# OPT-175B

**Repository Path**: boabuling/OPT-175B

## Basic Information

- **Project Name**: OPT-175B
- **Description**: OPT-175B 是 Meta 开源的大语言模型，拥有超过 1750 亿个参数——和 GPT-3 相当
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: https://www.oschina.net/p/opt-175b
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 4
- **Created**: 2023-04-02
- **Last Updated**: 2023-04-02

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README



# Metaseq
A codebase for working with [Open Pre-trained Transformers](projects/OPT).


## 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.

## 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), [Ruan Silva](https://github.com/ruanslv), [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), and [Yuchen Zhang](https://github.com/zycalice).

Previous maintainers include:
[Stephen Roller](https://github.com/stephenroller), [Anjali Sridhar](https://github.com/anj-s), [Christopher Dewan](https://github.com/m3rlin45).


## 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)