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
🚀 Big news! After months of hard work and incredible community contributions, we're thrilled to announce the bitsandbytes multi-backend alpha release! 💥
Now supporting:
- 🔥 AMD GPUs (ROCm)
- ⚡ Intel CPUs & GPUs
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 🤗
bitsandbytes is MIT licensed.
We thank Fabio Cannizzo for his work on FastBinarySearch which we use for CPU quantization.
