diff --git a/src/lighteval/metrics/llm_as_judge.py b/src/lighteval/metrics/llm_as_judge.py index e98a64aa4..81e1d7d30 100644 --- a/src/lighteval/metrics/llm_as_judge.py +++ b/src/lighteval/metrics/llm_as_judge.py @@ -28,6 +28,7 @@ from tqdm import tqdm +from lighteval.models.model_output import ModelResponse from lighteval.utils.imports import is_litellm_available, is_openai_available, is_vllm_available @@ -195,20 +196,30 @@ def __call_litellm(self, prompts): def __call_api(prompt): for _ in range(self.API_MAX_RETRY): try: - response = litellm.completion( - model=self.model, - messages=prompt, - response_format={"type": "text"}, - max_tokens=512, - n=1, - caching=True, - ) + kwargs = { + "model": self.model, + "messages": prompt, + "response_format": {"type": "text"}, + "max_tokens": 512, + "n": 1, + "caching": True, + } + response = litellm.completion(**kwargs) text = response.choices[0].message.content + if not text or response.failed: + kwargs["caching"] = False + response = litellm.completion(**kwargs) + text = response.choices[0].message.content + if not text or response.failed: + # Just return an error response if the second attempt fails too + return ModelResponse( + text="Failed to get response from the API.", model=self.model, failed=True + ) return text except Exception as e: logger.warning(f"{type(e), e}") time.sleep(self.API_RETRY_SLEEP) - raise Exception("Failed to get response from the API") + return ModelResponse(text="Failed to get response from the API.", model=self.model, failed=True) results = [] with ThreadPoolExecutor(100) as executor: diff --git a/src/lighteval/models/model_output.py b/src/lighteval/models/model_output.py index 7d0ba4818..b485371ca 100644 --- a/src/lighteval/models/model_output.py +++ b/src/lighteval/models/model_output.py @@ -33,6 +33,7 @@ class ModelResponse: generated_tokens: list[int] = field(default_factory=list) # model generations truncated_tokens_count: Optional[int] = 0 # How many tokens truncated padded_tokens_count: Optional[int] = 0 # How many tokens of padding + failed: bool = False def get_result_for_eval(self): raise NotImplementedError()