_size}")
+                    if np.unique(data).size != data.size:
+                        raise ValueError("EAGLE-3 d2t contains duplicate target ids")
+                data_qtype = gguf.GGMLQuantizationType.I64
+
+                shape_str = f"{{{', '.join(str(n) for n in reversed(data.shape))}}}"
+                logger.info(f"{name + ',':<30} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
+                self.gguf_writer.add_tensor(name, data, raw_dtype=data_qtype)
+
         if self._experts is not None:
             # flatten `list[dict[str, Tensor]]` into `list[str]`
             experts = [k for d in self._experts for k in d.keys()]

convert_hf_to_gguf.py

@@ -153,6 +153,15 @@ def parse_args() -> argparse.Namespace:
         help="Store tensors dequantized from FP8 as Q8_0 instead of BF16/F16.",
     )
 
+    parser.add_argument(
+        "--target-model-dir", type=str, default=None,
+        help=(
+            "path to the target model directory; required when converting a standalone draft model "
+            "(e.g. EAGLE3 / DFlash) that needs target-model metadata such as tokenizer, hidden size, and "
+            "layer count to populate its GGUF."
+        ),
+    )
+
     args = parser.parse_args()
     if not args.print_supported_models and args.model is None:
         parser.error("the following arguments are required: model")
@@ -269,6 +278,7 @@ def main() -> None:
                                      small_first_shard=args.no_tensor_first_split,
                                      remote_hf_model_id=hf_repo_id, disable_mistral_community_chat_template=disable_mistral_community_chat_template,
                                      sentence_transformers_dense_modules=args.sentence_transformers_dense_modules,
+                                     target_model_dir=Path(args.target_model_dir) if args.target_model_dir else None,
                                      fuse_gate_up_exps=args.fuse_gate_up_exps,
                                      fp8_as_q8=args.fp8_as_q8,
                                      )

ggml/include/ggml.h

@@ -2571,10 +2571,16 @@ extern "C" {
     // TODO: add ggml_gated_delta_net_set_bcast() to be able to configure Q, K broadcast type: tiled vs interleaved [TAG_GGML_GDN_BCAST]
     // ref: https://github.com/ggml-org/llama.cpp/pull/19468#discussion_r2786394306
     //
-    // state is a 3D tensor of shape (S_v*S_v*H, K, n_seqs):
-    //   K == 1: output carries the final state only.
-    //   K  > 1: output carries K snapshot slots; the kernel writes the last min(n_tokens, K)
-    //   per-token snapshots into the trailing slots
+    // tensor shapes (S_k == S_v, H_v % H_k == 0):
+    //   q, k  : [S_k, H_k, n_tokens, n_seqs]
+    //   v     : [S_v, H_v, n_tokens, n_seqs]
+    //   g     : [1, H_v, n_tokens, n_seqs] (scalar gate) or [S_v, H_v, n_tokens, n_seqs] (KDA)
+    //   beta  : [1, H_v, n_tokens, n_seqs]
+    //   state : [S_v, S_v, H_v, n_seqs] -- initial recurrent state s0
+    //
+    // the output packs the attention scores [S_v, H_v, n_tokens, n_seqs] followed by K state
+    // snapshots, most-recent first (slot 0 = final state, slot s = state s tokens back). K == 1
+    // keeps only the final state; when n_tokens < K only slots 0..n_tokens-1 are written.
     GGML_API struct ggml_tensor * ggml_gated_delta_net(
             struct ggml_context * ctx,
             struct ggml_tensor  * q,
@@ -2582,7 +2588,8 @@ extern "C" {
             struct ggml_tensor  * v,
             struct ggml_tensor  * g,
             struct ggml_tensor  * beta,
-            struct ggml_tensor  * state);
+            struct ggml_tensor  * state,
+            int64_t               K);
 
     // custom operators
 

ggml/src/ggml-backend-meta.cpp

@@ -776,8 +776,8 @@ static struct ggml_backend_meta_split_state ggml_backend_meta_get_split_state(
         GGML_ASSERT(src_ss[2].axis == GGML_BACKEND_SPLIT_AXIS_1);
         GGML_ASSERT(src_ss[3].axis == GGML_BACKEND_SPLIT_AXIS_1);
         GGML_ASSERT(src_ss[4].axis == GGML_BACKEND_SPLIT_AXIS_1);
-        // state shape is (S_v*S_v*H, K, n_seqs); the heads dim is nested inside axis 0,
-        // so a head-aligned split on the input cache reshapes to axis 0 here (not axis 2).
+        // state shape is [S_v, S_v, H_v, n_seqs] (s0 only); the heads dim is its own axis 2,
+        // so a head-aligned split on the input cache lands on axis 2 here.
         GGML_ASSERT(src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_2 || src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_1 || src_ss[5].axis == GGML_BACKEND_SPLIT_AXIS_0);
         return {GGML_BACKEND_SPLIT_AXIS_0, {0}, {1}, 1};
     };

ggml/src/ggml-cpu/ggml-cpu.c

@@ -3785,7 +3785,7 @@ struct ggml_cplan ggml_graph_plan(
                 case GGML_OP_GATED_DELTA_NET:
                     {
                         const int64_t S_v = node->src[2]->ne[0];
-                        const int64_t K   = node->src[5]->ne[1];  // state is (D, K, n_seqs)
+                        const int64_t K   = ggml_get_op_params_i32(node, 0);
                         const int64_t per_thread = S_v + (K > 1 ? S_v * S_v : 0);
                         cur = per_thread * sizeof(float) * n_tasks;
                     } break;

ggml/src/ggml-cpu/ops.cpp

@@ -10624,11 +10624,11 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
 
     const bool kda = (neg0 == S_v);
 
-    // state is 3D (S_v*S_v*H, K, n_seqs); K is the snapshot slot count.
-    const int64_t K = src_state->ne[1];
+    // K (snapshot slot count) is an op param; state holds s0 only [S_v, S_v, H, n_seqs].
+    const int64_t K = ggml_get_op_params_i32(dst, 0);
     GGML_ASSERT(K >= 1);
-    // per-seq stride in floats (slot 0 of seq s lives at state + s * seq_stride)
-    const int64_t state_seq_stride = src_state->nb[2] / sizeof(float);
+    // per-seq stride in floats (seq s starts at state + s * seq_stride)
+    const int64_t state_seq_stride = src_state->nb[3] / sizeof(float);
 
     const int64_t per_thread = S_v + (K > 1 ? S_v * S_v : 0);
     const int ith = params->ith;
@@ -10644,9 +10644,8 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
     float * attn_out_base  = (float *)dst->data;
     float * state_out_base = (float *)dst->data + attn_score_elems;
 
-    // snapshot slot mapping: target_slot = t - shift. When n_tokens < K only the last
-    // n_tokens slots are written; earlier slots are left untouched (caller-owned).
-    const int64_t shift = n_tokens - K;
+    // snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back.
+    // When n_tokens < K only slots 0..n_tokens-1 are written; older slots are caller-owned.
 
     const float * state_in_base = (const float *)src_state->data;
 
@@ -10674,7 +10673,7 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
             : state_out_base + (iv3 * H + iv1) * S_v * S_v;
 
         // copy input state into the working buffer and operate in-place
-        // state layout (D, K, n_seqs): slot 0 of seq iv3 starts at iv3 * state_seq_stride.
+        // state layout [S_v, S_v, H, n_seqs]: seq iv3 starts at iv3 * state_seq_stride.
         const float * s_in = state_in_base + iv3 * state_seq_stride + iv1 * S_v * S_v;
         memcpy(s_out, s_in, S_v * S_v * sizeof(float));
 
@@ -10727,7 +10726,7 @@ static void ggml_compute_forward_gated_delta_net_one_chunk(
             attn_data += S_v * H; // advance to next token
 
             if (K > 1) {
-                const int64_t target_slot = t - shift;
+                const int64_t target_slot = n_tokens - 1 - t;
                 if (target_slot >= 0 && target_slot < K) {
                     float * curr_state_o = state_out_base + target_slot * state_size_per_snap +
                                      (iv3 * H + iv1) * S_v * S_v;

ggml/src/ggml-cuda/concat.cu

@@ -1,16 +1,18 @@
 #include "concat.cuh"
 
+#include <stdint.h>
+
 // contiguous kernels
-template <int dim>
-static __global__ void __launch_bounds__(CUDA_CONCAT_BLOCK_SIZE) concat_f32_cont(const float * x,
-                                                                                 const float * y,
-                                                                                 float *       dst,
-                                                                                 int64_t       ne00,
-                                                                                 int64_t       ne01,
-                                                                                 int64_t       ne02,
-                                                                                 int64_t       ne0,
-                                                                                 int64_t       ne1,
-                                                                                 int64_t       ne2) {
+template <typename T, int dim>
+static __global__ void __launch_bounds__(CUDA_CONCAT_BLOCK_SIZE) concat_cont(const T * x,
+                                                                             const T * y,
+                                                                             T *       dst,
+                                                                             int64_t   ne00,
+                                                                             int64_t   ne01,
+                                                                             int64_t   ne02,
+                                                                             int64_t   ne0,
+                                                                             int64_t   ne1,
+                                                                             int64_t   ne2) {
     static_assert(dim >= 0 && dim <= 2, "dim must be in [0, 2]");
 
     const int64_t n = ne0 * ne1 * ne2;
@@ -50,37 +52,37 @@ static __global__ void __launch_bounds__(CUDA_CONCAT_BLOCK_SIZE) concat_f32_cont
     }
 }
 
-static void concat_f32_cuda(const float * x,
-                            const float * y,
-                            float *       dst,
-                            int64_t       ne00,
-                            int64_t       ne01,
-                            int64_t       ne02,
-                            int64_t       ne0,
-                            int64_t       ne1,
-                            int64_t       ne2,
-                            int           dim,
-                            cudaStream_t  stream) {
+template <typename T>
+static void concat_cont_cuda(const T * x,
+                             const T * y,
+                             T *       dst,
+                             int64_t   ne00,
+                             int64_t   ne01,
+                             int64_t   ne02,
+                             int64_t   ne0,
+                             int64_t   ne1,
+                             int64_t   ne2,
+                             int       dim,
+                             cudaStream_t stream) {
     const int64_t n          = ne0 * ne1 * ne2;
     const int     num_blocks = (n + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE;
 
     if (dim == 0) {
         const ggml_cuda_kernel_launch_params launch_params = ggml_cuda_kernel_launch_params(num_blocks, CUDA_CONCAT_BLOCK_SIZE, 0, stream);
-        ggml_cuda_kernel_launch(concat_f32_cont<0>, launch_params,x, y, dst, ne00, ne01, ne02, ne0, ne1, ne2);
+        ggml_cuda_kernel_launch(concat_cont<T, 0>, launch_params, x, y, dst, ne00, ne01, ne02, ne0, ne1, ne2);
         return;
     }
     if (dim == 1) {
-        concat_f32_cont<1>
-            <<<num_blocks, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne00, ne01, ne02, ne0, ne1, ne2);
+        concat_cont<T, 1><<<num_blocks, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne00, ne01, ne02, ne0, ne1, ne2);
         return;
     }
-    concat_f32_cont<2><<<num_blocks, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne00, ne01, ne02, ne0, ne1, ne2);
+    concat_cont<T, 2><<<num_blocks, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(x, y, dst, ne00, ne01, ne02, ne0, ne1, ne2);
 }
 
 // non-contiguous kernel (slow)
-template <int dim>
+template <typename T, int dim>
 static __global__ void __launch_bounds__(CUDA_CONCAT_BLOCK_SIZE)
-    concat_f32_non_cont(
+    concat_non_cont(
         const char * src0,
         const char * src1,
               char * dst,
@@ -107,61 +109,49 @@ static __global__ void __launch_bounds__(CUDA_CONCAT_BLOCK_SIZE)
           uint64_t   nb0,
           uint64_t   nb1,
           uint64_t   nb2,
-          uint64_t   nb3){
+          uint64_t   nb3) {
     static_assert(dim >= 0 && dim <= 3, "dim must be in [0, 3]");
 
     const int64_t i3 = blockIdx.z;
     const int64_t i2 = blockIdx.y;
     const int64_t i1 = blockIdx.x;
 
-    const float * x;
+    const T * x;
 
     for (int64_t i0 = threadIdx.x; i0 < ne0; i0 += blockDim.x) {
         if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) {
-            x = (const float *)(src0 + (i3       )*nb03 + (i2       )*nb02 + (i1       )*nb01 + (i0       )*nb00);
+            x = (const T *)(src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
         } else {
             if constexpr (dim == 0) {
-                x = (const float *) (src1 + i3 * nb13 + i2 * nb12 + i1 * nb11 + (i0 - ne00) * nb10);
+                x = (const T *)(src1 + i3*nb13 + i2*nb12 + i1*nb11 + (i0 - ne00)*nb10);
             } else if constexpr (dim == 1) {
-                x = (const float *) (src1 + i3 * nb13 + i2 * nb12 + (i1 - ne01) * nb11 + i0 * nb10);
+                x = (const T *)(src1 + i3*nb13 + i2*nb12 + (i1 - ne01)*nb11 + i0*nb10);
             } else if constexpr (dim == 2) {
-                x = (const float *) (src1 + i3 * nb13 + (i2 - ne02) * nb12 + i1 * nb11 + i0 * nb10);
+                x = (const T *)(src1 + i3*nb13 + (i2 - ne02)*nb12 + i1*nb11 + i0*nb10);
             } else if constexpr (dim == 3) {
-                x = (const float *) (src1 + (i3 - ne03) * nb13 + i2 * nb12 + i1 * nb11 + i0 * nb10);
+                x = (const T *)(src1 + (i3 - ne03)*nb13 + i2*nb12 + i1*nb11 + i0*nb10);
             }
         }
 
