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Lighteval - AI Evaluation Tools Tool

Overview

Lighteval is an open-source evaluation toolkit from Hugging Face that standardizes how researchers and engineers measure LLM performance across multiple backends. It focuses on sample-by-sample evaluation, producing exportable records that include the prompt, model response, reference(s), and per-sample scores so teams can inspect failures and aggregate results reproducibly. According to the GitHub repository, Lighteval is designed to run the same evaluation suite against hosted APIs and local models with minimal adapter configuration. The project emphasizes flexibility for real-world benchmarking: users can define custom task templates, supply few-shot examples, and plug in scoring functions appropriate to the task (classification, generative, or structured outputs). Lighteval also includes tooling to compare model outputs across backends, reproduce runs, and export results for downstream analysis. For full details and examples, see the repository README on GitHub.

Installation

Install via pip:

pip install lighteval
git clone https://github.com/huggingface/lighteval.git

Key Features

  • Multi-backend adapters to evaluate the same suite on hosted APIs and local models (e.g., Hugging Face inference, OpenAI).
  • Sample-by-sample output export (prompt, response, reference, scorer outputs) for forensic error analysis.
  • Task customization with templates and few-shot example injection for instruction-following and classification tasks.
  • Pluggable scorers and metrics so teams can attach task-appropriate evaluations (accuracy, F1-style scorers, custom checkers).
  • Reproducible runs and result comparison tooling to compare model behavior across backends and versions.

Community

Lighteval is maintained as an open-source project on the Hugging Face GitHub organization. According to the GitHub repository, it includes example notebooks, an issues tracker for bug reports and feature requests, and contribution guidance for external contributors. The project receives community feedback via GitHub issues and discussions; users typically reference the repository README and example configs when onboarding.

Last Refreshed: 2026-01-09

Key Information

  • Category: Evaluation Tools
  • Type: AI Evaluation Tools Tool
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