[Model] Support Qwen3.6 ModelOpt mixed NVFP4 (#27906)

python/sglang/srt/configs/model_config.py

@@ -1146,10 +1146,14 @@ class ModelConfig:
         quant_algo = json_quant_configs.get("quant_algo", None)
 
         if quant_algo == "MIXED_PRECISION":
-            architectures = getattr(self.hf_config, "architectures", []) or []
-            if getattr(self.hf_config, "model_type", None) == "nemotron_h" or any(
-                arch.startswith("NemotronH") for arch in architectures
-            ):
+            quantized_layers = json_quant_configs.get("quantized_layers") or {}
+            has_modelopt_nvfp4_layers = any(
+                str(layer_info.get("quant_algo", "")).upper()
+                in ("NVFP4", "W4A16_NVFP4")
+                for layer_info in quantized_layers.values()
+                if isinstance(layer_info, dict)
+            )
+            if has_modelopt_nvfp4_layers:
                 return {"quant_method": "modelopt_mixed", "quant_algo": quant_algo}
             return {"quant_method": "w4afp8", "quant_algo": quant_algo}
         elif quant_algo and ("FP4" in quant_algo or "NVFP4" in quant_algo):

python/sglang/srt/layers/logits_processor.py

@@ -61,6 +61,74 @@ logger = logging.getLogger(__name__)
 _is_npu = is_npu()
 _is_cpu = is_cpu()
 
+_UNQUANTIZED_LM_HEAD_METHODS = {
+    "UnquantizedEmbeddingMethod",
+    "UnquantizedLinearMethod",
+    "PackWeightMethod",
+}
+
+
+def _has_lm_head_runtime_attrs(lm_head, attr_names: Tuple[str, ...]) -> bool:
+    return all(hasattr(lm_head, attr_name) for attr_name in attr_names)
+
+
+def should_apply_lm_head_quant_method(lm_head, quant_method) -> bool:
+    if (
+        quant_method is None
+        or not hasattr(lm_head, "weight")
+        or not callable(getattr(quant_method, "apply", None))
+    ):
+        return False
+
+    method_name = type(quant_method).__name__
+    if method_name in _UNQUANTIZED_LM_HEAD_METHODS:
+        return False
+
+    # Some draft models share an unquantized target lm_head tensor while still
+    # carrying the draft model's stale ModelOpt quant_method. Only use the
+    # ModelOpt lm_head kernel when the runtime quantization state matches it.
+    if method_name == "ModelOptFp4LinearMethod":
+        if lm_head.weight.dtype == torch.int32 and _has_lm_head_runtime_attrs(
+            lm_head,
+            (
+                "weight_scale",
+                "weight_global_scale",
+                "workspace",
+                "input_size_per_partition",
+                "output_size_per_partition",
+            ),
+        ):
+            return True
+        return lm_head.weight.dtype == torch.uint8 and _has_lm_head_runtime_attrs(
+            lm_head,
+            (
+                "weight_scale_interleaved",
+                "alpha",
+                "input_scale_inv",
+                "input_size_per_partition",
+                "output_size_per_partition",
+            ),
+        )
+    if method_name == "ModelOptNvFp4A16LinearMethod":
+        return lm_head.weight.dtype == torch.int32 and _has_lm_head_runtime_attrs(
+            lm_head,
+            (
+                "weight_scale",
+                "weight_global_scale",
+                "workspace",
+                "input_size_per_partition",
+                "output_size_per_partition",
+            ),
+        )
+    if method_name == "ModelOptFp8LinearMethod":
+        return (
+            lm_head.weight.dtype == torch.float8_e4m3fn
+            and _has_lm_head_runtime_attrs(lm_head, ("weight_scale", "input_scale"))
+        )
+
+    return True
+
+
 # When set, LogitsProcessor.forward returns an empty output and skips the
 # LM head + tensor-parallel all-gather. FlashInfer autotune only profiles
 # attention/MoE/GEMM kernels, so the LM-head all-gather is wasted work --
@@ -883,9 +951,12 @@ class LogitsProcessor(nn.Module):
         lm_head: VocabParallelEmbedding,
         embedding_bias: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
+        quant_method = getattr(lm_head, "quant_method", None)
         if hasattr(lm_head, "set_lora") and hasattr(lm_head, "apply_lora"):
             # This is a LoRA-wrapped module, use its forward method
             logits = lm_head(hidden_states)
+        elif should_apply_lm_head_quant_method(lm_head, quant_method):
+            logits = quant_method.apply(lm_head, hidden_states, embedding_bias)
         elif hasattr(lm_head, "weight"):
             # Normal linear layer
             if self.use_fp32_lm_head:

