| Name | bitsandbytes JSON |
| Version |
0.47.0
JSON |
| download |
| home_page | None |
| Summary | k-bit optimizers and matrix multiplication routines. |
| upload_time | 2025-08-11 18:51:20 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License
Copyright (c) Facebook, Inc. and its affiliates.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
gpu
optimizers
optimization
8-bit
quantization
compression
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<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>
`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:
* 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.
## System Requirements
bitsandbytes has the following minimum requirements for all platforms:
* 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._
#### Accelerator support:
<small>Note: this table reflects the status of the current development branch. For the latest stable release, see the
[document in the 0.47.0 tag](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/0.47.0/README.md#accelerator-support).
</small>
##### Legend:
π§ = In Development,
γ°οΈ = Partially Supported,
β
= Supported,
β = Not Supported
<table>
<thead>
<tr>
<th>Platform</th>
<th>Accelerator</th>
<th>Hardware Requirements</th>
<th>LLM.int8()</th>
<th>QLoRA 4-bit</th>
<th>8-bit Optimizers</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="6">π§ <strong>Linux, glibc >= 2.24</strong></td>
</tr>
<tr>
<td align="right">x86-64</td>
<td>β»οΈ CPU</td>
<td>AVX2</td>
<td>β
</td>
<td>β
</td>
<td>β</td>
</tr>
<tr>
<td></td>
<td>π© NVIDIA GPU <br><code>cuda</code></td>
<td>SM50+ minimum<br>SM75+ recommended</td>
<td>β
</td>
<td>β
</td>
<td>β
</td>
</tr>
<tr>
<td></td>
<td>π₯ AMD GPU <br><code>cuda</code></td>
<td>
CDNA: gfx90a, gfx942<br>
RDNA: gfx1100
</td>
<td>π§</td>
<td>π§</td>
<td>π§</td>
</tr>
<tr>
<td></td>
<td>π¦ Intel GPU <br><code>xpu</code></td>
<td>
Data Center GPU Max Series<br>
Arc A-Series (Alchemist)<br>
Arc B-Series (Battlemage)
</td>
<td>π§</td>
<td>π§</td>
<td>π§</td>
</tr>
<tr>
<td></td>
<td>πͺ Intel Gaudi <br><code>hpu</code></td>
<td>Gaudi1, Gaudi2, Gaudi3</td>
<td>π§</td>
<td>π§</td>
<td>β</td>
</tr>
<tr>
<td align="right">aarch64</td>
<td>β»οΈ CPU</td>
<td></td>
<td>β
</td>
<td>β
</td>
<td>β</td>
</tr>
<tr>
<td></td>
<td>π© NVIDIA GPU <br><code>cuda</code></td>
<td>SM75+</td>
<td>β
</td>
<td>β
</td>
<td>β
</td>
</tr>
<tr>
<td colspan="6">πͺ <strong>Windows 11 / Windows Server 2019+</strong></td>
</tr>
<tr>
<td align="right">x86-64</td>
<td>β»οΈ CPU</td>
<td>AVX2</td>
<td>β
</td>
<td>β
</td>
<td>β</td>
</tr>
<tr>
<td></td>
<td>π© NVIDIA GPU <br><code>cuda</code></td>
<td>SM50+ minimum<br>SM75+ recommended</td>
<td>β
</td>
<td>β
</td>
<td>β
</td>
</tr>
<tr>
<td></td>
<td>π¦ Intel GPU <br><code>xpu</code></td>
<td>
Arc A-Series (Alchemist) <br>
Arc B-Series (Battlemage)
</td>
<td>π§</td>
<td>π§</td>
<td>π§</td>
</tr>
<tr>
<td colspan="6">π <strong>macOS 14+</strong></td>
</tr>
<tr>
<td align="right">arm64</td>
<td>β»οΈ CPU</td>
<td>Apple M1+</td>
<td>π§</td>
<td>π§</td>
<td>β</td>
</tr>
<tr>
<td></td>
<td>β¬ Metal <br><code>mps</code></td>
<td>Apple M1+</td>
<td>π§</td>
<td>π§</td>
<td>β</td>
</tbody>
</table>
## :book: Documentation
* [Official Documentation](https://huggingface.co/docs/bitsandbytes/main)
* π€ [Transformers](https://huggingface.co/docs/transformers/quantization/bitsandbytes)
