# lighteval
**Repository Path**: devdz/lighteval
## Basic Information
- **Project Name**: lighteval
- **Description**: Lighteval is your all-in-one toolkit for evaluating LLMs across multiple backends
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-03-09
- **Last Updated**: 2026-03-17
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
Your go-to toolkit for lightning-fast, flexible LLM evaluation, from Hugging Face's Leaderboard and Evals Team.
[](https://github.com/huggingface/lighteval/actions/workflows/tests.yaml?query=branch%3Amain)
[](https://github.com/huggingface/lighteval/actions/workflows/quality.yaml?query=branch%3Amain)
[](https://www.python.org/downloads/)
[](https://github.com/huggingface/lighteval/blob/main/LICENSE)
[](https://pypi.org/project/lighteval/)
---
---
**Lighteval** is your *all-in-one toolkit* for evaluating LLMs across multiple
backends—whether your model is being **served somewhere** or **already loaded in memory**.
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.
*Customization at your fingertips*: letting you either browse all our existing tasks and [metrics](https://huggingface.co/docs/lighteval/metric-list) or effortlessly create your own [custom task](https://huggingface.co/docs/lighteval/adding-a-custom-task) and [custom metric](https://huggingface.co/docs/lighteval/adding-a-new-metric), tailored to your needs.
## Available Tasks
Lighteval supports **1000+ evaluation tasks** across multiple domains and
languages. Use [this
space](https://huggingface.co/spaces/OpenEvals/open_benchmark_index) to find what
you need, or, here's an overview of some *popular benchmarks*:
### 📚 **Knowledge**
- **General Knowledge**: MMLU, MMLU-Pro, MMMU, BIG-Bench
- **Question Answering**: TriviaQA, Natural Questions, SimpleQA, Humanity's Last Exam (HLE)
- **Specialized**: GPQA, AGIEval
### 🧮 **Math and Code**
- **Math Problems**: GSM8K, GSM-Plus, MATH, MATH500
- **Competition Math**: AIME24, AIME25
- **Multilingual Math**: MGSM (Grade School Math in 10+ languages)
- **Coding Benchmarks**: LCB (LiveCodeBench)
### 🎯 **Chat Model Evaluation**
- **Instruction Following**: IFEval, IFEval-fr
- **Reasoning**: MUSR, DROP (discrete reasoning)
- **Long Context**: RULER
- **Dialogue**: MT-Bench
- **Holistic Evaluation**: HELM, BIG-Bench
### 🌍 **Multilingual Evaluation**
- **Cross-lingual**: XTREME, Flores200 (200 languages), XCOPA, XQuAD
- **Language-specific**:
- **Arabic**: ArabicMMLU
- **Filipino**: FilBench
- **French**: IFEval-fr, GPQA-fr, BAC-fr
- **German**: German RAG Eval
- **Serbian**: Serbian LLM Benchmark, OZ Eval
- **Turkic**: TUMLU (9 Turkic languages)
- **Chinese**: CMMLU, CEval, AGIEval
- **Russian**: RUMMLU, Russian SQuAD
- **Kyrgyz**: Kyrgyz LLM Benchmark
- **And many more...**
### 🧠 **Core Language Understanding**
- **NLU**: GLUE, SuperGLUE, TriviaQA, Natural Questions
- **Commonsense**: HellaSwag, WinoGrande, ProtoQA
- **Natural Language Inference**: XNLI
- **Reading Comprehension**: SQuAD, XQuAD, MLQA, Belebele
## ⚡️ Installation
> **Note**: lighteval is currently *completely untested on Windows*, and we don't support it yet. (*Should be fully functional on Mac/Linux*)
```bash
pip install lighteval
```
Lighteval allows for *many extras* when installing, see [here](https://huggingface.co/docs/lighteval/installation) for a **complete list**.
If you want to push results to the **Hugging Face Hub**, add your access token as
an environment variable:
```shell
hf auth login
```
## 🚀 Quickstart
Lighteval offers the following entry points for model evaluation:
- `lighteval eval`: Evaluation models using [inspect-ai](https://inspect.aisi.org.uk/) as a backend (prefered).
