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.

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)
• Multi-agent RAG System 🤖🤝🤖

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

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