jp6/cu126/: bitsandbytes-0.44.2.dev0 metadata and description

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k-bit optimizers and matrix multiplication routines.

author Tim Dettmers
author_email dettmers@cs.washington.edu
classifiers
  • Development Status :: 4 - Beta
  • Topic :: Scientific/Engineering :: Artificial Intelligence
description_content_type text/markdown
keywords gpu optimizers optimization 8-bit quantization compression
license MIT
requires_dist
  • torch
  • numpy
  • pandas; extra == "benchmark"
  • matplotlib; extra == "benchmark"
  • scipy; extra == "test"
  • lion-pytorch; extra == "test"

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bitsandbytes-0.44.2.dev0-cp310-cp310-linux_aarch64.whl
Size
10 MB
Type
Python Wheel
Python
3.10
  • Replaced 2 time(s)
  • Uploaded to jp6/cu126 by jp6 2024-10-15 15:24:28

bitsandbytes

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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!

🚀 Big news! After months of hard work and incredible community contributions, we're thrilled to announce the bitsandbytes multi-backend alpha release! 💥

Now supporting:

We’d love your early feedback! 🙏

👉 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 🤗

License

bitsandbytes is MIT licensed.

We thank Fabio Cannizzo for his work on FastBinarySearch which we use for CPU quantization.