fix: load lm_head.weight in outer model before forwarding to inner

lm_head lives on DeepseekV4ForCausalLM, not DeepseekV4Model. The inner
load_weights silently drops it (not in params_dict). Extract it in the
outer loader, load it directly, then forward the rest to the inner model.

vllm/patches/deepseek_v4.py

@@ -2205,11 +2205,24 @@ class DeepseekV4ForCausalLM(nn.Module):
         return getattr(self.model, "_mtp_hidden_buffer", None)
 
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        # lm_head lives on this outer model, not on the inner DeepseekV4Model.
+        # The inner load_weights silently drops lm_head.weight via
+        # "if name not in params_dict: continue". Extract it here.
+        rest = []
+        for name, loaded_weight in weights:
+            if name == "lm_head.weight" or name.endswith(".lm_head.weight"):
+                param = self.lm_head.weight
+                weight_loader = getattr(param, "weight_loader",
+                                        default_weight_loader)
+                weight_loader(param, loaded_weight)
+            else:
+                rest.append((name, loaded_weight))
         # Use the model-level loader which handles NVFP4 expert mapping,
         # uint8→bf16 unpacking for MergedColumnParallelLinear, and
         # bf16→NVFP4 quantization for unquantized layers.
         # AutoWeightsLoader bypasses this logic and would break NVFP4 loading.
-        loaded_params = self.model.load_weights(weights)
+        loaded_params = self.model.load_weights(rest)
+        loaded_params.add("lm_head.weight")
         self.model.finalize_mega_moe_weights()
         self.model._convert_nvfp4_post_load()
         if int(os.environ.get('NVFP4_DEBUG', '0')):