diff --git a/config.py b/config.py
index f170a2b..44e9f07 100644
--- a/config.py
+++ b/config.py
@@ -15,13 +15,13 @@ API_KEY = "此处填API密钥"    # 可同时填写多个API-KEY，用英文逗
 USE_PROXY = False
 if USE_PROXY:
     """
+    代理网络的地址，打开你的代理软件查看代理协议(socks5h / http)、地址(localhost)和端口(11284)
     填写格式是 [协议]://  [地址] :[端口]，填写之前不要忘记把USE_PROXY改成True，如果直接在海外服务器部署，此处不修改
             <配置教程&视频教程> https://github.com/binary-husky/gpt_academic/issues/1>
     [协议] 常见协议无非socks5h/http; 例如 v2**y 和 ss* 的默认本地协议是socks5h; 而cl**h 的默认本地协议是http
-    [地址] 懂的都懂，不懂就填localhost或者127.0.0.1肯定错不了（localhost意思是代理软件安装在本机上）
+    [地址] 填localhost或者127.0.0.1（localhost意思是代理软件安装在本机上）
     [端口] 在代理软件的设置里找。虽然不同的代理软件界面不一样，但端口号都应该在最显眼的位置上
     """
-    # 代理网络的地址，打开你的*学*网软件查看代理的协议(socks5h / http)、地址(localhost)和端口(11284)
     proxies = {
         #          [协议]://  [地址]  :[端口]
         "http":  "socks5h://localhost:11284",  # 再例如  "http":  "http://127.0.0.1:7890",
@@ -100,6 +100,10 @@ AVAIL_LLM_MODELS = ["gpt-3.5-turbo-1106","gpt-4-1106-preview","gpt-4-vision-prev
 MULTI_QUERY_LLM_MODELS = "gpt-3.5-turbo&chatglm3"
 
 
+# 选择本地模型变体（只有当AVAIL_LLM_MODELS包含了对应本地模型时，才会起作用）
+QWEN_MODEL_SELECTION = "Qwen/Qwen-1_8B-Chat-Int8"
+
+
 # 百度千帆（LLM_MODEL="qianfan"）
 BAIDU_CLOUD_API_KEY = ''
 BAIDU_CLOUD_SECRET_KEY = ''
diff --git a/request_llms/bridge_qwen_7B.py b/request_llms/bridge_qwen.py
similarity index 77%
rename from request_llms/bridge_qwen_7B.py
rename to request_llms/bridge_qwen.py
index dfe1fc4..1bd846b 100644
--- a/request_llms/bridge_qwen_7B.py
+++ b/request_llms/bridge_qwen.py
@@ -1,13 +1,7 @@
-model_name = "Qwen-7B"
+model_name = "Qwen"
 cmd_to_install = "`pip install -r request_llms/requirements_qwen.txt`"
 
-
-from transformers import AutoModel, AutoTokenizer
-import time
-import threading
-import importlib
-from toolbox import update_ui, get_conf, ProxyNetworkActivate
-from multiprocessing import Process, Pipe
+from toolbox import ProxyNetworkActivate, get_conf
 from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
 
 
@@ -24,16 +18,14 @@ class GetQwenLMHandle(LocalLLMHandle):
 
     def load_model_and_tokenizer(self):
         # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        import os, glob
-        import os
-        import platform
-        from modelscope import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
-
+        # from modelscope import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+        from transformers import AutoModelForCausalLM, AutoTokenizer
+        from transformers.generation import GenerationConfig
         with ProxyNetworkActivate('Download_LLM'):
-            model_id = 'qwen/Qwen-7B-Chat'  #在这里更改路径，如果你已经下载好了的话，同时，别忘记tokenizer
-            self._tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen-7B-Chat', trust_remote_code=True, resume_download=True)
+            model_id = get_conf('QWEN_MODEL_SELECTION') #在这里更改路径，如果你已经下载好了的话，同时，别忘记tokenizer
+            self._tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True, resume_download=True)
             # use fp16
-            model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True, fp16=True).eval()
+            model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True).eval()
             model.generation_config = GenerationConfig.from_pretrained(model_id, trust_remote_code=True)  # 可指定不同的生成长度、top_p等相关超参
             self._model = model
 
diff --git a/request_llms/requirements_qwen.txt b/request_llms/requirements_qwen.txt
index 3d7d62a..ea65dee 100644
--- a/request_llms/requirements_qwen.txt
+++ b/request_llms/requirements_qwen.txt
@@ -1,2 +1,4 @@
 modelscope
-transformers_stream_generator
\ No newline at end of file
+transformers_stream_generator
+auto-gptq
+optimum
\ No newline at end of file