diff --git a/README.md b/README.md
index 84f7bc1..14680b3 100644
--- a/README.md
+++ b/README.md
@@ -13,7 +13,7 @@ ChatGLM-6B 使用了和 ChatGPT 相似的技术，针对中文问答和对话进
 *Read this in [English](README_en.md).*
 
 ## 更新信息
-**[2023/03/23]** 增加API部署，感谢 [@LemonQu-GIT](https://github.com/LemonQu-GIT)
+**[2023/03/23]** 增加API部署（感谢 [@LemonQu-GIT](https://github.com/LemonQu-GIT)），增加Embedding量化模型[ChatGLM-6B-INT4-QE](https://huggingface.co/THUDM/chatglm-6b-int4-qe)
 
 **[2023/03/19]** 增加流式输出接口 `stream_chat`，已更新到网页版和命令行 Demo。修复输出中的中文标点。增加量化后的模型 [ChatGLM-6B-INT4](https://huggingface.co/THUDM/chatglm-6b-int4)
 
@@ -133,6 +133,13 @@ model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).ha
 model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True).half().cuda()
 ```
 
+**[2023/03/24]** 我们进一步提供了对Embedding量化后的模型，模型参数仅占用4.3 GB显存：
+```python
+model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4-qe", trust_remote_code=True).half().cuda()
+```
+
+
+
 ### CPU 部署
 如果你没有 GPU 硬件的话，也可以在 CPU 上进行推理，但是推理速度会更慢。使用方法如下（需要大概 32GB 内存）
 ```python
diff --git a/README_en.md b/README_en.md
index ba9366d..3a8b17b 100644
--- a/README_en.md
+++ b/README_en.md
@@ -9,7 +9,7 @@ ChatGLM-6B uses technology similar to ChatGPT, optimized for Chinese QA and dial
 Try the [online demo](https://huggingface.co/spaces/ysharma/ChatGLM-6b_Gradio_Streaming) on Huggingface Spaces.
 
 ## Update
-**[2023/03/23]** Add API deployment, thanks to [@LemonQu-GIT](https://github.com/LemonQu-GIT)
+**[2023/03/23]** Add API deployment, thanks to [@LemonQu-GIT](https://github.com/LemonQu-GIT). Add embedding-quantized model [ChatGLM-6B-INT4-QE](https://huggingface.co/THUDM/chatglm-6b-int4-qe)
 
 **[2023/03/19]** Add streaming output function `stream_chat`, already applied in web and CLI demo. Fix Chinese punctuations in output. Add quantized model [ChatGLM-6B-INT4](https://huggingface.co/THUDM/chatglm-6b-int4). 
 
@@ -129,6 +129,11 @@ Model quantization brings a certain performance decline. After testing, ChatGLM-
 model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4", trust_remote_code=True).half().cuda()
 ```
 
+**[2023/03/24]** We further provide an embedding-quantized model whose model parameters only cost 4.3GB GPU memory
+```python
+model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4-qe", trust_remote_code=True).half().cuda()
+```
+
 ### CPU Deployment
 
 If your computer is not equipped with GPU, you can also conduct inference on CPU, but the inference speed is slow (and taking about 32GB of memory):