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</div>
## 新闻
* [2023/09] [One Half-Day of Training Using a Few Hundred Dollars Yields Similar Results to Mainstream Large Models, Open-Source and Commercial-Free Domain-Specific Llm Solution](https://www.hpc-ai.tech/blog/one-half-day-of-training-using-a-few-hundred-dollars-yields-similar-results-to-mainstream-large-models-open-source-and-commercial-free-domain-specific-llm-solution)
* [2024/01] [Inference Performance Improved by 46%, Open Source Solution Breaks the Length Limit of LLM for Multi-Round Conversations](https://hpc-ai.com/blog/Colossal-AI-SwiftInfer)
* [2024/01] [Construct Refined 13B Private Model With Just $5000 USD, Upgraded Colossal-AI Llama-2 Open Source](https://hpc-ai.com/blog/colossal-llama-2-13b)
* [2023/11] [Enhanced MoE Parallelism, Open-source MoE Model Training Can Be 9 Times More Efficient](https://www.hpc-ai.tech/blog/enhanced-moe-parallelism-open-source-moe-model-training-can-be-9-times-more-efficient)
* [2023/09] [One Half-Day of Training Using a Few Hundred Dollars Yields Similar Results to Mainstream Large Models, Open-Source and Commercial-Free Domain-Specific LLM Solution](https://www.hpc-ai.tech/blog/one-half-day-of-training-using-a-few-hundred-dollars-yields-similar-results-to-mainstream-large-models-open-source-and-commercial-free-domain-specific-llm-solution)
* [2023/09] [70 Billion Parameter LLaMA2 Model Training Accelerated by 195%](https://www.hpc-ai.tech/blog/70b-llama2-training)
* [2023/07] [HPC-AI Tech Raises 22 Million USD in Series A Funding](https://www.hpc-ai.tech/blog/hpc-ai-tech-raises-22-million-usd-in-series-a-funding-to-fuel-team-expansion-and-business-growth)
* [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)
* [2023/02] [Open Source Solution Replicates ChatGPT Training Process! Ready to go with only 1.6GB GPU Memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt)
* [2023/01] [Hardware Savings Up to 46 Times for AIGC and Automatic Parallelism](https://medium.com/pytorch/latest-colossal-ai-boasts-novel-automatic-parallelism-and-offers-savings-up-to-46x-for-stable-1453b48f3f02)
## 目录
<ul>
@@ -51,6 +52,7 @@
<a href="#并行训练样例展示">并行训练样例展示</a>
<ul>
<li><a href="#LLaMA2">LLaMA 1/2</a></li>
<li><a href="#MoE">MoE</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>
@@ -68,8 +70,9 @@
</ul>
</li>
<li>
<a href="#推理-Energon-AI-样例展示">推理 (Energon-AI) 样例展示</a>
<a href="#推理">推理</a>
<ul>
<li><a href="#SwiftInfer">SwiftInfer:打破LLM多轮对话的长度限制，推理加速46%</a></li>
<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>
<li><a href="#BLOOM-Inference">1760亿参数 BLOOM</a></li>
@@ -114,41 +117,42 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
- [PatrickStar](https://arxiv.org/abs/2108.05818)
- 使用友好
- 基于参数文件的并行化
- 推理
- [Energon-AI](https://github.com/hpcaitech/EnergonAI)
<p align="right">(<a href="#top">返回顶端</a>)</p>
## Colossal-AI 成功案例
### Colossal-LLaMA-2
- 千元预算半天训练，效果媲美主流大模型，开源可商用中文LLaMA-2
- 7B：千元预算半天训练，效果媲美主流大模型，开源可商用中文LLaMA-2
