llama cookbook

It serves as the authoritative guide for practitioners adopting Llama models, demonstrating the recommended approaches for common use cases with working code that can be adapted directly to specific applications.

The repository covers the full Llama usage lifecycle: downloading and setting up Llama models from Hugging Face, fine-tuning with LoRA and QLoRA for domain adaptation, parameter-efficient training on domain-specific datasets, quantization for inference efficiency, deployment to various serving frameworks (vLLM, TorchServe, Ollama), and building RAG systems and agent workflows on top of Llama.

Multi-modal recipes covering Llama's vision capabilities and code-focused use cases are included as Llama's capabilities have expanded.

Developers building applications on Llama 3 and its successors, ML engineers fine-tuning Llama for domain-specific use cases (legal, medical, code, customer service), researchers studying Llama model behavior and capabilities, and organizations evaluating whether to use Llama as the foundation for their AI products use Llama Cookbook as the primary reference for production-quality implementation patterns.

Meta's official maintenance ensures recipes stay current with each new Llama release and reflect the latest recommended practices from the team that built and trained the models.