huggingface/evaluation-guidebook

Sharing both practical insights and theoretical knowledge about LLM evaluation that we gathered while managing the Open LLM Leaderboard and designing lighteval!

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This guide helps you verify that a large language model (LLM) performs as expected for your specific use case. It takes your LLM and task requirements as input and provides frameworks, methodologies, and practical tips for assessing model quality. This is ideal for anyone working with LLMs, from researchers to hobbyists, who needs to ensure their models are reliable and effective.

2,075 stars.

Use this if you need to systematically evaluate an LLM's performance, understand different evaluation methods like automatic benchmarks or human review, or design your own robust evaluation processes.

Not ideal if you are looking for an out-of-the-box software tool to run evaluations without needing to understand the underlying principles or design choices.

LLM development model validation AI research natural language processing performance testing
No Package No Dependents
Maintenance 6 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 17 / 25

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Jupyter Notebook

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Last pushed

Dec 03, 2025

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