Issues Huggingface Evaluation Guidebook Github

Leo Migdal

-Nov 17, 2025, 1:54 PM

issues huggingface evaluation guidebook github

There was an error while loading. Please reload this page. If you've ever wondered how to make sure an LLM performs well on your specific task, this guide is for you! It covers the different ways you can evaluate a model, guides on designing your own evaluations, and tips and tricks from practical experience. Whether working with production models, a researcher or a hobbyist, I hope you'll find what you need; and if not, open an issue (to suggest ameliorations or missing resources) and I'll complete the guide! In text, links prefixed by ⭐ are links I really enjoyed and recommend reading.

If you want an intro on the topic, you can read this blog on how and why we do evaluation! The most densely practical part of this guide. There was an error while loading. Please reload this page. There was an error while loading. Please reload this page.

There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs.

and get access to the augmented documentation experience You can evaluate AI models on the Hub in multiple ways and this page will guide you through the different options: Community leaderboards show how a model performs on a given task or domain. For example, there are leaderboards for question answering, reasoning, classification, vision, and audio. If you’re tackling a new task, you can use a leaderboard to see how a model performs on it. Here are some examples of community leaderboards:

There are many more leaderboards on the Hub. Check out all the leaderboards via this search or use this dedicated Space to find a leaderboard for your task. This document addresses challenges, solutions, and impact of LaTeX parsing problems when evaluating mathematical capabilities of Large Language Models (LLMs). Proper LaTeX parsing is critical for accurately evaluating model performance on mathematical tasks, particularly on the MATH benchmark. The evaluation process for mathematical tasks often requires comparing LaTeX expressions generated by models with ground truth expressions. The LM evaluation harness uses sympy (a Python library for symbolic mathematics) to parse and compare these expressions.

However, as sympy's own documentation acknowledges: When validating the ground truth against itself using sympy (which should ideally yield 100% accuracy), we only achieve approximately 94% accuracy due to parsing failures. Common examples of LaTeX expressions that sympy fails to parse include: Discover and explore top open-source AI tools and projects—updated daily. 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.

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. 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. 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.

The LLM Evaluation Guidebook provides comprehensive guidance on how to evaluate Large Language Models (LLMs) across different contexts and requirements. This page introduces the purpose, structure, and key components of the guidebook, serving as a starting point for understanding the available evaluation approaches and how to use them effectively. This guidebook helps you ensure that your LLM performs well on specific tasks by explaining: Whether you're working with production models, conducting academic research, or experimenting as a hobbyist, the guidebook offers relevant guidance tailored to your needs. The guidebook is organized around three core evaluation approaches, with supporting content for implementation and troubleshooting: The guidebook covers three primary evaluation methodologies, each with distinct characteristics and use cases:

Issues Huggingface Evaluation Guidebook Github

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There Are Many More Leaderboards On The Hub. Check Out