diff --git a/README.md b/README.md
index d6e2742..c08314c 100644
--- a/README.md
+++ b/README.md
@@ -225,7 +225,7 @@ git clone https://huggingface.co/jingyaogong/MiniMind2
 
 ```bash
 # load=0: load from pytorch model, load=1: load from transformers-hf model
-python eval_model.py --load 1
+python eval_model.py --load 1 --model_mode 2
 ```
 
 ### 4.或启动WebUI
diff --git a/README_en.md b/README_en.md
index 988ffea..e349805 100644
--- a/README_en.md
+++ b/README_en.md
@@ -239,7 +239,7 @@ git clone https://huggingface.co/jingyaogong/MiniMind2
 
 ```bash
 # load=0: load from pytorch model, load=1: load from transformers-hf model
-python eval_model.py --load 1
+python eval_model.py --load 1 --model_mode 2
 ```
 
 ### 4. Or Start WebUI
diff --git a/scripts/train_tokenizer.py b/scripts/train_tokenizer.py
index 6661ace..868099a 100644
--- a/scripts/train_tokenizer.py
+++ b/scripts/train_tokenizer.py
@@ -25,7 +25,7 @@ def train_tokenizer():
                 data = json.loads(line)
                 yield data['text']
 
-    data_path = '../dataset/tokenizer_train.jsonl'
+    data_path = '../dataset/pretrain_hq.jsonl'
 
     # 初始化tokenizer
     tokenizer = Tokenizer(models.BPE())
@@ -139,12 +139,12 @@ def eval_tokenizer():
     print('encoder长度：', len(model_inputs['input_ids']))
 
     input_ids = model_inputs['input_ids']
-    response = tokenizer.decode(input_ids, skip_special_tokens=True)
+    response = tokenizer.decode(input_ids, skip_special_tokens=False)
     print('decoder和原始文本是否一致：', response == new_prompt)
 
 
 def main():
-    # train_tokenizer()
+    train_tokenizer()
     eval_tokenizer()
 
 
diff --git a/train_distill_reason.py b/train_distill_reason.py
index 722deeb..1fe9ad1 100644
--- a/train_distill_reason.py
+++ b/train_distill_reason.py
@@ -35,7 +35,7 @@ def train_epoch(epoch, wandb):
     # 思考标签占位符
     start_of_think_ids = tokenizer('<think>').input_ids
     end_of_think_ids = tokenizer('</think>').input_ids
-    start_of_answer_ids = tokenizer('</answer>').input_ids
+    start_of_answer_ids = tokenizer('<answer>').input_ids
     end_of_answer_ids = tokenizer('</answer>').input_ids
     loss_fct = nn.CrossEntropyLoss(reduction='none')
     start_time = time.time()