fix #16 by also allowing a generationconfig object to be passed progr…

…ammatically if needed

src/lighteval/models/transformers/transformers_model.py

@@ -39,7 +39,7 @@
     GPTQConfig,
     PretrainedConfig,
 )
-from transformers.generation.utils import GenerateOutput
+from transformers.generation.utils import GenerateOutput, GenerationConfig
 from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
 
 from lighteval.data import GenerativeTaskDataset, LoglikelihoodDataset, LoglikelihoodSingleTokenDataset
@@ -126,6 +126,8 @@ class TransformersModelConfig:
             model at a quantized precision. Needed for 4-bit and 8-bit precision.
         trust_remote_code (bool): Whether to trust remote code during model
             loading.
+        generation_parameters (GenerationParameters): Range of parameters which will affect the generation.
+        generation_config (GenerationConfig): GenerationConfig object (only passed during manual creation)
 
     Methods:
         __post_init__(): Performs post-initialization checks on the configuration.
@@ -154,6 +156,7 @@ class TransformersModelConfig:
     use_chat_template: bool = False
     compile: bool = False
     generation_parameters: GenerationParameters = None
+    generation_config: GenerationConfig = None
 
     def __post_init__(self):
         # Making sure this parameter is a boolean
@@ -180,7 +183,12 @@ def __post_init__(self):
         if not isinstance(self.device, str):
             raise ValueError("Current device must be passed as string.")
 
-        if not self.generation_parameters:
+        if self.generation_config and self.generation_parameters:
+            raise ValueError(
+                "Can't use both generation_config and generation_parameters argument. Pass the generation parameters to your generation config object"
+            )
+
+        if not self.generation_parameters and not self.generation_config:
             self.generation_parameters = GenerationParameters()
 
     def _init_configs(self, model_name: str, env_config: EnvConfig) -> PretrainedConfig:
@@ -275,8 +283,11 @@ def __init__(
         self.model_sha = config.get_model_sha()
 
         self.precision = _get_dtype(config.dtype, config=self._config)
-        self.generation_parameters = config.generation_parameters
-        self.generation_config_dict = self.generation_parameters.to_transformers_dict()
+        if config.generation_config is None:
+            self.generation_parameters = config.generation_parameters
+            self.generation_config_dict = self.generation_parameters.to_transformers_dict()
+        else:
+            self.generation_config_dict = config.generation_config.to_dict()
 
         if is_accelerate_available():
             model_size, _ = calculate_maximum_sizes(self.model)