[news] add llama pretraining

README.md

@@ -25,6 +25,7 @@
 </div>
 
 ## Latest News
+* [2023/07] [65B Model Pretraining Accelerated by 38%, Best Practices for Building LLaMA-Like Base Models Open-Source](https://www.hpc-ai.tech/blog/large-model-pretraining)
 * [2023/03] [ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b)
 * [2023/03] [Intel and Colossal-AI Partner to Deliver Cost-Efficient Open-Source Solution for Protein Folding Structure Prediction](https://www.hpc-ai.tech/blog/intel-habana)
 * [2023/03] [AWS and Google Fund Colossal-AI with Startup Cloud Programs](https://www.hpc-ai.tech/blog/aws-and-google-fund-colossal-ai-with-startup-cloud-programs)
@@ -49,6 +50,7 @@
  <li>
    <a href="#Parallel-Training-Demo">Parallel Training Demo</a>
    <ul>
+     <li><a href="#LLaMA">LLaMA</a></li>
      <li><a href="#GPT-3">GPT-3</a></li>
      <li><a href="#GPT-2">GPT-2</a></li>
      <li><a href="#BERT">BERT</a></li>
@@ -216,6 +218,15 @@ Acceleration of [AlphaFold Protein Structure](https://alphafold.ebi.ac.uk/)
 
 ## Parallel Training Demo
 
+### LLaMA
+<p align="center">
+<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/LLaMA_pretraining.png" width=600/>
+</p>
+
+- 65-billion-parameter large model pretraining accelerated by 38%
+[[code]](https://github.com/hpcaitech/ColossalAI/tree/example/llama/examples/language/llama)
+[[blog]](https://www.hpc-ai.tech/blog/large-model-pretraining)
+
 ### GPT-3
 <p align="center">
 <img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT3-v5.png" width=700/>

docs/README-zh-Hans.md

@@ -24,6 +24,7 @@
 </div>
 
 ## 新闻
+* [2023/07] [65B Model Pretraining Accelerated by 38%, Best Practices for Building LLaMA-Like Base Models Open-Source](https://www.hpc-ai.tech/blog/large-model-pretraining)
 * [2023/03] [ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b)
 * [2023/03] [Intel and Colossal-AI Partner to Deliver Cost-Efficient Open-Source Solution for Protein Folding Structure Prediction](https://www.hpc-ai.tech/blog/intel-habana)
 * [2023/03] [AWS and Google Fund Colossal-AI with Startup Cloud Programs](https://www.hpc-ai.tech/blog/aws-and-google-fund-colossal-ai-with-startup-cloud-programs)
@@ -49,6 +50,7 @@
  <li>
    <a href="#并行训练样例展示">并行训练样例展示</a>
    <ul>
+     <li><a href="#LLaMA">LLaMA</a></li>
      <li><a href="#GPT-3">GPT-3</a></li>
      <li><a href="#GPT-2">GPT-2</a></li>
      <li><a href="#BERT">BERT</a></li>
@@ -209,6 +211,14 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
 <p align="right">(<a href="#top">返回顶端</a>)</p>
 
 ## 并行训练样例展示
+### LLaMA
+<p align="center">
+<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/LLaMA_pretraining.png" width=600/>
+</p>
+
+- 650亿参数大模型预训练加速38%
+[[代码]](https://github.com/hpcaitech/ColossalAI/tree/example/llama/examples/language/llama)
+[[博客]](https://www.hpc-ai.tech/blog/large-model-pretraining)
 
 ### GPT-3
 <p align="center">

examples/language/llama/README.md

@@ -0,0 +1,11 @@
+# Pretraining LLaMA: best practices for building LLaMA-like base models
+
+<p id="ColossalChat-Speed" align="center">
+<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/LLaMA_pretraining.png" width=600/>
+</p>
+
+- 65-billion-parameter large model pretraining accelerated by 38%
+[[code]](https://github.com/hpcaitech/ColossalAI/tree/example/llama/examples/language/llama)
+[[blog]](https://www.hpc-ai.tech/blog/large-model-pretraining)
+
+> Since the main branch is being updated, in order to maintain the stability of the code, this example is temporarily kept as an [independent branch](https://github.com/hpcaitech/ColossalAI/tree/example/llama/examples/language/llama).