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DAQ: Density-Aware Post-Training Weight-Only Quantization For LLMs

Install

  1. Clone this repository and navigate to DAQ folder
git clone http://this/repo
cd daq
  1. Install Package
conda create -n daq python=3.10 -y
conda activate daq
pip install --upgrade pip  # enable PEP 660 support
pip install -e .

Usage

To run DAQ:

python awq/entry.py --sample 1 --model_path /Llama-2-7b-hf --run_daq --tasks wikitext --w_bit 4 --q_group_size -1 --q_backend fake --dump daq_cache/Llama-2-7b-hf.pt

To run DAQ+AWQ:

python awq/entry.py --model_path /Llama-2-7b-hf --calibration daq --run_awq --tasks wikitext --w_bit 4 --q_group_size -1 --q_backend fake --dump awq_cache/Llama-2-7b-hf.pt --sample 2 --data_type nf4

Acknowledgements

We would like to express our gratitude to the AWQ project for their pioneering work in weight quantization for LLMs. Our work builds upon their insights and implementations.