- `lighteval accelerate`: Evaluate models on CPU or one or more GPUs using [🤗
Accelerate](https://github.com/huggingface/accelerate)
- `lighteval nanotron`: Evaluate models in distributed settings using [⚡️
Nanotron](https://github.com/huggingface/nanotron)
- `lighteval vllm`: Evaluate models on one or more GPUs using [🚀
VLLM](https://github.com/vllm-project/vllm)
- `lighteval sglang`: Evaluate models using [SGLang](https://github.com/sgl-project/sglang) as backend
- `lighteval endpoint`: Evaluate models using various endpoints as backend
- `lighteval endpoint inference-endpoint`: Evaluate models using Hugging Face's [Inference Endpoints API](https://huggingface.co/inference-endpoints/dedicated)
- `lighteval endpoint tgi`: Evaluate models using [🔗 Text Generation Inference](https://huggingface.co/docs/text-generation-inference/en/index) running locally
- `lighteval endpoint litellm`: Evaluate models on any compatible API using [LiteLLM](https://www.litellm.ai/)
- `lighteval endpoint inference-providers`: Evaluate models using [HuggingFace's inference providers](https://huggingface.co/docs/inference-providers/en/index) as backend
Did not find what you need ? You can always make your custom model API by following [this guide](https://huggingface.co/docs/lighteval/main/en/evaluating-a-custom-model)
- `lighteval custom`: Evaluate custom models (can be anything)
Here's a **quick command** to evaluate using a remote inference service:
```shell
lighteval eval "hf-inference-providers/openai/gpt-oss-20b" gpqa:diamond
```
Or use the **Python API** to run a model *already loaded in memory*!
```python
from transformers import AutoModelForCausalLM
from lighteval.logging.evaluation_tracker import EvaluationTracker
from lighteval.models.transformers.transformers_model import TransformersModel, TransformersModelConfig
from lighteval.pipeline import ParallelismManager, Pipeline, PipelineParameters
MODEL_NAME = "meta-llama/Meta-Llama-3-8B-Instruct"
BENCHMARKS = "gsm8k"
evaluation_tracker = EvaluationTracker(output_dir="./results")
pipeline_params = PipelineParameters(
launcher_type=ParallelismManager.NONE,
max_samples=2
)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME, device_map="auto"
)
config = TransformersModelConfig(model_name=MODEL_NAME, batch_size=1)
model = TransformersModel.from_model(model, config)
pipeline = Pipeline(
model=model,
pipeline_parameters=pipeline_params,
evaluation_tracker=evaluation_tracker,
tasks=BENCHMARKS,
)
results = pipeline.evaluate()
pipeline.show_results()
results = pipeline.get_results()
```
## 🙏 Acknowledgements
Lighteval took inspiration from the following *amazing* frameworks: Eleuther's [AI Harness](https://github.com/EleutherAI/lm-evaluation-harness) and Stanford's
[HELM](https://crfm.stanford.edu/helm/latest/). We are grateful to their teams for their **pioneering work** on LLM evaluations.
We'd also like to offer our thanks to all the community members who have contributed to the library, adding new features and reporting or fixing bugs.
## 🌟 Contributions Welcome 💙💚💛💜🧡
**Got ideas?** Found a bug? Want to add a
[task](https://huggingface.co/docs/lighteval/adding-a-custom-task) or
[metric](https://huggingface.co/docs/lighteval/adding-a-new-metric)?
Contributions are *warmly welcomed*!
If you're adding a **new feature**, please *open an issue first*.
If you open a PR, don't forget to **run the styling**!
```bash
pip install -e ".[dev]"
pre-commit install
pre-commit run --all-files
```
## 📜 Citation
```bibtex
@misc{lighteval,
author = {Habib, Nathan and Fourrier, Clémentine and Kydlíček, Hynek and Wolf, Thomas and Tunstall, Lewis},
title = {LightEval: A lightweight framework for LLM evaluation},
year = {2023},
version = {0.11.0},
url = {https://github.com/huggingface/lighteval}
}
```