jp6/cu128/: bitsandbytes-0.45.2 metadata and description
k-bit optimizers and matrix multiplication routines.
author_email | Tim Dettmers <dettmers@cs.washington.edu> |
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description_content_type | text/markdown |
keywords | gpu,optimizers,optimization,8-bit,quantization,compression |
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. |
maintainer_email | Titus von Kรถller <titus@huggingface.co>, Matthew Douglas <matthew.douglas@huggingface.co> |
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provides_extras | test |
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requires_python | >=3.8 |
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no releases from root/pypi are included.
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bitsandbytes-0.45.2-cp310-cp310-linux_aarch64.whl
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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
bitsandbytes
multi-backend alpha release is out!
๐ Big news! After months of hard work and incredible community contributions, we're thrilled to announce the bitsandbytes multi-backend alpha release! ๐ฅ
Now supporting:
- ๐ฅ AMD GPUs (ROCm)
- โก Intel CPUs & GPUs
Weโd love your early feedback! ๐
๐ Instructions for your pip install
here
We're super excited about these recent developments and grateful for any constructive input or support that you can give to help us make this a reality (e.g. helping us with the upcoming Apple Silicon backend or reporting bugs). BNB is a community project and we're excited for your collaboration ๐ค
License
bitsandbytes
is MIT licensed.
We thank Fabio Cannizzo for his work on FastBinarySearch which we use for CPU quantization.