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[NFC] add OPT serving (hpcaitech#1581)
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binmakeswell authored Sep 9, 2022
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12 changes: 10 additions & 2 deletions README-zh-Hans.md
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<a href="#推理-Energon-AI-样例展示">推理 (Energon-AI) 样例展示</a>
<ul>
<li><a href="#GPT-3-Inference">GPT-3</a></li>
<li><a href="#OPT-Serving">1750亿参数OPT在线推理服务</a></li>
</ul>
</li>
<li>
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/OPT_update.png" width=800/>

- [Open Pretrained Transformer (OPT)](https://github.com/facebookresearch/metaseq), 由Meta发布的1750亿语言模型,由于完全公开了预训练参数权重,因此促进了下游任务和应用部署的发展。
- 加速45%,仅用几行代码以低成本微调OPT。[[样例]](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/language/opt)
- 加速45%,仅用几行代码以低成本微调OPT。[[样例]](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/language/opt) [[在线推理]](https://service.colossalai.org/opt)

请访问我们的 [文档](https://www.colossalai.org/)[例程](https://github.com/hpcaitech/ColossalAI-Examples) 以了解详情。

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## 推理 (Energon-AI) 样例展示

### GPT-3
<p id="GPT-3-Inference" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/inference_GPT-3.jpg" width=800/>
</p>

- [Energon-AI](https://github.com/hpcaitech/EnergonAI) :用相同的硬件推理加速50%

<p id="OPT-Serving" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/OPT_serving.png" width=800/>
</p>

- [OPT推理服务](https://service.colossalai.org/opt): 无需注册,免费体验1750亿参数OPT在线推理服务


<p align="right">(<a href="#top">back to top</a>)</p>

## Colossal-AI 成功案例
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- [xTrimoMultimer](https://github.com/biomap-research/xTrimoMultimer): 11倍加速蛋白质单体与复合物结构预测


## 安装

### 从官方安装
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<a href="#Inference-Energon-AI-Demo">Inference (Energon-AI) Demo</a>
<ul>
<li><a href="#GPT-3-Inference">GPT-3</a></li>
<li><a href="#OPT-Serving">OPT-175B Online Serving for Text Generation</a></li>
</ul>
</li>
<li>
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/OPT_update.png" width=800/>

- [Open Pretrained Transformer (OPT)](https://github.com/facebookresearch/metaseq), a 175-Billion parameter AI language model released by Meta, which stimulates AI programmers to perform various downstream tasks and application deployments because public pretrained model weights.
- 45% speedup fine-tuning OPT at low cost in lines. [[Example]](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/language/opt)
- 45% speedup fine-tuning OPT at low cost in lines. [[Example]](https://github.com/hpcaitech/ColossalAI-Examples/tree/main/language/opt) [[Online Serving]](https://service.colossalai.org/opt)

Please visit our [documentation](https://www.colossalai.org/) and [examples](https://github.com/hpcaitech/ColossalAI-Examples) for more details.

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## Inference (Energon-AI) Demo

### GPT-3
<p id="GPT-3-Inference" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/inference_GPT-3.jpg" width=800/>
</p>

- [Energon-AI](https://github.com/hpcaitech/EnergonAI): 50% inference acceleration on the same hardware

<p id="OPT-Serving" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/OPT_serving.png" width=800/>
</p>

- [OPT Serving](https://service.colossalai.org/opt): Try 175-billion-parameter OPT online services for free, without any registration whatsoever.

<p align="right">(<a href="#top">back to top</a>)</p>

## Colossal-AI in the Real World
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