python/sglang/srt/layers/quantization/modelopt_quant.py

@@ -4,7 +4,7 @@
 from __future__ import annotations
 
 import logging
-from typing import TYPE_CHECKING, Any, Dict, List, Optional
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
 
 import regex as re
 import torch
@@ -65,6 +65,7 @@ from sglang.srt.utils.common import (
     is_sm100_supported,
     is_sm120_supported,
     round_up,
+    set_weight_attrs,
 )
 from sglang.srt.utils.custom_op import register_custom_op
 from sglang.srt.utils.patch_torch import register_fake_if_exists
@@ -77,6 +78,25 @@ if TYPE_CHECKING:
     )
     from sglang.srt.models.utils import WeightsMapper
 
+
+def _make_per_tensor_scale_parameter(
+    shape,
+    weight_loader,
+    *,
+    fill_value: Optional[float] = None,
+    needs_scalar_to_array: bool = False,
+) -> PerTensorScaleParameter:
+    data = (
+        torch.empty(shape, dtype=torch.float32)
+        if fill_value is None
+        else torch.full(shape, fill_value, dtype=torch.float32)
+    )
+    scale = PerTensorScaleParameter(data=data, weight_loader=weight_loader)
+    if needs_scalar_to_array:
+        set_weight_attrs(scale, {"needs_scalar_to_array": True})
+    return scale
+
+
 try:
     from flashinfer import mm_fp4 as flashinfer_fp4_gemm
     from flashinfer import reorder_rows_for_gated_act_gemm, shuffle_matrix_sf_a
@@ -281,8 +301,9 @@ class ModelOptQuantConfig(QuantizationConfig):
     ) -> Optional[QuantizeMethodBase]:
         from sglang.srt.layers.linear import LinearBase
         from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
+        from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
 
-        if isinstance(layer, LinearBase):
+        if isinstance(layer, (LinearBase, ParallelLMHead)):
             if is_layer_skipped(
                 prefix, self.exclude_modules, self.packed_modules_mapping
             ) or self.is_layer_excluded(prefix):
@@ -433,6 +454,8 @@ class ModelOptFp8Config(ModelOptQuantConfig):
                     and kv_cache_scheme.get("num_bits") == 8
                 ):
                     kv_cache_quant_method = "FP8"
+            else:
+                kv_cache_quant_method = config.get("kv_cache_quant_algo")
 
             # Map 'ignore' field to 'exclude_modules'
             exclude_modules = config.get("ignore")
@@ -536,17 +559,13 @@ class ModelOptFp8LinearMethod(LinearMethodBase):
         if self.quant_config.is_checkpoint_fp8_serialized:
             # Register weight and input scales
             for scale_name in ["weight_scale", "input_scale"]:
-                layer.register_parameter(
-                    scale_name,
-                    PerTensorScaleParameter(
-                        data=torch.full(
-                            (len(output_partition_sizes),),
-                            torch.finfo(torch.float32).min,
-                            dtype=torch.float32,
-                        ),
-                        weight_loader=weight_loader,
-                    ),
+                scale = _make_per_tensor_scale_parameter(
+                    (len(output_partition_sizes),),
+                    weight_loader=weight_loader,
+                    fill_value=torch.finfo(torch.float32).min,
+                    needs_scalar_to_array=True,
                 )
+                layer.register_parameter(scale_name, scale)
 
     def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
         """Requantizes weights after loading using the maximum scale."""
@@ -604,11 +623,13 @@ class ModelOptMixedPrecisionConfig(ModelOptQuantConfig):
         quantized_layers: Dict[str, Dict[str, Any]],
         fp8_config: ModelOptFp8Config,
         nvfp4_config: ModelOptFp4Config,
+        nvfp4a16_config: ModelOptFp4Config,
     ) -> None:
         super().__init__(kv_cache_quant_algo, exclude_modules, packed_modules_mapping)
         self.quantized_layers = quantized_layers
         self.fp8_config = fp8_config
         self.nvfp4_config = nvfp4_config
+        self.nvfp4a16_config = nvfp4a16_config
 
