Github Huggingface Lighteval Lighteval Is Your All In One Toolkit

Your go-to toolkit for lightning-fast, flexible LLM evaluation, from Hugging Face's Leaderboard and Evals Team. Lighteval is your all-in-one toolkit for evaluating LLMs across multiple backends—whether your model is being served somewhere or already loaded in memory. Dive deep into your model's performance by saving and exploring detailed, sample-by-sample results to debug and see how your models stack-up. Customization at your fingertips: letting you either browse all our existing tasks and metrics or effortlessly create your own custom task and custom metric, tailored to your needs. Lighteval supports 1000+ evaluation tasks across multiple domains and languages. Use this space to find what you need, or, here's an overview of some popular benchmarks:

Note: lighteval is currently completely untested on Windows, and we don't support it yet. (Should be fully functional on Mac/Linux) and get access to the augmented documentation experience 🤗 Lighteval is your all-in-one toolkit for evaluating Large Language Models (LLMs) across multiple backends with ease. Dive deep into your model’s performance by saving and exploring detailed, sample-by-sample results to debug and see how your models stack up. Evaluate your models using the most popular and efficient inference backends:

Customization at your fingertips: create new tasks, metrics or model tailored to your needs, or browse all our existing tasks and metrics. Seamlessly experiment, benchmark, and store your results on the Hugging Face Hub, S3, or locally. pip install lighteval Copy PIP instructions A lightweight and configurable evaluation package Your go-to toolkit for lightning-fast, flexible LLM evaluation, from Hugging Face's Leaderboard and Evals Team. Lighteval is your all-in-one toolkit for evaluating LLMs across multiple backends—whether your model is being served somewhere or already loaded in memory.

Dive deep into your model's performance by saving and exploring detailed, sample-by-sample results to debug and see how your models stack-up. Customization at your fingertips: letting you either browse all our existing tasks and metrics or effortlessly create your own custom task and custom metric, tailored to your needs. ML Enginneer at Hugging Face X: @nathanhabib1011 🔥 Evaluating LLMs? You need Lighteval — the fastest, most flexible toolkit for benchmarking models, built by @huggingface Now with: ✅ Plug & play custom model inference (evaluate any backend) 📈 Tasks like AIME, GPQA:diamond, SimpleQA, and... Built by Hugging Face's OpenEvals team, it lets you: Run standardized benchmarks Compare models and backends side-by-side Scale across backends (vLLM, HF Hub, litellm, nanotron, sglang, transformers, etc.) 📊 New Benchmarks added and others...

Bring any model to lighteval, your backend, your rules. Use Lighteval to benchmark it like any other supported model. This makes evals reproducible & comparable on your backends 💥 ✨ Bonus goodies: Hugging Face Hub inference for LLM-as-Judge CoT prompting in vLLM W&B logging to track everything 🐛 Tons of bug fixes: vLLM... 🪨 🪨 Major props to the contributors who made this release happen 🙌 @JoelNiklaus @_lewtun @ailozovskaya @clefourrier @alvind319 HERIUN @_EldarKurtic @mariagrandury jnanliu @qubvelx Check out the release & try it out: 🔗 https://lnkd.in/emZNQWUd LightEval is an open-source evaluation framework by Hugging Face for large language models (LLMs). It provides a unified toolkit to assess LLM performance across many benchmarks and settings.

LightEval’s architecture centers on a flexible evaluation pipeline that supports multiple backends and a rich library of evaluation tasks and metrics. Its goal is to make rigorous model evaluation as accessible and customizable as model training, enabling researchers and developers to easily measure how models “stack up” on various benchmarks ​github.com. LightEval integrates seamlessly with Hugging Face’s ecosystem – for example, it works with the 🤗 Transformers library, the Accelerate library for multi-GPU execution, and even Hugging Face Hub for storing results​ source1, source2. By building on prior work (it started as an extension of EleutherAI’s LM Evaluation Harness and drew inspiration from Stanford’s HELM project​ github.com), LightEval combines speed, flexibility, and transparency in one framework. At a high level, LightEval’s architecture consists of the following components: LightEval was built with integration in mind.

It plugs into Hugging Face’s training and inference stack: for example, it can integrate with Hugging Face’s Accelerate library to run multi-GPU or distributed evaluations with minimal fuss​ venturebeat.com. It also ties into tools like Hugging Face’s data processing (Datasets) and Hub for sharing results. In fact, LightEval is the framework powering Hugging Face’s Open LLM Leaderboard evaluations, forming part of a “complete pipeline for AI development” alongside Hugging Face’s training library (Nanotron) and data pipelines​ venturebeat.com. This tight integration means you can evaluate models in the same environment you train them, and easily compare your model’s performance with community benchmarks. Overall, LightEval’s architecture balances user-friendliness and extensibility – it’s intended to be usable by those without deep technical expertise (simple CLI commands or Python calls), while still offering advanced configuration for precise needs​ venturebeat.com. There was an error while loading.

Please reload this page. and get access to the augmented documentation experience 🤗 Lighteval is your all-in-one toolkit for evaluating Large Language Models (LLMs) across multiple backends with ease. Dive deep into your model’s performance by saving and exploring detailed, sample-by-sample results to debug and see how your models stack up. Evaluate your models using the most popular and efficient inference backends: Customization at your fingertips: create new tasks, metrics or model tailored to your needs, or browse all our existing tasks and metrics.

Seamlessly experiment, benchmark, and store your results on the Hugging Face Hub, S3, or locally. 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. There was an error while loading. Please reload this page. Evaluating large language models (LLMs) is no small feat.

With diverse architectures, deployment environments, and use cases, assessing an LLM’s performance demands flexibility, precision, and scalability. That’s where Lighteval comes in—a comprehensive toolkit designed to simplify and enhance the evaluation process for LLMs across multiple backends, including transformers, VLLM, Nanotron, and more. Whether you're an AI researcher, developer, or enthusiast, this guide will walk you through the essentials of Lighteval, from installation to running your first evaluation. Lighteval is an evaluation toolkit that allows you to: To get started quickly, install Lighteval using pip: If you plan to contribute or need the latest features, clone the repository:

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