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@ -3,7 +3,7 @@
[![logo](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/colossal-ai_logo_vertical.png)](https://www.colossalai.org/)
Colossal-AI: Making large AI models cheaper, faster and more accessible
Colossal-AI: Making large AI models cheaper, faster, and more accessible
<h3> <a href="https://arxiv.org/abs/2110.14883"> Paper </a> |
<a href="https://www.colossalai.org/"> Documentation </a> |
@ -115,7 +115,7 @@ distributed training and inference in a few lines.
- [PatrickStar](https://arxiv.org/abs/2108.05818)
- Friendly Usage
- Parallelism based on configuration file
- Parallelism based on the configuration file
- Inference
- [Energon-AI](https://github.com/hpcaitech/EnergonAI)
@ -129,7 +129,7 @@ distributed training and inference in a few lines.
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT3-v5.png" width=700/>
</p>
- Save 50% GPU resources, and 10.7% acceleration
- Save 50% GPU resources and 10.7% acceleration
### GPT-2
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/GPT2.png" width=800/>
@ -151,7 +151,7 @@ distributed training and inference in a few lines.
### OPT
<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.
- [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 of public pre-trained model weights.
- 45% speedup fine-tuning OPT at low cost in lines. [[Example]](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/opt) [[Online Serving]](https://colossalai.org/docs/advanced_tutorials/opt_service)
Please visit our [documentation](https://www.colossalai.org/) and [examples](https://github.com/hpcaitech/ColossalAI/tree/main/examples) for more details.
@ -245,7 +245,7 @@ Please visit our [documentation](https://www.colossalai.org/) and [examples](htt
</p>
- Increase the capacity of the fine-tuning model by up to 3.7 times on a single GPU
- Keep in a sufficiently high running speed
- Keep at a sufficiently high running speed
<p align="right">(<a href="#top">back to top</a>)</p>
@ -304,7 +304,7 @@ Requirements:
- Python >= 3.7
- CUDA >= 11.0
If you encounter any problem about installation, you may want to raise an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose) in this repository.
If you encounter any problem with installation, you may want to raise an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose) in this repository.
### Install from PyPI
@ -322,9 +322,9 @@ However, if you want to build the PyTorch extensions during installation, you ca
CUDA_EXT=1 pip install colossalai
```
**Otherwise, CUDA kernels will be built during runtime when you actually need it.**
**Otherwise, CUDA kernels will be built during runtime when you actually need them.**
We also keep release the nightly version to PyPI on a weekly basis. This allows you to access the unreleased features and bug fixes in the main branch.
We also keep releasing the nightly version to PyPI every week. This allows you to access the unreleased features and bug fixes in the main branch.
Installation can be made via
```bash
@ -333,7 +333,7 @@ pip install colossalai-nightly
### Download From Source
> The version of Colossal-AI will be in line with the main branch of the repository. Feel free to raise an issue if you encounter any problem. :)
> The version of Colossal-AI will be in line with the main branch of the repository. Feel free to raise an issue if you encounter any problems. :)
```shell
git clone https://github.com/hpcaitech/ColossalAI.git
@ -423,6 +423,6 @@ To cite this project, you can use the following BibTeX citation.
}
```
Colossal-AI has been accepted as official tutorials by top conference [SC](https://sc22.supercomputing.org/), [AAAI](https://aaai.org/Conferences/AAAI-23/), [PPoPP](https://ppopp23.sigplan.org/), [CVPR](https://cvpr2023.thecvf.com/), [ISC](https://www.isc-hpc.com/), etc.
Colossal-AI has been accepted as official tutorial by top conferences [SC](https://sc22.supercomputing.org/), [AAAI](https://aaai.org/Conferences/AAAI-23/), [PPoPP](https://ppopp23.sigplan.org/), [CVPR](https://cvpr2023.thecvf.com/), [ISC](https://www.isc-hpc.com/), etc.
<p align="right">(<a href="#top">back to top</a>)</p>