     @classmethod
     def override_quantization_method(cls, hf_quant_config, user_quant):
@@ -652,6 +673,8 @@ class ModelOptMixedPrecisionConfig(ModelOptQuantConfig):
                     kv_cache_quant_algo = "NVFP4"
                 else:
                     kv_cache_quant_algo = "auto"
+            else:
+                kv_cache_quant_algo = config.get("kv_cache_quant_algo")
             exclude_modules = config.get("ignore")
             quantized_layers = config.get("quantized_layers", {})
         else:
@@ -672,7 +695,10 @@ class ModelOptMixedPrecisionConfig(ModelOptQuantConfig):
 
         group_size = None
         for layer_info in quantized_layers.values():
-            if layer_info.get("quant_algo", "").upper() == "NVFP4":
+            if layer_info.get("quant_algo", "").upper() in (
+                "NVFP4",
+                "W4A16_NVFP4",
+            ):
                 group_size = layer_info.get("group_size", 16)
                 break
         if group_size is None:
@@ -692,6 +718,14 @@ class ModelOptMixedPrecisionConfig(ModelOptQuantConfig):
             packed_modules_mapping=packed_modules_mapping,
             group_size=group_size,
         )
+        nvfp4a16_config = ModelOptFp4Config(
+            is_checkpoint_nvfp4_serialized=True,
+            kv_cache_quant_algo=kv_cache_quant_algo,
+            exclude_modules=[],
+            packed_modules_mapping=packed_modules_mapping,
+            group_size=group_size,
+            use_per_token_activation=False,
+        )
 
         return cls(
             kv_cache_quant_algo=kv_cache_quant_algo,
@@ -700,6 +734,7 @@ class ModelOptMixedPrecisionConfig(ModelOptQuantConfig):
             quantized_layers=quantized_layers,
             fp8_config=fp8_config,
             nvfp4_config=nvfp4_config,
+            nvfp4a16_config=nvfp4a16_config,
         )
 
     def apply_weight_name_mapper(self, hf_to_sglang_mapper: WeightsMapper):
@@ -710,17 +745,21 @@ class ModelOptMixedPrecisionConfig(ModelOptQuantConfig):
             )
 
     def _resolve_quant_algo(self, prefix: str) -> Optional[str]:
-        if prefix in self.quantized_layers:
-            return self.quantized_layers[prefix]["quant_algo"].upper()
+        for candidate in self._quantized_layer_prefix_candidates(prefix):
+            if candidate in self.quantized_layers:
+                return self.quantized_layers[candidate]["quant_algo"].upper()
 
         proj_name = prefix.rsplit(".", 1)[-1]
         if self.packed_modules_mapping and proj_name in self.packed_modules_mapping:
             algos = set()
             base = prefix.rsplit(".", 1)[0]
-            for shard_name in self.packed_modules_mapping[proj_name]:
-                shard_prefix = f"{base}.{shard_name}"
-                if shard_prefix in self.quantized_layers:
-                    algos.add(self.quantized_layers[shard_prefix]["quant_algo"].upper())
+            for base_candidate in self._quantized_layer_prefix_candidates(base):
+                for shard_name in self.packed_modules_mapping[proj_name]:
+                    shard_prefix = f"{base_candidate}.{shard_name}"
+                    if shard_prefix in self.quantized_layers:
+                        algos.add(
+                            self.quantized_layers[shard_prefix]["quant_algo"].upper()
+                        )
             if len(algos) == 1:
                 return algos.pop()
             if len(algos) > 1:
@@ -729,22 +768,42 @@ class ModelOptMixedPrecisionConfig(ModelOptQuantConfig):
                     "All shards must use the same quantization."
                 )
 
-        prefix_dot = prefix + "."
-        for key, info in self.quantized_layers.items():
-            if key.startswith(prefix_dot):
-                return info["quant_algo"].upper()
+        for candidate in self._quantized_layer_prefix_candidates(prefix):
+            prefix_dot = candidate + "."
+            for key, info in self.quantized_layers.items():
+                if key.startswith(prefix_dot):
+                    return info["quant_algo"].upper()
 
         return None
 
+    @staticmethod
+    def _quantized_layer_prefix_candidates(prefix: str) -> Tuple[str, ...]:
+        candidates = [prefix]
+
+        if prefix.endswith(".lm_head"):
+            candidates.append("lm_head")
+
+        if prefix.startswith("language_model.model."):
+            candidates.append(
+                "model.language_model." + prefix[len("language_model.model.") :]
+            )
+        elif prefix.startswith("model.language_model."):
+            candidates.append(
+                "language_model.model." + prefix[len("model.language_model.") :]
+            )
+
+        return tuple(dict.fromkeys(candidates))
+
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
     ) -> Optional[QuantizeMethodBase]:
         from sglang.srt.layers.linear import LinearBase
         from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
+        from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
 
         quant_algo = self._resolve_quant_algo(prefix)
 
