add deepseekv3 and llama4

vllm-v0.6.2/vllm/model_executor/models/llama4.py

@@ -446,6 +446,12 @@ class Llama4ForCausalLM(nn.Module, SupportsPP):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
+        # Llama4ForConditionalGeneration uses top-level Llama4Config
+        # which has text_config sub-config. Extract it for text model.
+        text_config = getattr(config, "text_config", None)
+        if text_config is not None:
+            vllm_config.model_config.hf_config = text_config
+            config = text_config
         quant_config = vllm_config.quant_config
         lora_config = vllm_config.lora_config
         self.config = config
@@ -553,8 +559,19 @@ class Llama4ForCausalLM(nn.Module, SupportsPP):
                 if getattr(self.config, "tie_word_embeddings", False)
                 else None),
         )
-        weights = [
-            self.permute_qk_weight_for_rotary(name, loaded_weight)
-            for name, loaded_weight in weights
-        ]
-        loader.load_weights(weights)
+
+        def _process_weights(weights):
+            for name, loaded_weight in weights:
+                # Strip language_model. prefix for Llama4ForConditionalGeneration
+                if name.startswith("language_model."):
+                    name = name[len("language_model."):]
+                # Skip vision encoder weights
+                elif name.startswith("multi_modal_projector.") or \
+                     name.startswith("vision_encoder.") or \
+                     name.startswith("vision_model."):
+                    continue
+                name, loaded_weight = self.permute_qk_weight_for_rotary(
+                    name, loaded_weight)
+                yield name, loaded_weight
+
+        loader.load_weights(_process_weights(weights))

vllm-v0.6.2/vllm_mlu/vllm_mlu/model_executor/models/llama4.py

@@ -389,6 +389,15 @@ def vllm__llama4__Llama4ForCausalLM__load_weights(
         if "rotary_emb.inv_freq" in name:
             continue
 
+        # Strip language_model. prefix for Llama4ForConditionalGeneration
+        if name.startswith("language_model."):
+            name = name[len("language_model."):]
+        # Skip vision encoder weights
+        elif (name.startswith("multi_modal_projector.")
+              or name.startswith("vision_encoder.")
+              or name.startswith("vision_model.")):
+            continue
+
         # Permute Q/K weights for rotary embedding
         name, loaded_weight = self.permute_qk_weight_for_rotary(
             name, loaded_weight)