[[代码]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Colossal-LLaMA-2)
[[博客]](https://www.hpc-ai.tech/blog/one-half-day-of-training-using-a-few-hundred-dollars-yields-similar-results-to-mainstream-large-models-open-source-and-commercial-free-domain-specific-llm-solution)
[[模型权重]](https://huggingface.co/hpcai-tech/Colossal-LLaMA-2-7b-base)
| | Backbone | Tokens Consumed | | MMLU | CMMLU | AGIEval | GAOKAO | CEval |
| :----------------------------: | :--------: | :-------------: | :------------------: | :-----------: | :-----: | :----: | :----: | :------------------------------: |
| | | - | | 5-shot | 5-shot | 5-shot | 0-shot | 5-shot |
| Baichuan-7B | - | 1.2T | | 42.32 (42.30) | 44.53 (44.02) | 38.72 | 36.74 | 42.80 |
| Baichuan-13B-Base | - | 1.4T | | 50.51 (51.60) | 55.73 (55.30) | 47.20 | 51.41 | 53.60 |
| Baichuan2-7B-Base | - | 2.6T | | 46.97 (54.16) | 57.67 (57.07) | 45.76 | 52.60 | 54.00 |
| Baichuan2-13B-Base | - | 2.6T | | 54.84 (59.17) | 62.62 (61.97) | 52.08 | 58.25 | 58.10 |
| ChatGLM-6B | - | 1.0T | | 39.67 (40.63) | 41.17 (-) | 40.10 | 36.53 | 38.90 |
| ChatGLM2-6B | - | 1.4T | | 44.74 (45.46) | 49.40 (-) | 46.36 | 45.49 | 51.70 |
| InternLM-7B | - | 1.6T | | 46.70 (51.00) | 52.00 (-) | 44.77 | 61.64 | 52.80 |
| Qwen-7B | - | 2.2T | | 54.29 (56.70) | 56.03 (58.80) | 52.47 | 56.42 | 59.60 |
| | | | | | | | | |
| Llama-2-7B | - | 2.0T | | 44.47 (45.30) | 32.97 (-) | 32.60 | 25.46 | - |
| Linly-AI/Chinese-LLaMA-2-7B-hf | Llama-2-7B | 1.0T | | 37.43 | 29.92 | 32.00 | 27.57 | - |
| wenge-research/yayi-7b-llama2 | Llama-2-7B | - | | 38.56 | 31.52 | 30.99 | 25.95 | - |
| ziqingyang/chinese-llama-2-7b | Llama-2-7B | - | | 33.86 | 34.69 | 34.52 | 25.18 | 34.2 |
| TigerResearch/tigerbot-7b-base | Llama-2-7B | 0.3T | | 43.73 | 42.04 | 37.64 | 30.61 | - |
| LinkSoul/Chinese-Llama-2-7b | Llama-2-7B | - | | 48.41 | 38.31 | 38.45 | 27.72 | - |
| FlagAlpha/Atom-7B | Llama-2-7B | 0.1T | | 49.96 | 41.10 | 39.83 | 33.00 | - |
| IDEA-CCNL/Ziya-LLaMA-13B-v1.1 | Llama-13B | 0.11T | | 50.25 | 40.99 | 40.04 | 30.54 | - |
| | | | | | | | | |
| **Colossal-LLaMA-2-7b-base** | Llama-2-7B | **0.0085T** | | 53.06 | 49.89 | 51.48 | 58.82 | 50.2 |
- 13B: 万元预算打造高质量13B私有模型
[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Colossal-LLaMA-2)
[[blog]](https://hpc-ai.com/blog/colossal-llama-2-13b)
[[HuggingFace model weights]](https://huggingface.co/hpcai-tech/Colossal-LLaMA-2-13b-base)
[[Modelscope model weights]](https://www.modelscope.cn/models/colossalai/Colossal-LLaMA-2-13b-base/summary)
| Model | Backbone | Tokens Consumed | MMLU (5-shot) | CMMLU (5-shot)| AGIEval (5-shot) | GAOKAO (0-shot) | CEval (5-shot) |
| :----------------------------: | :--------: | :-------------: | :------------------: | :-----------: | :--------------: | :-------------: | :-------------: |
| Baichuan-7B | - | 1.2T | 42.32 (42.30) | 44.53 (44.02) | 38.72 | 36.74 | 42.80 |
| Baichuan-13B-Base | - | 1.4T | 50.51 (51.60) | 55.73 (55.30) | 47.20 | 51.41 | 53.60 |
