llm-quantkit 0.26

cli tool for downloading and quantizing LLMs

quantkit

A tool for downloading and converting HuggingFace models without drama.

Install

If you're on a machine with an NVIDIA/CUDA GPU and want AWQ/GPTQ support:

pip3 install llm-quantkit[cuda]

Otherwise, the default install works.

pip3 install llm-quantkit

Requirements

If you need a device specific torch, install it first.

This project depends on torch, awq, exl2, gptq, and hqq libraries.
Some of these dependencies do not support Python 3.12 yet.
Supported Pythons: 3.8, 3.9, 3.10, and 3.11

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.
  gguf        Download and/or convert a model to GGUF format.
  gptq        Download and/or convert a model to GPTQ format.
  hqq         Download and/or convert a model to HQQ 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.
GGUF defaults to no imatrix but there is no default quant-type.
HQQ defaults to 4 bits, group size 64, zero_point=True.

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 from specific revision of a huggingface repo:

uantkit download turboderp/TinyLlama-1B-32k-exl2 --branch 6.0bpw --no-cache -out TinyLlama-1B-32k-exl2-b6

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 GGUF (Q5_K):

quantkit gguf TinyLlama/TinyLlama-1.1B-Chat-v1.0 -out TinyLlama-1.1B-Q5_K.gguf Q5_K

Download and convert a model to GGUF using an imatrix, offloading 200 layers:

quantkit gguf TinyLlama/TinyLlama-1.1B-Chat-v1.0 -out TinyLlama-1.1B-IQ4_XS.gguf IQ4_XS --built-in-imatrix -ngl 200

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

Convert a model to HQQ:

quantkit hqq mistralai/Mistral-7B-v0.1 -out Mistral-7B-HQQ-w4-gs64

Hardware Requirements

Here's what has worked for me in testing. Drop a PR or Issue with updates for what is possible on various size cards.
GGUF conversion doesn't need a GPU except for iMatrix and Exllamav2 requires that the largest layer fits on single GPU.

Model Size	Quant	VRAM	Successful
7B	AWQ	24GB	✅
7B	EXL2	24GB	✅
7B	GGUF	24GB	✅
7B	GPTQ	24GB	✅
7B	HQQ	24GB	✅
13B	AWQ	24GB	✅
13B	EXL2	24GB	✅
13B	GGUF	24GB	✅
13B	GPTQ	24GB	:x:
13B	HQQ	24GB	?
34B	AWQ	24GB	:x:
34B	EXL2	24GB	✅
34B	GGUF	24GB	✅
34B	GPTQ	24GB	:x:
34B	HQQ	24GB	?
70B	AWQ	24GB	:x:
70B	EXL2	24GB	✅
70B	GGUF	24GB	✅
70B	GPTQ	24GB	:x:
70B	HQQ	24GB	?

Notes

Still in beta. Llama.cpp offloading is probably not going to work on your platform unless you uninstall llama-cpp-conv and reinstall it with the proper build flags. Look at the llama-cpp-python documentation and follow the relevant command but replace llama-cpp-python with llama-cpp-conv.