diff --git a/README.md b/README.md
index 37e16ad..af6aae6 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
  * @Author: LiangSong(sl12160010@gmail.com)
  * @Date: 2023-03-10 21:18:35
  * @LastEditors: LiangSong(sl12160010@gmail.com)
- * @LastEditTime: 2023-04-28 22:44:21
+ * @LastEditTime: 2023-04-29 11:41:10
  * @FilePath: /Open-Llama/README.md
  * @Description: 
  * 
@@ -24,7 +24,7 @@ Open-Llama是一个开源项目，提供了一整套用于构建大型语言模
 
 经过Instruct-tuning的CheckPoint已开源在[HuggingFace: s-JoL/Open-Llama-V1](https://huggingface.co/s-JoL/Open-Llama-V1)。使用ckpt需要先用下面命令安装最新版本Transformers
 ``` python
-pip install git+https://github.com/s-JoL/transformers.git@dev
+pip install git+https://github.com/huggingface/transformers.git
 
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
@@ -181,7 +181,7 @@ python3 dataset/dataset.py
 我们基于Transformers库中的[Llama](https://github.com/facebookresearch/llama)参考论文原文中的2.4 Efficient implementation一节进行了修改，
 同时还参考了一些其他论文引入了一些优化。具体来说，我们引入了由META开源的[xformers库](https://github.com/facebookresearch/xformers)中的memory_efficient_attention操作来进行
 Self Attention的计算，这对于性能有明显的提升，提升大约30%。
-具体可以参见[modeling_llama.py](https://github.com/s-JoL/transformers/blob/dev/src/transformers/models/open_llama/modeling_open_llama.py#L230)
+具体可以参见[modeling_llama.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/open_llama/modeling_open_llama.py#L229)
 
 同时我们还参考了[Bloom](https://huggingface.co/bigscience/bloom)，对于Token Embedding引入了Stable Embedding以更好的稳定训练。
 
diff --git a/README_en.md b/README_en.md
index f3d0f3f..e0f6960 100644
--- a/README_en.md
+++ b/README_en.md
@@ -2,7 +2,7 @@
  * @Author: LiangSong(sl12160010@gmail.com)
  * @Date: 2023-03-10 21:18:35
  * @LastEditors: LiangSong(sl12160010@gmail.com)
- * @LastEditTime: 2023-04-28 22:44:27
+ * @LastEditTime: 2023-04-29 11:41:20
  * @FilePath: /Open-Llama/README_en.md
  * @Description: 
  * 
@@ -24,7 +24,7 @@ Open-Llama is an open-source project that offers a complete training pipeline fo
 
 The CheckPoint after Instruct-tuning is open-source on [HuggingFace: s-JoL/Open-Llama-V1](https://huggingface.co/s-JoL/Open-Llama-V1). To use the CheckPoint, first, install the latest version of Transformers with the following command:
 ``` python
-pip install git+https://github.com/s-JoL/transformers.git@dev
+pip install git+https://github.com/huggingface/transformers.git
 
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
@@ -190,7 +190,7 @@ python3 dataset/dataset.py
 
 ### Model Structure
 
-We modified according to the section 2.4 Efficient implementation of the [Llama](https://github.com/facebookresearch/llama) paper in the Transformers library, and also referenced other papers to introduce some optimizations. Specifically, we used the memory_efficient_attention operation from the [xformers library](https://github.com/facebookresearch/xformers) open-sourced by META for Self Attention computation, which has a significant performance improvement of approximately 30%. Further details can be found in [modeling_llama.py](https://github.com/s-JoL/transformers/blob/dev/src/transformers/models/open_llama/modeling_open_llama.py#L230).
+We modified according to the section 2.4 Efficient implementation of the [Llama](https://github.com/facebookresearch/llama) paper in the Transformers library, and also referenced other papers to introduce some optimizations. Specifically, we used the memory_efficient_attention operation from the [xformers library](https://github.com/facebookresearch/xformers) open-sourced by META for Self Attention computation, which has a significant performance improvement of approximately 30%. Further details can be found in [modeling_llama.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/open_llama/modeling_open_llama.py#L229).
 
 Additionally, we referred to [Bloom](https://huggingface.co/bigscience/bloom) and introduced Stable Embedding for Token Embedding to better stabilize training.