evaluation-guidebook
Sharing both practical insights and theoretical knowledge about LLM evaluation that we gathered while managing the Open LLM Leaderboard and designing lighteval!
What is evaluation-guidebook?
Sharing both practical insights and theoretical knowledge about LLM evaluation that we gathered while managing the Open LLM Leaderboard and designing lighteval!
Overview of evaluation-guidebook
What it is
This is a guidebook about evaluating large language models (LLMs). It shares both practical insights and theoretical knowledge gathered while managing the Open LLM Leaderboard and designing lighteval. The guide covers different ways to evaluate a model, guides on designing your own evaluations, and tips and tricks from practical experience. It is intended for users working with production models, researchers, or hobbyists. Note: this guidebook is no longer maintained; the latest version (as of Dec 2025) lives at a Hugging Face Space.
Key features
- Covers automatic benchmarks, human evaluation, and LLM-as-a-judge approaches - Includes sections on designing evaluations, troubleshooting inference and reproducibility - Provides general knowledge chapters on model inference, tokenization, and other LLM basics - Contains yearly dives on evaluation topics (2023: Open Source, 2024: what evaluation should be, 2025: evaluations for real-life useful models)
- Offers community translations in Chinese and French - Lists resources and links recommended by the author
Use cases
- Beginners can start with the Basics sections in each chapter and the General knowledge chapters - Advanced users can focus on Tips and Tricks, Troubleshooting, and Designing sections - Anyone needing to ensure an LLM performs well on a specific task
Getting started
See the repository README for instructions on how to read the guide. No installation commands are provided.
License and pricing
The license is not stated (NOASSERTION). The project is open source.
Based on the project's public README and repository metadata.
