[Frontend] Support using chat template as custom score template for reranking models (#30550)

Signed-off-by: Jakub Zakrzewski <jzakrzewski@nvidia.com>
Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io>
Signed-off-by: wang.yuqi <noooop@126.com>
Co-authored-by: wang.yuqi <yuqi.wang@daocloud.io>

vllm/model_executor/models/config.py

@@ -88,6 +88,26 @@ class JinaRobertaModelConfig(VerifyAndUpdateConfig):
             }
 
 
+class LlamaBidirectionalConfig(VerifyAndUpdateConfig):
+    @staticmethod
+    def verify_and_update_config(vllm_config: "VllmConfig") -> None:
+        from vllm.config.pooler import PoolingTypeStr
+
+        hf_config = vllm_config.model_config.hf_config
+        hf_config.is_causal = False
+
+        pooling_type_map: dict[str, PoolingTypeStr] = {
+            "avg": "MEAN",
+            "cls": "CLS",
+            "last": "LAST",
+        }
+
+        pooling_type = pooling_type_map.get(hf_config.pooling, None)
+        if pooling_type is None:
+            raise ValueError(f"pool_type {hf_config.pooling} not supported")
+        vllm_config.model_config.pooler_config.pooling_type = pooling_type
+
+
 class NomicBertModelConfig(VerifyAndUpdateConfig):
     @staticmethod
     def verify_and_update_config(vllm_config: "VllmConfig") -> None:
@@ -509,6 +529,8 @@ MODELS_CONFIG_MAP: dict[str, type[VerifyAndUpdateConfig]] = {
     "GteNewModel": GteNewModelConfig,
     "GteNewForSequenceClassification": GteNewModelConfig,
     "Gemma3TextModel": Gemma3TextModelConfig,
+    "LlamaBidirectionalForSequenceClassification": LlamaBidirectionalConfig,
+    "LlamaBidirectionalModel": LlamaBidirectionalConfig,
     "NomicBertModel": NomicBertModelConfig,
     "Qwen2ForProcessRewardModel": Qwen2ForProcessRewardModelConfig,
     "Qwen2ForRewardModel": Qwen2ForRewardModelConfig,

vllm/model_executor/models/llama.py

@@ -57,7 +57,14 @@ from vllm.model_executor.model_loader.weight_utils import (
 )
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import SupportsEagle, SupportsEagle3, SupportsLoRA, SupportsPP
+from .adapters import as_embedding_model, as_seq_cls_model
+from .interfaces import (
+    SupportsEagle,
+    SupportsEagle3,
+    SupportsLoRA,
+    SupportsPP,
+)
+from .interfaces_base import attn_type
 from .utils import (
     AutoWeightsLoader,
     PPMissingLayer,
@@ -698,3 +705,17 @@ class LlamaForCausalLM(
                 name = name.replace(item, mapping[item])
 
         return name, loaded_weight
+
+
+@attn_type("encoder_only")
+class LlamaBidirectionalForSequenceClassification(as_seq_cls_model(LlamaForCausalLM)):
+    # This class sets the correct attention type and pooling type
+    # through LlamaBidirectionalConfig.
+    pass
+
+
+@attn_type("encoder_only")
+class LlamaBidirectionalModel(as_embedding_model(LlamaForCausalLM)):
+    # This class sets the correct attention type and pooling type
+    # through LlamaBidirectionalConfig.
+    pass

vllm/model_executor/models/registry.py

@@ -203,6 +203,7 @@ _EMBEDDING_MODELS = {
     "GteNewModel": ("bert_with_rope", "GteNewModel"),
     "InternLM2ForRewardModel": ("internlm2", "InternLM2ForRewardModel"),
     "JambaForSequenceClassification": ("jamba", "JambaForSequenceClassification"),  # noqa: E501
+    "LlamaBidirectionalModel": ("llama", "LlamaBidirectionalModel"),
     "LlamaModel": ("llama", "LlamaForCausalLM"),
     **{
         # Multiple models share the same architecture, so we include them all
@@ -246,6 +247,11 @@ _CROSS_ENCODER_MODELS = {
         "bert_with_rope",
         "GteNewForSequenceClassification",
     ),
+    "JinaVLForRanking": ("jina_vl", "JinaVLForSequenceClassification"),
+    "LlamaBidirectionalForSequenceClassification": (
+        "llama",
+        "LlamaBidirectionalForSequenceClassification",
+    ),
     "ModernBertForSequenceClassification": (
         "modernbert",
         "ModernBertForSequenceClassification",
@@ -259,8 +265,6 @@ _CROSS_ENCODER_MODELS = {
         "roberta",
         "RobertaForSequenceClassification",
     ),
-    # [Auto-converted (see adapters.py)]
-    "JinaVLForRanking": ("jina_vl", "JinaVLForSequenceClassification"),  # noqa: E501,
 }
 
 _MULTIMODAL_MODELS = {