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

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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>
This commit is contained in:
Jakub Zakrzewski
2025-12-23 12:19:16 +01:00
committed by GitHub
parent 27c6c2f98c
commit 23daef548d
19 changed files with 663 additions and 46 deletions
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11
vllm/entrypoints/llm.py
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@@ -1280,6 +1280,7 @@ class LLM:
pooling_params: PoolingParams | None = None,
lora_request: list[LoRARequest] | LoRARequest | None = None,
tokenization_kwargs: dict[str, Any] | None = None,
score_template: str | None = None,
) -> list[ScoringRequestOutput]:
model_config = self.model_config
@@ -1313,6 +1314,7 @@ class LLM:
data_2=d,
tokenizer=tokenizer,
tokenization_kwargs=tokenization_kwargs,
score_template=score_template,
)
if token_type_ids := engine_prompt.pop("token_type_ids", None):
@@ -1347,6 +1349,7 @@ class LLM:
use_tqdm: bool | Callable[..., tqdm] = True,
pooling_params: PoolingParams | None = None,
lora_request: list[LoRARequest] | LoRARequest | None = None,
chat_template: str | None = None,
) -> list[ScoringRequestOutput]:
"""Generate similarity scores for all pairs `<text,text_pair>` or
`<multi-modal data, multi-modal data pair>`.
@@ -1379,6 +1382,8 @@ class LLM:
lora_request: LoRA request to use for generation, if any.
pooling_params: The pooling parameters for pooling. If None, we
use the default pooling parameters.
chat_template: The chat template to use for the scoring. If None, we
use the model's default chat template.
Returns:
A list of `ScoringRequestOutput` objects containing the
generated scores in the same order as the input prompts.
@@ -1406,6 +1411,11 @@ class LLM:
):
raise ValueError("Score API is only enabled for num_labels == 1.")
if not model_config.is_cross_encoder and chat_template is not None:
raise ValueError(
"chat_template is only supported for cross-encoder models."
)
# the tokenizer for models such as
# "cross-encoder/ms-marco-MiniLM-L-6-v2" doesn't support passing
# lists of tokens to the `text` and `text_pair` kwargs
@@ -1475,6 +1485,7 @@ class LLM:
use_tqdm,
pooling_params,
lora_request,
score_template=chat_template,
)
else:
return self._embedding_score(