-        if isinstance(layer, LinearBase):
+        if isinstance(layer, (LinearBase, ParallelLMHead)):
             if is_layer_skipped(
                 prefix, self.exclude_modules, self.packed_modules_mapping
             ) or self.is_layer_excluded(prefix):
@@ -753,6 +812,8 @@ class ModelOptMixedPrecisionConfig(ModelOptQuantConfig):
                 return ModelOptFp8LinearMethod(self.fp8_config)
             if quant_algo == "NVFP4":
                 return ModelOptFp4LinearMethod(self.nvfp4_config)
+            if quant_algo == "W4A16_NVFP4":
+                return ModelOptNvFp4A16LinearMethod(self.nvfp4a16_config)
             return UnquantizedLinearMethod()
 
         if self.kv_cache_quant_algo and isinstance(layer, RadixAttention):
@@ -765,6 +826,8 @@ class ModelOptMixedPrecisionConfig(ModelOptQuantConfig):
                 return ModelOptFp8MoEMethod(self.fp8_config)
             if quant_algo == "NVFP4":
                 return ModelOptNvFp4FusedMoEMethod(self.nvfp4_config)
+            if quant_algo == "W4A16_NVFP4":
+                return ModelOptNvFp4FusedMoEMethod(self.nvfp4a16_config)
             return None
 
         return None
@@ -1139,8 +1202,9 @@ class ModelOptFp4Config(ModelOptQuantConfig):
             )
         self.group_size = group_size
         self.use_per_token_activation = (
-            use_per_token_activation
-            or envs.SGLANG_FLASHINFER_NVFP4_PER_TOKEN_ACTIVATION.get()
+            envs.SGLANG_FLASHINFER_NVFP4_PER_TOKEN_ACTIVATION.get()
+            if use_per_token_activation is None
+            else use_per_token_activation
         )
 
     @classmethod
@@ -1229,7 +1293,7 @@ class ModelOptFp4Config(ModelOptQuantConfig):
                 else:
                     kv_cache_quant_algo = "auto"
             else:
-                kv_cache_quant_algo = "auto"
+                kv_cache_quant_algo = config.get("kv_cache_quant_algo") or "auto"
 
             group_size = config.get("group_size")
             # If group_size is not at top level, try to extract from config_groups
@@ -1400,16 +1464,17 @@ class ModelOptFp4LinearMethod(LinearMethodBase):
         )
         layer.register_parameter("weight", weight)
 
-        input_scale = PerTensorScaleParameter(
-            data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
+        input_scale = _make_per_tensor_scale_parameter(
+            (len(output_partition_sizes),),
             weight_loader=weight_loader,
+            needs_scalar_to_array=True,
         )
-
         layer.register_parameter("input_scale", input_scale)
 
-        weight_scale_2 = PerTensorScaleParameter(
-            data=torch.empty(len(output_partition_sizes), dtype=torch.float32),
+        weight_scale_2 = _make_per_tensor_scale_parameter(
+            (len(output_partition_sizes),),
             weight_loader=weight_loader,
+            needs_scalar_to_array=True,
         )
         layer.register_parameter("weight_scale_2", weight_scale_2)
 
@@ -1450,6 +1515,7 @@ class ModelOptFp4LinearMethod(LinearMethodBase):
                 )
             copy_or_rebind_param(layer, "input_global_scale", input_scale_2)
             copy_or_rebind_param(layer, "weight_global_scale", weight_scale_2)
+            layer.quant_config = self.quant_config
             prepare_nvfp4_layer_for_marlin(layer)
             layer.weights_padding_cols = 0
             return
@@ -1668,6 +1734,131 @@ class ModelOptFp4LinearMethod(LinearMethodBase):
         return out.view(*output_shape)
 
