diff --git a/configs/model_config.py b/configs/model_config.py
index ea24c36..4a51c5c 100644
--- a/configs/model_config.py
+++ b/configs/model_config.py
@@ -203,7 +203,7 @@ llm_model_dict = {
 }
 
 # LLM 名称
-LLM_MODEL = "fastchat-chatglm-6b-int4"
+LLM_MODEL = "fastchat-chatglm"
 # 量化加载8bit 模型
 LOAD_IN_8BIT = False
 # Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.
@@ -220,7 +220,7 @@ STREAMING = True
 
 # Use p-tuning-v2 PrefixEncoder
 USE_PTUNING_V2 = False
-PTUNING_DIR='./ptuing-v2'
+PTUNING_DIR='./ptuning-v2'
 # LLM running device
 LLM_DEVICE = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
 
diff --git a/models/chatglm_llm.py b/models/chatglm_llm.py
index 81878ce..0d19ee6 100644
--- a/models/chatglm_llm.py
+++ b/models/chatglm_llm.py
@@ -2,14 +2,14 @@ from abc import ABC
 from langchain.chains.base import Chain
 from typing import Any, Dict, List, Optional, Generator
 from langchain.callbacks.manager import CallbackManagerForChainRun
-from transformers.generation.logits_process import LogitsProcessor
-from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
+# from transformers.generation.logits_process import LogitsProcessor
+# from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
 from models.loader import LoaderCheckPoint
 from models.base import (BaseAnswer,
                          AnswerResult,
                          AnswerResultStream,
                          AnswerResultQueueSentinelTokenListenerQueue)
-import torch
+# import torch
 import transformers
 
 
diff --git a/models/loader/args.py b/models/loader/args.py
index e7df76b..cd3e78b 100644
--- a/models/loader/args.py
+++ b/models/loader/args.py
@@ -44,7 +44,7 @@ parser.add_argument('--no-remote-model', action='store_true', help='remote in th
 parser.add_argument('--model-name', type=str, default=LLM_MODEL, help='Name of the model to load by default.')
 parser.add_argument('--lora', type=str, help='Name of the LoRA to apply to the model by default.')
 parser.add_argument("--lora-dir", type=str, default=LORA_DIR, help="Path to directory with all the loras")
-parser.add_argument('--use-ptuning-v2',type=str,default=USE_PTUNING_V2,help="whether use ptuning-v2 checkpoint")
+parser.add_argument('--use-ptuning-v2',action='store_true',help="whether use ptuning-v2 checkpoint")
 parser.add_argument("--ptuning-dir",type=str,default=PTUNING_DIR,help="the dir of ptuning-v2 checkpoint")
 # Accelerate/transformers
 parser.add_argument('--load-in-8bit', action='store_true', default=LOAD_IN_8BIT,
diff --git a/models/loader/loader.py b/models/loader/loader.py
index 986f5c4..f43bb1d 100644
--- a/models/loader/loader.py
+++ b/models/loader/loader.py
@@ -441,7 +441,7 @@ class LoaderCheckPoint:
 
         if self.use_ptuning_v2:
             try:
-                prefix_encoder_file = open(Path(f'{self.ptuning_dir}/config.json'), 'r')
+                prefix_encoder_file = open(Path(f'{os.path.abspath(self.ptuning_dir)}/config.json'), 'r')
                 prefix_encoder_config = json.loads(prefix_encoder_file.read())
                 prefix_encoder_file.close()
                 self.model_config.pre_seq_len = prefix_encoder_config['pre_seq_len']
@@ -457,13 +457,14 @@ class LoaderCheckPoint:
 
         if self.use_ptuning_v2:
             try:
-                prefix_state_dict = torch.load(Path(f'{self.ptuning_dir}/pytorch_model.bin'))
+                prefix_state_dict = torch.load(Path(f'{os.path.abspath(self.ptuning_dir)}/pytorch_model.bin'))
                 new_prefix_state_dict = {}
                 for k, v in prefix_state_dict.items():
                     if k.startswith("transformer.prefix_encoder."):
                         new_prefix_state_dict[k[len("transformer.prefix_encoder."):]] = v
                 self.model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
                 self.model.transformer.prefix_encoder.float()
+                print("加载ptuning检查点成功！")
             except Exception as e:
                 print(e)
                 print("加载PrefixEncoder模型参数失败")