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Lighteval Is Your All In One Toolkit For Evaluating Llms Across

Leo Migdal
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lighteval is your all in one toolkit for evaluating llms across

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. 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: 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. Lighteval is an all-in-one toolkit for evaluating large language models across multiple backends. It provides detailed sample-by-sample performance metrics and options to customize evaluation tasks. The project is available as a GitHub repository and focuses on configurable, reproducible LLM evaluations rather than model training or hosting.

Hosted on GitHub; no pricing or commercial terms listed in the provided data. DeepEval is an open-source evaluation toolkit for AI models that provides advanced metrics for both text and multimodal outputs. It supports features like multimodal G-Eval, conversational evaluation using a list of Turns, and integrates platform support along with comprehensive documentation. A tool that monitors datasets and tracks models trained on them, helping users manage and oversee AI model performance. seismometer is an open-source Python package for evaluating AI model performance with a focus on healthcare. It provides templates and tools to analyze statistical performance, fairness, and the impact of interventions on outcomes using local patient data.

Although designed for healthcare applications, it can be used to validate models in any field. There was an error while loading. Please reload this page. Lighteval, developed by Hugging Face, is a comprehensive and efficient toolkit for evaluating large language models. It simplifies the process of running standard benchmarks across a multitude of models available on the Hugging Face Hub, making it a go-to for researchers and developers in that ecosystem. 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. 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. 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:

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Your Go-to Toolkit For Lightning-fast, Flexible LLM Evaluation, From Hugging

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 ...

Note: Lighteval Is Currently Completely Untested On Windows, And We

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 ...

Customization At Your Fingertips: Create New Tasks, Metrics Or Model

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. Evaluating large language models (LLMs) is no small feat. With diverse architectures, deployment environments, and use cases, assessing an LLM’s performance demand...

Lighteval Is An Evaluation Toolkit That Allows You To: To

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: 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

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 effo...