This repository provides a comprehensive guide to evaluating Large Language Models (LLMs), aimed at researchers, developers, and hobbyists. It offers practical insights and theoretical knowledge for assessing LLM performance on specific tasks, designing custom evaluations, and troubleshooting common issues.

How It Works

The guide covers various evaluation methodologies, including automatic benchmarks, human evaluation, and LLM-as-a-judge approaches. It breaks down complex topics into foundational concepts and advanced techniques, providing practical tips and troubleshooting advice derived from managing the Open LLM Leaderboard and developing the lighteval framework.

Quick Start & Requirements

• Installation: No direct installation is required as this is a documentation repository.
• Prerequisites: Access to LLMs for practical application of the guide's concepts. Jupyter notebooks are provided for hands-on experience.
• Resources: Links to external resources and blog posts are included for further learning.

Highlighted Details

• Covers automatic benchmarks, human evaluation, and LLM-as-a-judge methodologies.
• Includes practical sections on designing evaluations and troubleshooting inference/reproducibility.
• Provides beginner-friendly explanations of core LLM concepts like model inference and tokenization.
• Offers Jupyter notebooks for hands-on experimentation with evaluation techniques.

Maintenance & Community

The guide is a community-driven effort, inspired by the ML Engineering Guidebook and contributions from numerous individuals and teams within Hugging Face and the broader AI community. Suggestions for improvements or missing resources can be made via GitHub issues.

Licensing & Compatibility

The repository content is likely under a permissive license, similar to other Hugging Face community projects, allowing for broad use and adaptation. Specific licensing details would need to be confirmed within the repository itself.

Limitations & Caveats

This is a guide and not a runnable software library, meaning it does not have direct installation or execution requirements beyond accessing the content. The practical application of the guide's advice will depend on the user's existing LLM infrastructure and tools.