[Model] Automatic conversion of TokenClassification model

tests/models/language/pooling/test_token_classification.py

@@ -68,3 +68,34 @@ def test_modernbert_models(
         hf_output = torch.tensor(hf_output).cpu().float()
         vllm_output = torch.tensor(vllm_output).cpu().float()
         assert torch.allclose(hf_output, vllm_output, atol=1e-2)
+
+
+@pytest.mark.parametrize("model", ["bd2lcco/Qwen3-0.6B-finetuned"])
+@pytest.mark.parametrize("dtype", ["float"])
+@torch.inference_mode
+def test_auto_conversion(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+) -> None:
+    with vllm_runner(model, max_model_len=1024, dtype=dtype) as vllm_model:
+        vllm_outputs = vllm_model.token_classify(example_prompts)
+
+    with hf_runner(
+        model, dtype=dtype, auto_cls=AutoModelForTokenClassification
+    ) as hf_model:
+        tokenizer = hf_model.tokenizer
+        hf_outputs = []
+        for prompt in example_prompts:
+            inputs = tokenizer([prompt], return_tensors="pt")
+            inputs = hf_model.wrap_device(inputs)
+            output = hf_model.model(**inputs)
+            hf_outputs.append(softmax(output.logits[0]))
+
+    # check logits difference
+    for hf_output, vllm_output in zip(hf_outputs, vllm_outputs):
+        hf_output = torch.tensor(hf_output).cpu().float()
+        vllm_output = torch.tensor(vllm_output).cpu().float()
+        assert torch.allclose(hf_output, vllm_output, atol=1e-2)

tests/models/registry.py

@@ -573,6 +573,7 @@ def check_available_online(
     "Qwen3ForSequenceClassification": _HfExamplesInfo(
         "tomaarsen/Qwen3-Reranker-0.6B-seq-cls"
     ),
+    "Qwen3ForTokenClassification": _HfExamplesInfo("bd2lcco/Qwen3-0.6B-finetuned"),
 }
 
 _MULTIMODAL_EXAMPLE_MODELS = {

vllm/config/model.py

@@ -1796,6 +1796,7 @@ def get_served_model_name(model: str, served_model_name: str | list[str] | None)
     ("ForTextEncoding", ("pooling", "embed")),
     ("EmbeddingModel", ("pooling", "embed")),
     ("ForSequenceClassification", ("pooling", "classify")),
+    ("ForTokenClassification", ("pooling", "classify")),
     ("ForAudioClassification", ("pooling", "classify")),
     ("ForImageClassification", ("pooling", "classify")),
     ("ForVideoClassification", ("pooling", "classify")),

vllm/model_executor/models/adapters.py

@@ -337,6 +337,18 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
             tokens = getattr(text_config, "classifier_from_token", None)
             method = getattr(text_config, "method", None)
 
+            def auto_set_score_bias(weights):
+                for name, weight in weights:
+                    if name == "score.bias":
+                        device = self.score.weight.device
+                        dtype = self.score.weight.dtype
+                        bias = weight.to(device).to(dtype)
+                        self.score.bias = torch.nn.Parameter(bias)
+                        self.score.skip_bias_add = False
+                    else:
+                        yield name, weight
+
+            weights = auto_set_score_bias(weights)
             if tokens is None and method is None:
                 return super().load_weights(weights)
             else: