llm-quantkit 0.20

cli tool for downloading and quantizing LLMs

quantkit

A tool for downloading and converting HuggingFace models without drama.

Install

pip3 install llm-quantkit

Usage

Usage: quantkit [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  download    Download model from huggingface.
  safetensor  Download and/or convert a pytorch model to safetensor format.
  awq         Download and/or convert a model to AWQ format.
  exl2        Download and/or convert a model to EXL2 format.
  gptq        Download and/or convert a model to GPTQ format.

The first argument after command should be an HF repo id (mistralai/Mistral-7B-v0.1) or a local directory with model files in it already.

The download command defaults to downloading into the HF cache and producing symlinks in the output dir, but there is a --no-cache option which places the model files in the output directory.

AWQ defaults to 4 bits, group size 128, zero-point True.
GPTQ defaults are 4 bits, group size 128, activation-order False.
EXL2 defaults to 8 head bits but there is no default bitrate.

Examples

Download a model from HF and don't use HF cache:

quantkit download teknium/Hermes-Trismegistus-Mistral-7B --no-cache

Only download the safetensors version of a model (useful for models that have torch and safetensor):

quantkit download mistralai/Mistral-7B-v0.1 --no-cache --safetensors-only -out mistral7b

Download and convert a model to safetensor, deleting the original pytorch bins:

quantkit safetensor migtissera/Tess-10.7B-v1.5b --delete-original

Download and convert a model to AWQ:

quantkit awq mistralai/Mistral-7B-v0.1 -out Mistral-7B-v0.1-AWQ

Convert a model to GPTQ (4 bits / group-size 32):

quantkit gptq mistral7b -out Mistral-7B-v0.1-GPTQ -b 4 --group-size 32

Convert a model to exllamav2:

quantkit exl2 mistralai/Mistral-7B-v0.1 -out Mistral-7B-v0.1-exl2-b8-h8 -b 8 -hb 8

Still in beta.