Forward fix nightly errors from #44589

vllm/model_executor/models/commandr.py

@@ -348,17 +348,18 @@ class CohereForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsQuant):
             ".v_proj": (".qkv_proj", "v"),
             ".gate_proj": (".gate_up_proj", 0),
             ".up_proj": (".gate_up_proj", 1),
-        }
+        },
+        # ModelOpt NVFP4 checkpoints carry raw quantizer-module state
+        # (e.g. "*.weight_quantizer._double_scale"); drop them before loading.
+        # See #41925.
+        orig_to_new_substr={"_quantizer.": None},
     )
     packed_modules_mapping = {
         "qkv_proj": ["q_proj", "k_proj", "v_proj"],
         "gate_up_proj": ["gate_proj", "up_proj"],
     }
     # LoRA specific attributes
     embedding_modules = {"embed_tokens": "input_embeddings"}
-    # ModelOpt NVFP4 checkpoints carry raw quantizer-module state
-    # (e.g. "*.weight_quantizer._double_scale"); drop them before loading. See #41925.
-    hf_to_vllm_mapper = WeightsMapper(orig_to_new_substr={"_quantizer.": None})
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()

vllm/model_executor/models/gemma3.py

@@ -291,6 +291,17 @@ def forward(
 
 @support_torch_compile
 class Gemma3Model(nn.Module):
+    hf_to_vllm_mapper = WeightsMapper(
+        orig_to_new_stacked={
+            # weight_name: (param_name, shard_id)
+            ".q_proj": (".qkv_proj", "q"),
+            ".k_proj": (".qkv_proj", "k"),
+            ".v_proj": (".qkv_proj", "v"),
+            ".gate_proj": (".gate_up_proj", 0),
+            ".up_proj": (".gate_up_proj", 1),
+        }
+    )
+
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__()
         config = vllm_config.model_config.hf_config
@@ -361,18 +372,13 @@ def forward(
         hidden_states, _ = self.norm(hidden_states, residual)
         return hidden_states
 
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(self)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
+
 
 class Gemma3ForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
-    hf_to_vllm_mapper = WeightsMapper(
-        orig_to_new_stacked={
-            # weight_name: (param_name, shard_id)
-            ".q_proj": (".qkv_proj", "q"),
-            ".k_proj": (".qkv_proj", "k"),
-            ".v_proj": (".qkv_proj", "v"),
-            ".gate_proj": (".gate_up_proj", 0),
-            ".up_proj": (".gate_up_proj", 1),
-        }
-    )
+    hf_to_vllm_mapper = Gemma3Model.hf_to_vllm_mapper
     packed_modules_mapping = {
         "qkv_proj": [
             "q_proj",

vllm/model_executor/models/jina.py

@@ -25,7 +25,7 @@
 from .interfaces import SupportsLateInteraction
 from .interfaces_base import VllmModelForPooling
 from .qwen3 import Qwen3ForCausalLM, Qwen3Model
-from .utils import AutoWeightsLoader, maybe_prefix
+from .utils import AutoWeightsLoader, WeightsMapper, maybe_prefix
 
 logger = logging.getLogger(__name__)
 
@@ -193,6 +193,9 @@ class JinaEmbeddingsV5Model(Qwen3ForCausalLM, VllmModelForPooling):
     """
 
     is_pooling_model = True
+    hf_to_vllm_mapper = Qwen3ForCausalLM.hf_to_vllm_mapper | WeightsMapper(
+        orig_to_new_prefix={"": "model."}
+    )
 
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
         super().__init__(vllm_config=vllm_config, prefix=prefix)
@@ -254,6 +257,6 @@ def _merge_weights(
                         tensor = tensor + (lora_B @ lora_A) * scaling
                 yield name, tensor
 
-        loader = AutoWeightsLoader(self.model, ignore_unexpected_prefixes=["lm_head."])
+        loader = AutoWeightsLoader(self, ignore_unexpected_prefixes=["lm_head."])
         weights = _merge_weights(weights)
-        return loader.load_weights(weights, mapper=self.model.hf_to_vllm_mapper)
+        return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)