inferenceendpoint renamed to ie

src/lighteval/models/endpoints/endpoint_model.py

@@ -316,9 +316,7 @@ def __init__(  # noqa: C901
             model_size=-1,
         )
         self.generation_parameters = config.generation_parameters
-        self.generation_config = TextGenerationInputGenerateParameters(
-            **self.generation_parameters.to_tgi_inferenceendpoint_dict()
-        )
+        self.generation_config = TextGenerationInputGenerateParameters(**self.generation_parameters.to_tgi_ie_dict())
 
     @staticmethod
     def get_larger_hardware_suggestion(cur_instance_type: str = None, cur_instance_size: str = None):

src/lighteval/models/endpoints/tgi_model.py

@@ -88,9 +88,7 @@ def __init__(self, config: TGIModelConfig) -> None:
 
         self.client = AsyncClient(config.inference_server_address, headers=headers, timeout=240)
         self.generation_parameters = config.generation_parameters
-        self.generation_config = TextGenerationInputGenerateParameters(
-            **self.generation_parameters.to_tgi_inferenceendpoint_dict()
-        )
+        self.generation_config = TextGenerationInputGenerateParameters(**self.generation_parameters.to_tgi_ie_dict())
         self._max_gen_toks = 256
         self.model_info = requests.get(f"{config.inference_server_address}/info", headers=headers).json()
         if "model_id" not in self.model_info:

src/lighteval/models/model_input.py

@@ -97,7 +97,7 @@ def to_transformers_dict(self) -> dict:
         }
         return {k: v for k, v in args.items() if v is not None}
 
-    def to_tgi_inferenceendpoint_dict(self) -> dict:
+    def to_tgi_ie_dict(self) -> dict:
         """Selects relevant generation and sampling parameters for tgi or inference endpoints models.
         Doc: https://huggingface.co/docs/huggingface_hub/v0.26.3/en/package_reference/inference_types#huggingface_hub.TextGenerationInputGenerateParameters