diff --git a/README_CN.md b/README_CN.md
index de3a656..c98bca2 100644
--- a/README_CN.md
+++ b/README_CN.md
@@ -335,6 +335,53 @@ model = AutoModelForCausalLM.from_pretrained(
 ```
 <br>
 
+## Batch推理
+千问支持batch批量推理。在开启flash-attention的状态下，使用batch推理可以约40%的提速。示例代码如下所示：
+```
+import torch
+from tokenization_qwen import QWenTokenizer
+from modeling_qwen import QWenLMHeadModel
+from transformers import GenerationConfig
+from qwen_generation_utils import make_context
+
+
+tokenizer = QWenTokenizer.from_pretrained('./', pad_token='<|extra_0|>', eos_token='<|endoftext|>', padding_side='left')
+model = QWenLMHeadModel.from_pretrained('./', device_map="auto").eval()
+model.generation_config = GenerationConfig.from_pretrained('./')
+
+all_raw_text = ["我想听你说爱我。", "今天我想吃点啥，甜甜的，推荐下", "我马上迟到了，怎么做才能不迟到"]
+batch_question = []
+for q in all_raw_text:
+    raw_text, _ = make_context(
+        tokenizer,
+        q,
+        system="You are a helpful assistant.",
+        max_window_size=model.generation_config.max_window_size,
+        chat_format=model.generation_config.chat_format,
+    )
+    batch_question.append(raw_text)
+
+batch_input_ids = tokenizer(batch_question, padding='longest')
+print(batch_input_ids)
+
+batch_input_ids1 = torch.LongTensor(batch_input_ids['input_ids']).to(model.device)
+batch_out_ids = model.generate(
+    input_ids=batch_input_ids1
+    ,return_dict_in_generate=False
+)
+batch_response = [tokenizer.decode(o, skip_special_tokens=True) for o in batch_out_ids]
+print(batch_response)
+
+response, _ = model.chat(tokenizer, "我想听你说爱我。", history=None)
+print(response)
+
+response, _ = model.chat(tokenizer, "今天我想吃点啥，甜甜的，推荐下", history=None)
+print(response)
+
+response, _ = model.chat(tokenizer, "我马上迟到了，怎么做才能不迟到", history=None)
+print(response)
+```
+
 ## 微调
 
 ### 使用方法