-        float * y = (float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
+        T * y = (T *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0);
 
         *y = *x;
     }
 }
 
-
-void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
-    const ggml_tensor * src0 = dst->src[0];
-    const ggml_tensor * src1 = dst->src[1];
-
-    cudaStream_t stream = ctx.stream();
-
-    const int32_t dim = ((int32_t *) dst->op_params)[0];
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT(src1->type == GGML_TYPE_F32);
-    GGML_ASSERT(dst->type  == GGML_TYPE_F32);
-
+template <typename T>
+static void concat_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, int dim, cudaStream_t stream) {
     if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) {
-        const float * src0_d = (const float *)src0->data;
-        const float * src1_d = (const float *)src1->data;
-
-        float * dst_d = (float *)dst->data;
+        const T * src0_d = (const T *) src0->data;
+        const T * src1_d = (const T *) src1->data;
+        T *       dst_d  = (T *) dst->data;
 
         if (dim != 3) {
-            for (int i3 = 0; i3 < dst->ne[3]; i3++) {
-                concat_f32_cuda(
-                        src0_d + i3 * (src0->nb[3] / 4),
-                        src1_d + i3 * (src1->nb[3] / 4),
-                        dst_d + i3 * ( dst->nb[3] / 4),
+            for (int64_t i3 = 0; i3 < dst->ne[3]; i3++) {
+                concat_cont_cuda(
+                        src0_d + i3*(src0->nb[3] / sizeof(T)),
+                        src1_d + i3*(src1->nb[3] / sizeof(T)),
+                        dst_d  + i3*( dst->nb[3] / sizeof(T)),
                         src0->ne[0], src0->ne[1], src0->ne[2],
                         dst->ne[0],  dst->ne[1],  dst->ne[2], dim, stream);
             }
@@ -169,13 +159,13 @@ void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
             const size_t size0 = ggml_nbytes(src0);
             const size_t size1 = ggml_nbytes(src1);
 
-            CUDA_CHECK(cudaMemcpyAsync(dst_d,           src0_d, size0, cudaMemcpyDeviceToDevice, stream));
-            CUDA_CHECK(cudaMemcpyAsync(dst_d + size0/4, src1_d, size1, cudaMemcpyDeviceToDevice, stream));
+            CUDA_CHECK(cudaMemcpyAsync((char *) dst->data,         src0->data, size0, cudaMemcpyDeviceToDevice, stream));
+            CUDA_CHECK(cudaMemcpyAsync((char *) dst->data + size0, src1->data, size1, cudaMemcpyDeviceToDevice, stream));
         }
     } else {
         dim3 grid_dim(dst->ne[1], dst->ne[2], dst->ne[3]);
         auto launch_kernel = [&](auto dim) {
-            concat_f32_non_cont<dim><<<grid_dim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(
+            concat_non_cont<T, dim><<<grid_dim, CUDA_CONCAT_BLOCK_SIZE, 0, stream>>>(
                 (const char *) src0->data, (const char *) src1->data, (char *) dst->data,
                 src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3],
                 src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3],
@@ -203,3 +193,35 @@ void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
         }
     }
 }
+
+void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+
+    cudaStream_t stream = ctx.stream();
+
+    const int32_t dim = ((int32_t *) dst->op_params)[0];
+
+    GGML_ASSERT(src0->type == src1->type);
+    GGML_ASSERT(dst->type  == src0->type);
+    GGML_ASSERT(!ggml_is_quantized(src0->type));
+    GGML_ASSERT(ggml_blck_size(src0->type) == 1);
+
+    switch (ggml_type_size(src0->type)) {
+        case 1:
+            concat_cuda<uint8_t>(src0, src1, dst, dim, stream);
+            break;
+        case 2:
+            concat_cuda<uint16_t>(src0, src1, dst, dim, stream);
+            break;
+        case 4:
+            concat_cuda<uint32_t>(src0, src1, dst, dim, stream);
+            break;
+        case 8:
+            concat_cuda<uint64_t>(src0, src1, dst, dim, stream);
+            break;
+        default:
+            GGML_ABORT("Unsupported type size: %zu", ggml_type_size(src0->type));
+            break;
+    }
+}

ggml/src/ggml-cuda/gated_delta_net.cu

@@ -39,9 +39,9 @@ gated_delta_net_cuda(const float * q,
     float *       attn_data        = dst;
     float *       state            = dst + attn_score_elems;
 
-    // input state layout (D, K, n_seqs) — seq stride is K * D = K * H * S_v * S_v.
+    // input state holds s0 only: [S_v, S_v, H, n_seqs] — seq stride is D = H * S_v * S_v.
     // output state layout (per-slot D * n_seqs) — same per-(seq,head) offset as before.
-    const int64_t state_in_offset      = sequence * K * H * S_v * S_v + h_idx * S_v * S_v;
+    const int64_t state_in_offset      = sequence * H * S_v * S_v + h_idx * S_v * S_v;
     const int64_t state_out_offset     = (sequence * H + h_idx) * S_v * S_v;
     state += state_out_offset;
     curr_state += state_in_offset + col * S_v;
@@ -143,12 +143,10 @@ gated_delta_net_cuda(const float * q,
         attn_data += S_v * H;
 
         if constexpr (keep_rs_t) {
-            // slot mapping: target_slot = t - shift. When n_tokens < K only the last n_tokens slots
-            // are written; earlier slots are left untouched (caller-owned).
-            const int shift = (int) n_tokens - K;
-
+            // snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back.
+            // When n_tokens < K only slots 0..n_tokens-1 are written; older slots are caller-owned.
             const int64_t state_size_per_token = S_v * S_v * H * n_seqs; // per-slot stride in output
-            const int target_slot = t - shift;
+            const int target_slot = (int) n_tokens - 1 - t;
             if (target_slot >= 0 && target_slot < K) {
                 float * curr_state = (dst + attn_score_elems) + target_slot * state_size_per_token + state_out_offset;
 #pragma unroll
@@ -286,8 +284,8 @@ void ggml_cuda_op_gated_delta_net(ggml_backend_cuda_context & ctx, ggml_tensor *
 
     cudaStream_t stream = ctx.stream();
 
-    // state is 3D (S_v*S_v*H, K, n_seqs); K is the snapshot slot count.
-    const int K = (int) src_state->ne[1];
+    // K (snapshot slot count) is an op param; state holds s0 only [S_v, S_v, H, n_seqs].
+    const int K = ggml_get_op_params_i32(dst, 0);
     const bool keep_rs = K > 1;
 
     if (kda) {

ggml/src/ggml-cuda/ggml-cuda.cu

@@ -5366,7 +5366,15 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
         case GGML_OP_CONCAT:
             {
                 ggml_type src0_type = op->src[0]->type;
-                return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16;
+                ggml_type src1_type = op->src[1]->type;
+                return src0_type == src1_type &&
+                       src0_type == op->type &&
+                       !ggml_is_quantized(src0_type) &&
+                       ggml_blck_size(src0_type) == 1 &&
+                       (ggml_type_size(src0_type) == 1 ||
+                        ggml_type_size(src0_type) == 2 ||
+                        ggml_type_size(src0_type) == 4 ||
+                        ggml_type_size(src0_type) == 8);
             } break;
         case GGML_OP_CONV_TRANSPOSE_1D:
             {

ggml/src/ggml-metal/ggml-metal-device.cpp

@@ -590,8 +590,8 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_gated_delta_net(
     const int ne20 = op->src[2]->ne[0]; // S_v
     const int ne21 = op->src[2]->ne[1]; // H
     const int ne30 = op->src[3]->ne[0]; // G
-    // state is src[5], 3D (S_v*S_v*H, K, n_seqs); K is the snapshot slot count.
-    const int K = op->src[5]->ne[1];
+    // state is src[5], 4D [S_v, S_v, H_v, n_seqs] (s0 only); K is op param 0.
+    const int K = ggml_get_op_params_i32(op, 0);
 
     const int nsg = op->src[2]->ne[0]/32;
 

ggml/src/ggml-metal/ggml-metal-device.m

@@ -1126,8 +1126,17 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
         case GGML_OP_VIEW:
         case GGML_OP_TRANSPOSE:
         case GGML_OP_PERMUTE:
-        case GGML_OP_CONCAT:
             return true;
+        case GGML_OP_CONCAT:
+            {
+                // kernel_concat copies one float-sized value per element.
+                // Other scalar types need a type-generic copy kernel first.
+                const enum ggml_type src0_type = op->src[0]->type;
+                const enum ggml_type src1_type = op->src[1]->type;
+                return src0_type == src1_type &&
+                       src0_type == op->type &&
+                       (src0_type == GGML_TYPE_F32 || src0_type == GGML_TYPE_I32);
+            }
         case GGML_OP_ADD:
         case GGML_OP_SUB:
         case GGML_OP_MUL:

ggml/src/ggml-metal/ggml-metal.metal

@@ -2599,9 +2599,9 @@ kernel void kernel_gated_delta_net_impl(
 
     const float scale = 1.0f / sqrt((float)S_v);
 
-    // input state layout (D, K, n_seqs): per-seq stride is K*H*D; we read slot 0.
+    // input state layout [S_v, S_v, H, n_seqs] (s0 only): per-seq stride is H*D.
     // state is stored transposed: M[i20][is] = S[is][i20], so row i20 is contiguous
-    const uint state_in_base = (i23*K*args.ne21 + i21)*S_v*S_v + i20*S_v;
+    const uint state_in_base = (i23*args.ne21 + i21)*S_v*S_v + i20*S_v;
     device const float * s_ptr = (device const float *) (s) + state_in_base;
 
     float ls[NSG];
@@ -2620,9 +2620,8 @@ kernel void kernel_gated_delta_net_impl(
     device const float * b_ptr = (device const float *) (b) + (i23*args.ne22*args.ne21 + i21);
     device const float * g_ptr = (device const float *) (g) + (i23*args.ne22*args.ne21 + i21)*G;
 
-    // snapshot slot mapping: target_slot = t - shift. When n_tokens < K, only the last
-    // n_tokens slots are written; earlier slots are left untouched (caller-owned).
-    const int shift = (int)args.ne22 - (int)K;
+    // snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back.
+    // When n_tokens < K, only slots 0..n_tokens-1 are written; older slots are caller-owned.
 
     // output state base offset: after attention scores
     const uint attn_size = args.ne22 * args.ne21 * S_v * args.ne23;
@@ -2680,7 +2679,7 @@ kernel void kernel_gated_delta_net_impl(
         g_ptr += args.ne21*G;
 
         if (K > 1) {
-            const int target_slot = (int)t - shift;
+            const int target_slot = (int)args.ne22 - 1 - (int)t;
             if (target_slot >= 0 && target_slot < (int)K) {
                 device float * dst_state = (device float *) (dst) + attn_size + (uint)target_slot * state_size_per_snap + state_out_base;
                 FOR_UNROLL (short j = 0; j < NSG; j++) {

ggml/src/ggml-vulkan/ggml-vulkan.cpp

@@ -6224,6 +6224,19 @@ static vk_device ggml_vk_get_device(size_t idx) {
                 break;
             }
 
+#if VK_HEADER_VERSION >= 287
+            // Honeykrisp driver for Asahi Linux doesn't report VK_VENDOR_ID_APPLE.
+            // Check for Honeykrisp driver and force same configuration as the VK_VENDOR_ID_APPLE case.
+            if (device->driver_id == vk::DriverId::eMesaHoneykrisp) {
+                device->mul_mat_l[i] = false;
+                device->mul_mat_m[i] = true;
+                device->mul_mat_s[i] = false;
+                device->mul_mat_id_l[i] = false;
+                device->mul_mat_id_m[i] = true;
+                device->mul_mat_id_s[i] = false;
+            }
+#endif
+
             device->mul_mat_l_int[i]    = device->mul_mat_l[i];
             device->mul_mat_m_int[i]    = device->mul_mat_m[i];
             device->mul_mat_s_int[i]    = device->mul_mat_s[i];
@@ -7636,8 +7649,12 @@ static void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void *
     if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) {
         GGML_ASSERT(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
 
-        for (size_t i = 0; i < height; i++) {
-            memcpy((uint8_t *)dst->ptr + offset + i * dpitch, (const uint8_t *) src + i * spitch, width);
+        if (width == spitch && width == dpitch) {
+            memcpy((uint8_t *)dst->ptr + offset, src, width * height);
+        } else {
+            for (size_t i = 0; i < height; i++) {
+                memcpy((uint8_t *)dst->ptr + offset + i * dpitch, (const uint8_t *) src + i * spitch, width);
+            }
         }
     } else {
         std::lock_guard<std::recursive_mutex> guard(dst->device->mutex);
@@ -7756,8 +7773,12 @@ static void ggml_vk_buffer_read_2d(vk_buffer& src, size_t offset, void * dst, si
     if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible && src->device->uma) {
         GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent);
 
