diff --git a/README.md b/README.md
index 591f257..d18f7f7 100644
--- a/README.md
+++ b/README.md
@@ -405,6 +405,9 @@ model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan2-7B-Chat-4bi
 对于 8bits 离线量化，我们没有提供相应的版本，因为 Hugging Face transformers 库提供了相应的 API 接口，可以很方便的实现 8bits 量化模型的保存和加载。用户可以自行按照如下方式实现 8bits 的模型保存和加载：
 ```python
 # Model saving: model_id is the original model directory, and quant8_saved_dir is the directory where the 8bits quantized model is saved.
+tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False, trust_remote_code=True)
+tokenizer.save_pretrained(quant8_saved_dir)
+
 model = AutoModelForCausalLM.from_pretrained(model_id, load_in_8bit=True, device_map="auto", trust_remote_code=True)
 model.save_pretrained(quant8_saved_dir)
 model = AutoModelForCausalLM.from_pretrained(quant8_saved_dir, device_map="auto", trust_remote_code=True)
diff --git a/README_EN.md b/README_EN.md
index 4ff49e9..046ed96 100644
--- a/README_EN.md
+++ b/README_EN.md
@@ -405,6 +405,9 @@ model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan2-7B-Chat-4bi
 For 8bits offline quantization, we haven't provided a corresponding version since the Hugging Face transformers library offers the necessary API interfaces. This makes the saving and loading of 8bits quantized models very convenient. Users can implement the saving and loading of 8bits models in the following manner:
 ```python
 # Model saving: model_id is the original model directory, and quant8_saved_dir is the directory where the 8bits quantized model is saved.
+tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False, trust_remote_code=True)
+tokenizer.save_pretrained(quant8_saved_dir)
+
 model = AutoModelForCausalLM.from_pretrained(model_id, load_in_8bit=True, device_map="auto", trust_remote_code=True)
 model.save_pretrained(quant8_saved_dir)
 model = AutoModelForCausalLM.from_pretrained(quant8_saved_dir, device_map="auto", trust_remote_code=True)