diff --git a/AITrain/main_controller.py b/AITrain/main_controller.py
index 717f6a9..ad1784f 100644
--- a/AITrain/main_controller.py
+++ b/AITrain/main_controller.py
@@ -221,11 +221,11 @@ def run_dialogue_system():
                 turns = int(turns_input) if turns_input.isdigit() else 4
                 
                 # 询问历史上下文设置
-                history_input = input("使用历史对话轮数 (默认3): ").strip()
-                history_count = int(history_input) if history_input.isdigit() else 3
+                history_input = input("使用历史对话轮数 (默认10): ").strip()
+                history_count = int(history_input) if history_input.isdigit() else 10
                 
-                context_input = input("使用上下文信息数量 (默认10): ").strip()
-                context_info_count = int(context_input) if context_input.isdigit() else 10
+                context_input = input("使用上下文信息数量 (默认5): ").strip()
+                context_info_count = int(context_input) if context_input.isdigit() else 5
                 
                 print(f"\n开始对话 - 主题: {user_input}")
                 print(f"轮数: {turns}, 历史: {history_count}, 上下文: {context_info_count}")
diff --git a/AITrain/npc_dialogue_generator.py b/AITrain/npc_dialogue_generator.py
index b936bf1..5ccce21 100644
--- a/AITrain/npc_dialogue_generator.py
+++ b/AITrain/npc_dialogue_generator.py
@@ -136,7 +136,7 @@ class NPCDialogueGenerator:
         if self.lora_model_path:
             print(f"Loading LoRA weights from: {self.lora_model_path}")
             self.model = PeftModel.from_pretrained(self.model, self.lora_model_path)
-    
+       
     def generate_character_dialogue(
         self, 
         character_name: str, 
@@ -193,7 +193,17 @@ class NPCDialogueGenerator:
         
         # 移动到设备
         inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
+        # 计算input token数并与模型最大token数比较
+        input_token_count = inputs['input_ids'].shape[1]
+        try:
+            max_model_tokens = self.model.config.max_position_embeddings
+        except AttributeError:
+            max_model_tokens = 2048
         
+        if input_token_count + max_new_tokens > max_model_tokens:
+            print(f"警告：当前输入token数({input_token_count})加上最大生成token数({max_new_tokens})超过模型最大token数({max_model_tokens})，可能导致生成结果不完整或报错。")
+        
+      
         # 生成对话
         with torch.no_grad():
             outputs = self.model.generate(