-        for (size_t i = 0; i < height; i++) {
-            memcpy((uint8_t *) dst + i * dpitch, (const uint8_t *) src->ptr + offset + i * spitch, width);
+        if (width == spitch && width == dpitch) {
+            memcpy(dst, (const uint8_t *) src->ptr + offset, width * height);
+        } else {
+            for (size_t i = 0; i < height; i++) {
+                memcpy((uint8_t *) dst + i * dpitch, (const uint8_t *) src->ptr + offset + i * spitch, width);
+            }
         }
     } else {
         std::lock_guard<std::recursive_mutex> guard(src->device->mutex);
@@ -11560,7 +11581,6 @@ static void ggml_vk_gated_delta_net(ggml_backend_vk_context * ctx, vk_context& s
     const ggml_tensor * src_q     = dst->src[0];
     const ggml_tensor * src_v     = dst->src[2];
     const ggml_tensor * src_beta  = dst->src[4];
-    const ggml_tensor * src_state = dst->src[5];
 
     GGML_ASSERT(dst->buffer != nullptr);
 
@@ -11569,8 +11589,8 @@ static void ggml_vk_gated_delta_net(ggml_backend_vk_context * ctx, vk_context& s
     const uint32_t n_tokens = (uint32_t)src_v->ne[2];
     const uint32_t n_seqs   = (uint32_t)src_v->ne[3];
 
-    // state is 3D (S_v*S_v*H, K, n_seqs); K is the snapshot slot count.
-    const uint32_t K = (uint32_t)src_state->ne[1];
+    // K (snapshot slot count) is an op param; state holds s0 only [S_v, S_v, H, n_seqs].
+    const uint32_t K = (uint32_t)ggml_get_op_params_i32(dst, 0);
 
     const uint32_t s_off = S_v * H * n_tokens * n_seqs;
 
@@ -17986,7 +18006,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
             src_clone[4], src_clone[5], src_clone[6]);
         } else if (tensor->op == GGML_OP_GATED_DELTA_NET) {
             tensor_clone = ggml_gated_delta_net(ggml_ctx, src_clone[0], src_clone[1],
-            src_clone[2], src_clone[3], src_clone[4], src_clone[5]);
+            src_clone[2], src_clone[3], src_clone[4], src_clone[5],
+            ggml_get_op_params_i32(tensor, 0));
         } else if (tensor->op == GGML_OP_OPT_STEP_ADAMW) {
             src_clone[0]->flags = tensor->src[0]->flags;
             tensor_clone = ggml_opt_step_adamw(ggml_ctx, src_clone[0], src_clone[1],

ggml/src/ggml-vulkan/vulkan-shaders/gated_delta_net.comp

@@ -102,8 +102,8 @@ void main() {
     const uint iq3 = seq_id / rq3;
 
     const uint state_size = S_V * S_V;
-    // input state layout (D, K, n_seqs): per-seq stride is K*H*D; we read slot 0.
-    const uint state_in_base       = (seq_id * K * H + head_id) * state_size;
+    // input state holds s0 only [S_v, S_v, H, n_seqs]: per-seq stride is H*D.
+    const uint state_in_base       = (seq_id * H + head_id) * state_size;
     // output state layout per slot: same per-(seq,head) offset as the single-slot case.
     const uint state_out_base      = (seq_id * H + head_id) * state_size;
     const uint state_size_per_snap = state_size * H * n_seqs;
@@ -113,9 +113,8 @@ void main() {
         s_shard[r] = FLOAT_TYPE(data_state[state_in_base + col * S_V + r * LANES_PER_COLUMN + lane]);
     }
 
-    // snapshot slot mapping: target_slot = t - shift. When n_tokens < K, only the last
-    // n_tokens slots are written; earlier slots are left untouched (caller-owned).
-    const int shift = int(n_tokens) - int(K);
+    // snapshot slot mapping: slot 0 = most recent state, slot s = s tokens back.
+    // When n_tokens < K, only slots 0..n_tokens-1 are written; older slots are caller-owned.
 
     uint attn_off = (seq_id * n_tokens * H + head_id) * S_V;
 
@@ -172,7 +171,7 @@ void main() {
         attn_off += S_V * H;
 
         if (K > 1u) {
-            const int target_slot = int(t) - shift;
+            const int target_slot = int(n_tokens) - 1 - int(t);
             if (target_slot >= 0 && target_slot < int(K)) {
                 const uint slot_base = s_off + uint(target_slot) * state_size_per_snap + state_out_base;
                 [[unroll]] for (uint r = 0; r < ROWS_PER_LANE; r++) {

ggml/src/ggml.c

@@ -6239,7 +6239,8 @@ struct ggml_tensor * ggml_gated_delta_net(
         struct ggml_tensor  * v,
         struct ggml_tensor  * g,
         struct ggml_tensor  * beta,
-        struct ggml_tensor  * state) {
+        struct ggml_tensor  * state,
+        int64_t               K) {
     GGML_ASSERT(ggml_is_contiguous_rows(q));
     GGML_ASSERT(ggml_is_contiguous_rows(k));
     GGML_ASSERT(ggml_is_contiguous_rows(v));
@@ -6263,15 +6264,18 @@ struct ggml_tensor * ggml_gated_delta_net(
     GGML_ASSERT(g->ne[0] == 1 || g->ne[0] == S_v);
     GGML_ASSERT(beta->ne[0] == 1);
 
-    // state is a 3D tensor (S_v*S_v*H, K, n_seqs). K is the snapshot slot count.
-    GGML_ASSERT(state->ne[0] == S_v * S_v * H);
-    GGML_ASSERT(state->ne[2] == n_seqs);
-    GGML_ASSERT(state->ne[3] == 1);
-    const int64_t K = state->ne[1];
+    // state holds the initial state s0 only: [S_v, S_v, H, n_seqs]. K (snapshot slot count) is an op param.
+    GGML_ASSERT(state->ne[0] == S_v);
+    GGML_ASSERT(state->ne[1] == S_v);
+    GGML_ASSERT(state->ne[2] == H);
+    GGML_ASSERT(state->ne[3] == n_seqs);
+    GGML_ASSERT(K >= 1);
     const int64_t state_rows = K * S_v * n_seqs;
     const int64_t ne[4] = { S_v * H, n_tokens * n_seqs + state_rows, 1, 1 };
     struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne);
 
+    ggml_set_op_params_i32(result, 0, (int32_t) K);
+
     result->op     = GGML_OP_GATED_DELTA_NET;
     result->src[0] = q;
     result->src[1] = k;

gguf-py/gguf/constants.py

@@ -154,6 +154,9 @@ class Keys:
         HIDDEN_ACT                        = "{arch}.hidden_activation"
         DENSE_FEAT_IN_SIZE                = "{arch}.{dense}_feat_in"
         DENSE_FEAT_OUT_SIZE               = "{arch}.{dense}_feat_out"
+        TARGET_LAYERS                     = "{arch}.target_layers"
+        TARGET_HIDDEN_SIZE                = "{arch}.target_hidden_size"
+        NORM_BEFORE_RESIDUAL              = "{arch}.norm_before_residual"
 
     class Attention:
         HEAD_COUNT                   = "{arch}.attention.head_count"
@@ -272,7 +275,8 @@ class Keys:
         CHAT_TEMPLATE_N      = "tokenizer.chat_template.{name}"
         CHAT_TEMPLATES       = "tokenizer.chat_templates"
         # Normalizer constants
-        NORMALIZER_LOWERCASE = "tokenizer.ggml.normalizer.lowercase"
+        NORMALIZER_LOWERCASE     = "tokenizer.ggml.normalizer.lowercase"
+        NORMALIZER_STRIP_ACCENTS = "tokenizer.ggml.normalizer.strip_accents"
         # FIM/Infill special tokens constants
         FIM_PRE_ID           = "tokenizer.ggml.fim_pre_token_id"
         FIM_SUF_ID           = "tokenizer.ggml.fim_suf_token_id"
@@ -510,6 +514,7 @@ class MODEL_ARCH(IntEnum):
     RND1             = auto()
     PANGU_EMBED      = auto()
     MISTRAL3         = auto()
+    EAGLE3           = auto()
     MISTRAL4         = auto()
     PADDLEOCR        = auto()
     MIMO2            = auto()
@@ -900,14 +905,17 @@ class MODEL_TENSOR(IntEnum):
     A_PER_DIM_K_SCALE     = auto() # gemma4
     A_PER_DIM_SCALE       = auto() # gemma4
     # nextn/mtp
-    NEXTN_PROJ_PRE       = auto()
-    NEXTN_PROJ_POST      = auto()
-    NEXTN_EH_PROJ        = auto()
-    NEXTN_EMBED_TOKENS   = auto()
-    NEXTN_ENORM          = auto()
-    NEXTN_HNORM          = auto()
+    NEXTN_PROJ_PRE         = auto()
+    NEXTN_PROJ_POST        = auto()
+    NEXTN_EH_PROJ          = auto()
+    NEXTN_EMBED_TOKENS     = auto()
+    NEXTN_ENORM            = auto()
+    NEXTN_HNORM            = auto()
     NEXTN_SHARED_HEAD_HEAD = auto()
     NEXTN_SHARED_HEAD_NORM = auto()
+    # eagle3
+    FC                     = auto()  # feature fusion layer
+    D2T                    = auto()  # draft to target vocabulary mapping
     # lfm2 audio
     A_ENC_NORM_CONV        = auto()
     A_ENC_LINEAR_POS       = auto()
@@ -1062,6 +1070,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.RND1:             "rnd1",
     MODEL_ARCH.PANGU_EMBED:      "pangu-embedded",
     MODEL_ARCH.MISTRAL3:         "mistral3",
+    MODEL_ARCH.EAGLE3:           "eagle3",
     MODEL_ARCH.MISTRAL4:         "mistral4",
     MODEL_ARCH.PADDLEOCR:        "paddleocr",
     MODEL_ARCH.MIMO2:            "mimo2",
@@ -1094,8 +1103,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.POS_EMBD:                  "position_embd",
     MODEL_TENSOR.OUTPUT_NORM:               "output_norm",
     MODEL_TENSOR.OUTPUT:                    "output",
-    MODEL_TENSOR.DENSE_2_OUT:                "dense_2", # embeddinggemma 2_Dense
-    MODEL_TENSOR.DENSE_3_OUT:                "dense_3", # embeddinggemma 2_Dense
+    MODEL_TENSOR.DENSE_2_OUT:               "dense_2", # embeddinggemma 2_Dense
+    MODEL_TENSOR.DENSE_3_OUT:               "dense_3", # embeddinggemma 2_Dense
     MODEL_TENSOR.ROPE_FREQS:                "rope_freqs",
     MODEL_TENSOR.ROPE_FACTORS_LONG:         "rope_factors_long",
     MODEL_TENSOR.ROPE_FACTORS_SHORT:        "rope_factors_short",
@@ -1487,6 +1496,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.NEXTN_HNORM:               "blk.{bid}.nextn.hnorm",
     MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD:    "blk.{bid}.nextn.shared_head_head",
     MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM:    "blk.{bid}.nextn.shared_head_norm",
+    MODEL_TENSOR.FC:                        "fc",
+    MODEL_TENSOR.D2T:                       "d2t",
 }
 
 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -4027,6 +4038,24 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN_EXP,
         MODEL_TENSOR.FFN_UP_EXP,
     ],
+    MODEL_ARCH.EAGLE3: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_NORM_2,
+        MODEL_TENSOR.ATTN_Q,
+        MODEL_TENSOR.ATTN_K,
+        MODEL_TENSOR.ATTN_V,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+        MODEL_TENSOR.FC,
+        MODEL_TENSOR.D2T,
+    ],
     MODEL_ARCH.MISTRAL4: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,

gguf-py/gguf/gguf_writer.py

@@ -1124,6 +1124,9 @@ class GGUFWriter:
     def add_normalizer_lowercase(self, value: bool) -> None:
         self.add_bool(Keys.Tokenizer.NORMALIZER_LOWERCASE, value)
 
