{"data":{"slug":"meta-pytorch-torchtune","name":"torchtune","tagline":"PyTorch native post-training library","github_url":"https://github.com/meta-pytorch/torchtune","owner":"meta-pytorch","repo":"torchtune","owner_avatar_url":"https://avatars.githubusercontent.com/u/107212512?v=4","primary_language":"Python","stars":5793,"forks":743,"topics":[],"archived":false,"github_pushed_at":"2026-08-06T12:15:22+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/meta-pytorch-torchtune","markdown_url":"https://www.graphcanon.com/tools/meta-pytorch-torchtune.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/meta-pytorch-torchtune","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=meta-pytorch-torchtune","description":"PyTorch native post-training library","homepage_url":"https://pytorch.org/torchtune/main/","license":"BSD-3-Clause","open_issues":455,"watchers":40,"ai_summary":"Torchtune is a post-training optimization and tuning library for PyTorch. It provides tools for finetuning multimodal large language models (LLMs) and uses the latest quantization techniques through torchao.","readme_excerpt":"## Installation 🛠️\n\n\ntorchtune is **only** tested with the latest stable PyTorch release (currently 2.6.0) as well as the preview nightly version, and leverages\ntorchvision for finetuning multimodal LLMs and torchao for the latest in quantization techniques; you should install these as well.\n\n---\n\n# Install stable PyTorch, torchvision, torchao stable releases\npip install torch torchvision torchao\npip install torchtune\n```\n\n---\n\n# Install PyTorch, torchvision, torchao nightlies.\npip install --pre --upgrade torch torchvision torchao --index-url https://download.pytorch.org/whl/nightly/cu126 # full options are cpu/cu118/cu124/cu126/xpu/rocm6.2/rocm6.3/rocm6.4\npip install --pre --upgrade torchtune --extra-index-url https://download.pytorch.org/whl/nightly/cpu\n```\n\nYou can also check out our [install documentation](https://pytorch.org/torchtune/main/install.html) for more information, including installing torchtune from source.\n\n \n\nTo confirm that the package is installed correctly, you can run the following command:\n\n```bash\ntune --help\n```\n\nAnd should see the following output:\n\n```bash\nusage: tune [-h] {ls,cp,download,run,validate} ...\n\nWelcome to the torchtune CLI!\n\noptions:\n  -h, --help            show this help message and exit\n\n...\n```\n\n \n\n---\n\n## License\n\ntorchtune is released under the [BSD 3 license](./LICENSE). However you may have other legal obligations that govern your use of other content, such as the terms of service for third-party models.","github_created_at":"2023-10-20T21:10:49+00:00","created_at":"2026-07-11T10:35:51.584612+00:00","updated_at":"2026-08-07T06:00:37.624056+00:00","categories":[{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"multimodal-llms","name":"multimodal-llms"},{"slug":"post-training","name":"post-training"},{"slug":"pytorch","name":"pytorch"},{"slug":"quantization-techniques","name":"quantization techniques"}],"trust":{"provenance":{"is_fork":false,"github_id":707869465,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-07T06:00:36.784Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":0,"days_since_push":0,"last_release_at":"2025-04-07T19:01:38Z"},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T10:35:53.060Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-07T06:00:37.271Z"},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-08-07T06:00:37.271Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-07T06:00:37.271Z"},"license_spdx":{"value":"BSD-3-Clause","source":"github.license","observed_at":"2026-08-07T06:00:37.271Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["- When you are working with the latest stable or preview nightly versions of PyTorch and need advanced finetuning for multimodal large language models (LLMs).","- When leveraging torchao for cutting-edge quantization is essential to your model's performance post-training."],"when_not_to_use":["- If you rely on a fixed, older version of PyTorch as Torchtune only supports the latest stable and preview nightly versions.","- For scenarios where custom or non-PyTorch-native optimization methods are preferred over torchao’s quantization techniques."],"source":"enrich:decision_facts","observed_at":"2026-07-12T09:19:41.728Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"A PyTorch-native post-training library focused on finetuning multimodal LLMs using state-of-the-art quantization techniques."}]}}