update eval-chat

2-eval.py

@@ -39,7 +39,7 @@ def init_model(lm_config):
         # 加载到模型中
         model.load_state_dict(state_dict, strict=False)
     else:
-        model = AutoModelForCausalLM.from_pretrained('minimind', trust_remote_code=True)
+        model = AutoModelForCausalLM.from_pretrained('./minimind-v1-small', trust_remote_code=True)
     model = model.to(device)
 
     print(f'模型参数: {count_parameters(model) / 1e6} 百万 = {count_parameters(model) / 1e9} B (Billion)')
@@ -55,13 +55,13 @@ def setup_seed(seed):
     torch.backends.cudnn.deterministic = True  # 确保每次返回的卷积算法是确定的
     torch.backends.cudnn.benchmark = False  # 关闭 cuDNN 的自动调优，避免不确定性
 
+
 if __name__ == "__main__":
     # -----------------------------------------------------------------------------
     out_dir = 'out'
     start = ""
-    temperature = 0.5
+    temperature = 0.7
     top_k = 16
-    setup_seed(1337)
     # device = 'cpu'
     device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
     dtype = 'bfloat16'
@@ -84,8 +84,8 @@ if __name__ == "__main__":
     stream = True
 
     prompt_datas = [
-        '你叫什么名字啊？',
-        '你叫什么名字？',
+        '你叫什么名字',
+        '你是谁',
         '中国有哪些比较好的大学？',
         '全世界最好的大学是什么？',
         '你知道光速是多少吗？',
@@ -115,6 +115,9 @@ if __name__ == "__main__":
 
     i = 0
     while i < len(prompt_datas):
+        # Generate a random seed
+        random_seed = random.randint(0, 2 ** 32 - 1)
+        setup_seed(random_seed)
         if not contain_history_chat:
             messages = messages_origin.copy()
 
@@ -125,6 +128,7 @@ if __name__ == "__main__":
             print(f'[Q]: {prompt}')
             i += 1
 
+        prompt = '请问，' + prompt
         messages.append({"role": "user", "content": prompt})
 
         # print(messages)