| Baichuan2-7B-Base | - | 2.6T | 46.97 (54.16) | 57.67 (57.07) | 45.76 | 52.60 | 54.00 |
| Baichuan2-13B-Base | - | 2.6T | 54.84 (59.17) | 62.62 (61.97) | 52.08 | 58.25 | 58.10 |
| ChatGLM-6B | - | 1.0T | 39.67 (40.63) | 41.17 (-) | 40.10 | 36.53 | 38.90 |
| ChatGLM2-6B | - | 1.4T | 44.74 (45.46) | 49.40 (-) | 46.36 | 45.49 | 51.70 |
| InternLM-7B | - | 1.6T | 46.70 (51.00) | 52.00 (-) | 44.77 | 61.64 | 52.80 |
| Qwen-7B | - | 2.2T | 54.29 (56.70) | 56.03 (58.80) | 52.47 | 56.42 | 59.60 |
| Llama-2-7B | - | 2.0T | 44.47 (45.30) | 32.97 (-) | 32.60 | 25.46 | - |
| Linly-AI/Chinese-LLaMA-2-7B-hf | Llama-2-7B | 1.0T | 37.43 | 29.92 | 32.00 | 27.57 | - |
| wenge-research/yayi-7b-llama2 | Llama-2-7B | - | 38.56 | 31.52 | 30.99 | 25.95 | - |
| ziqingyang/chinese-llama-2-7b | Llama-2-7B | - | 33.86 | 34.69 | 34.52 | 25.18 | 34.2 |
| TigerResearch/tigerbot-7b-base | Llama-2-7B | 0.3T | 43.73 | 42.04 | 37.64 | 30.61 | - |
| LinkSoul/Chinese-Llama-2-7b | Llama-2-7B | - | 48.41 | 38.31 | 38.45 | 27.72 | - |
| FlagAlpha/Atom-7B | Llama-2-7B | 0.1T | 49.96 | 41.10 | 39.83 | 33.00 | - |
| IDEA-CCNL/Ziya-LLaMA-13B-v1.1 | Llama-13B | 0.11T | 50.25 | 40.99 | 40.04 | 30.54 | - |
| **Colossal-LLaMA-2-7b-base** | Llama-2-7B | **0.0085T** | 53.06 | 49.89 | 51.48 | 58.82 | 50.2 |
### ColossalChat
@@ -208,7 +212,7 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
- [DreamBooth微调](https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/dreambooth): 仅需3-5张目标主题图像个性化微调
<p id="inference" align="center">
<p id="inference-sd" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/Stable%20Diffusion%20Inference.jpg" width=800/>
</p>
@@ -260,6 +264,15 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
[[代码]](https://github.com/hpcaitech/ColossalAI/tree/example/llama/examples/language/llama)
[[博客]](https://www.hpc-ai.tech/blog/large-model-pretraining)
### MoE
<p align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/examples/images/MOE_training.png" width=800/>
</p>
- 专家并行再升级，开源MoE模型训练效率提升9倍
[[代码]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/openmoe)
[[博客]](https://www.hpc-ai.tech/blog/enhanced-moe-parallelism-open-source-moe-model-training-can-be-9-times-more-efficient)
### GPT-3
<p align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT3-v5.png" width=700/>
@@ -331,7 +344,12 @@ 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>
## 推理 (Energon-AI) 样例展示
## 推理
<p id="SwiftInfer" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/SwiftInfer.jpg" width=800/>
</p>
- [SwiftInfer](https://github.com/hpcaitech/SwiftInfer): Inference performance improved by 46%, open source solution breaks the length limit of LLM for multi-round conversations
<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/>
@@ -357,7 +375,7 @@ Colossal-AI 为您提供了一系列并行组件。我们的目标是让您的
环境要求:
- PyTorch >= 1.11 (PyTorch 2.x 正在适配中)
- PyTorch >= 1.11 并且 PyTorch <= 2.1
- Python >= 3.7
- CUDA >= 11.0
- [NVIDIA GPU Compute Capability](https://developer.nvidia.com/cuda-gpus) >= 7.0 (V100/RTX20 and higher)

