diff --git a/eval_llm.py b/eval_llm.py
index 86709ae..3c187a4 100755
--- a/eval_llm.py
+++ b/eval_llm.py
@@ -1,7 +1,7 @@
+import time
 import argparse
 import random
 import warnings
-import numpy as np
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
 from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
@@ -43,6 +43,7 @@ def main():
     parser.add_argument('--temperature', default=0.85, type=float, help="生成温度，控制随机性（0-1，越大越随机）")
     parser.add_argument('--top_p', default=0.85, type=float, help="nucleus采样阈值（0-1）")
     parser.add_argument('--historys', default=0, type=int, help="携带历史对话轮数（需为偶数，0表示不携带历史）")
+    parser.add_argument('--show_speed', default=1, type=int, help="显示decode速度（tokens/s）")
     parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str, help="运行设备")
     args = parser.parse_args()
     
@@ -62,10 +63,10 @@ def main():
     input_mode = int(input('[0] 自动测试\n[1] 手动输入\n'))
     streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
     
-    prompt_iter = prompts if input_mode == 0 else iter(lambda: input('👶: '), '')
+    prompt_iter = prompts if input_mode == 0 else iter(lambda: input('💬: '), '')
     for prompt in prompt_iter:
         setup_seed(2026) # or setup_seed(random.randint(0, 2048))
-        if input_mode == 0: print(f'👶: {prompt}')
+        if input_mode == 0: print(f'💬: {prompt}')
         conversation = conversation[-args.historys:] if args.historys else []
         conversation.append({"role": "user", "content": prompt})
 
@@ -74,7 +75,8 @@ def main():
         inputs = tokenizer.apply_chat_template(**templates) if args.weight != 'pretrain' else (tokenizer.bos_token + prompt)
         inputs = tokenizer(inputs, return_tensors="pt", truncation=True).to(args.device)
 
-        print('🤖️: ', end='')
+        print('🤖: ', end='')
+        st = time.time()
         generated_ids = model.generate(
             inputs=inputs["input_ids"], attention_mask=inputs["attention_mask"],
             max_new_tokens=args.max_new_tokens, do_sample=True, streamer=streamer,
@@ -83,7 +85,8 @@ def main():
         )
         response = tokenizer.decode(generated_ids[0][len(inputs["input_ids"][0]):], skip_special_tokens=True)
         conversation.append({"role": "assistant", "content": response})
-        print('\n\n')
+        gen_tokens = len(generated_ids[0]) - len(inputs["input_ids"][0])
+        print(f'\n[Speed]: {gen_tokens / (time.time() - st):.2f} tokens/s\n\n') if args.show_speed else print('\n\n')
 
 if __name__ == "__main__":
     main()
\ No newline at end of file