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https://github.com/facebookresearch/metaseq

Repo for external large-scale work
https://github.com/facebookresearch/metaseq

Last synced: about 1 month ago
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Repo for external large-scale work

Host: GitHub
URL: https://github.com/facebookresearch/metaseq
Owner: facebookresearch
License: mit
Created: 2022-05-02T22:09:05.000Z (over 2 years ago)
Default Branch: main
Last Pushed: 2024-04-27T22:15:40.000Z (6 months ago)
Last Synced: 2024-09-26T22:02:17.638Z (about 1 month ago)
Language: Python
Homepage:
Size: 25.7 MB
Stars: 6,457
Watchers: 112
Forks: 722
Open Issues: 155
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: docs/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Codeowners: CODEOWNERS

Awesome Lists containing this project

awesome-transformer-nlp - Metaseq - A codebase for working with [Open Pre-trained Transformers (OPT)](https://arxiv.org/abs/2205.01068). (Articles / Large Language Model (LLM))
awesome-ChatGPT-repositories - metaseq - Repo for external large-scale work (Others)
awesome-prompt-engineering - OPT - 175B | Decoder | open | (LLMs)
StarryDivineSky - facebookresearch/metaseq - 175B，Open Pre-trained Transformers,不仅参数比GPT-3的3750亿更少，效果还完全不输GPT-3。从完整模型到训练代码、部署代码全部开放。 (预训练模型)

README

        

# Metaseq

A codebase for working with [Open Pre-trained Transformers](projects/OPT), originally forked from [fairseq](https://github.com/facebookresearch/fairseq).

## Community Integrations

### Using OPT with 🤗 Transformers

The OPT 125M--66B models are now available in [Hugging Face Transformers](https://github.com/huggingface/transformers/releases/tag/v4.19.0). You can access them under the `facebook` organization on the [Hugging Face Hub](https://huggingface.co/facebook)

### Using OPT-175B with Alpa

The OPT 125M--175B models are now supported in the [Alpa project](https://alpa-projects.github.io/tutorials/opt_serving.html), which 

enables serving OPT-175B with more flexible parallelisms on older generations of GPUs, such as 40GB A100, V100, T4, M60, etc.

### Using OPT with Colossal-AI

The OPT models are now supported in the [Colossal-AI](https://github.com/hpcaitech/ColossalAI#OPT), which helps users to efficiently and quickly deploy OPT models training and inference, reducing large AI model budgets and scaling down the labor cost of learning and deployment.

### Using OPT with CTranslate2

The OPT 125M--66B models can be executed with [CTranslate2](https://github.com/OpenNMT/CTranslate2/), which is a fast inference engine for Transformer models. The project integrates the [SmoothQuant](https://github.com/mit-han-lab/smoothquant) technique to allow 8-bit quantization of OPT models. See the [usage example](https://opennmt.net/CTranslate2/guides/transformers.html#opt) to get started.

### Using OPT with FasterTransformer

The OPT models can be served with [FasterTransformer](https://github.com/NVIDIA/FasterTransformer), a highly optimized inference framework written and maintained by NVIDIA. We provide instructions to convert OPT checkpoints into FasterTransformer format and [a usage example](docs/faster-transformer.md) with some benchmark results.

### Using OPT with DeepSpeed

The OPT models can be finetuned using [DeepSpeed](https://github.com/microsoft/DeepSpeed). See the [DeepSpeed-Chat example](https://github.com/microsoft/DeepSpeedExamples/tree/master/applications/DeepSpeed-Chat) to get started.

## Getting Started in Metaseq

Follow [setup instructions here](docs/setup.md) to get started.

### Documentation on workflows

* [Training](docs/training.md)

* [API](docs/api.md)

### Background Info

* [Background & relationship to fairseq](docs/history.md)

* [Chronicles of training OPT-175B](projects/OPT/chronicles/README.md)

## Support

If you have any questions, bug reports, or feature requests regarding either the codebase or the models released in the projects section, please don't hesitate to post on our [Github Issues page](https://github.com/facebookresearch/metaseq/issues).

Please remember to follow our [Code of Conduct](CODE_OF_CONDUCT.md).

## Contributing

We welcome PRs from the community!

You can find information about contributing to metaseq in our [Contributing](docs/CONTRIBUTING.md) document.

## The Team

Metaseq is currently maintained by the CODEOWNERS: [Susan Zhang](https://github.com/suchenzang), [Naman Goyal](https://github.com/ngoyal2707), [Punit Singh Koura](https://github.com/punitkoura), [Moya Chen](https://github.com/moyapchen), [Kurt Shuster](https://github.com/klshuster), [David Esiobu](https://github.com/davides), [Igor Molybog](https://github.com/igormolybogFB), [Peter Albert](https://github.com/Xirider), [Andrew Poulton](https://github.com/andrewPoulton), [Nikolay Bashlykov](https://github.com/bashnick), [Binh Tang](https://github.com/tangbinh), [Uriel Singer](https://github.com/urielsinger), [Yuchen Zhang](https://github.com/zycalice), [Armen Aghajanya](https://github.com/ArmenAg), [Lili Yu](https://github.com/lilisierrayu), and [Adam Polyak](https://github.com/adampolyak).

## License

The majority of metaseq is licensed under the MIT license, however portions of the project are available under separate license terms: 

* Megatron-LM is licensed under the [Megatron-LM license](https://github.com/NVIDIA/Megatron-LM/blob/main/LICENSE)