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| Blip2Model | transformers |
| Blip2ForConditionalGeneration | transformers |
<!-- doc-test-command: torchrun --standalone --nproc_per_node=2 lazy_iniy.py -->
<!-- doc-test-command: torchrun --standalone --nproc_per_node=2 lazy_init.py -->

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booster = Booster(plugin=plugin)
```
Boost these train componts with the booster created.
Boost these train components with the booster created.
```python
model, optimizer, _criterion, _, lr_scheduler = booster.boost(model,
optimizer,

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<td nowrap="nowrap" align="center">❌</td>
<td nowrap="nowrap" align="center">❌</td>
</tr>
<tr>
<td nowrap="nowrap">Falcon</td>
<td nowrap="nowrap" align="center">✔️</td>
<td nowrap="nowrap" align="center">✔️</td>
<td nowrap="nowrap" align="center">✔️</td>
<td nowrap="nowrap" align="center">✔️</td>
<td nowrap="nowrap" align="center">✔️</td>
<td nowrap="nowrap" align="center">❌</td>
<td nowrap="nowrap" align="center">✔️</td>
<td nowrap="nowrap" align="center">❌</td>
<td nowrap="nowrap" align="center">❌</td>
</tr>
<tr>
<td colspan="39"></td>
</tr>

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# Setup
Requirements:
- PyTorch >= 1.11 (PyTorch 2.x in progress)
- PyTorch >= 1.11 and PyTorch <= 2.1
- Python >= 3.7
- CUDA >= 11.0
- [NVIDIA GPU Compute Capability](https://developer.nvidia.com/cuda-gpus) >= 7.0 (V100/RTX20 and higher)

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Booster 插件是管理并行配置的重要组件（eg：gemini 插件封装了 gemini 加速方案）。目前支持的插件如下：
**_HybridParallelPlugin:_** HybirdParallelPlugin 插件封装了混合并行的加速解决方案。它提供的接口可以在张量并行，流水线并行以及两种数据并行方法（DDP, Zero）间进行任意的组合。
**_HybridParallelPlugin:_** HybridParallelPlugin 插件封装了混合并行的加速解决方案。它提供的接口可以在张量并行，流水线并行以及两种数据并行方法（DDP, Zero）间进行任意的组合。
**_GeminiPlugin:_** GeminiPlugin 插件封装了 gemini 加速解决方案，即基于块内存管理的 ZeRO 优化方案。

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@@ -73,4 +73,4 @@ model, *_ = booster.boost(model)
| Blip2Model | transformers |
| Blip2ForConditionalGeneration | transformers |
<!-- doc-test-command: torchrun --standalone --nproc_per_node=2 lazy_iniy.py -->
<!-- doc-test-command: torchrun --standalone --nproc_per_node=2 lazy_init.py -->

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<td nowrap="nowrap" align="center">❌</td>
<td nowrap="nowrap" align="center">❌</td>
</tr>
<tr>
<td nowrap="nowrap">Falcon</td>
<td nowrap="nowrap" align="center">✔️</td>
<td nowrap="nowrap" align="center">✔️</td>
<td nowrap="nowrap" align="center">✔️</td>
<td nowrap="nowrap" align="center">✔️</td>
<td nowrap="nowrap" align="center">✔️</td>
<td nowrap="nowrap" align="center">❌</td>
<td nowrap="nowrap" align="center">✔️</td>
<td nowrap="nowrap" align="center">❌</td>
<td nowrap="nowrap" align="center">❌</td>
</tr>
<tr>
<td colspan="39"></td>
</tr>

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环境要求:
- PyTorch >= 1.11 (PyTorch 2.x 正在适配中)
- PyTorch >= 1.11 并且 PyTorch <= 2.1
- Python >= 3.7
- CUDA >= 11.0
- [NVIDIA GPU Compute Capability](https://developer.nvidia.com/cuda-gpus) >= 7.0 (V100/RTX20 and higher)