Detailed Introduction
The Evaluation Guidebook is a practical and theoretical handbook published by Hugging Face for evaluating Large Language Models (LLMs) and related models. It consolidates lessons learned from managing the Open LLM Leaderboard and designing lightweight evaluation tools, helping engineers, researchers and evaluators systematically design evaluation pipelines, select metrics, and interpret results. The guide balances theoretical context with actionable recommendations for model selection, benchmark construction, and reproducibility.
Main Features
- Systematic methodology: principles for evaluation flow, dataset selection, and metric trade-offs.
- Practical examples and best practices: evaluation patterns and caveats for common tasks.
- Reproducibility and result interpretation: emphasizes metadata and experiment logging for fair comparisons.
- Ecosystem integration: guidance on connecting with Hugging Face evaluation tools and benchmark platforms.
Use Cases
- Researchers designing model comparison experiments can use it to build robust evaluation plans.
- Engineers evaluating or selecting models before deployment can determine key metrics and acceptance criteria.
- Evaluation teams building leaderboards and benchmarks can use it as governance guidance for processes, data, and metrics.
Technical Features
- Covers evaluation dimensions across text and multimodal tasks, combining automated metrics and human evaluation.
- Stresses the contextual nature of metrics and recommends selecting measurement approaches and reporting uncertainty.
- Practically aligned with Hugging Face libraries and community leaderboards such as LightEval for reporting and sharing results.