* π€ [Diffusers](https://huggingface.co/docs/diffusers/quantization/bitsandbytes)
* π€ [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}
}
```
Raw data
{
"_id": null,
"home_page": null,
"name": "bitsandbytes",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": "Titus von K\u00f6ller <titus@huggingface.co>, Matthew Douglas <matthew.douglas@huggingface.co>",
"keywords": "gpu, optimizers, optimization, 8-bit, quantization, compression",
"author": null,
"author_email": "Tim Dettmers <dettmers@cs.washington.edu>",
"download_url": null,
"platform": null,
"description": "<p align=\"center\"><img src=\"https://avatars.githubusercontent.com/u/175231607?s=200&v=4\" alt=\"\"></p>\n<h1 align=\"center\">bitsandbytes</h1>\n<p align=\"center\">\n <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>\n <a href=\"https://pepy.tech/project/bitsandbytes\"><img alt=\"Downloads\" src=\"https://static.pepy.tech/badge/bitsandbytes/month\"></a>\n <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>\n <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>\n <a href=\"https://pypi.org/project/bitsandbytes/\"><img alt=\"PyPI - Python Version\" src=\"https://img.shields.io/pypi/pyversions/bitsandbytes\"></a>\n</p>\n\n`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:\n\n* 8-bit optimizers uses block-wise quantization to maintain 32-bit performance at a small fraction of the memory cost.\n* 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.\n* 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.\n\nThe 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.\n\n## System Requirements\nbitsandbytes has the following minimum requirements for all platforms:\n\n* Python 3.9+\n* [PyTorch](https://pytorch.org/get-started/locally/) 2.2+\n * _Note: While we aim to provide wide backwards compatibility, we recommend using the latest version of PyTorch for the best experience._\n\n#### Accelerator support:\n\n<small>Note: this table reflects the status of the current development branch. For the latest stable release, see the\n[document in the 0.47.0 tag](https://github.com/bitsandbytes-foundation/bitsandbytes/blob/0.47.0/README.md#accelerator-support).\n</small>\n\n##### Legend:\n\ud83d\udea7 = In Development,\n\u3030\ufe0f = Partially Supported,\n\u2705 = Supported,\n\u274c = Not Supported\n\n<table>\n <thead>\n <tr>\n <th>Platform</th>\n <th>Accelerator</th>\n <th>Hardware Requirements</th>\n <th>LLM.int8()</th>\n <th>QLoRA 4-bit</th>\n <th>8-bit Optimizers</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td colspan=\"6\">\ud83d\udc27 <strong>Linux, glibc >= 2.24</strong></td>\n </tr>\n <tr>\n <td align=\"right\">x86-64</td>\n <td>\u25fb\ufe0f CPU</td>\n <td>AVX2</td>\n <td>\u2705</td>\n <td>\u2705</td>\n <td>\u274c</td>\n </tr>\n <tr>\n <td></td>\n <td>\ud83d\udfe9 NVIDIA GPU <br><code>cuda</code></td>\n <td>SM50+ minimum<br>SM75+ recommended</td>\n <td>\u2705</td>\n <td>\u2705</td>\n <td>\u2705</td>\n </tr>\n <tr>\n <td></td>\n <td>\ud83d\udfe5 AMD GPU <br><code>cuda</code></td>\n <td>\n CDNA: gfx90a, gfx942<br>\n RDNA: gfx1100\n </td>\n <td>\ud83d\udea7</td>\n <td>\ud83d\udea7</td>\n <td>\ud83d\udea7</td>\n </tr>\n <tr>\n <td></td>\n <td>\ud83d\udfe6 Intel GPU <br><code>xpu</code></td>\n <td>\n Data Center GPU Max Series<br>\n Arc A-Series (Alchemist)<br>\n Arc B-Series (Battlemage)\n </td>\n <td>\ud83d\udea7</td>\n <td>\ud83d\udea7</td>\n <td>\ud83d\udea7</td>\n </tr>\n <tr>\n <td></td>\n <td>\ud83d\udfea Intel Gaudi <br><code>hpu</code></td>\n <td>Gaudi1, Gaudi2, Gaudi3</td>\n <td>\ud83d\udea7</td>\n <td>\ud83d\udea7</td>\n <td>\u274c</td>\n </tr>\n <tr>\n <td align=\"right\">aarch64</td>\n <td>\u25fb\ufe0f CPU</td>\n <td></td>\n <td>\u2705</td>\n <td>\u2705</td>\n <td>\u274c</td>\n </tr>\n <tr>\n <td></td>\n <td>\ud83d\udfe9 NVIDIA GPU <br><code>cuda</code></td>\n <td>SM75+</td>\n <td>\u2705</td>\n <td>\u2705</td>\n <td>\u2705</td>\n </tr>\n <tr>\n <td colspan=\"6\">\ud83e\ude9f <strong>Windows 11 / Windows Server 2019+</strong></td>\n </tr>\n <tr>\n <td align=\"right\">x86-64</td>\n <td>\u25fb\ufe0f CPU</td>\n <td>AVX2</td>\n <td>\u2705</td>\n <td>\u2705</td>\n <td>\u274c</td>\n </tr>\n <tr>\n <td></td>\n <td>\ud83d\udfe9 NVIDIA GPU <br><code>cuda</code></td>\n <td>SM50+ minimum<br>SM75+ recommended</td>\n <td>\u2705</td>\n <td>\u2705</td>\n <td>\u2705</td>\n </tr>\n <tr>\n <td></td>\n <td>\ud83d\udfe6 Intel GPU <br><code>xpu</code></td>\n <td>\n Arc A-Series (Alchemist) <br>\n Arc B-Series (Battlemage)\n </td>\n <td>\ud83d\udea7</td>\n <td>\ud83d\udea7</td>\n <td>\ud83d\udea7</td>\n </tr>\n <tr>\n <td colspan=\"6\">\ud83c\udf4e <strong>macOS 14+</strong></td>\n </tr>\n <tr>\n <td align=\"right\">arm64</td>\n <td>\u25fb\ufe0f CPU</td>\n <td>Apple M1+</td>\n <td>\ud83d\udea7</td>\n <td>\ud83d\udea7</td>\n <td>\u274c</td>\n </tr>\n <tr>\n <td></td>\n <td>\u2b1c Metal <br><code>mps</code></td>\n <td>Apple M1+</td>\n <td>\ud83d\udea7</td>\n <td>\ud83d\udea7</td>\n <td>\u274c</td>\n </tbody>\n</table>\n\n## :book: Documentation\n* [Official Documentation](https://huggingface.co/docs/bitsandbytes/main)\n* \ud83e\udd17 [Transformers](https://huggingface.co/docs/transformers/quantization/bitsandbytes)\n* \ud83e\udd17 [Diffusers](https://huggingface.co/docs/diffusers/quantization/bitsandbytes)\n* \ud83e\udd17 [PEFT](https://huggingface.co/docs/peft/developer_guides/quantization#quantize-a-model)\n\n## :heart: Sponsors\nThe 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.\n\n<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>\n\n## License\n`bitsandbytes` is MIT licensed.\n\nWe thank Fabio Cannizzo for his work on [FastBinarySearch](https://github.com/fabiocannizzo/FastBinarySearch) which we use for CPU quantization.\n\n## How to cite us\nIf you found this library useful, please consider citing our work:\n\n### QLoRA\n\n```bibtex\n@article{dettmers2023qlora,\n title={Qlora: Efficient finetuning of quantized llms},\n author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},\n journal={arXiv preprint arXiv:2305.14314},\n year={2023}\n}\n```\n\n### LLM.int8()\n\n```bibtex\n@article{dettmers2022llmint8,\n title={LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale},\n author={Dettmers, Tim and Lewis, Mike and Belkada, Younes and Zettlemoyer, Luke},\n journal={arXiv preprint arXiv:2208.07339},\n year={2022}\n}\n```\n\n### 8-bit Optimizers\n\n```bibtex\n@article{dettmers2022optimizers,\n title={8-bit Optimizers via Block-wise Quantization},\n author={Dettmers, Tim and Lewis, Mike and Shleifer, Sam and Zettlemoyer, Luke},\n journal={9th International Conference on Learning Representations, ICLR},\n year={2022}\n}\n```\n",
"bugtrack_url": null,
