#
Lighteval

🤗 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.

> Share your evaluation results with the community by pushing them to the Hugging Face Hub. If you open Pull Requests on model repositories with evaluation results, we will automatically show the results on benchmark dataset repositories. Let’s decentralize evaluation! Check out the docs.

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Key Features

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🚀 Multi-Backend Support

Evaluate your models using the most popular and efficient inference backends:

- `eval`: Use inspect-ai as backend to evaluate and inspect your models! (prefered way)
- `transformers`: Evaluate models on CPU or one or more GPUs using 🤗 Accelerate
- `nanotron`: Evaluate models in distributed settings using ⚡️ Nanotron
- `vllm`: Evaluate models on one or more GPUs using 🚀 VLLM
- `custom`: Evaluate custom models (can be anything)
- `sglang`: Evaluate models using SGLang as backend
- `inference-endpoint`: Evaluate models using Hugging Face’s Inference Endpoints API
- `tgi`: Evaluate models using 🔗 Text Generation Inference running locally
- `litellm`: Evaluate models on any compatible API using LiteLLM
- `inference-providers`: Evaluate models using HuggingFace’s inference providers as backend**: Distributed training and evaluation

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📊 Comprehensive Evaluation

- Extensive Task Library: 1000s pre-built evaluation tasks
- Custom Task Creation: Build your own evaluation tasks
- Flexible Metrics: Support for custom metrics and scoring
- Detailed Analysis: Sample-by-sample results for deep insights

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🔧 Easy Customization

Customization at your fingertips: create new tasks, metrics or model tailored to your needs, or browse all our existing tasks and metrics.

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☁️ Seamless Integration

Seamlessly experiment, benchmark, and store your results on the Hugging Face Hub, S3, or locally.

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Quick Start

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Installation

    pip install lighteval

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Basic Usage

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Find a task

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Run your benchmark and push details to the hub

    lighteval eval "hf-inference-providers/openai/gpt-oss-20b" \
        gpqa:diamond \
        --bundle-dir gpt-oss-bundle \
        --repo-id OpenEvals/evals

Resulting Space:

Update on GitHub