Lighteval Documentation — Evaluating LLMs Across Multiple Backends
by Hugging Face
Run the same benchmark against vLLM, SGLang or a hosted API and get comparable numbers.
Overview
Lighteval is Hugging Face's evaluation toolkit, and its documentation is organised as a practical reference rather than a course. The index page establishes the core idea — one task definition, many execution backends — and lists ten of them: `eval` using inspect-ai (the preferred path), `transformers` via Accelerate for CPU or multi-GPU, `nanotron` for distributed settings, `vllm` and `sglang` for high-throughput local serving, `tgi` for a local Text Generation Inference server, `inference-endpoint` and `inference-providers` for Hugging Face hosted models, `litellm` for any compatible third-party API, and `custom` for anything else. From there the docs branch into a quicktour, a page on using the Python API, guides for adding a custom task, adding a new metric and evaluating a custom model, a page on evaluating a model served on a server or container, and a package reference covering the metric catalogue. The library ships thousands of pre-built tasks across knowledge, math, code, chat and multilingual domains, and stores detailed sample-by-sample results to the Hugging Face Hub, S3 or local disk so a score regression can be traced to individual generations. It is MIT-licensed, carries roughly 2.5k stars and 552 forks, is maintained by Hugging Face's Leaderboard and Evals team, and openly credits EleutherAI's lm-evaluation-harness as its inspiration.
At a Glance
- Topic
- Frameworks
- Level
- Intermediate
- Format
- Documentation
- Cost
- Free
- Duration
- Reference docs — first eval running in ~15 minutes, ~2 hours to read the guides end to end
- Provider
- Hugging Face
- Hands-on
- Yes — code/exercises
- Certificate
- None
What You’ll Learn
- ✓Run a full benchmark from the CLI with a single lighteval eval command
- ✓Evaluate one model across vLLM, SGLang, Transformers and nanotron backends
- ✓Score hosted API models through LiteLLM or Hugging Face inference providers
- ✓Write a custom task definition when no pre-built benchmark matches your use case
- ✓Implement a custom metric and register it with the evaluation pipeline
- ✓Inspect sample-by-sample generations to find why a benchmark score moved
- ✓Push evaluation bundles to the Hugging Face Hub, S3 or local storage
- ✓Evaluate a model already served on a remote endpoint or inside a container
Highlights
- •Thousands of pre-built tasks spanning knowledge, math, code, chat and multilingual benchmarks
- •Uses inspect-ai as its preferred backend, so an eval is inspectable rather than a single opaque number
- •The same task definition runs on a local GPU, a distributed nanotron job or a hosted API
- •Maintained by Hugging Face's Leaderboard and Evals team, who run the Open LLM Leaderboard lineage
- •MIT-licensed with ~2.5k stars; credits EleutherAI's lm-evaluation-harness as prior art rather than replacing it silently
Who It’s For
Best For
- ✓Engineers who need reproducible benchmark numbers before promoting a model
- ✓Teams fine-tuning models who must compare checkpoints on identical tasks
- ✓Anyone standing up an internal leaderboard across several inference backends
- ✓Researchers who need per-sample outputs, not just aggregate scores
Prerequisites
- •Working Python and pip; a GPU for the local backends, otherwise an API key
- •Understanding of what benchmarks such as GPQA or MMLU actually measure
- •Familiarity with at least one inference stack — Transformers, vLLM or a hosted API
FAQ
What is Lighteval Documentation — Evaluating LLMs Across Multiple Backends?
The official documentation for Lighteval, Hugging Face's all-in-one LLM evaluation toolkit. It covers running benchmarks from the CLI or Python API across ten inference backends, writing custom tasks and metrics, and reading sample-by-sample results. Aimed at engineers who need reproducible evaluation numbers when comparing fine-tuned checkpoints, base models or hosted APIs before shipping anything.
Is Lighteval Documentation — Evaluating LLMs Across Multiple Backends free?
Lighteval Documentation — Evaluating LLMs Across Multiple Backends is free to access.
What level is Lighteval Documentation — Evaluating LLMs Across Multiple Backends for?
Lighteval Documentation — Evaluating LLMs Across Multiple Backends is aimed at a intermediate audience. Recommended background: Working Python and pip; a GPU for the local backends, otherwise an API key, Understanding of what benchmarks such as GPQA or MMLU actually measure, Familiarity with at least one inference stack — Transformers, vLLM or a hosted API.
How long does Lighteval Documentation — Evaluating LLMs Across Multiple Backends take?
Expect roughly Reference docs — first eval running in ~15 minutes, ~2 hours to read the guides end to end. Most learners work through it at their own pace.
What will I learn from Lighteval Documentation — Evaluating LLMs Across Multiple Backends?
You'll learn: Run a full benchmark from the CLI with a single lighteval eval command; Evaluate one model across vLLM, SGLang, Transformers and nanotron backends; Score hosted API models through LiteLLM or Hugging Face inference providers; Write a custom task definition when no pre-built benchmark matches your use case; Implement a custom metric and register it with the evaluation pipeline; Inspect sample-by-sample generations to find why a benchmark score moved; Push evaluation bundles to the Hugging Face Hub, S3 or local storage; Evaluate a model already served on a remote endpoint or inside a container.
Topics
Sources
This page was written from 2 sources, 1 on domains other than huggingface.co.