#
Open-Source AI Cookbook

The Open-Source AI Cookbook is a collection of notebooks illustrating practical aspects of building AI applications and solving various machine learning tasks using open-source tools and models.

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Latest notebooks

Check out the recently added notebooks:

- Phoenix Observability Dashboard on HF Spaces
- Have several agents collaborate in a multi-agent hierarchy 🤖🤝🤖
- Semantic reranking with Elasticsearch
- Benchmarking TGI
- Generate a Preference Dataset with distilabel
- Clean an Existing Preference Dataset with LLMs as Judges
- Building RAG with Custom Unstructured Data
- Agentic RAG: turbocharge your RAG with query reformulation and self-query! 🚀
- Create a Transformers Agent from any LLM inference provider
- Fine-tuning LLM to Generate Persian Product Catalogs in JSON Format
- Agent for text-to-SQL with automatic error correction
- Information Extraction with Haystack and NuExtract
- RAG with Hugging Face and Milvus
- Data analyst agent: get your data’s insights in the blink of an eye ✨
- Enhancing RAG Reasoning with Knowledge Graphs
- Fine-Tuning Object Detection on a Custom Dataset 🖼, Deployment in Spaces, and Gradio API Integration
- Fine-Tuning a Semantic Segmentation Model on a Custom Dataset and Usage via the Inference API
- Multimodal Retrieval-Augmented Generation (RAG) with Document Retrieval (ColPali) and Vision Language Models (VLMs)
- Fine-Tuning a Vision Language Model (Qwen2-VL-7B) with the Hugging Face Ecosystem (TRL)

You can also check out the notebooks in the cookbook’s GitHub repo.

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Contributing

The Open-Source AI Cookbook is a community effort, and we welcome contributions from everyone! Check out the cookbook’s Contribution guide to learn how you can add your “recipe”.

< > Update on GitHub