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https://github.com/huggingface/lighteval
LightEval is a lightweight LLM evaluation suite that Hugging Face has been using internally with the recently released LLM data processing library datatrove and LLM training library nanotron.
https://github.com/huggingface/lighteval
evaluation evaluation-framework evaluation-metrics huggingface
Last synced: 6 days ago
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LightEval is a lightweight LLM evaluation suite that Hugging Face has been using internally with the recently released LLM data processing library datatrove and LLM training library nanotron.
- Host: GitHub
- URL: https://github.com/huggingface/lighteval
- Owner: huggingface
- License: mit
- Created: 2024-01-26T13:15:39.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-06-27T16:22:13.000Z (5 months ago)
- Last Synced: 2024-06-28T14:03:20.354Z (5 months ago)
- Topics: evaluation, evaluation-framework, evaluation-metrics, huggingface
- Language: Python
- Homepage:
- Size: 882 KB
- Stars: 461
- Watchers: 32
- Forks: 53
- Open Issues: 62
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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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/)
---
**Documentation**: Lighteval's Wiki
---
### Unlock the Power of LLM Evaluation with Lighteval 🚀
**Lighteval** is your all-in-one toolkit for evaluating LLMs across multiple
backends—whether it's
[transformers](https://github.com/huggingface/transformers),
[tgi](https://github.com/huggingface/text-generation-inference),
[vllm](https://github.com/vllm-project/vllm), or
[nanotron](https://github.com/huggingface/nanotron)—with
ease. 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](https://github.com/huggingface/lighteval/wiki/Available-Tasks) and [metrics](https://github.com/huggingface/lighteval/wiki/Metric-List) or effortlessly [create your own](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task), tailored to your needs.
Seamlessly experiment, benchmark, and store your results on the Hugging Face
Hub, S3, or locally.
## 🔑 Key Features
- **Speed**: [Use vllm as backend for fast evals](https://github.com/huggingface/lighteval/wiki/Use-VLLM-as-backend).
- **Completeness**: [Use the accelerate backend to launch any models hosted on Hugging Face](https://github.com/huggingface/lighteval/wiki/Quicktour#accelerate).
- **Seamless Storage**: [Save results in S3 or Hugging Face Datasets](https://github.com/huggingface/lighteval/wiki/Saving-and-reading-results).
- **Python API**: [Simple integration with the Python API](https://github.com/huggingface/lighteval/wiki/Using-the-Python-API).
- **Custom Tasks**: [Easily add custom tasks](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task).
- **Versatility**: Tons of [metrics](https://github.com/huggingface/lighteval/wiki/Metric-List) and [tasks](https://github.com/huggingface/lighteval/wiki/Available-Tasks) ready to go.
## ⚡️ Installation
```bash
pip install lighteval[accelerate]
```
Lighteval allows for many extras when installing, see [here](https://github.com/huggingface/lighteval/wiki/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
huggingface-cli login
```
## 🚀 Quickstart
Lighteval offers two main entry points for model evaluation:
* `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).
Here’s a quick command to evaluate using the Accelerate backend:
```shell
lighteval accelerate \
--model_args "pretrained=gpt2" \
--tasks "leaderboard|truthfulqa:mc|0|0" \
--override_batch_size 1 \
--output_dir="./evals/"
```
## 🙏 Acknowledgements
Lighteval started as an extension of the fantastic [Eleuther AI
Harness](https://github.com/EleutherAI/lm-evaluation-harness) (which powers the
[Open LLM
Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard))
and draws inspiration from the amazing
[HELM](https://crfm.stanford.edu/helm/latest/) framework.
While evolving Lighteval into its own standalone tool, we are grateful to the
Harness and HELM teams for their pioneering work on LLM evaluations.
## 🌟 Contributions Welcome 💙💚💛💜🧡
Got ideas? Found a bug? Want to add a
[task](https://github.com/huggingface/lighteval/wiki/Adding-a-Custom-Task) or
[metric](https://github.com/huggingface/lighteval/wiki/Adding-a-New-Metric)?
Contributions are warmly
welcomed!
## 📜 Citation
```bibtex
@misc{lighteval,
author = {Fourrier, Clémentine and Habib, Nathan and Wolf, Thomas and Tunstall, Lewis},
title = {LightEval: A lightweight framework for LLM evaluation},
year = {2023},
version = {0.5.0},
url = {https://github.com/huggingface/lighteval}
}
```