Dflash support for nemotron-3.5 (#26905)

* conversion: skip untrained DFlash embeddings

* Add Nemotron DFlash support

* Add DFlash NVFP4 support

* Address review comments

* add missing output_s for nvfp4

* Include change for keeping residual for last layer also if requested in future dflash models

* Update conversion/qwen.py

Defensive check, not needed

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>

* Fixing bug introduced by merge conflict

---------

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>

conversion/base.py

@@ -829,7 +829,7 @@ class ModelBase:
             elif any(str(v.get("quant_algo")).endswith("NVFP4") for v in quant_layers.values() if isinstance(v, dict)):
                 quant_algo = "NVFP4"
 
-        self._is_nvfp4 = quant_algo == "NVFP4"
+        self._is_nvfp4 = quant_algo in ("NVFP4", "W4A16_NVFP4")
         self._is_mxfp4 = quant_method == "mxfp4"
 
         # NVFP4 weights are repacked and written directly to gguf_writer.

conversion/qwen.py

@@ -647,10 +647,13 @@ class DFlashModel(Qwen3Model):
         # own tokenizer logic, not the Qwen default).
         from . import get_model_class
         with open(self.target_model_dir / "config.json", "r", encoding="utf-8") as f:
-            target_arch = json.load(f)["architectures"][0]
+            target_hparams = json.load(f)
+            target_arch = target_hparams["architectures"][0]
         target_cls = get_model_class(target_arch)
 
         if target_cls is not type(self):
+            if target_arch == "NemotronHForCausalLM":
+                setattr(self, "is_moe", "num_experts_per_tok" in target_hparams)
             target_cls.set_vocab(self)  # ty: ignore[unresolved-attribute]
         else:
             super().set_vocab()
@@ -688,6 +691,12 @@ class DFlashModel(Qwen3Model):
             name = "model." + name
         return super().filter_tensors((name, gen))
 
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name == "model.embed_tokens.weight" and not self.hparams.get("has_embed_tokens", True):
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
 
 @ModelBase.register("Qwen3DSparkModel")
 class DSparkModel(DFlashModel):

gguf-py/gguf/constants.py

@@ -4726,6 +4726,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.D2T,
     ],
     MODEL_ARCH.DFLASH: [
+        MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
         MODEL_TENSOR.ATTN_NORM,
         MODEL_TENSOR.ATTN_Q,

src/llama-model.h

@@ -623,8 +623,9 @@ 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
+    // eagle3 / dflash feature fusion layer
+    struct ggml_tensor * fc   = nullptr;
+    struct ggml_tensor * fc_s = nullptr;
     struct ggml_tensor * d2t = nullptr;  // draft to target vocabulary mapping
 
     // dspark

src/models/dflash.cpp

@@ -79,6 +79,7 @@ void llama_model_dflash::load_arch_tensors(llama_model_loader &) {
 
     const int64_t n_embd_inp = hparams.n_embd_inp_enc();
 
+    tok_embd        = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD,       "weight"), { n_embd, n_vocab }, TENSOR_NOT_REQUIRED);
     // DSpark = DFlash + a semi-autoregressive Markov head and Confidence head
     //
     // TODO: only Qwen3-style backbones are supported for now; other backbones (e.g. Gemma4)
@@ -97,6 +98,7 @@ void llama_model_dflash::load_arch_tensors(llama_model_loader &) {
     }
 
     fc              = create_tensor(tn(LLM_TENSOR_FC,              "weight"), { n_embd_inp, n_embd }, 0);
+    fc_s            = create_tensor(tn(LLM_TENSOR_FC,              "scale"),  { 1 }, TENSOR_NOT_REQUIRED);
     output_norm_enc = create_tensor(tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), { n_embd }, 0); // encoder hidden_norm (after fc)
     output_norm     = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM,    "weight"), { n_embd }, 0); // decoder final norm
 