+    def add_normalizer_strip_accents(self, value: bool) -> None:
+        self.add_bool(Keys.Tokenizer.NORMALIZER_STRIP_ACCENTS, value)
+
     def add_eot_token_id(self, id: int) -> None:
         self.add_uint32(Keys.Tokenizer.EOT_ID, id)
 

gguf-py/gguf/vocab.py

@@ -53,6 +53,7 @@ class SpecialVocab:
     special_token_ids: dict[str, int]
     chat_template: str | Sequence[Mapping[str, str]] | None
     normalizer_lowercase: bool | None
+    normalizer_strip_accents: bool | None
 
     def __init__(
         self, path: str | os.PathLike[str], load_merges: bool = False,
@@ -66,6 +67,7 @@ class SpecialVocab:
         self.merges = []
         self.chat_template = None
         self.normalizer_lowercase = None
+        self.normalizer_strip_accents = None
         if special_token_types is not None:
             self.special_token_types = special_token_types
         else:
@@ -108,6 +110,10 @@ class SpecialVocab:
             if not quiet:
                 logger.info(f'Setting normalizer_lowercase to {self.normalizer_lowercase}')
             gw.add_normalizer_lowercase(self.normalizer_lowercase)
+        if self.normalizer_strip_accents is not None:
+            if not quiet:
+                logger.info(f'Setting normalizer_strip_accents to {self.normalizer_strip_accents}')
+            gw.add_normalizer_strip_accents(self.normalizer_strip_accents)
 
     def _load(self, path: Path) -> None:
         self._try_load_from_tokenizer_json(path)
@@ -155,17 +161,21 @@ class SpecialVocab:
     def _parse_normalizer(self, normalizer: dict) -> None:
         # ref: https://huggingface.co/docs/tokenizers/api/normalizers
         #
-        # Detects lowercase normalization in three possible formats:
-        # 1. Standalone: {"type": "Lowercase"}
-        # 2. BertNormalizer attribute: {"type": "BertNormalizer", "lowercase": true, ...}
-        # 3. Nested in Sequence: {"type": "Sequence", "normalizers": [...]}
+        # Extracts normalizer flags from three possible formats:
+        # 1. Standalone:           {"type": "Lowercase"}
+        # 2. BertNormalizer attrs: {"type": "BertNormalizer", ...}
+        # 3. Nested in Sequence:   {"type": "Sequence", "normalizers": [...]}
 
         normalizer_type = normalizer.get('type')
         if normalizer_type == 'Lowercase':
             self.normalizer_lowercase = True
+        elif normalizer_type == 'StripAccents':
+            self.normalizer_strip_accents = True
         elif normalizer_type == 'BertNormalizer':
             if 'lowercase' in normalizer:
                 self.normalizer_lowercase = normalizer['lowercase']
+            if 'strip_accents' in normalizer:
+                self.normalizer_strip_accents = normalizer['strip_accents']
         elif normalizer_type == 'Sequence':
             for norm in normalizer.get('normalizers', []):
                 self._parse_normalizer(norm)
@@ -246,6 +256,11 @@ class SpecialVocab:
                             if special_first := tmpl_single[0].get('SpecialToken', {}).get('id'):
                                 if not tokenizer_config:
                                     special_bos = special_first
+                                elif special_first not in (special_bos, special_cls):
+                                    if not special_bos:
+                                        tokenizer_config['bos_token'] = special_bos = special_first
+                                    if not special_cls:
+                                        tokenizer_config['cls_token'] = special_cls = special_first
                                 self.add_special_token['bos'] = True if special_first in (special_bos, special_cls) else False
                                 if special_first not in (special_bos, special_cls):
                                     logger.warning(f'Unknown leading special token {special_first!r} in TemplateProcessing<single>')

src/llama-arch.cpp

@@ -3,7 +3,6 @@
 #include "llama-impl.h"
 
 #include <map>
-#include <set>
 #include <vector>
 
 static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
@@ -128,6 +127,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_RND1,             "rnd1"             },
     { LLM_ARCH_PANGU_EMBED,      "pangu-embedded"   },
     { LLM_ARCH_MISTRAL3,         "mistral3"         },
+    { LLM_ARCH_EAGLE3,           "eagle3"           },
     { LLM_ARCH_MISTRAL4,         "mistral4"         },
     { LLM_ARCH_PADDLEOCR,        "paddleocr"        },
     { LLM_ARCH_MIMO2,            "mimo2"            },
@@ -292,46 +292,51 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
 
     { LLM_KV_CLASSIFIER_OUTPUT_LABELS, "%s.classifier.output_labels" },
 
+    { LLM_KV_TARGET_LAYERS,         "%s.target_layers"        },
+    { LLM_KV_TARGET_HIDDEN_SIZE,    "%s.target_hidden_size"   },
+    { LLM_KV_NORM_BEFORE_RESIDUAL,  "%s.norm_before_residual" },
+
     { LLM_KV_SHORTCONV_L_CACHE, "%s.shortconv.l_cache" },
     // sentence-transformers dense modules feature dims
     { LLM_KV_DENSE_2_FEAT_IN,        "%s.dense_2_feat_in"  },
-    { LLM_KV_DENSE_2_FEAT_OUT,       "%s.dense_2_feat_out"  },
-    { LLM_KV_DENSE_3_FEAT_IN,        "%s.dense_3_feat_in"   },
-    { LLM_KV_DENSE_3_FEAT_OUT,       "%s.dense_3_feat_out"  },
+    { LLM_KV_DENSE_2_FEAT_OUT,       "%s.dense_2_feat_out" },
+    { LLM_KV_DENSE_3_FEAT_IN,        "%s.dense_3_feat_in"  },
+    { LLM_KV_DENSE_3_FEAT_OUT,       "%s.dense_3_feat_out" },
 
-    { LLM_KV_TOKENIZER_MODEL,                "tokenizer.ggml.model"                    },
-    { LLM_KV_TOKENIZER_PRE,                  "tokenizer.ggml.pre"                      },
-    { LLM_KV_TOKENIZER_LIST,                 "tokenizer.ggml.tokens"                   },
-    { LLM_KV_TOKENIZER_TOKEN_TYPE,           "tokenizer.ggml.token_type"               },
-    { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,     "tokenizer.ggml.token_type_count"         },
-    { LLM_KV_TOKENIZER_SCORES,               "tokenizer.ggml.scores"                   },
-    { LLM_KV_TOKENIZER_MERGES,               "tokenizer.ggml.merges"                   },
-    { LLM_KV_TOKENIZER_BOS_ID,               "tokenizer.ggml.bos_token_id"             },
-    { LLM_KV_TOKENIZER_EOS_ID,               "tokenizer.ggml.eos_token_id"             },
-    { LLM_KV_TOKENIZER_EOT_ID,               "tokenizer.ggml.eot_token_id"             },
-    { LLM_KV_TOKENIZER_EOM_ID,               "tokenizer.ggml.eom_token_id"             },
-    { LLM_KV_TOKENIZER_UNK_ID,               "tokenizer.ggml.unknown_token_id"         },
-    { LLM_KV_TOKENIZER_SEP_ID,               "tokenizer.ggml.seperator_token_id"       },
-    { LLM_KV_TOKENIZER_PAD_ID,               "tokenizer.ggml.padding_token_id"         },
-    { LLM_KV_TOKENIZER_CLS_ID,               "tokenizer.ggml.cls_token_id"             },
-    { LLM_KV_TOKENIZER_MASK_ID,              "tokenizer.ggml.mask_token_id"            },
-    { LLM_KV_TOKENIZER_ADD_BOS,              "tokenizer.ggml.add_bos_token"            },
-    { LLM_KV_TOKENIZER_ADD_EOS,              "tokenizer.ggml.add_eos_token"            },
-    { LLM_KV_TOKENIZER_ADD_SEP,              "tokenizer.ggml.add_sep_token"            },
-    { LLM_KV_TOKENIZER_ADD_PREFIX,           "tokenizer.ggml.add_space_prefix"         },
-    { LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,      "tokenizer.ggml.remove_extra_whitespaces" },
-    { LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP, "tokenizer.ggml.precompiled_charsmap"     },
-    { LLM_KV_TOKENIZER_HF_JSON,              "tokenizer.huggingface.json"              },
-    { LLM_KV_TOKENIZER_RWKV,                 "tokenizer.rwkv.world"                    },
-    { LLM_KV_TOKENIZER_CHAT_TEMPLATE,        "tokenizer.chat_template"                 },
-    { LLM_KV_TOKENIZER_NORMALIZER_LOWERCASE, "tokenizer.ggml.normalizer.lowercase"     },
-    { LLM_KV_TOKENIZER_FIM_PRE_ID,           "tokenizer.ggml.fim_pre_token_id"         },
-    { LLM_KV_TOKENIZER_FIM_SUF_ID,           "tokenizer.ggml.fim_suf_token_id"         },
-    { LLM_KV_TOKENIZER_FIM_MID_ID,           "tokenizer.ggml.fim_mid_token_id"         },
-    { LLM_KV_TOKENIZER_FIM_PAD_ID,           "tokenizer.ggml.fim_pad_token_id"         },
-    { LLM_KV_TOKENIZER_FIM_REP_ID,           "tokenizer.ggml.fim_rep_token_id"         },
-    { LLM_KV_TOKENIZER_FIM_SEP_ID,           "tokenizer.ggml.fim_sep_token_id"         },
-    { LLM_KV_TOKENIZER_SUPPRESS_TOKENS,      "tokenizer.ggml.suppress_tokens"          },
+    { LLM_KV_TOKENIZER_MODEL,                    "tokenizer.ggml.model"                    },
+    { LLM_KV_TOKENIZER_PRE,                      "tokenizer.ggml.pre"                      },
+    { LLM_KV_TOKENIZER_LIST,                     "tokenizer.ggml.tokens"                   },
+    { LLM_KV_TOKENIZER_TOKEN_TYPE,               "tokenizer.ggml.token_type"               },
+    { LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT,         "tokenizer.ggml.token_type_count"         },
+    { LLM_KV_TOKENIZER_SCORES,                   "tokenizer.ggml.scores"                   },
+    { LLM_KV_TOKENIZER_MERGES,                   "tokenizer.ggml.merges"                   },
+    { LLM_KV_TOKENIZER_BOS_ID,                   "tokenizer.ggml.bos_token_id"             },
+    { LLM_KV_TOKENIZER_EOS_ID,                   "tokenizer.ggml.eos_token_id"             },
+    { LLM_KV_TOKENIZER_EOT_ID,                   "tokenizer.ggml.eot_token_id"             },
+    { LLM_KV_TOKENIZER_EOM_ID,                   "tokenizer.ggml.eom_token_id"             },
+    { LLM_KV_TOKENIZER_UNK_ID,                   "tokenizer.ggml.unknown_token_id"         },
+    { LLM_KV_TOKENIZER_SEP_ID,                   "tokenizer.ggml.seperator_token_id"       },
+    { LLM_KV_TOKENIZER_PAD_ID,                   "tokenizer.ggml.padding_token_id"         },
+    { LLM_KV_TOKENIZER_CLS_ID,                   "tokenizer.ggml.cls_token_id"             },
+    { LLM_KV_TOKENIZER_MASK_ID,                  "tokenizer.ggml.mask_token_id"            },
+    { LLM_KV_TOKENIZER_ADD_BOS,                  "tokenizer.ggml.add_bos_token"            },
+    { LLM_KV_TOKENIZER_ADD_EOS,                  "tokenizer.ggml.add_eos_token"            },
+    { LLM_KV_TOKENIZER_ADD_SEP,                  "tokenizer.ggml.add_sep_token"            },
+    { LLM_KV_TOKENIZER_ADD_PREFIX,               "tokenizer.ggml.add_space_prefix"         },
+    { LLM_KV_TOKENIZER_REMOVE_EXTRA_WS,          "tokenizer.ggml.remove_extra_whitespaces" },
+    { LLM_KV_TOKENIZER_PRECOMPILED_CHARSMAP,     "tokenizer.ggml.precompiled_charsmap"     },
+    { LLM_KV_TOKENIZER_HF_JSON,                  "tokenizer.huggingface.json"              },
+    { LLM_KV_TOKENIZER_RWKV,                     "tokenizer.rwkv.world"                    },
+    { LLM_KV_TOKENIZER_CHAT_TEMPLATE,            "tokenizer.chat_template"                 },
+    { LLM_KV_TOKENIZER_NORMALIZER_LOWERCASE,     "tokenizer.ggml.normalizer.lowercase"     },
+    { LLM_KV_TOKENIZER_NORMALIZER_STRIP_ACCENTS, "tokenizer.ggml.normalizer.strip_accents" },
+    { LLM_KV_TOKENIZER_FIM_PRE_ID,               "tokenizer.ggml.fim_pre_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_SUF_ID,               "tokenizer.ggml.fim_suf_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_MID_ID,               "tokenizer.ggml.fim_mid_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_PAD_ID,               "tokenizer.ggml.fim_pad_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_REP_ID,               "tokenizer.ggml.fim_rep_token_id"         },
+    { LLM_KV_TOKENIZER_FIM_SEP_ID,               "tokenizer.ggml.fim_sep_token_id"         },
+    { LLM_KV_TOKENIZER_SUPPRESS_TOKENS,          "tokenizer.ggml.suppress_tokens"          },
 
     { LLM_KV_ADAPTER_TYPE,                    "adapter.type"               },
     { LLM_KV_ADAPTER_LORA_ALPHA,              "adapter.lora.alpha"         },
@@ -561,6 +566,8 @@ static const std::map<llm_tensor, const char *> LLM_TENSOR_NAMES = {
     { LLM_TENSOR_INDEXER_ATTN_Q_B,                       "blk.%d.indexer.attn_q_b" },
     { LLM_TENSOR_MASKED_EMBD_CENTROIDS,                  "masked_embd_centroids" },
     { LLM_TENSOR_MASKED_EMBD_ORDERING,                   "masked_embd_ordering" },
+    { LLM_TENSOR_FC,                                     "fc" },
+    { LLM_TENSOR_D2T,                                    "d2t" },
 };
 