"license": "MIT License\n \n Copyright (c) Facebook, Inc. and its affiliates.\n \n Permission is hereby granted, free of charge, to any person obtaining a copy\n of this software and associated documentation files (the \"Software\"), to deal\n in the Software without restriction, including without limitation the rights\n to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n \n The above copyright notice and this permission notice shall be included in all\n copies or substantial portions of the Software.\n \n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n SOFTWARE.\n ",
"summary": "k-bit optimizers and matrix multiplication routines.",
"version": "0.47.0",
"project_urls": {
"changelog": "https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/CHANGELOG.md",
"docs": "https://huggingface.co/docs/bitsandbytes/main",
"homepage": "https://github.com/bitsandbytes-foundation/bitsandbytes",
"issues": "https://github.com/bitsandbytes-foundation/bitsandbytes/issues"
},
"split_keywords": [
"gpu",
" optimizers",
" optimization",
" 8-bit",
" quantization",
" compression"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "aaeb477d6b5602f469c7305fd43eec71d890c39909f615c1d7138f6e7d226eff",
"md5": "34623b680accc4e22e7eed17561bbbcc",
"sha256": "2f805b76891a596025e9e13318b675d08481b9ee650d65e5d2f9d844084c6521"
},
"downloads": -1,
"filename": "bitsandbytes-0.47.0-py3-none-manylinux_2_24_aarch64.whl",
"has_sig": false,
"md5_digest": "34623b680accc4e22e7eed17561bbbcc",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 30004641,
"upload_time": "2025-08-11T18:51:20",
"upload_time_iso_8601": "2025-08-11T18:51:20.524667Z",
"url": "https://files.pythonhosted.org/packages/aa/eb/477d6b5602f469c7305fd43eec71d890c39909f615c1d7138f6e7d226eff/bitsandbytes-0.47.0-py3-none-manylinux_2_24_aarch64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "9c4091f1a5a694f434bc13cba160045fdc4e867032e627b001bf411048fefd9c",
"md5": "95d59458d7bd5d3d666c69f541693595",
"sha256": "68f3fffd494a47ed1fd7593bfc5dd2ac69b68260599b71b4c4b3a32f90f3b184"
},
"downloads": -1,
"filename": "bitsandbytes-0.47.0-py3-none-manylinux_2_24_x86_64.whl",
"has_sig": false,
"md5_digest": "95d59458d7bd5d3d666c69f541693595",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 61284639,
"upload_time": "2025-08-11T18:51:23",
"upload_time_iso_8601": "2025-08-11T18:51:23.581508Z",
"url": "https://files.pythonhosted.org/packages/9c/40/91f1a5a694f434bc13cba160045fdc4e867032e627b001bf411048fefd9c/bitsandbytes-0.47.0-py3-none-manylinux_2_24_x86_64.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "18a9e07a227f1cd6562844cea2f05ee576b0991a9a91f45965c06034178ba0f6",
"md5": "c1153094c015730aa0549439e0f3d824",
"sha256": "4880a6d42ca9628b5a571c8cc3093dc3f5f52511e5a9e47d52d569807975531a"
},
"downloads": -1,
"filename": "bitsandbytes-0.47.0-py3-none-win_amd64.whl",
"has_sig": false,
"md5_digest": "c1153094c015730aa0549439e0f3d824",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.9",
"size": 60725121,
"upload_time": "2025-08-11T18:51:27",
"upload_time_iso_8601": "2025-08-11T18:51:27.543951Z",
"url": "https://files.pythonhosted.org/packages/18/a9/e07a227f1cd6562844cea2f05ee576b0991a9a91f45965c06034178ba0f6/bitsandbytes-0.47.0-py3-none-win_amd64.whl",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-08-11 18:51:20",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "bitsandbytes-foundation",
"github_project": "bitsandbytes",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "bitsandbytes"
}
