lighteval
Freea lightweight LLM evaluation suite that Hugging Face has been using internally.
FreeFree tier
About lighteval
Lighteval is an open-source, all-in-one toolkit for evaluating large language models (LLMs) across multiple backends, developed by Hugging Face's Leaderboard and Evals Team. It supports lightning-fast, flexible evaluation whether a model is served remotely or loaded in memory, and offers over 1,000 evaluation tasks spanning general knowledge, math, code, multilingual, chat, and core language understanding. Users can dive deep into model performance with sample-by-sample results, customize tasks and metrics, and push results to the Hugging Face Hub. Installation is via pip, and backends include accelerate, vllm, sglang, nanotron, endpoint, and inspect-ai.
Key Features
Evaluates LLMs across multiple backends: accelerate, vllm, sglang, nanotron, endpoint, inspect-ai
Supports 1000+ evaluation tasks across domains: knowledge, math, code, multilingual, chat, core NLU
Saves and explores detailed, sample-by-sample results for debugging and performance analysis
Allows creation of custom tasks and custom metrics tailored to specific needs
Lightweight design optimized for speed and flexibility
Push results directly to the Hugging Face Hub via hf auth login
Backed by Hugging Face's Leaderboard and Evals Team
Pros & Cons
Pros
- Completely open-source and free to use
- Developed and maintained by Hugging Face, ensuring strong community support and integration with the Hugging Face ecosystem
- Broad coverage of over 1,000 evaluation tasks across numerous languages and domains
- Supports multiple evaluation backends, giving flexibility in deployment and scaling
- Enables granular control with custom task and metric definitions
- Provides sample-by-sample results for detailed analysis and debugging
Cons
- Currently unsupported on Windows (only tested on Mac/Linux)
- May require significant GPU resources for large model evaluation, especially with certain backends
- Steep learning curve for users unfamiliar with command-line tools or configuration files
- Some backends (e.g., vllm, sglang) have additional dependencies beyond the base installation
Best For
Benchmarking LLMs for leaderboards and research papers using standard tasks like MMLU, GSM8K, HellaSwagEvaluating multilingual models with cross-lingual benchmarks such as XTREME and Flores200Assessing chat model performance with instruction following, reasoning, and long-context tasksTesting coding and math abilities using benchmarks like LCB, AIME24, and MATH500Creating custom evaluation pipelines for specialized domains or proprietary modelsDebugging model behavior by inspecting individual sample results
FAQ
What is Lighteval?
Lighteval is an open-source toolkit from Hugging Face for evaluating large language models across multiple backends. It supports over 1,000 tasks, custom metrics, and sample-level result analysis.
How do I install Lighteval?
You can install Lighteval via pip: 'pip install lighteval'. Additional extras are available for specific backends. See the README for details.
What backends does Lighteval support?
Lighteval supports multiple backends: accelerate, vllm, sglang, nanotron, endpoint, and inspect-ai. You can choose the appropriate one based on your model serving and hardware setup.
Can I create custom evaluation tasks?
Yes, Lighteval allows you to create custom tasks and custom metrics to tailor evaluations to your specific needs. The README provides guidance on how to define them.