Allow for model kwargs when loading transformers from pretrained (#754)

## Pull Request Overview

This PR introduces support for passing custom keyword arguments when loading pretrained transformer models, enabling more flexible configuration of model loading. It also replaces the fixed "generation_size" parameter with a more general "model_loading_kwargs" field.
- Removed the fixed generation_size parameter.
- Added a new model_loading_kwargs field to the configuration.
- Updated the auto model creation to copy the provided kwargs.

Co-authored-by: Copilot <[email protected]>

* suggestion from copilot

---------

Co-authored-by: Copilot <[email protected]>

Author: NathanHB

src/lighteval/models/transformers/transformers_model.py

@@ -27,7 +27,7 @@
 import torch
 import torch.nn.functional as F
 import transformers
-from pydantic import PositiveInt
+from pydantic import Field, PositiveInt
 from torch.nn.utils.rnn import pad_sequence
 from torch.utils.data import DataLoader
 from tqdm import tqdm
@@ -137,8 +137,8 @@ class TransformersModelConfig(ModelConfig):
     subfolder: str | None = None
     revision: str = "main"
     batch_size: PositiveInt | None = None
-    generation_size: PositiveInt = 256
     max_length: PositiveInt | None = None
+    model_loading_kwargs: dict = Field(default_factory=dict)
     add_special_tokens: bool = True
     model_parallel: bool | None = None
     dtype: str | None = None
@@ -384,7 +384,7 @@ def _create_auto_model(self) -> transformers.PreTrainedModel:
 
         pretrained_config = self.transformers_config
 
-        kwargs = {}
+        kwargs = self.config.model_loading_kwargs.copy()
         if "quantization_config" not in pretrained_config.to_dict():
             kwargs["quantization_config"] = quantization_config