 // declare information about the model weight tensors:
@@ -787,6 +794,9 @@ static const std::map<llm_tensor, llm_tensor_info> LLM_TENSOR_INFOS = {
     {LLM_TENSOR_FFN_LATENT_UP,              {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
     {LLM_TENSOR_MASKED_EMBD_CENTROIDS,      {LLM_TENSOR_LAYER_INPUT,     GGML_OP_NONE}},
     {LLM_TENSOR_MASKED_EMBD_ORDERING,       {LLM_TENSOR_LAYER_INPUT,     GGML_OP_NONE}},
+    // eagle3
+    {LLM_TENSOR_FC,                         {LLM_TENSOR_LAYER_OUTPUT,    GGML_OP_MUL_MAT}},
+    {LLM_TENSOR_D2T,                        {LLM_TENSOR_LAYER_OUTPUT,    GGML_OP_GET_ROWS}},
 };
 
 LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {}

src/llama-arch.h

@@ -141,6 +141,7 @@ enum llm_arch {
     LLM_ARCH_KIMI_LINEAR,
     LLM_ARCH_TALKIE,
     LLM_ARCH_MELLUM,
+    LLM_ARCH_EAGLE3,
     LLM_ARCH_UNKNOWN,
 };
 
@@ -314,6 +315,7 @@ enum llm_kv {
     LLM_KV_TOKENIZER_RWKV,
     LLM_KV_TOKENIZER_CHAT_TEMPLATE,
     LLM_KV_TOKENIZER_NORMALIZER_LOWERCASE,
+    LLM_KV_TOKENIZER_NORMALIZER_STRIP_ACCENTS,
     LLM_KV_TOKENIZER_FIM_PRE_ID,
     LLM_KV_TOKENIZER_FIM_SUF_ID,
     LLM_KV_TOKENIZER_FIM_MID_ID,
@@ -336,6 +338,10 @@ enum llm_kv {
 
     LLM_KV_CLASSIFIER_OUTPUT_LABELS,
 
+    LLM_KV_TARGET_LAYERS,
+    LLM_KV_TARGET_HIDDEN_SIZE,
+    LLM_KV_NORM_BEFORE_RESIDUAL,
+
     LLM_KV_SHORTCONV_L_CACHE,
 
     LLM_KV_XIELU_ALPHA_N,
@@ -568,6 +574,8 @@ enum llm_tensor {
     LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,
     LLM_TENSOR_MASKED_EMBD_CENTROIDS,
     LLM_TENSOR_MASKED_EMBD_ORDERING,
+    LLM_TENSOR_FC,
+    LLM_TENSOR_D2T,
 };
 
 

src/llama-context.cpp

@@ -74,6 +74,9 @@ llama_context::llama_context(
     cparams.no_perf                 = params.no_perf;
     cparams.warmup                  = false;
 
+    cparams.embeddings_layer_inp.resize(hparams.n_layer(), false);
+    embd_layer_inp.resize(hparams.n_layer());
+
     cparams.ctx_type     = params.ctx_type;
     cparams.pooling_type = params.pooling_type;
 
@@ -94,12 +97,21 @@ llama_context::llama_context(
     if (model.arch == LLM_ARCH_GEMMA4_ASSISTANT) {
         if (params.ctx_other == nullptr) {
             // TODO: change from runtime_error to llama_exception to avoid printing error message
-            throw std::runtime_error("Gemma4Assistant requires ctx_other to be set (this is normal during memory fitting)");
+            throw std::runtime_error("Gemma4Assistant requires ctx_other to be set (this warning is normal during memory fitting)");
         }
 
         cparams.ctx_other = params.ctx_other;
     }
 
+    if (model.arch == LLM_ARCH_EAGLE3) {
+        if (model.tok_embd == nullptr || model.output == nullptr) {
+            if (params.ctx_other == nullptr) {
+                throw std::runtime_error("EAGLE3 requires ctx_other to be set (this warning is normal during memory fitting)");
+            }
+            cparams.ctx_other = params.ctx_other;
+        }
+    }
+
     // Initialize backend samplers here so they are part of the sampling graph
     // before the reserve passes run later in this function. This avoids a later
     // re-reserve when graph nodes change.
@@ -197,7 +209,7 @@ llama_context::llama_context(
 
     cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
 
-    cparams.n_outputs_max = params.n_outputs_max == 0 ? cparams.n_batch : params.n_outputs_max;
+    cparams.n_outputs_max = params.n_outputs_max == 0 || llama_model_has_encoder(&model) ? cparams.n_batch : params.n_outputs_max;
 
     cparams.op_offload = params.op_offload;
     cparams.kv_unified = params.kv_unified;
@@ -948,6 +960,14 @@ float * llama_context::get_embeddings_nextn_ith(int32_t i) {
     }
 }
 
+float * llama_context::get_embeddings_layer_inp(uint32_t lid) {
+    output_reorder();
+
+    GGML_ASSERT(lid < embd_layer_inp.size() && embd_layer_inp[lid].has_data());
+
+    return embd_layer_inp[lid].data;
+}
+
 llama_token llama_context::get_sampled_token_ith(int32_t idx) {
     output_reorder();
 
@@ -1135,6 +1155,17 @@ void llama_context::set_embeddings_nextn(bool value, bool masked) {
     cparams.embeddings_nextn_masked = masked;
 }
 
+void llama_context::set_embeddings_layer_inp(uint32_t lid, bool enable) {
+    LLAMA_LOG_DEBUG("%s: lid = %d, enable = %d\n", __func__, lid, enable);
+
+    GGML_ASSERT(lid < model.hparams.n_layer());
+
+    cparams.embeddings_layer_inp[lid] = enable;
+
+    // note: without this reserve, the draft acceptance drops to zero. not sure why - this is unexpected
+    sched_need_reserve = true;
+}
+
 void llama_context::set_causal_attn(bool value) {
     LLAMA_LOG_DEBUG("%s: value = %d\n", __func__, value);
 
@@ -1360,7 +1391,8 @@ int llama_context::encode(const llama_batch & batch_inp) {
 
     const auto & hparams = model.hparams;
 
-    const int64_t n_embd  = hparams.n_embd_inp();
+    // eagle3/DFlash: features as encoder input, and non-draft paths fall back to model's input dim
+    const int64_t n_embd = hparams.n_embd_inp();
     const int64_t n_vocab = model.vocab.n_tokens();
 
     // note: during encode, we always pass the full sequence starting from pos = 0
@@ -1935,6 +1967,8 @@ int llama_context::decode(const llama_batch & batch_inp) {
             }
         }
 
+        extract_layer_inputs(res, n_tokens_prev, ubatch.n_tokens);
+
         // extract nextn embeddings before
         // only meaningful in LLAMA_POOLING_TYPE_NONE (per-token); other pooling modes are ignored.
         {
@@ -2039,6 +2073,7 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
 
     const auto n_batch    = cparams.n_batch;
     const auto n_vocab    = vocab.n_tokens();
+    const auto n_embd     = hparams.n_embd;
     const auto n_embd_out = hparams.n_embd_out();
 
     bool has_logits     = true;
@@ -2051,9 +2086,9 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
         has_embd   = true;
     }
 
-
     size_t backend_float_count = 0;
     size_t backend_token_count = 0;
+    size_t embd_layer_inp_float_count = 0;
 
     logits.size     = has_logits     ? n_vocab*n_outputs_max     : 0;
     embd.size       = has_embd       ? n_embd_out*n_outputs_max  : 0;
@@ -2065,6 +2100,12 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
         embd_nextn.size = (size_t) n_embd_out * n_batch;
     }
 
+    for (bool enabled : cparams.embeddings_layer_inp) {
+        if (enabled) {
+            embd_layer_inp_float_count += (size_t) n_embd * n_batch;
+        }
+    }
+
     // Allocate backend sampling output buffers if there are backend samplers configured.
     const bool has_sampling = !sampling.samplers.empty();
     if (has_sampling) {
@@ -2079,8 +2120,8 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
 
     const size_t prev_size = buf_output ? ggml_backend_buffer_get_size(buf_output.get()) : 0;
     const size_t new_size  =
-        (logits.size + embd.size + embd_nextn.size + backend_float_count) * sizeof(float) +
-        (                                               backend_token_count) * sizeof(llama_token);
+        (logits.size + embd.size + embd_nextn.size + embd_layer_inp_float_count + backend_float_count) * sizeof(float) +
+        (                                                                         backend_token_count) * sizeof(llama_token);
 
     // alloc only when more than the current capacity is required
     // TODO: also consider shrinking the buffer
@@ -2097,6 +2138,9 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
             logits.data = nullptr;
             embd.data = nullptr;
             embd_nextn.data = nullptr;
+            for (auto & layer_inp : embd_layer_inp) {
+                layer_inp = {nullptr, 0};
+            }
         }
 
         auto * buft = ggml_backend_cpu_buffer_type();
@@ -2128,6 +2172,15 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
     embd_nextn = has_embd_nextn ? buffer_view<float>{(float *) (base + offset), embd_nextn.size} : buffer_view<float>{nullptr, 0};
     offset += embd_nextn.size * sizeof(float);
 
+    for (uint32_t il = 0; il < embd_layer_inp.size(); ++il) {
+        if (cparams.embeddings_layer_inp[il]) {
+            embd_layer_inp[il] = buffer_view<float>{(float *) (base + offset), (size_t) n_embd * n_batch};
+            offset += embd_layer_inp[il].size * sizeof(float);
+        } else {
+            embd_layer_inp[il] = buffer_view<float>{nullptr, 0};
+        }
+    }
+
     if (has_sampling) {
         sampling.logits = {(float *) (base + offset), (size_t)(n_vocab*n_outputs_max)};
         offset += sampling.logits.size * sizeof(float);
@@ -2174,6 +2227,34 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) {
     return n_outputs_max;
 }
 
+void llama_context::extract_layer_inputs(const llm_graph_result * res, size_t token_offset, size_t n_tokens) {
+    for (uint32_t il = 0; il < cparams.embeddings_layer_inp.size(); ++il) {
+        if (!cparams.embeddings_layer_inp[il]) {
+            continue;
+        }
+        if (!embd_layer_inp[il].has_data()) {
+            GGML_ABORT("output layer input buffer not allocated");
+        }
+        ggml_tensor * t = res->get_layer_inp((int) il);
+        if (!t) {
+            GGML_ABORT("layer input tensor not found");
+        }
+
+        const size_t nbytes = ggml_nbytes(t);
+        const size_t nfloats = nbytes / sizeof(float);
+        GGML_ASSERT(n_tokens > 0);
+        GGML_ASSERT(nfloats % n_tokens == 0);
+
+        const size_t row_floats = nfloats / n_tokens;
+        const size_t dst_offset = token_offset * row_floats;
+        GGML_ASSERT(dst_offset + nfloats <= embd_layer_inp[il].size);
+
+        ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched.get(), t);
+        GGML_ASSERT(backend != nullptr);
+        ggml_backend_tensor_get_async(backend, t, embd_layer_inp[il].data + dst_offset, 0, nbytes);
+    }
+}
+
 void llama_context::output_reorder() {
     const uint64_t n_vocab = model.vocab.n_tokens();
     const uint64_t n_embd  = model.hparams.n_embd;
@@ -2200,6 +2281,16 @@ void llama_context::output_reorder() {
             }
         }
 
+        if (embd_layer_inp.size() > 0) {
+            for (int lid = 0; lid < (int) embd_layer_inp.size(); ++lid) {
+                if (embd_layer_inp[lid].size > 0) {
+                    for (uint64_t k = 0; k < n_embd; ++k) {
+                        std::swap(embd_layer_inp[lid].data[i0*n_embd + k], embd_layer_inp[lid].data[i1*n_embd + k]);
+                    }
+                }
+            }
+        }
+
         if (!sampling.samplers.empty()) {
             assert(sampling.logits.size > 0);
             assert(sampling.probs.size > 0);
@@ -3614,6 +3705,10 @@ void llama_set_embeddings_nextn(llama_context * ctx, bool value, bool masked) {
     ctx->set_embeddings_nextn(value, masked);
 }
 
+void llama_set_embeddings_layer_inp(llama_context * ctx, uint32_t lid, bool value) {
+    ctx->set_embeddings_layer_inp(lid, value);
+}
+
 llama_memory_t llama_get_memory(const struct llama_context * ctx) {
     if (!ctx) {
         return nullptr;
@@ -3634,6 +3729,12 @@ float * llama_get_embeddings_nextn_ith(llama_context * ctx, int32_t i) {
     return ctx->get_embeddings_nextn_ith(i);
 }
 
+float * llama_get_embeddings_layer_inp(llama_context * ctx, uint32_t lid) {
+    ctx->synchronize();
+
+    return ctx->get_embeddings_layer_inp(lid);
+}
+
 bool llama_set_sampler(llama_context * ctx, llama_seq_id seq_id, llama_sampler * smpl) {
     return ctx->set_sampler(seq_id, smpl);
 }

src/llama-context.h

@@ -88,6 +88,8 @@ struct llama_context {
     float * get_embeddings_nextn();
     float * get_embeddings_nextn_ith(int32_t i);
 
+    float * get_embeddings_layer_inp(uint32_t lid);
+
     llama_token * get_sampled_tokens() const;
     llama_token   get_sampled_token_ith(int32_t idx);
 
@@ -112,6 +114,7 @@ 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_causal_attn(bool value);
     void set_warmup(bool value);
 
@@ -226,6 +229,10 @@ private:
     // map the output row index `i` to batch index
     int64_t output_resolve_row(int32_t i) const;
 
+    // async-copy enabled layer-input tensors (per cparams.output_layer_inp)
+    // from backend into host-side embd_layer_inp buffers
+    void extract_layer_inputs(const llm_graph_result * res, size_t token_offset, size_t n_tokens);
+
     //
     // graph
     //
@@ -288,6 +295,10 @@ private:
     // sets llm_graph_result::t_h_nextn
     buffer_view<float> embd_nextn = {nullptr, 0};
 
+    // host buffers for output layer input embeddings, per layer
+    // populated when cparams.output_layer_inp[il] is true
+    std::vector<buffer_view<float>> embd_layer_inp;
+
     struct sampling_info {
         // !samplers.empty() to check if any samplers are active
         std::map<llama_seq_id, llama_sampler *> samplers;

src/llama-cparams.h

@@ -3,6 +3,7 @@
 #include "llama.h"
 
 #include <cstdint>
+#include <vector>
 
 #define LLAMA_MAX_SEQ 256
 
@@ -44,6 +45,8 @@ struct llama_cparams {
     bool kv_unified;
     bool pipeline_parallel;
 
+    std::vector<bool> embeddings_layer_inp; // [n_layer()] extract input embeddings for layer
+
     enum llama_context_type ctx_type;
     enum llama_pooling_type pooling_type;
 

src/llama-ext.h

@@ -101,4 +101,20 @@ LLAMA_API float * llama_get_embeddings_nextn(struct llama_context * ctx);
 // LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i);
 LLAMA_API float * llama_get_embeddings_nextn_ith(struct llama_context * ctx, int32_t i);
 
+// Set whether the context outputs the input embeddings of a specific layer
+LLAMA_API void llama_set_embeddings_layer_inp(struct llama_context * ctx, uint32_t lid, bool value);
+
+// mirrors:
+// LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
+LLAMA_API float * llama_get_embeddings_layer_inp(struct llama_context * ctx, uint32_t lid);
+
 LLAMA_API llama_context * llama_get_ctx_other(struct llama_context * ctx);
+
+//
+// model/context data extraction
+//
+
+// returns pointer to the target-model layer indices
+LLAMA_API const int32_t * llama_model_target_layer_ids  (const struct llama_model * model);
+// returns the number of extracted layers from target model
+LLAMA_API uint32_t        llama_model_target_layer_ids_n(const struct llama_model * model);

src/llama-graph.cpp

@@ -904,6 +904,10 @@ void llm_graph_result::reset() {
     t_logits      = nullptr;
     t_embd        = nullptr;
     t_embd_pooled = nullptr;
+
+    t_layer_inp.resize(LLAMA_MAX_LAYERS);
+    std::fill(t_layer_inp.begin(), t_layer_inp.end(), nullptr);
+
     t_sampled.clear();
     t_sampled_probs.clear();
     t_sampled_logits.clear();
@@ -932,7 +936,7 @@ void llm_graph_result::set_inputs(const llama_ubatch * ubatch) {
     }
 }
 
-void llm_graph_result::set_outputs() {
+void llm_graph_result::set_outputs(const llm_graph_params & params) {
     if (t_logits != nullptr) {
         ggml_set_output(t_logits);
     }
@@ -945,6 +949,15 @@ void llm_graph_result::set_outputs() {
     if (t_h_nextn != nullptr) {
         ggml_set_output(t_h_nextn);
     }
+    {
+        const auto & embeddings_layer_inp = params.cparams.embeddings_layer_inp;
+        for (size_t il = 0; il < embeddings_layer_inp.size(); ++il) {
+            if (embeddings_layer_inp[il]) {
+                GGML_ASSERT(t_layer_inp[il] != nullptr && "layer input tensor is null");
+                ggml_set_output(t_layer_inp[il]);
+            }
+        }
+    }
     for (auto & [seq_id, t] : t_sampled) {
         if (t != nullptr) {
             ggml_set_output(t);

src/llama-graph.h

@@ -705,6 +705,8 @@ public:
     ggml_tensor * get_embd_pooled() const { return t_embd_pooled; }
     ggml_tensor * get_h_nextn()     const { return t_h_nextn; }
 
+    ggml_tensor * get_layer_inp(int il) const { return t_layer_inp[il]; }
+
     ggml_cgraph  * get_gf()  const { return gf; }
     ggml_context * get_ctx() const { return ctx_compute.get(); }
 
@@ -713,7 +715,7 @@ public:
     void reset();
 
     void set_inputs(const llama_ubatch * ubatch);
-    void set_outputs();
+    void set_outputs(const llm_graph_params & params);
 
     // try to update the existing graph result using the new graph parameters in order to reuse it
     // this can only be done if we determine that the resulting graph using the new graph parameters
@@ -734,10 +736,12 @@ public:
     ggml_tensor * t_embd_pooled = nullptr;
     ggml_tensor * t_h_nextn     = nullptr; // [n_embd, n_outputs] hidden state before final output norm
 
-    std::map<llama_seq_id, ggml_tensor*> t_sampled_logits;
-    std::map<llama_seq_id, ggml_tensor*> t_candidates;
-    std::map<llama_seq_id, ggml_tensor*> t_sampled;
-    std::map<llama_seq_id, ggml_tensor*> t_sampled_probs;
+    std::vector<ggml_tensor *> t_layer_inp;
+
+    std::map<llama_seq_id, ggml_tensor *> t_sampled_logits;
+    std::map<llama_seq_id, ggml_tensor *> t_candidates;
+    std::map<llama_seq_id, ggml_tensor *> t_sampled;
+    std::map<llama_seq_id, ggml_tensor *> t_sampled_probs;
 
     std::vector<llm_graph_input_ptr> inputs;
 

src/llama-hparams.h

@@ -45,6 +45,7 @@ struct llama_hparams {
     bool rope_finetuned;
     bool use_par_res;
     bool swin_norm;
+    bool norm_before_residual = false;
 
     uint32_t n_ctx_train; // context size the model was trained on
     uint32_t n_embd;

src/llama-model-loader.cpp

@@ -394,6 +394,7 @@ namespace GGUFMeta {
 
     template bool llama_model_loader::get_arr<std::vector<std::string>>(enum llm_kv kid, std::vector<std::string> & result, bool required);
     template bool llama_model_loader::get_arr<std::array<int32_t, 512>>(enum llm_kv kid, std::array<int32_t, 512> & result, bool required);
+    template bool llama_model_loader::get_arr<std::vector<int32_t>>(enum llm_kv kid, std::vector<int32_t> & result, bool required);
 
     template<typename T>
     bool llama_model_loader::get_key(const std::string & key, T & result, bool required) {

src/llama-model.cpp

@@ -62,6 +62,7 @@
 #include "models/delta-net-base.cpp"
 #include "models/dots1.cpp"
 #include "models/dream.cpp"
+#include "models/eagle3.cpp"
 #include "models/ernie4-5-moe.cpp"
 #include "models/ernie4-5.cpp"
 #include "models/eurobert.cpp"
@@ -421,6 +422,8 @@ static llama_model * llama_model_mapping(llm_arch arch, const llama_model_params
             return new llama_model_qwen35moe(params);
         case LLM_ARCH_MISTRAL3:
             return new llama_model_mistral3(params);
+        case LLM_ARCH_EAGLE3:
+            return new llama_model_eagle3(params);
         case LLM_ARCH_MIMO2:
             return new llama_model_mimo2(params);
         case LLM_ARCH_KIMI_LINEAR:
@@ -2372,7 +2375,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
     // TODO: move reranking logic here and generalize
     llm->build_dense_out(dense_2_out_layers, dense_2_out_layers_b, dense_3_out_layers);
 
-    llm->res->set_outputs();
+    llm->res->set_outputs(params);
 
     return llm->res->get_gf();
 }
@@ -2540,6 +2543,7 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
         case LLM_ARCH_ERNIE4_5:
         case LLM_ARCH_ERNIE4_5_MOE:
         case LLM_ARCH_MISTRAL3:
+        case LLM_ARCH_EAGLE3:
         case LLM_ARCH_MISTRAL4:
         case LLM_ARCH_LLAMA_EMBED:
         case LLM_ARCH_MAINCODER:
@@ -2734,8 +2738,9 @@ uint64_t llama_model_n_params(const llama_model * model) {
 
 bool llama_model_has_encoder(const llama_model * model) {
     switch (model->arch) {
-        case LLM_ARCH_T5:        return true;
-        case LLM_ARCH_T5ENCODER: return true;
+        case LLM_ARCH_T5:
+        case LLM_ARCH_T5ENCODER:
+        case LLM_ARCH_EAGLE3:    return true;
         default:                 return false;
     }
 }
@@ -2821,3 +2826,12 @@ void llama_model_base::create_tensor_qkv(llama_layer & layer, int bid,
         layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", bid), {n_embd_v_}, TENSOR_NOT_REQUIRED);
     }
 }
+
+const int32_t * llama_model_target_layer_ids(const struct llama_model * model) {
+    const auto & v = model->target_layer_ids;
+    return v.empty() ? nullptr : v.data();
+}
+
+uint32_t llama_model_target_layer_ids_n(const struct llama_model * model) {
+    return (uint32_t) model->target_layer_ids.size();
+}

src/llama-model.h

@@ -569,6 +569,13 @@ struct llama_model {
     struct ggml_tensor * per_layer_model_proj = nullptr;
     struct ggml_tensor * per_layer_proj_norm  = nullptr;
 
+    // eagle3
+    struct ggml_tensor * fc  = nullptr;  // feature fusion layer
+    struct ggml_tensor * d2t = nullptr;  // draft to target vocabulary mapping
+
+    // unified vector to store target-model extracted layer ids in eagle3, dflash, etc.
+    std::vector<int32_t> target_layer_ids;
+
     std::vector<llama_layer> layers;
 
     //Dense linear projections for SentenceTransformers models like embeddinggemma

src/llama-vocab.cpp

@@ -989,7 +989,7 @@ struct llm_tokenizer_wpm_session {
 
     void tokenize(const std::string & text, std::vector<llama_token> & output) {
         // normalize and split by whitespace
-        std::vector<std::string> words = preprocess(text, vocab.get_normalizer_lowercase());
+        std::vector<std::string> words = preprocess(text, vocab.get_normalizer_opts());
         // bos token prepended already
 
         // find the longest tokens that form the words
@@ -1034,11 +1034,14 @@ struct llm_tokenizer_wpm_session {
     }
 
     // TODO: reduce string copies by using cpts_offs array
-    static std::vector<std::string> preprocess(const std::string & text, bool lowercase)  {
-        const std::vector<uint32_t> cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
+    static std::vector<std::string> preprocess(const std::string & text, const llama_vocab::normalizer_options & normalizer_opts)  {
+        std::vector<uint32_t> cpts = unicode_cpts_from_utf8(text);
+        if (normalizer_opts.strip_accents) {
+            cpts = unicode_cpts_normalize_nfd(cpts);
+        }
         std::vector<std::string> words(1, "");
 
-        for (const uint32_t cpt : cpts_nfd) {
+        for (const uint32_t cpt : cpts) {
             const auto flags = unicode_cpt_flags_from_cpt(cpt);
 
             if (flags.is_whitespace) {
@@ -1053,7 +1056,11 @@ struct llm_tokenizer_wpm_session {
                 continue;
             }
 
-            const std::string s = unicode_cpt_to_utf8(lowercase ? unicode_tolower(cpt) : cpt);
+            if (normalizer_opts.strip_accents && flags.is_accent_mark) {
+                continue;
+            }
+
+            const std::string s = unicode_cpt_to_utf8(normalizer_opts.lowercase ? unicode_tolower(cpt) : cpt);
             if (flags.is_punctuation || ( cpt < 0x7F && flags.is_symbol ) || is_chinese_char(cpt)) {
                 if (words.back().size()) {  // finish previous word if any
                     words.emplace_back();
@@ -1917,7 +1924,7 @@ struct llm_tokenizer_whitespace_session : llm_tokenizer_bpe_session {
     llm_tokenizer_whitespace_session(const llama_vocab & vocab, const llm_tokenizer_bpe & tokenizer) : llm_tokenizer_bpe_session{vocab, tokenizer}, vocab{vocab} {}
 
     void tokenize(const std::string & text, std::vector<llama_token> & output) override {
-        const bool lowercase = vocab.get_normalizer_lowercase();
+        const bool lowercase = vocab.get_normalizer_opts().lowercase;
 
         std::string segment;
         auto flush = [&]() {
@@ -2022,7 +2029,9 @@ struct llama_vocab::impl {
     bool remove_extra_whitespaces   = false;
     bool escape_whitespaces         = true;
     bool treat_whitespace_as_suffix = false;
-    bool normalizer_lowercase       = true; // Lowercase normalizer (tokenizer.json)
+
+    // BertNormalizer options
+    llama_vocab::normalizer_options normalizer_opts;
 
     std::unordered_map<std::string, llama_token> token_to_id;
     std::vector<token_data>                      id_to_token;
@@ -2408,7 +2417,7 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
             } else if (
                     tokenizer_pre == "whitespace") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_WHITESPACE;
-                normalizer_lowercase = false;
+                normalizer_opts.lowercase = false;
             } else if (
                     tokenizer_pre == "refact") {
                 pre_type = LLAMA_VOCAB_PRE_TYPE_REFACT;
@@ -2771,8 +2780,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
             }
         }
 
