{"id":13545324,"url":"https://github.com/facebookresearch/metaseq","last_synced_at":"2025-10-23T03:31:05.055Z","repository":{"id":37036320,"uuid":"488004650","full_name":"facebookresearch/metaseq","owner":"facebookresearch","description":"Repo for external large-scale work","archived":true,"fork":false,"pushed_at":"2024-04-27T22:15:40.000Z","size":26972,"stargazers_count":6517,"open_issues_count":154,"forks_count":727,"subscribers_count":111,"default_branch":"main","last_synced_at":"2025-01-21T05:24:23.798Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/facebookresearch.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"docs/CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":"CODEOWNERS","security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-05-02T22:09:05.000Z","updated_at":"2025-01-19T23:33:25.000Z","dependencies_parsed_at":"2022-07-09T19:46:05.793Z","dependency_job_id":"779f6e35-73ea-4b69-b980-2cf453a307d6","html_url":"https://github.com/facebookresearch/metaseq","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/facebookresearch%2Fmetaseq","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/facebookresearch%2Fmetaseq/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/facebookresearch%2Fmetaseq/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/facebookresearch%2Fmetaseq/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/facebookresearch","download_url":"https://codeload.github.com/facebookresearch/metaseq/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":237775565,"owners_count":19364272,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-01T11:01:00.932Z","updated_at":"2025-10-23T03:31:03.457Z","avatar_url":"https://github.com/facebookresearch.png","language":"Python","funding_links":[],"categories":["Python","Articles","Others","LLMs","预训练模型"],"sub_categories":["Large Language Model (LLM)"],"readme":"\n\n# Metaseq\nA codebase for working with [Open Pre-trained Transformers](projects/OPT), originally forked from [fairseq](https://github.com/facebookresearch/fairseq).\n\n\n## Community Integrations\n\n### Using OPT with 🤗 Transformers\n\nThe 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)\n\n### Using OPT-175B with Alpa\n\nThe OPT 125M--175B models are now supported in the [Alpa project](https://alpa-projects.github.io/tutorials/opt_serving.html), which \nenables serving OPT-175B with more flexible parallelisms on older generations of GPUs, such as 40GB A100, V100, T4, M60, etc.\n\n### Using OPT with Colossal-AI\n\nThe 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.\n\n### Using OPT with CTranslate2\n\nThe 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.\n\n### Using OPT with FasterTransformer\n\nThe 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.\n\n### Using OPT with DeepSpeed\n\nThe 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.\n\n## Getting Started in Metaseq\nFollow [setup instructions here](docs/setup.md) to get started.\n\n### Documentation on workflows\n* [Training](docs/training.md)\n* [API](docs/api.md)\n\n### Background Info\n* [Background \u0026 relationship to fairseq](docs/history.md)\n* [Chronicles of training OPT-175B](projects/OPT/chronicles/README.md)\n\n## Support\nIf 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).\n\nPlease remember to follow our [Code of Conduct](CODE_OF_CONDUCT.md).\n\n## Contributing\nWe welcome PRs from the community!\n\nYou can find information about contributing to metaseq in our [Contributing](docs/CONTRIBUTING.md) document.\n\n## The Team\nMetaseq 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).\n\n## License\n\nThe majority of metaseq is licensed under the MIT license, however portions of the project are available under separate license terms: \n* Megatron-LM is licensed under the [Megatron-LM license](https://github.com/NVIDIA/Megatron-LM/blob/main/LICENSE)\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffacebookresearch%2Fmetaseq","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffacebookresearch%2Fmetaseq","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffacebookresearch%2Fmetaseq/lists"}