From b0a23dc4fc8a0651268e98568a95ab87452ce754 Mon Sep 17 00:00:00 2001
From: Jiarui Fang <fangjiarui123@gmail.com>
Date: Sun, 16 Oct 2022 21:57:50 +0800
Subject: [PATCH] [embeddings] add doc in readme (#1711)

---
 README-zh-Hans.md | 5 +++++
 README.md         | 3 +++
 2 files changed, 8 insertions(+)

diff --git a/README-zh-Hans.md b/README-zh-Hans.md
index ae8fbd9b7..c5c8c4d70 100644
--- a/README-zh-Hans.md
+++ b/README-zh-Hans.md
@@ -148,6 +148,11 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
 
 请访问我们的 [文档](https://www.colossalai.org/) 和 [例程](https://github.com/hpcaitech/ColossalAI-Examples) 以了解详情。
 
+
+### 推荐系统模型
+- [Cached Embedding](https://github.com/hpcaitech/FreqCacheEmbedding), 使用软件Cache实现Embeddings，用更少GPU显存训练更大的模型。
+
+
 <p align="right">(<a href="#top">返回顶端</a>)</p>
 
 ## 单GPU训练样例展示
diff --git a/README.md b/README.md
index 278dfad10..f844b8994 100644
--- a/README.md
+++ b/README.md
@@ -153,6 +153,9 @@ distributed training and inference in a few lines.
 
 Please visit our [documentation](https://www.colossalai.org/) and [examples](https://github.com/hpcaitech/ColossalAI-Examples) for more details.
 
+### Recommendation System Models
+- [Cached Embedding](https://github.com/hpcaitech/FreqCacheEmbedding), utilize software cache to train larger embedding tables with a smaller GPU memory budget.
+
 <p align="right">(<a href="#top">back to top</a>)</p>
 
 ## Single GPU Training Demo