Update docstrings to files.

src/lighteval/models/endpoints/openai_model.py

@@ -66,18 +66,44 @@ class OpenAIModelConfig:
     Configuration class to create an [[OpenAIModel]], to call via its API at inference for evaluation.
 
     Attributes:
-    model: name or identifier of the OpenAI model to be used for inference.
+    model (str): name or identifier of the OpenAI model to be used for inference.
+    generation_parameters(None,GenerationParameters): Parameters for model generation. If not
+                                                     provided, defaults to a new instance
+                                                     of `GenerationParameters`.
     """
 
     model: str
     generation_parameters: GenerationParameters = None
 
     def __post_init__(self):
+        """
+
+        Post-initialization that ensures the `generation_parameters` is set
+        to a valid `GenerationParameters`. If not provided, initializes a default one.
+        """
         if not self.generation_parameters:
             self.generation_parameters = GenerationParameters()
 
     @classmethod
     def from_path(cls, path: str) -> "OpenAIModelConfig":
+        """
+        Creates an instance of `OpenAIModelConfig` from a YAML configuration file.
+
+        Loads the model configuration from a given file path and initializes the
+        `OpenAIModelConfig` with the model name and corresponding `GenerationParameters` parsed
+        from the file.
+
+        Args:
+            path (str): Path to the YAML configuration file containing the model configuration.
+
+        Returns:
+            OpenAIModelConfig: An instance of `OpenAIModelConfig` with the configuration loaded
+                               from the specified YAML file.
+
+        Raises:
+            FileNotFoundError: If the specified file path does not exist.
+            KeyError: If required keys are missing in the YAML configuration file.
+        """
         import yaml
 
         with open(path, "r") as f:
@@ -169,11 +195,11 @@ def greedy_until(
         Generates responses using a greedy decoding strategy until certain ending conditions are met.
 
         Args:
-            requests (list[Request]): list of requests containing the context and ending conditions.
+            requests (list[GreedyUntilRequest]): list of requests containing the context and ending conditions.
             override_bs (int, optional): Override the batch size for generation. Defaults to None.
 
         Returns:
-            list[GenerativeResponse]: list of generated responses.
+            list [GenerativeResponse]: list of generated responses.
         """
         for request in requests:
             request.tokenized_context = self.tok_encode(request.context)

src/lighteval/models/transformers/adapter_model.py

@@ -44,6 +44,10 @@ class AdapterModelConfig(BaseModelConfig):
     """
     Manages the configuration of adapter models. Adapter models are designed to extend or adapt a
     base model's functionality for specific tasks while keeping most of the base model's parameters frozen.
+
+    Attributes:
+        base_model (str): The name of the parent base model. This model provides the tokenizer and configuration for the adapter model.
+                          Defaults to None if not specified.
     """
 
     # Adapter models have the specificity that they look at the base model (= the parent) for the tokenizer and config
@@ -66,17 +70,14 @@ def init_configs(self, env_config: EnvConfig):
             env_configs(EnvConfig): An instance of EnvConfig.
 
         Returns:
-            Any:
+            Any: Result of the configuration initialization.
         """
         return self._init_configs(self.base_model, env_config)
 
 
 class AdapterModel(BaseModel):
     """
     Integrates the adapter models with a pre-trained base model.
-
-    Args:
-
     """
 
     def _create_auto_tokenizer(self, config: AdapterModelConfig, env_config: EnvConfig) -> PreTrainedTokenizer: