huggingface/lighteval

Lighteval is your all-in-one toolkit for evaluating LLMs across multiple backends

What it solves

Lighteval provides a unified toolkit for evaluating Large Language Models (LLMs) across various backends. It eliminates the need to manually set up complex benchmarks by offering a massive library of pre-defined tasks and the flexibility to create custom evaluation metrics and tasks for specific needs.

How it works

The library acts as an evaluation layer that can connect to models regardless of how they are hosted. It supports multiple entry points for different environments, including local GPU execution via Accelerate, vLLM, or SGLang, distributed settings via Nanotron, and remote API endpoints via Hugging Face Inference Endpoints, TGI, or LiteLLM. Users can run evaluations through a command-line interface or a Python API for models already loaded in memory.

Who it’s for

It is designed for AI researchers and developers who need to benchmark their LLMs against industry-standard datasets or validate model performance on specialized, multilingual, or domain-specific tasks.

Highlights

  • Extensive Task Library: Supports over 1,000 evaluation tasks covering general knowledge, math, coding, instruction following, and core language understanding.
  • Broad Backend Support: Compatible with a wide range of inference engines and API providers.
  • Multilingual Capabilities: Includes benchmarks for dozens of languages, including Arabic, French, German, Chinese, and Russian.
  • Detailed Debugging: Allows saving sample-by-sample results to analyze exactly where a model is failing.
  • Customizable: Users can easily implement their own custom tasks and metrics.

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