added doc

src/lighteval/models/model_input.py

@@ -44,7 +44,19 @@ class GenerationParameters:
     truncate_prompt: Optional[bool] = None  # vllm, tgi
 
     @classmethod
-    def from_dict(cls, config_dict):
+    def from_dict(cls, config_dict: dict):
+        """Creates a GenerationParameters object from a config dictionary
+
+        Args:
+            config_dict (dict): Config dictionary. Must obey the following shape:
+            {"generation_parameters":
+                {
+                    "early_stopping": value,
+                    ...
+                    "truncate_prompt": value
+                }
+            }
+        """
         if "generation_parameters" not in config_dict:
             return cls
         cls.early_stopping = config_dict["generation_parameters"].get("early_stopping", None)
@@ -63,7 +75,13 @@ def from_dict(cls, config_dict):
         cls.truncate_prompt = config_dict["generation_parameters"].get("truncate_prompt", None)
         return cls
 
-    def to_vllm_openai_dict(self):
+    def to_vllm_openai_dict(self) -> dict:
+        """Selects relevant generation and sampling parameters for vllm and openai models.
+        Doc: https://docs.vllm.ai/en/v0.5.5/dev/sampling_params.html
+
+        Returns:
+            dict: The parameters to create a vllm.SamplingParams or just provide OpenAI params as such in the model config.
+        """
         # Task specific sampling params to set in model: n, best_of, use_beam_search
         # Generation specific params to set in model: logprobs, prompt_logprobs
         args = {
@@ -84,7 +102,13 @@ def to_vllm_openai_dict(self):
         }
         return {k: v for k, v in args.items() if v is not None}
 
-    def to_transformers_dict(self):
+    def to_transformers_dict(self) -> dict:
+        """Selects relevant generation and sampling parameters for transformers models.
+        Doc: https://huggingface.co/docs/transformers/v4.46.3/en/main_classes/text_generation#transformers.GenerationConfig
+
+        Returns:
+            dict: The parameters to create a transformers.GenerationConfig in the model config.
+        """
         # Task specific sampling params to set in model: do_sample, num_return_sequences, num_beans
         args = {
             "max_new_tokens": self.max_new_tokens,
@@ -104,7 +128,13 @@ def to_transformers_dict(self):
         # we still create the object as it uses validation steps
         return {k: v for k, v in args.items() if v is not None}
 
-    def to_tgi_inferenceendpoint_dict(self):
+    def to_tgi_inferenceendpoint_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
+
+        Returns:
+            dict: The parameters to create a huggingface_hub.TextGenerationInputGenerateParameters in the model config.
+        """
         # Task specific sampling params to set in model: best_of, do_sample
         args = {
             "decoder_input_details": True,