@@ -205,7 +207,7 @@ template <>
 llama_model_dflash::graph<true>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
     ggml_tensor * cur = build_inp_embd_enc();
 
-    cur = build_lora_mm(model.fc, cur);
+    cur = build_lora_mm(model.fc, cur, model.fc_s);
     cb(cur, "fc_out", -1);
 
     cur = build_norm(cur, model.output_norm_enc, NULL, LLM_NORM_RMS, -1);
@@ -460,9 +462,9 @@ llama_model_dflash::graph<false>::graph(const llama_model & model, const llm_gra
         cb(cur, "ffn_norm", il);
 
         cur = build_ffn(cur,
-                layer.ffn_up,   NULL, NULL,
-                layer.ffn_gate, NULL, NULL,
-                layer.ffn_down, NULL, NULL,
+                layer.ffn_up,   NULL, layer.ffn_up_s,
+                layer.ffn_gate, NULL, layer.ffn_gate_s,
+                layer.ffn_down, NULL, layer.ffn_down_s,
                 NULL,
                 LLM_FFN_SILU, LLM_FFN_PAR, il);
         cb(cur, "ffn_out", il);
@@ -479,15 +481,17 @@ llama_model_dflash::graph<false>::graph(const llama_model & model, const llm_gra
     res->t_embd = cur;
 
     // lm_head from the target model (shared via ctx_other)
-    auto * output = model.output;
+    auto * output   = model.output;
+    auto * output_s = model.output_s;
     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 && "DFlash decoder requires the target model's output projection");
-        output = model_other->output;
+        output   = model_other->output;
+        output_s = model_other->output_s;
     }
 
-    cur = build_lora_mm(output, cur);
+    cur = build_lora_mm(output, cur, output_s);
     cb(cur, "result_output", -1);
     res->t_logits = cur;
 
@@ -655,15 +659,17 @@ llama_model_dflash::graph_dsv4::graph_dsv4(const llama_model & model, const llm_
     cb(cur, "result_norm", -1);
 
     // lm_head from the target model (shared via ctx_other)
-    auto * output = model.output;
+    auto * output   = model.output;
+    auto * output_s = model.output_s;
     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 && "DSpark decoder requires the target model's output projection");
-        output = model_other->output;
+        output   = model_other->output;
+        output_s = model_other->output_s;
     }
 
-    cur = build_lora_mm(output, cur);
+    cur = build_lora_mm(output, cur, output_s);
     cb(cur, "result_output", -1);
     res->t_logits = cur;
 

src/models/nemotron-h.cpp

@@ -177,8 +177,11 @@ llama_model_nemotron_h::graph::graph(const llama_model & model, const llm_graph_
     auto * inp = build_inp_mem_hybrid();
 
     ggml_tensor * inp_out_ids = build_inp_out_ids();
+    const bool extract_final_inp = (size_t) n_layer < cparams.embeddings_layer_inp.size() && cparams.embeddings_layer_inp[n_layer];
 
     for (int il = 0; il < n_layer; ++il) {
+        res->t_layer_inp[il] = inpL;
+
         struct ggml_tensor * inpSA = inpL;
 
         // norm
@@ -195,7 +198,7 @@ llama_model_nemotron_h::graph::graph(const llama_model & model, const llm_graph_
             cur = build_ffn_layer(cur, model, il);
         }
 
-        if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
+        if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked && !extract_final_inp) {
             cur   = ggml_get_rows(ctx0, cur, inp_out_ids);
             inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
         }
@@ -209,6 +212,13 @@ llama_model_nemotron_h::graph::graph(const llama_model & model, const llm_graph_
     }
 
     cur = inpL;
+    if (extract_final_inp) {
+        res->t_layer_inp[n_layer] = cur;
+
+        if (inp_out_ids && cparams.embeddings_nextn_masked) {
+            cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+        }
+    }
 
     cur = build_norm(cur, model.output_norm, NULL, LLM_NORM_RMS, -1);