Default to empty string for model file

awq/models/auto.py

@@ -35,7 +35,7 @@ class AutoAWQForCausalLM:
         )
 
     @classmethod
-    def from_quantized(self, quant_path, quant_filename='pytorch_model.bin', max_new_tokens=None,
+    def from_quantized(self, quant_path, quant_filename='', max_new_tokens=None,
                        device='balanced', trust_remote_code=True, fuse_layers=True,
                        batch_size=1, safetensors=False) -> BaseAWQForCausalLM:
         os.environ["AWQ_BATCH_SIZE"] = str(batch_size)

awq/models/base.py

@@ -284,7 +284,7 @@ class BaseAWQForCausalLM(nn.Module):
         )
 
     @classmethod
-    def from_quantized(self, model_path, model_type, model_filename='pytorch_model.bin', 
+    def from_quantized(self, model_path, model_type, model_filename='', 
                              max_new_tokens=None, device='balanced', torch_dtype=torch.float16, 
                              trust_remote_code=True, safetensors=False, is_quantized=True, 
                              fuse_layers=False, version='GEMM'):
@@ -298,7 +298,10 @@ class BaseAWQForCausalLM(nn.Module):
             
             model_path = snapshot_download(model_path, ignore_patterns=ignore_patterns)
         
+        if model_filename != '':
             model_weights_path = model_path + f'/{model_filename}'
+        else:
+            model_weights_path = model_path
 
         # [STEP 2] Load config and set sequence length
         # TODO: Create BaseAWQConfig class
@@ -343,7 +346,7 @@ class BaseAWQForCausalLM(nn.Module):
         if is_quantized:
             load_checkpoint_in_model(
                 model,
-                checkpoint=model_path if safetensors else model_weights_path,
+                checkpoint=model_weights_path,
                 device_map=device_map
             )