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README.md
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# `bitsandbytes`
<p align="center"><img src="https://avatars.githubusercontent.com/u/175231607?s=200&v=4" alt=""></p>
<h1 align="center">bitsandbytes</h1>
<p align="center">
<a href="https://github.com/bitsandbytes-foundation/bitsandbytes/main/LICENSE">
<img alt="License" src="https://img.shields.io/github/license/bitsandbytes-foundation/bitsandbytes.svg?color=blue">
</a>
<a href="https://pepy.tech/project/bitsandbytes">
<img alt="Downloads" src="https://static.pepy.tech/badge/bitsandbytes/month">
</a>
<a href="https://github.com/bitsandbytes-foundation/bitsandbytes/actions/workflows/tests.yml">
<img alt="Nightly Unit Tests" src="https://img.shields.io/github/actions/workflow/status/bitsandbytes-foundation/bitsandbytes/tests.yml?logo=github&label=Nightly%20Tests">
</a>
<a href="https://github.com/bitsandbytes-foundation/bitsandbytes/releases">
<img alt="GitHub Release" src="https://img.shields.io/github/v/release/bitsandbytes-foundation/bitsandbytes">
</a>
<a href="https://pypi.org/project/bitsandbytes/">
<img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/bitsandbytes">
</a>
</p>
[![Downloads](https://static.pepy.tech/badge/bitsandbytes)](https://pepy.tech/project/bitsandbytes) [![Downloads](https://static.pepy.tech/badge/bitsandbytes/month)](https://pepy.tech/project/bitsandbytes) [![Downloads](https://static.pepy.tech/badge/bitsandbytes/week)](https://pepy.tech/project/bitsandbytes)
`bitsandbytes` enables accessible large language models via k-bit quantization for PyTorch. We provide three main features for dramatically reducing memory consumption for inference and training:
The `bitsandbytes` library is a lightweight Python wrapper around CUDA custom functions, in particular 8-bit optimizers, matrix multiplication (LLM.int8()), and 8 & 4-bit quantization functions.
* 8-bit optimizers uses block-wise quantization to maintain 32-bit performance at a small fraction of the memory cost.
* LLM.int8() or 8-bit quantization enables large language model inference with only half the required memory and without any performance degradation. This method is based on vector-wise quantization to quantize most features to 8-bits and separately treating outliers with 16-bit matrix multiplication.
* QLoRA or 4-bit quantization enables large language model training with several memory-saving techniques that don't compromise performance. This method quantizes a model to 4-bits and inserts a small set of trainable low-rank adaptation (LoRA) weights to allow training.
The library includes quantization primitives for 8-bit & 4-bit operations, through `bitsandbytes.nn.Linear8bitLt` and `bitsandbytes.nn.Linear4bit` and 8-bit optimizers through `bitsandbytes.optim` module.
There are ongoing efforts to support further hardware backends, i.e. Intel CPU + GPU, AMD GPU, Apple Silicon, hopefully NPU.
