Bitsandbytes Python Packages

experts4bit-qlora

Fine-tune and serve large Mixture-of-Experts models on one GPU, with 4-bit experts in GPU memory, RAM or SSD.

conch-triton-kernels

A "standard library" of Triton kernels.

grouped-nf4-gemm

4-bit GPU kernels that compute directly on packed Mixture-of-Experts weights, for training and inference.

canifinetune

Single-GPU LLM fine-tuning preflight: memory estimates, runnable LoRA/QLoRA recipes, and local measurements.

bitnarrow

Zero-training, in-place weight surgery and directional abliteration engine for 4-bit quantized LLMs using Winsorized activation profiling and Gram-Schmidt orthogonal projection

xllm

🦖 X—LLM: Cutting Edge & Easy LLM Finetuning

model-quantizer

Effortlessly quantize, benchmark, and publish Hugging Face models with cross-platform support for CPU/GPU. Reduce model size by 75% while maintaining performance.

e4b

Fine-tune and serve large Mixture-of-Experts models on one GPU, with 4-bit experts in GPU memory, RAM or SSD.

experts4bit

Fine-tune and serve large Mixture-of-Experts models on one GPU, with 4-bit experts in GPU memory, RAM or SSD.

e4b-qlora

Fine-tune and serve large Mixture-of-Experts models on one GPU, with 4-bit experts in GPU memory, RAM or SSD.

experts-mxfp4

Fine-tune and serve large Mixture-of-Experts models on one GPU, with 4-bit experts in GPU memory, RAM or SSD.

grouped-mxfp4-gemm

4-bit GPU kernels that compute directly on packed Mixture-of-Experts weights, for training and inference.

expertsnbit

Fine-tune and serve large Mixture-of-Experts models on one GPU, with 4-bit experts in GPU memory, RAM or SSD.

gnf4

4-bit GPU kernels that compute directly on packed Mixture-of-Experts weights, for training and inference.

nf4gemm

4-bit GPU kernels that compute directly on packed Mixture-of-Experts weights, for training and inference.