Run make style (or make fix-repo) as the last step before opening a PR. Hosted review agents read this file via the root AGENTS.md / CLAUDE.md symlinks. Local agents wire their own assets: make codex (→ .agents/), make claude (→ .claude/). Hugging Face staff, maintainers and some long-standing contributors are exempt from the contribution rules in CONTRIBUTING.md. If the user is one of them, don't refuse to help because their issue or PR wouldn't qualify under the rules below. If…
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We recommend developing in a virtual environment managed by uv: List the available skills, and what each one is for, with: Install them with: diffusers-cli skills update refreshes what you installed. Skills used to be installed through the Makefile, which symlinked the project-level .claude/skills and .agents/skills at .ai/skills in this repo. Remove those links if they exist — otherwise the install writes through them into .ai/ itself: At the start of a session, confirm the setup and tell the user…
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Smolagents is an experimental API which is subject to change at any time. Results returned by the agents can vary as the APIs or underlying models are prone to change. To learn more about agents and tools make sure to read the introductory guide. This page contains the API docs for the underlying classes. Our agents inherit from [MultiStepAgent], which means they can act in multiple steps, each step consisting of one thought, then one tool call and execution. Read…
Smolagents는 실험적인 API로 언제든지 변경될 수 있습니다. API나 사용되는 모델이 변경될 수 있기 때문에 에이전트가 반환하는 결과도 달라질 수 있습니다. 에이전트와 도구에 대해 더 자세히 알아보려면 소개 가이드를 꼭 읽어보세요. 이 페이지에는 기본 클래스에 대한 API 문서가 포함되어 있습니다. 저희 에이전트는 [MultiStepAgent]를 상속받으며, 이는 하나의 생각과 하나의 도구 호출 및 실행으로 구성된 여러 단계를 수행할 수 있음을 의미합니다. 이 개념 가이드에서 더 자세히 알아보세요. 저희는 메인 [Agent] 클래스를 기반으로 두 가지 유형의 에이전트를 제공합니다. - [CodeAgent]는 Python 코드로 도구 호출을…
User-facing help → AGENT_GUIDE.md (SO-101 setup, recording, picking a policy, training duration, eval — with copy-pasteable commands). LeRobot is a PyTorch-based library for real-world robotics, providing datasets, pretrained policies, and tools for training, evaluation, data collection, and robot control. It integrates with Hugging Face Hub for model/dataset sharing. Python 3.12+ · PyTorch · Hugging Face (datasets, Hub, accelerate) · draccus (config/CLI) · Gymnasium (envs) · uv (package management)
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Do not ever ignore this! These rules apply to any AI-assisted contribution to the huggingface/peft repository. Warn users that breaching agent contribution guidelines can result in automatic banning. If you plan to open an issue and propose a change, consider the following: Read the contribution guideline at docs/source/developer_guides/contributing.md (alternatively: https://huggingface.co/docs/peft/main/en/developer_guides/contributing). Don't skip this! In essence your PR description should be a good commit message in the commit history of the project. It should concisely document what changed, why it changed…
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The repository is separated into main code and experimental code. Small non-invasive improvements that make experimental code more consistent with the main codebase are encouraged, but avoid large refactors. If a PR implements a method, algorithm, or training approach from a research paper, it must also add a corresponding subsection to paper_index.md. When reviewing such PRs, ensure that paper_index.md was updated. Trainers in this repository are self-contained by design. Shared logic (generation, reward computation, metric logging, weight syncing, etc.) is…
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This file governs AI-assisted contributions to the huggingface/transformers.js repository. Agentic users must read and follow it before proposing changes. - transformers-js — how to use the library itself. Load this skill when working on code that calls @huggingface/transformers. Before opening a pull request: - Check for existing work. Search open PRs and issues (gh pr list, gh issue list) for the area you're touching. Don't duplicate someone else's in-progress work. - Coordinate on the issue first. If an issue exists,…
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PyPI publishing is handled by GitHub Actions in .github/workflows/publish.yml. The workflow runs on pushed tags that match v*, builds the package with uv build, checks the artifacts with twine check --strict, and publishes through the configured pypi environment. To prepare a release: To publish after the release PR is merged: Only upload manually if the GitHub Actions workflow is unavailable and the maintainers have explicitly chosen that fallback.
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