This repository serves as a comprehensive, curated guide for LLM engineers, aiming to bridge the gap between basic LLM demos and production-grade applications. It offers a structured collection of resources covering the entire LLM lifecycle, from training and fine-tuning to serving, prompt optimization, and LLMOps, empowering users to build robust and scalable LLM solutions.

How It Works

The handbook organizes resources into five key categories: Libraries & Frameworks & Tools, Learning Resources, Understanding LLMs, Social Accounts & Community, and Contribution guidelines. It highlights essential tools and frameworks for various stages of LLM development, including application building (e.g., LangChain, LlamaIndex), fine-tuning (e.g., Hugging Face Transformers, Unsloth), and efficient serving (e.g., vLLM, TensorRT-LLM). The structure facilitates a systematic approach to learning and implementing LLM technologies.

Quick Start & Requirements

This is a curated list of resources, not a runnable software package. No installation or specific requirements are needed to access the information. Links to official documentation, tutorials, and courses are provided within the handbook for relevant tools and frameworks.

Highlighted Details

• Extensive coverage of LLM application frameworks like DSPy, LangChain, and Haystack.
• Detailed sections on fine-tuning techniques and libraries such as Unsloth and LitGPT, emphasizing performance gains.
• Comprehensive overview of serving solutions including NVIDIA TensorRT-LLM, vLLM, and ollama for efficient deployment.
• Resources for prompt engineering, evaluation (e.g., ragas), and LLMOps practices.

Maintenance & Community

The repository is actively maintained and encourages community contributions via issues and pull requests. It lists prominent figures and organizations in the LLM space, such as SylphAI, Hugging Face, OpenAI, and Microsoft, indicating a strong connection to the broader AI community. A Discord server is available for discussions.

Licensing & Compatibility

The repository itself is a collection of links and information; the licensing of the linked resources varies. Users should consult the individual licenses of the tools and frameworks mentioned.

Limitations & Caveats

As a curated list, the handbook's content is dependent on the maintainers' selection and the availability of external resources. While comprehensive, it is an opinionated guide and may not cover every emerging tool or technique in the rapidly evolving LLM landscape.