-        // Lowercase normalizer flag (consulted by WPM / whitespace BPE)
-        ml.get_key(LLM_KV_TOKENIZER_NORMALIZER_LOWERCASE, normalizer_lowercase, false);
+        // BertNormalizer options
+        ml.get_key(LLM_KV_TOKENIZER_NORMALIZER_LOWERCASE,     normalizer_opts.lowercase,     false);
+        normalizer_opts.strip_accents = normalizer_opts.lowercase;
+        ml.get_key(LLM_KV_TOKENIZER_NORMALIZER_STRIP_ACCENTS, normalizer_opts.strip_accents, false);
 
         // suppress tokens
         {
@@ -4255,8 +4266,8 @@ bool llama_vocab::get_treat_whitespace_as_suffix() const {
     return pimpl->treat_whitespace_as_suffix;
 }
 
-bool llama_vocab::get_normalizer_lowercase() const {
-    return pimpl->normalizer_lowercase;
+const llama_vocab::normalizer_options & llama_vocab::get_normalizer_opts() const {
+    return pimpl->normalizer_opts;
 }
 
 const std::vector<llama_token> & llama_vocab::get_suppress_tokens() const {

src/llama-vocab.h

@@ -77,6 +77,12 @@ struct llama_vocab {
         llama_token_attr attr;
     };
 
+    struct normalizer_options {
+        bool lowercase     = true;
+        bool strip_accents = true;
+        // TODO: clean_text, handle_chinese_chars
+    };
+
     llama_vocab();
     ~llama_vocab();
 
@@ -143,7 +149,7 @@ struct llama_vocab {
     bool get_remove_extra_whitespaces  () const;
     bool get_escape_whitespaces        () const;
     bool get_treat_whitespace_as_suffix() const;
-    bool get_normalizer_lowercase      () const;
+    const normalizer_options & get_normalizer_opts() const;
 
     const std::vector<llama_token> & get_suppress_tokens() const;
 

src/models/delta-net-base.cpp

@@ -398,9 +398,8 @@ std::pair<ggml_tensor *, ggml_tensor *> llm_build_delta_net_base::build_delta_ne
     GGML_ASSERT(b->ne[0] == 1   && b->ne[1] == H_v && b->ne[2] == n_tokens && b->ne[3] == n_seqs);
     GGML_ASSERT(s->ne[0] == S_v && s->ne[1] == S_v && s->ne[2] == H_v      && s->ne[3] == n_seqs);
 
-    // K=1 (final state only): reshape to 3D (S_v*S_v*H_v, 1, n_seqs) for ggml_gated_delta_net.
-    ggml_tensor * s_3d = ggml_reshape_3d(ctx0, s, S_v * S_v * H_v, 1, n_seqs);
-    ggml_tensor * result = ggml_gated_delta_net(ctx0, q, k, v, g, b, s_3d);
+    // K=1: output carries the final state only. state s is 4D [S_v, S_v, H_v, n_seqs].
+    ggml_tensor * result = ggml_gated_delta_net(ctx0, q, k, v, g, b, s, /*K=*/1);
     if (n_tokens == 1) {
         cb(result, LLAMA_TENSOR_NAME_FGDN_AR, il);
     } else {
@@ -564,11 +563,8 @@ ggml_tensor * llm_build_delta_net_base::build_recurrent_attn(
     const int64_t D = S_v * S_v * H_v;
     const int64_t K = cparams.n_rs_seq + 1;
 
-    // TODO: remove pad + simplify
-    ggml_tensor * s_3d     = ggml_reshape_3d(ctx0, s, D, 1, n_seqs);
-    ggml_tensor * s_3d_pad = ggml_pad       (ctx0, s_3d, 0, K - 1, 0, 0);
-
-    ggml_tensor * gdn_out = ggml_gated_delta_net(ctx0, q, k, v, g, b, s_3d_pad);
+    // state s is 4D [S_v, S_v, H_v, n_seqs]; K snapshot slots are written into the output.
+    ggml_tensor * gdn_out = ggml_gated_delta_net(ctx0, q, k, v, g, b, s, K);
     if (n_seq_tokens > 1) {
         cb(gdn_out, LLAMA_TENSOR_NAME_FGDN_CH, il);
     } else {
@@ -587,21 +583,24 @@ ggml_tensor * llm_build_delta_net_base::build_recurrent_attn(
     cb(output, "attn_output", il);
 
     const size_t row_size = hparams.n_embd_s() * ggml_element_size(ssm_states_all);
-    for (int64_t k_i = 0; k_i < K; ++k_i) {
-        const uint32_t cache_slot = (uint32_t) (K - 1 - k_i);
-        ggml_tensor * src = ggml_view_4d(ctx0, gdn_out,
-            S_v, S_v, H_v, n_seqs,
-            ggml_row_size(gdn_out->type, S_v),
-            ggml_row_size(gdn_out->type, S_v * S_v),
-            ggml_row_size(gdn_out->type, S_v * S_v * H_v),
-            ggml_row_size(gdn_out->type, attn_score_elems + k_i * state_size_per_snap));
 
-        ggml_tensor * dst = ggml_view_2d(ctx0, ssm_states_all,
-            hparams.n_embd_s(), n_seqs, ssm_states_all->nb[1],
-            ((size_t) cache_slot * mem_size + kv_head) * row_size);
+    // op writes the last min(n_seq_tokens, K) snapshots; trailing slots are left unwritten
+    const int64_t n_written = std::min<int64_t>(n_seq_tokens, K);
 
-        ggml_build_forward_expand(gf, ggml_cpy(ctx0, src, dst));
-    }
+    // write the produced snapshots into the recurrent cache (snapshot slot i -> rollback group i)
+    ggml_tensor * src = ggml_view_3d(ctx0, gdn_out,
+        D, n_seqs, n_written,
+        ggml_row_size(gdn_out->type, D),
+        ggml_row_size(gdn_out->type, state_size_per_snap),
+        ggml_row_size(gdn_out->type, attn_score_elems));
+
+    ggml_tensor * dst = ggml_view_3d(ctx0, ssm_states_all,
+        D, n_seqs, n_written,
+        ssm_states_all->nb[1],
+        (size_t) mem_size * row_size,
+        (size_t) kv_head * row_size);
+
+    ggml_build_forward_expand(gf, ggml_cpy(ctx0, src, dst));
 
     return output;
 }

src/models/eagle3.cpp

@@ -0,0 +1,323 @@
+#include "models.h"
+
+void llama_model_eagle3::load_arch_hparams(llama_model_loader & ml) {
+    ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+    if (!ml.get_arr(LLM_KV_TARGET_LAYERS, target_layer_ids, false)) {
+        throw std::runtime_error("EAGLE3 model requires 'extract_layers' in GGUF metadata");
+    }
+    if (target_layer_ids.size() != 3) {
+        throw std::runtime_error("EAGLE3 requires exactly 3 entries in 'extract_layers'");
+    }
+    LLAMA_LOG_INFO("%s: EAGLE3 extract_layers = [%d, %d, %d]\n", __func__,
+            target_layer_ids[0],
+            target_layer_ids[1],
+            target_layer_ids[2]);
+
+    uint32_t n_embd_tgt = 0;
+
+    ml.get_key(LLM_KV_TARGET_HIDDEN_SIZE, n_embd_tgt);
+    LLAMA_LOG_INFO("%s: EAGLE3 n_embd_tgt = %u (draft n_embd = %u)\n", __func__, n_embd_tgt, hparams.n_embd);
+
+    hparams.n_embd_inp_impl = (uint32_t) target_layer_ids.size() * n_embd_tgt;
+
+    // eagle3 norm_before_residual (optional, default false)
+    // compatible with Readhat eagle3 speculator model
+    ml.get_key(LLM_KV_NORM_BEFORE_RESIDUAL, hparams.norm_before_residual, false);
+    if (hparams.norm_before_residual) {
+        LLAMA_LOG_INFO("%s: EAGLE3gnorm_before_residual = true\n", __func__);
+    }
+
+    type = LLM_TYPE_UNKNOWN;
+}
+
+void llama_model_eagle3::load_arch_tensors(llama_model_loader &) {
+    LLAMA_LOAD_LOCALS;
+
+    const int64_t n_embd_inp = hparams.n_embd_inp();
+    const int64_t n_embd_attn_input = 2 * n_embd;
+
+    // Get vocab size from the d2t tensor in the GGUF file (optional - only needed if eagle3 has different vocab_size than target)
+    // d2t: draft to target vocabulary mapping
+    int64_t n_draft_vocab = n_vocab;  // Default: same as target vocab
+    const struct ggml_tensor * d2t_meta = ml->get_tensor_meta("d2t");
+    if (d2t_meta) {
+        n_draft_vocab = d2t_meta->ne[0]; // update draft vocab size
+        d2t = create_tensor(tn(LLM_TENSOR_D2T), {n_draft_vocab}, 0);
+        LLAMA_LOG_INFO("%s: EAGLE3 using d2t mapping (draft_vocab_size = %lld)\n", __func__, (long long)n_draft_vocab);
+    } else {
+        d2t = nullptr; // no d2t, use default vocab size
+        LLAMA_LOG_INFO("%s: EAGLE3 without d2t - sharing same vocab_size with target (vocab_size = %lld)\n", __func__, (long long)n_draft_vocab);
+    }
+
+    // Feature fusion layer: projects 3 target layers to draft hidden size
+    fc = create_tensor(tn(LLM_TENSOR_FC, "weight"), {n_embd_inp, n_embd}, 0);
+
+    // Output layer (uses draft vocab size)
+    output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+    output      = create_tensor(tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_draft_vocab}, TENSOR_NOT_REQUIRED);
+
+    // Token embeddings (optional - Llama 3.3 70B EAGLE3 has its own)
+    const struct ggml_tensor * tok_embd_meta = ml->get_tensor_meta(tn(LLM_TENSOR_TOKEN_EMBD, "weight").str().c_str());
+    if (tok_embd_meta) {
+        const int64_t n_target_vocab = tok_embd_meta->ne[1];
+        tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_target_vocab}, 0);
+        LLAMA_LOG_INFO("%s: EAGLE3 using its own token_embd (vocab = %lld)\n", __func__, (long long)n_target_vocab);
+    }
+
+    // Single decoder layer
+    for (int i = 0; i < n_layer; ++i) {
+        auto & layer = layers[i];
+
+        // input_layernorm: applied to token embeddings
+        layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+        // eagle3 specific: hidden_norm applied to fused target features
+        layer.attn_norm_2 = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, 0);
+
+        // Attention takes input_embeds_normed + fused_target_normed as input
+        layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd_attn_input, n_embd_head_k * n_head}, 0);
+        layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd_attn_input, n_embd_k_gqa}, 0);
+        layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd_attn_input, n_embd_v_gqa}, 0);
+        layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+        layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+        layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff}, 0);
+        layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd}, 0);
+        layer.ffn_up   = create_tensor(tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff}, 0);
+
+        // rope_freqs for llama3 rope scaling (optional - only if eagle3 config has rope_scaling)
+        layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED);
+    }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_eagle3::build_arch_graph(const llm_graph_params & params) const {
+    switch (params.gtype) {
+        case LLM_GRAPH_TYPE_ENCODER:
+            return std::make_unique<graph<true>>(*this, params);
+        case LLM_GRAPH_TYPE_DEFAULT:
+        case LLM_GRAPH_TYPE_DECODER:
+            return std::make_unique<graph<false>>(*this, params);
+        default:
+            GGML_ABORT("invalid graph type");
+    };
+}
+
+template <>
+ggml_tensor * llama_model_eagle3::graph<true>::build_inp_embd_enc() const {
+    ggml_tensor * cur = nullptr;
+
+    // Input: Target model features (3 layers concatenated: low, mid, high)
+    // Data will be provided via ubatch->embd in encode_eagle3_features()
+    auto inp_target = std::make_unique<llm_graph_input_embd>(hparams.n_embd_inp());
+    inp_target->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32,hparams.n_embd_inp(), n_tokens);
+    ggml_set_input(inp_target->embd);
+
+    cur = inp_target->embd;
+    cb(cur, "inp_embd", -1);
+
+    res->add_input(std::move(inp_target));
+
+    return cur;
+}
+
+// eagle3 Encoder: processes target model features through feature fusion layer
+// Input: target_features e.g. [12288, n_tokens] from target model layers low, middle, high
+// Output: g_embeddings e.g. [4096, n_tokens] stored in context
+template <>
+llama_model_eagle3::graph<true>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+    ggml_tensor * cur = nullptr;
+
+    cur = build_inp_embd_enc();
+
+    // Feature fusion layer
+    cur = build_lora_mm(model.fc, cur);
+    cb(cur, "fc_out", -1);
+
+    // Output: g_embeddings e.g. [4096, n_tokens]
+    // store in t_h_nextn (same as MTP) so can be read via llama_get_embeddings_nextn(ctx_dft)
+    ggml_set_output(cur);
+    res->t_h_nextn = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}
+
+// eagle3 Decoder: processes draft tokens using g_embeddings from encoder
+// Input: draft tokens + g_embeddings from encoder
+// Output: draft logits
+template <>
+llama_model_eagle3::graph<false>::graph(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_layer == 1);  // eagle3 has only one decoder layer
+
+    ggml_tensor * cur;
+    ggml_tensor * inpL;
+
+    // eagle3 Decoder receives:
+    // 1. Token embeddings (e.g.from eagle3's own tok_embd for Llama 3.3 70B, or target model for Llama 3.1 8B)
+    // 2. g_embeddings from encoder
+    auto * tok_embd = model.tok_embd;
+    if (model.tok_embd == nullptr) {
+        GGML_ASSERT(cparams.ctx_other != nullptr);
+        const auto * model_other = llama_get_model(cparams.ctx_other);
+
+        GGML_ASSERT(model_other->tok_embd != nullptr && "EAGLE3 decoder requires token embeddings (own or from target model)");
+        tok_embd = model_other->tok_embd;
+    }
+
+    auto inp = std::make_unique<llm_graph_input_embd>(n_embd);
+
+    inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+    ggml_set_input(inp->tokens);
+
+    inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
+    ggml_set_input(inp->embd);
+
+    ggml_tensor * inp_embd = ggml_get_rows(ctx0, tok_embd, inp->tokens);
+    cb(inp_embd, "inp_embd", -1);
+
+    ggml_tensor * inp_g = inp->embd;
+    cb(inp_g, "inp_g_embeddings", -1);
+
+    res->add_input(std::move(inp));
+
+    inpL = inp_g;
+
+    // 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));
+
+    // Single decoder layer (il = 0)
+    const int il = 0;
+    {
+        // Apply input_layernorm to the token embeddings
+        ggml_tensor * embd_norm = build_norm(inp_embd,
+                model.layers[il].attn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(embd_norm, "embd_norm", il);
+
+        // Apply hidden_norm to inp_g
+        ggml_tensor * g_norm = build_norm(inp_g,
+                model.layers[il].attn_norm_2, NULL,
+                LLM_NORM_RMS, -1);
+        cb(g_norm, "g_norm", il);
+
+        // norm_before_residual: determines what goes into the residual connection (compatible with Readhat eagle3 speculator model)
+        // - false (default): use raw inp_g for residual
+        // - true: use normalized g_norm for residual
+        // inpL is the concatenated input (normalized inp_embd + normalized inp_g)
+        ggml_tensor * inpSA = hparams.norm_before_residual ? g_norm : inpL;
+
+        // Concatenate normalized inp_embd and normalized inp_g
+        cur = ggml_concat(ctx0, embd_norm, g_norm, il);
+        cb(cur, "concat_embd", il);
+
+        // Self-attention with concatenated input
+        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);
+
+        // rope freq factors, returns nullptr if not available
+        ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+        // 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_rope", il);
+        cb(Kcur, "Kcur_rope", il);
+
+        cur = build_attn(inp_attn,
+                model.layers[il].wo, NULL, nullptr,
+                Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+
+        // Add residual and update it
+        ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+        cb(ffn_inp, "ffn_inp", il);
+
+        // Apply FFN norm to the sum
+        cur = build_norm(ffn_inp,
+                model.layers[il].ffn_norm, NULL,
+                LLM_NORM_RMS, il);
+        cb(cur, "post_attn_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);
+
+        // Output norm with residual
+        cur = ggml_add(ctx0, cur, ffn_inp);
+        cb(cur, "eagle3_prenorm", il);
+
+        inpL = cur;
+    }
+
+    cur = inpL;
+
+    // Output prenorm state (for next token's g_embeddings in autoregressive generation)
+    ggml_set_output(cur);
+    res->t_h_nextn = cur;
+
+    cur = build_norm(cur,
+            model.output_norm, NULL,
+            LLM_NORM_RMS, -1);
+    cb(cur, "result_norm", -1);
+
+    // lm_head - projects to draft vocabulary
+    // if the draft has no own output projection, inherit the target model's lm_head
+    auto * output = model.output;
+    if (output == nullptr) {
+        GGML_ASSERT(cparams.ctx_other != nullptr);
+        const auto * model_other = llama_get_model(cparams.ctx_other);
+
+        GGML_ASSERT(model_other->output != nullptr && "EAGLE3 decoder requires an output projection (own or from target model)");
+        output = model_other->output;
+    }
+    cur = build_lora_mm(output, cur);
+
+    if (model.d2t) {
+        const int64_t n_draft_vocab = cur->ne[0];
+        const int64_t n_outputs     = cur->ne[1];
+        const int64_t n_vocab       = (int64_t) model.vocab.n_tokens();
+
+        GGML_ASSERT(model.d2t->type == GGML_TYPE_I64);
+        GGML_ASSERT(model.d2t->ne[0] == n_draft_vocab);
+
+        ggml_tensor * logits = ggml_fill(ctx0, ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 1, n_vocab, n_outputs), -INFINITY);
+        cur = ggml_set_rows(ctx0, logits,
+                ggml_reshape_3d(ctx0, cur,       1,             n_draft_vocab, n_outputs),
+                ggml_reshape_3d(ctx0, model.d2t, n_draft_vocab, 1,             1));
+        cur = ggml_reshape_2d(ctx0, cur, n_vocab, n_outputs);
+    }
+
+    cb(cur, "result_output", -1);
+    res->t_logits = cur;
+
+    ggml_build_forward_expand(gf, cur);
+}

