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Brevitas: neural network quantization in PyTorch
| Date | Stars |
|---|---|
| 2026-07-24 | 1555 |
| 2026-07-25 | 1556 |
| 2026-07-28 | 1556 |
| 2026-07-30 | 1556 |
| 2026-07-31 | 1557 |
| 2026-08-02 | 1558 |
| 2026-08-05 | 1559 |
| 2026-08-06 | 1560 |
Today
+1 stars today
This week
+4 stars this week
This month
— stars this month
Momentum
43.0
growth rate 0.26%/day
# Brevitas
[](https://pepy.tech/project/brevitas)
[](https://github.com/Xilinx/brevitas/actions/workflows/pytest.yml)
[](https://github.com/Xilinx/brevitas/actions/workflows/examples_pytest.yml)
[](https://zenodo.org/badge/latestdoi/140494324)
Brevitas is a PyTorch library for neural network quantization, with support for both *post-training quantization (PTQ)* and *quantization-aware training (QAT)*.
**Please note that Brevitas is a research project and not an official Xilinx product.**
If you like this project please consider ⭐ this repo, as it is the simplest and best way to support it.
## Requirements
* Python >= 3.10
* [Pytorch](https://pytorch.org) >= 1.13, <= 2.12.1 (more recent versions would be untested).
* Windows, Linux or macOS.
* GPU training-time acceleration (*Optional* but recommended).
## Installation
You can install the latest release from PyPI:
```bash
pip install brevitas
```
## Getting Started
Brevitas currently offers quantized implementations of the most common PyTorch layers used in DNN under `brevitas.nn`, such as `QuantConv1d`, `QuantConv2d`, `QuantConvTranspose1d`, `QuantConvTranspose2d`, `QuantMultiheadAttention`, `QuantRNN`, `QuantLSTM` etc., for adoption within PTQ and/or QAT.
For each one of these layers, quantization of different tensors (inputs, weights, bias, outputs, etc) can be individually tuned according to a wide range of quantization settings.
As a reference for PTQ, Brevitas provides an example user flow for ImageNet classification models under [`brevitas_examples.imagenet_classification.ptq`](https://github.com/Xilinx/brevitas/blob/master/src/brevitas_examples/imagenet_classification/ptq/ptq_evaluate.py) that quantizes an input torchvision model using PTQ under different quantization configurations (e.g. bit-width, granularity of scale, etc).
For more info, checkout our [documentation](https://xilinx.github.io/brevitas/).
## Cite as
If you adopt Brevitas in your work, please cite it as:
```
@software{brevitas,
author = {Franco, Giuseppe and Monteagudo-Lago, Pablo and Colbert, Ian and Pappalardo, Alessandro and Fraser, Nicholas J},
title = {Xilinx/brevitas},
year = {2026},
publisher = {Zenodo},
doi = {10.5281/zenodo.3333552},
url = {https://doi.org/10.5281/zenodo.3333552}
}
```
## History
- *2026/07/14* - Release version 0.13.0, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.13.0).
- *2025/08/28* - Release version 0.12.1, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.12.1).
- *2025/05/09* - Release version 0.12.0, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.12.0).
- *2024/10/10* - Release version 0.11.0, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.11.0).
- *2024/07/23* - Minor release version 0.10.3, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.10.3).
- *2024/02/19* - Minor release version 0.10.2, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.10.2).
- *2024/02/15* - Minor release version 0.10.1, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.10.1).
- *2023/12/08* - Release version 0.10.0, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.10.0).
- *2023/04/28* - Minor release version 0.9.1, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.9.1).
- *2023/04/21* - Release version 0.9.0, see the [release notes](https://github.com/Xilinx/brevitas/releases/tag/v0.9.0).
- *2023/01/10* - Release version 0.8.0, see the [release notes](https://github.com/Xilinx/brevitas/releasExcerpt of 5,237 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b140239a61bc7793, topic:deep-learning, topic:pytorch
matched fp:b140239a61bc7793, topic:quantization, desc:quantization, readme:quantization
