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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 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.
bitsandbytes has the following minimum requirements for all platforms:
Platform | Accelerator | Hardware Requirements | Support Status |
---|---|---|---|
🐧 Linux, glibc >= 2.24 | |||
x86-64 | ◻️ CPU | AVX2 | 〰️ Partial Support |
🟩 NVIDIA GPU cuda | SM50+ minimum SM75+ recommended | ✅ Full Support | |
🟥 AMD GPU cuda |
CDNA: gfx90a, gfx942 RDNA: gfx1100, gfx1200 | 🚧 In Development | |
🟦 Intel GPU xpu |
Data Center GPU Max Series Arc A-Series (Alchemist) Arc B-Series (Battlemage) | 🚧 In Development | |
🟪 Intel Gaudi hpu | Gaudi1, Gaudi2, Gaudi3 | 🚧 In Development | |
aarch64 | ◻️ CPU | 〰️ Partial Support | |
🟩 NVIDIA GPU cuda | SM75, SM80, SM90, SM100 | ✅ Full Support | |
🪟 Windows 11 / Windows Server 2019+ | |||
x86-64 | ◻️ CPU | AVX2 | 〰️ Partial Support |
🟩 NVIDIA GPU cuda | SM50+ minimum SM75+ recommended | ✅ Full Support | |
🟦 Intel GPU xpu |
Arc A-Series (Alchemist) Arc B-Series (Battlemage) | 🚧 In Development | |
🍎 macOS 13.1+ | |||
arm64 | ◻️ CPU | Apple M1+ | 🛣️ Future Roadmap |
⬜ Metal mps | Apple M1+ | 🛣️ Future Roadmap |
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.
bitsandbytes
is MIT licensed.
We thank Fabio Cannizzo for his work on FastBinarySearch which we use for CPU quantization.
If you found this library useful, please consider citing our work:
@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}
}
@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}
}
@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}
}
FAQs
k-bit optimizers and matrix multiplication routines.
We found that bitsandbytes demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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