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    Python
  • License
    MIT License
  • Created over 2 years ago
  • Updated over 1 year ago
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Repository Details

Repo for external large-scale work

Metaseq

A codebase for working with Open Pre-trained Transformers, originally forked from fairseq.

Community Integrations

Using OPT with 🤗 Transformers

The OPT 125M--66B models are now available in Hugging Face Transformers. You can access them under the facebook organization on the Hugging Face Hub

Using OPT-175B with Alpa

The OPT 125M--175B models are now supported in the Alpa project, 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, 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, which is a fast inference engine for Transformer models. The project integrates the SmoothQuant technique to allow 8-bit quantization of OPT models. See the usage example to get started.

Using OPT with FasterTransformer

The OPT models can be served with 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 with some benchmark results.

Using OPT with DeepSpeed

The OPT models can be finetuned using DeepSpeed. See the DeepSpeed-Chat example to get started.

Getting Started in Metaseq

Follow setup instructions here to get started.

Documentation on workflows

Background Info

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.

Please remember to follow our Code of Conduct.

Contributing

We welcome PRs from the community!

You can find information about contributing to metaseq in our Contributing document.

The Team

Metaseq is currently maintained by the CODEOWNERS: Susan Zhang, Naman Goyal, Punit Singh Koura, Moya Chen, Kurt Shuster, David Esiobu, Igor Molybog, Peter Albert, Andrew Poulton, Nikolay Bashlykov, Binh Tang, Uriel Singer, Yuchen Zhang, Armen Aghajanya, Lili Yu, and Adam Polyak.

License

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

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