 
+class ModelOptNvFp4A16LinearMethod(LinearMethodBase):
+    """Linear method for ModelOpt NVFP4A16 checkpoints.
+
+    Loads packed NVFP4 weights with fp16/bf16 activations. ModelOpt may still
+    provide input_scale tensors for fused loader compatibility; they are
+    consumed during loading and discarded before runtime.
+    """
+
+    def __init__(self, quant_config: ModelOptFp4Config):
+        self.quant_config = quant_config
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: List[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        del input_size, output_size
+        if not self.quant_config.is_checkpoint_nvfp4_serialized:
+            raise ValueError(
+                "W4A16_NVFP4 quantization was selected, "
+                "dynamic quantization is not supported."
+            )
+
+        output_size_per_partition = sum(output_partition_sizes)
+        weight_loader = extra_weight_attrs.get("weight_loader")
+
+        layer.logical_widths = output_partition_sizes
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_size_per_partition = output_size_per_partition
+        layer.params_dtype = params_dtype
+        layer.quant_config = self.quant_config
+
+        if input_size_per_partition % 16 != 0:
+            raise ValueError(
+                "Unsupported model when input feature size is not a multiple of 16"
+            )
+
+        weight = ModelWeightParameter(
+            data=torch.empty(
+                output_size_per_partition,
+                input_size_per_partition // 2,
+                dtype=torch.uint8,
+            ),
+            input_dim=1,
+            output_dim=0,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("weight", weight)
+
+        weight_scale_2 = _make_per_tensor_scale_parameter(
+            (len(output_partition_sizes),),
+            weight_loader=weight_loader,
+            needs_scalar_to_array=True,
+        )
+        layer.register_parameter("weight_scale_2", weight_scale_2)
+
+        weight_scale = ModelWeightParameter(
+            data=torch.empty(
+                output_size_per_partition,
+                input_size_per_partition // self.quant_config.group_size,
+                dtype=torch.float8_e4m3fn,
+            ),
+            input_dim=1,
+            output_dim=0,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("weight_scale", weight_scale)
+
+        # Some ModelOpt checkpoints may still include input_scale entries in
+        # fused-loader paths. NVFP4A16 does not use them, but registering the
+        # placeholder lets the generic loader consume those tensors harmlessly.
+        input_scale = _make_per_tensor_scale_parameter(
+            (len(output_partition_sizes),),
+            weight_loader=weight_loader,
+            needs_scalar_to_array=True,
+        )
+        layer.register_parameter("input_scale", input_scale)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        if hasattr(layer, "input_scale"):
+            del layer.input_scale
+
+        if torch.unique(layer.weight_scale_2).numel() != 1:
+            logger.warning(
+                "In NVFP4A16 linear, weight_scale_2 differs across fused "
+                "parallel layers. Accuracy may be degraded."
+            )
+
+        copy_or_rebind_param(
+            layer,
+            "weight_global_scale",
+            layer.weight_scale_2.max().to(torch.float32),
+        )
+        del layer.weight_scale_2
+
+        if self.quant_config.group_size != 16:
+            raise ValueError(
+                f"NVFP4A16 Marlin requires group_size=16, got {self.quant_config.group_size}."
+            )
+        layer.quant_config = self.quant_config
+        prepare_nvfp4_layer_for_marlin(layer)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        return apply_fp4_marlin_linear(
+            input=x,
+            weight=layer.weight,
+            weight_scale=layer.weight_scale,
+            weight_global_scale=layer.weight_global_scale,
+            workspace=layer.workspace,
+            size_n=layer.output_size_per_partition,
+            size_k=layer.input_size_per_partition,
+            bias=bias,
+        )
+
+
 def _compute_gemm1_alphas(
     w13_weight_scale_2: torch.Tensor,
     w13_input_scale: torch.Tensor,

python/sglang/srt/layers/vocab_parallel_embedding.py

@@ -297,7 +297,7 @@ class VocabParallelEmbedding(torch.nn.Module):
         # If we are making an embedding layer, then our quantization linear
         # method must implement the embedding operation. If we are another
         # layer type like ParallelLMHead, this is not important.
-        is_embedding_layer = type(self.__class__) is VocabParallelEmbedding
+        is_embedding_layer = type(self) is VocabParallelEmbedding
         quant_method_implements_embedding = method_has_implemented_embedding(
             type(quant_method)
         )
@@ -459,6 +459,12 @@ class VocabParallelEmbedding(torch.nn.Module):
         # If parameter does not have output dim, then it should
         # be copied onto all gpus (e.g. g_idx for act_order gptq).
         if output_dim is None:
+            if (
+                loaded_weight.ndim == 0
+                and param.data.ndim == 1
+                and param.data.numel() == 1
+            ):
+                loaded_weight = loaded_weight.reshape(1)
             assert param.data.shape == loaded_weight.shape
             param.data.copy_(loaded_weight)
             return

python/sglang/srt/model_loader/weight_utils.py

@@ -74,6 +74,7 @@ logger = logging.getLogger(__name__)
 
