Metadata-Version: 2.1
Name: bitsandbytes
Version: 0.43.0
Summary: k-bit optimizers and matrix multiplication routines.
Home-page: https://github.com/TimDettmers/bitsandbytes
Author: Tim Dettmers
Author-email: dettmers@cs.washington.edu
License: MIT
Keywords: gpu optimizers optimization 8-bit quantization compression
Classifier: Development Status :: 4 - Beta
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: NOTICE.md
Requires-Dist: torch
Requires-Dist: numpy
Provides-Extra: benchmark
Requires-Dist: pandas ; extra == 'benchmark'
Requires-Dist: matplotlib ; extra == 'benchmark'
Provides-Extra: test
Requires-Dist: scipy ; extra == 'test'

# `bitsandbytes`

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.

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. Windows support is quite far along and is on its way as well.

**Please head to the official documentation page:**

**[https://huggingface.co/docs/bitsandbytes/main](https://huggingface.co/docs/bitsandbytes/main)**

## License

The majority of bitsandbytes is licensed under MIT, however small portions of the project are available under separate license terms, as the parts adapted from Pytorch are licensed under the BSD license.

We thank Fabio Cannizzo for his work on [FastBinarySearch](https://github.com/fabiocannizzo/FastBinarySearch) which we use for CPU quantization.
