The Hugging Face Cookbook is an open-source collection of practical, community-driven Jupyter notebooks that demonstrate how to build AI applications with open models and tools. It covers tasks ranging from model fine-tuning and inference to RAG, multimodal examples, and deployment.
Key Features
- Practical recipes: end-to-end, runnable notebooks that illustrate common workflows.
- Wide coverage: text, vision, multimodal tasks, and integration with Hub/Datasets.
- Community contributions: clear guidelines for contributing new notebooks and translations.
- Documentation integration: linked with the Hugging Face Learn platform for curated tutorials.
Use Cases
- Reproducing research experiments and adapting them for production.
- Validating vector search and RAG pipelines with real examples.
- Learning best practices for model fine-tuning and deployment.
Technical Highlights
- Executable Jupyter notebooks emphasizing reproducibility.
- Deep integrations with Hugging Face Hub, Transformers, and Datasets.
- CI and localization support for multi-language notebooks.
Note: This is a concise overview — see the repository and Learn pages for full examples and contribution instructions.