## System Requirements
bitsandbytes has the following minimum requirements for all platforms:
**Please head to the official documentation page:**
* Python 3.9+
* [PyTorch](https://pytorch.org/get-started/locally/) 2.2+
* _Note: While we aim to provide wide backwards compatibility, we recommend using the latest version of PyTorch for the best experience._
**[https://huggingface.co/docs/bitsandbytes/main](https://huggingface.co/docs/bitsandbytes/main)**
#### Accelerator support:
## License
<table>
<thead>
<tr>
<th>Platform</th>
<th>Accelerator</th>
<th>Hardware Requirements</th>
<th>Support Status</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="4">🐧 <strong>Linux</strong></td>
</tr>
<tr>
<td align="right">x86-64</td>
<td>◻️ CPU</td>
<td></td>
<td>〰️ Partial Support</td>
</tr>
<tr>
<td></td>
<td>🟩 NVIDIA GPU</td>
<td>SM50+ minimum<br>SM75+ recommended</td>
<td>✅ Full Support *</td>
</tr>
<tr>
<td></td>
<td>🟥 AMD GPU</td>
<td>gfx90a, gfx942, gfx1100</td>
<td>🚧 In Development</td>
</tr>
<tr>
<td></td>
<td>🟦 Intel XPU</td>
<td>
Data Center GPU Max Series (Ponte Vecchio) <br>
Arc A-Series (Alchemist) <br>
Arc B-Series (Battlemage)
</td>
<td>🚧 In Development</td>
</tr>
<!--
<tr>
<td></td>
<td>🟦 Intel HPU</td>
<td>Gaudi1, Gaudi2, Gaudi3</td>
<td>🚧</td>
</tr>
--->
<tr>
<td align="right">aarch64</td>
<td>◻️ CPU</td>
<td></td>
<td>〰️ Partial Support</td>
</tr>
<tr>
<td></td>
<td>🟩 NVIDIA GPU</td>
<td>SM75, SM80, SM90, SM100</td>
<td>✅ Full Support *</td>
</tr>
<tr>
<td colspan="4">🪟 <strong>Windows</strong></td>
</tr>
<tr>
<td align="right">x86-64</td>
<td>◻️ CPU</td>
<td>AVX2</td>
<td>〰️ Partial Support</td>
</tr>
<tr>
<td></td>
<td>🟩 NVIDIA GPU</td>
<td>SM50+ minimum<br>SM75+ recommended</td>
<td>✅ Full Support *</td>
</tr>
<tr>
<td></td>
<td>🟦 Intel XPU</td>
<td>
Arc A-Series (Alchemist) <br>
Arc B-Series (Battlemage)
</td>
<td>🚧 In Development</td>
</tr>
<tr>
<td colspan="4">🍎 <strong>macOS</strong></td>
</tr>
<tr>
<td align="right">arm64</td>
<td>◻️ CPU / Metal</td>
<td>Apple M1+</td>
<td>❌ Under consideration</td>
</tr>
</tbody>
</table>
\* Accelerated INT8 requires SM75+.
## :book: Documentation
* [Official Documentation](https://huggingface.co/docs/bitsandbytes/main)
* 🤗 [Transformers](https://huggingface.co/docs/transformers/quantization/bitsandbytes)
* 🤗 [Diffusers](https://huggingface.co/docs/diffusers/quantization/bitsandbytes)
* 🤗 [PEFT](https://huggingface.co/docs/peft/developer_guides/quantization#quantize-a-model)
## :heart: Sponsors
The continued maintenance and development of `bitsandbytes` is made possible thanks to the generous support of our sponsors. Their contributions help ensure that we can keep improving the project and delivering valuable updates to the community.
<a href="https://hf.co" target="_blank"><img width="100" src="https://huggingface.co/datasets/huggingface/brand-assets/resolve/main/hf-logo.svg" alt="Hugging Face"></a>
## License
`bitsandbytes` is MIT licensed.
We thank Fabio Cannizzo for his work on [FastBinarySearch](https://github.com/fabiocannizzo/FastBinarySearch) which we use for CPU quantization.
## How to cite us
If you found this library useful, please consider citing our work:
### QLoRA
```bibtex
@article{dettmers2023qlora,
title={Qlora: Efficient finetuning of quantized llms},
author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:2305.14314},
year={2023}
}
```
### LLM.int8()
```bibtex
@article{dettmers2022llmint8,
title={LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale},
author={Dettmers, Tim and Lewis, Mike and Belkada, Younes and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:2208.07339},
year={2022}
}
```
### 8-bit Optimizers
```bibtex
@article{dettmers2022optimizers,
title={8-bit Optimizers via Block-wise Quantization},
author={Dettmers, Tim and Lewis, Mike and Shleifer, Sam and Zettlemoyer, Luke},
journal={9th International Conference on Learning Representations, ICLR},
year={2022}
}
```
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