Add - 4bit-related args

lm_eval/models/huggingface.py

@@ -91,6 +91,8 @@ class HuggingFaceAutoLM(BaseLM):
         load_in_4bit: Optional[bool] = False,
         trust_remote_code: Optional[bool] = False,
         gptq_use_triton: Optional[bool] = False,
+        bnb_4bit_quant_type: Optional[str] = None,
+        bnb_4bit_compute_dtype: Optional[Union[str, torch.dtype]] = None,
     ):
         """Initializes a HuggingFace `AutoModel` and `AutoTokenizer` for evaluation.
         Args:
@@ -152,6 +154,13 @@ class HuggingFaceAutoLM(BaseLM):
                 If True, will trust the remote code when loading the model.
             gptq_use_triton (bool, optional, defaults to False):
                 Use Triton for GPTQ inference.
+            bnb_4bit_quant_type (str, optional, defaults to None): 
+                The quantization type to use for BnB 4bit quantization. See:
+                https://github.com/huggingface/transformers/blob/main/src/transformers/utils/quantization_config.py#L77
+            bnb_4bit_compute_dtype (Union[str, torch.dtype], optional, defaults to None):
+                The compute dtype to use for BnB 4bit quantization. See:
+                https://github.com/huggingface/transformers/blob/main/src/transformers/utils/quantization_config.py#L74
+
         """
         super().__init__()
 
@@ -215,6 +224,8 @@ class HuggingFaceAutoLM(BaseLM):
             gptq_use_triton=gptq_use_triton,
             load_in_8bit=load_in_8bit,
             load_in_4bit=load_in_4bit,
+            bnb_4bit_quant_type=bnb_4bit_quant_type,
+            bnb_4bit_compute_dtype=bnb_4bit_compute_dtype,
             **model_kwargs,
         )
         # note: peft_path can be different than pretrained model path
@@ -253,6 +264,8 @@ class HuggingFaceAutoLM(BaseLM):
         trust_remote_code: Optional[bool] = False,
         torch_dtype: Optional[Union[str, torch.dtype]] = None,
         gptq_use_triton: Optional[bool] = False,
+        bnb_4bit_quant_type: Optional[str] = None,
+        bnb_4bit_compute_dtype: Optional[Union[str, torch.dtype]] = None,
     ) -> transformers.AutoModel:
         """Returns a pre-trained pytorch model from a pre-trained model configuration."""
         if not quantized:
@@ -261,6 +274,9 @@ class HuggingFaceAutoLM(BaseLM):
             model_kwargs = {}
             if transformers.__version__ >= "4.30.0":
                 model_kwargs["load_in_4bit"] = load_in_4bit
+                if load_in_4bit:
+                    model_kwargs["bnb_4bit_quant_type"] = bnb_4bit_quant_type
+                    model_kwargs["bnb_4bit_compute_dtype"] = getattr(torch, bnb_4bit_compute_dtype)
             model = self.AUTO_MODEL_CLASS.from_pretrained(
                 pretrained,
                 revision=revision + ("/" + subfolder if subfolder is not None else ""),