Lighteval

Lighteval is an open-source LLM evaluation framework built by Hugging Face's Leaderboard and Evals Team, first released in October 2023. It is the actual framework powering Hugging Face's Open LLM Leaderboard, which means evaluation results produced locally with Lighteval are directly comparable to the community rankings that researchers use to benchmark Llama, DeepSeek, Mistral, and every other open-weight model. The primary authors include Clémentine Fourrier and Nathan Habib, who have maintained HF's evaluation infrastructure since 2022, alongside HF co-founder Thomas Wolf. Teams building chatbots or RAG pipelines and looking for production monitoring or CI/CD regression testing should note that Lighteval is designed for model capability benchmarking, not application quality testing.

Key Capabilities

• Multi-backend evaluation: Runs against transformers, vLLM, TGI, Nanotron, and HF Inference Endpoints through a single interface, with the vLLM backend providing fast inference and the Nanotron backend enabling evaluation of models during active training runs

• 1,000+ pre-built evaluation tasks: Covers MMLU, MMLU-Pro, TriviaQA, Natural Questions, BIG-Bench, Humanity's Last Exam, and hundreds of additional tasks across general knowledge, reasoning, coding, and multilingual benchmarks

• Open LLM Leaderboard reproducibility: Because Lighteval is the code that runs the leaderboard, researchers can reproduce official rankings exactly rather than approximating them with a different framework or methodology

• Inspect AI backend integration: Lighteval natively supports Inspect AI as an evaluation backend, allowing the same model to run through both Hugging Face's benchmarking infrastructure and the UK AI Security Institute's safety evaluation framework

• Hugging Face Hub result storage: Evaluation results push directly to the HF Hub, with automatic display on benchmark dataset repositories when results are submitted via pull requests on model pages

• Custom task and metric creation: Custom evaluation tasks register through Python packages or YAML configuration, with sample-by-sample result storage for debugging model behavior on specific inputs