src/models/gemma4.cpp

@@ -210,6 +210,8 @@ llama_model_gemma4::graph::graph(const llama_model & model, const llm_graph_para
         const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
         const int   n_rot_l      = hparams.n_rot(il);
 
+        res->t_layer_inp[il] = inpL;
+
         // norm
         cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
         cb(cur, "attn_norm", il);

src/models/llama.cpp

@@ -124,6 +124,8 @@ llama_model_llama::graph<embed>::graph(const llama_model & model, const llm_grap
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
     for (int il = 0; il < n_layer; ++il) {
+        res->t_layer_inp[il] = inpL;
+
         ggml_tensor * inpSA = inpL;
 
         // norm

src/models/models.h

@@ -46,7 +46,7 @@ struct llm_build_delta_net_base : public llm_graph_context {
                 ggml_tensor * s,
                 int           il);
 
-    // use the ggml_gated_delta_net fused operator (K=1; state has shape (D, 1, n_seqs))
+    // use the ggml_gated_delta_net fused operator (K=1; state has shape [S_v, S_v, H_v, n_seqs])
     std::pair<ggml_tensor *, ggml_tensor *> build_delta_net_fused(
                 ggml_tensor * q,
                 ggml_tensor * k,
@@ -1089,6 +1089,21 @@ struct llama_model_glm_dsa : public llama_model_base {
     std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
 };
 
+struct llama_model_eagle3 : public llama_model_base {
+    llama_model_eagle3(const struct llama_model_params & params) : llama_model_base(params) {}
+    void load_arch_hparams(llama_model_loader & ml) override;
+    void load_arch_tensors(llama_model_loader & ml) override;
+
+    template <bool is_enc>
+    struct graph : public llm_graph_context {
+        graph(const llama_model & model, const llm_graph_params & params);
+
+        ggml_tensor * build_inp_embd_enc() const;
+    };
+
+    std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
 
 struct llama_model_mistral4 : public llama_model_deepseek2 {
     llama_model_mistral4(const struct llama_model_params & params) : llama_model_deepseek2(params) {}

src/models/openai-moe.cpp

@@ -75,6 +75,8 @@ llama_model_openai_moe::graph::graph(const llama_model & model, const llm_graph_
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
     for (int il = 0; il < n_layer; ++il) {
+        res->t_layer_inp[il] = inpL;
+
         const float freq_base_l  = model.get_rope_freq_base (cparams, il);
         const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
 

src/models/qwen3.cpp

@@ -69,6 +69,8 @@ llama_model_qwen3::graph::graph(const llama_model & model, const llm_graph_param
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
     for (int il = 0; il < n_layer; ++il) {
+        res->t_layer_inp[il] = inpL;
+
         ggml_tensor * inpSA = inpL;
 
         // norm

src/models/qwen35.cpp

@@ -173,7 +173,7 @@ llama_model_qwen35::graph::graph(const llama_model & model, const llm_graph_para
         }
 
         if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
-            cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
+            cur   = ggml_get_rows(ctx0, cur,   inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
 

src/models/qwen3moe.cpp

@@ -78,6 +78,8 @@ llama_model_qwen3moe::graph::graph(const llama_model & model, const llm_graph_pa
     ggml_tensor * inp_out_ids = build_inp_out_ids();
 
     for (int il = 0; il < n_layer; ++il) {
+        res->t_layer_inp[il] = inpL;
+
         ggml_tensor * inpSA = inpL;
 
         // norm

tools/server/server-context.cpp

@@ -2046,6 +2046,9 @@ private:
 
         auto & cur = slot.prompt.checkpoints.emplace_back();
 
+        // [TAG_CHECKPOINTS_FIX_POS_MIN]
+        // TODO: here we incorrectly deterimne that the saved checkpoint data covers the [pos_min, pos_max] range
+        //       this is not true for SWA models: https://github.com/ggml-org/llama.cpp/pull/24411#issuecomment-4677983225
         cur.update_pos(slot.prompt.n_tokens() - n_tokens_cur, pos_min, pos_max);
 
         cur.update_tgt(ctx_tgt,       slot.id, LLAMA_STATE_SEQ_FLAGS_PARTIAL_ONLY);
@@ -2860,6 +2863,10 @@ private:
                                             // guarantee that a checkpoint will result in at least one token being processed [TAG_PROMPT_LOGITS]
                                             LOG_INF("slot %12.*s: id %2d | task %d | Checking checkpoint with [%d, %d] against %d...\n", 12,
                                                 func_name, (slot).id, ((slot).task ? (slot).task->id : -1), cur.pos_min, cur.pos_max, pos_min_thold);
+                                            // workaround for [TAG_CHECKPOINTS_FIX_POS_MIN]
+                                            if (cur.pos_max > pos_next) {
+                                                return false;
+                                            }
                                             return cur.pos_min < pos_min_thold || cur.pos_min == 0;
                                         }
                                     );

tools/server/server.cpp

@@ -94,20 +94,22 @@ int llama_server(int argc, char ** argv) {
     const bool is_router_server = params.model.path.empty();
     common_params_print_info(params, !is_router_server);
 
-    // validate batch size for embeddings
-    // embeddings require all tokens to be processed in a single ubatch
-    // see https://github.com/ggml-org/llama.cpp/issues/12836
-    if (params.embedding && params.n_batch > params.n_ubatch) {
-        SRV_WRN("embeddings enabled with n_batch (%d) > n_ubatch (%d)\n", params.n_batch, params.n_ubatch);
-        SRV_WRN("setting n_batch = n_ubatch = %d to avoid assertion failure\n", params.n_ubatch);
-        params.n_batch = params.n_ubatch;
-    }
+    if (!is_router_server) {
+        // validate batch size for embeddings
+        // embeddings require all tokens to be processed in a single ubatch
+        // see https://github.com/ggml-org/llama.cpp/issues/12836
+        if (params.embedding && params.n_batch > params.n_ubatch) {
+            SRV_WRN("embeddings enabled with n_batch (%d) > n_ubatch (%d)\n", params.n_batch, params.n_ubatch);
+            SRV_WRN("setting n_batch = n_ubatch = %d to avoid assertion failure\n", params.n_ubatch);
+            params.n_batch = params.n_ubatch;
+        }
 
-    if (params.n_parallel < 0) {
-        SRV_INF("%s", "n_parallel is set to auto, using n_parallel = 4 and kv_unified = true\n");
+        if (params.n_parallel < 0) {
+            SRV_INF("%s", "n_parallel is set to auto, using n_parallel = 4 and kv_unified = true\n");
 
-        params.n_parallel = 4;
-        params.kv_unified = true;
+            params.n_parallel = 4;
+            params.kv_unified = true;
+        }
     }
 
     // for consistency between server router mode and single-model mode, we set the same model name as alias

vendor/cpp-httplib/CMakeLists.txt

@@ -81,7 +81,7 @@ if (LLAMA_BUILD_BORINGSSL)
     target_link_libraries(${TARGET} PUBLIC ssl crypto)
 
 elseif (LLAMA_BUILD_LIBRESSL)
-    set(LIBRESSL_VERSION "4.3.1" CACHE STRING "LibreSSL version")
+    set(LIBRESSL_VERSION "4.3.2" CACHE STRING "LibreSSL version")
 
     message(STATUS "Fetching LibreSSL version ${LIBRESSL_VERSION}")