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Lighteval Pypi

Leo Migdal
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lighteval pypi

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.

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. and get access to the augmented documentation experience Lighteval can be installed from PyPI or from source. This guide covers all installation options and dependencies. The simplest way to install Lighteval is from PyPI:

This installs the core package with all essential dependencies for basic evaluation tasks. Source installation is recommended for developers who want to contribute to Lighteval or need the latest features: 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: There was an error while loading. Please reload this page. 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) 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. 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. 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 it's transformers, tgi, vllm, or nanotron—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. 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. Seamlessly experiment, benchmark, and store your results on the Hugging Face Hub, S3, or locally. Note that lighteval is currently completely untested on Windows, and we don't support it yet. (Should be fully functional on Mac/Linux)

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