 RUNAI_STREAMER_TENSOR_ATTR = "_sglang_runai_streamer_tensor"
 
+
 # Matches routed-expert weight keys in both HF-style layouts
 # (``...mlp.experts.<N>.{gate,up,down}_proj.weight``) and DeepSeek V4
 # layouts (``...ffn.experts.<N>.w{1,2,3}.weight``). ``shared_experts`` is
@@ -258,8 +259,24 @@ def get_quant_config(
     if hf_quant_config is not None:
         if not isinstance(hf_quant_config, dict):
             hf_quant_config = hf_quant_config.to_dict()
-        hf_quant_config["packed_modules_mapping"] = packed_modules_mapping
-        return quant_cls.from_config(hf_quant_config)
+
+        # For modelopt_mixed, config.json's quantization_config may not
+        # contain all runtime metadata. Fall through to the file-based
+        # hf_quant_config.json path when the per-layer map or KV-cache
+        # quantization metadata is missing.
+        modelopt_mixed_config_incomplete = (
+            model_config.quantization == "modelopt_mixed"
+            and (
+                "quantized_layers" not in hf_quant_config
+                or (
+                    "kv_cache_quant_algo" not in hf_quant_config
+                    and "kv_cache_scheme" not in hf_quant_config
+                )
+            )
+        )
+        if not modelopt_mixed_config_incomplete:
+            hf_quant_config["packed_modules_mapping"] = packed_modules_mapping
+            return quant_cls.from_config(hf_quant_config)
 
     # In case of bitsandbytes/QLoRA, get quant config from the adapter model.
     if model_config.quantization == "bitsandbytes":

python/sglang/srt/models/qwen3_5_mtp.py

@@ -59,7 +59,10 @@ class Qwen3_5ForCausalLMMTP(nn.Module):
         config = copy.deepcopy(config)
 
         # The MTP model is unquantized in the nvfp4 checkpoint.
-        if quant_config and quant_config.get_name() == "modelopt_fp4":
+        if quant_config and quant_config.get_name() in (
+            "modelopt_fp4",
+            "modelopt_mixed",
+        ):
             quant_config = None
         if (
             is_npu()
@@ -135,6 +138,12 @@ class Qwen3_5ForCausalLMMTP(nn.Module):
         torch.cuda.empty_cache()
         torch.cuda.synchronize()
 
+    def set_lm_head_from_target(self, target_lm_head):
+        if self.config.tie_word_embeddings:
+            return
+
+        self.lm_head = target_lm_head
+
     @torch.no_grad()
     def forward(
         self,

python/sglang/srt/speculative/eagle_worker_v2.py

@@ -316,6 +316,17 @@ class EagleDraftWorker(EagleDraftWorkerBase):
 
     def init_lm_head(self):
         embed, head = self.target_worker.model_runner.model.get_embed_and_head()
+        target_lm_head = getattr(self.target_worker.model_runner.model, "lm_head", None)
+
+        def maybe_share_target_lm_head():
+            if (
+                target_lm_head is not None
+                and self.hot_token_id is None
+                and getattr(self.draft_runner.model, "hot_token_id", None) is None
+                and hasattr(self.draft_runner.model, "set_lm_head_from_target")
+            ):
+                self.draft_runner.model.set_lm_head_from_target(target_lm_head)
+
         if self.speculative_algorithm.is_eagle3():
             # most cases EAGLE3 models don't share lm_head
             # but some models (e.g. nvidia/gpt-oss-120b-Eagle3) shares
@@ -324,6 +335,7 @@ class EagleDraftWorker(EagleDraftWorkerBase):
                 and self.draft_runner.model.load_lm_head_from_target
             ):
                 self.draft_runner.model.set_embed_and_head(embed, head)
+                maybe_share_target_lm_head()
             else:
                 self.draft_runner.model.set_embed(embed)
 
@@ -341,6 +353,7 @@ class EagleDraftWorker(EagleDraftWorkerBase):
 
             # Share the embedding and lm_head
             self.draft_runner.model.set_embed_and_head(embed, head)
+            maybe_share_target_lm_head()
 
     def init_attention_backend(self):
         # Create multi-step attn backends and cuda graph runners