diff --git a/quantize/quantize_eval.py b/quantize/quantize_eval.py
index ec8ea64..82d350c 100644
--- a/quantize/quantize_eval.py
+++ b/quantize/quantize_eval.py
@@ -3,27 +3,27 @@ import torch.nn as nn
 from tqdm import tqdm
 from datasets import load_dataset
 from transformers import AutoModelForCausalLM, AutoTokenizer
-#import GPUtil
+import GPUtil
 import argparse
 
 parser = argparse.ArgumentParser(description="========量化困惑度测试========")
 parser.add_argument(
     "--model_path",  
     type=str,  
-    default='/root/ld/ld_model_pretrained/miniCPM-bf16',  
+    default='',  
     help="未量化前的模型路径。"  
 )
 parser.add_argument(
     "--awq_path",  
     type=str,  
-    default='/root/ld/ld_project/pull_request/MiniCPM/quantize/awq_cpm_2b_4bit',  
+    default='',  
     help="awq量化后的模型保存路径。"  
 )
 #we will support gptq later
 parser.add_argument(
     "--gptq_path",  
     type=str,  
-    default='/root/ld/ld_project/AutoGPTQ/examples/quantization/minicpm_2b_4bit',  
+    default='',  
     help="gptq量化后的模型保存路径。"  
 )
 parser.add_argument(
@@ -82,7 +82,7 @@ if __name__ == "__main__":
     
     args = parser.parse_args()
 
-    if args.model_path:
+    if args.model_path != "":
         model = AutoModelForCausalLM.from_pretrained(args.model_path, torch_dtype=torch.bfloat16, device_map='cuda', trust_remote_code=True)
         tokenizer = AutoTokenizer.from_pretrained(args.model_path)
         print("pretrained model：",args.model_path.split('/')[-1])
@@ -91,7 +91,7 @@ if __name__ == "__main__":
         evaluate_perplexity(model, tokenizer, args.data_path)
         del model
 
-    if args.awq_path:
+    if args.awq_path != "":
         from awq import AutoAWQForCausalLM
 
         model = AutoAWQForCausalLM.from_quantized(args.awq_path, fuse_layers=True,device_map={"":'cuda:0'})
@@ -103,7 +103,7 @@ if __name__ == "__main__":
         del model
 
 #we will support the autogptq  later
-    if args.gptq_path:
+    if args.gptq_path != "":
         from auto_gptq import AutoGPTQForCausalLM
 
         tokenizer = AutoTokenizer.from_pretrained(args.gptq_path, use_fast=True)