#
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:

- Using LLM-as-a-judge 🧑‍⚖️ for an automated and versatile evaluation
- Create a legal preference dataset
- Suggestions for Data Annotation with SetFit in Zero-shot Text Classification
- Implementing semantic cache to improve a RAG system
- Building A RAG Ebook “Librarian” Using LlamaIndex
- Stable Diffusion Interpolation
- Building A RAG System with Gemma, MongoDB and Open Source Models
- Prompt Tuning with PEFT Library
- Migrating from OpenAI to Open LLMs Using TGI’s Messages API
- Automatic Embeddings with TEI through Inference Endpoints
- Simple RAG for GitHub issues using Hugging Face Zephyr and LangChain
- Embedding multimodal data for similarity search using 🤗 transformers, 🤗 datasets and FAISS
- Fine-tuning a Code LLM on Custom Code on a single GPU
- RAG Evaluation Using Synthetic data and LLM-As-A-Judge
- Advanced RAG on HuggingFace documentation using LangChain
- Detecting Issues in a Text Dataset with Cleanlab
- Annotate text data using Active Learning with Cleanlab

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”.