QA-LoRA

This repository provides the PyTorch implementation of QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models

QA-LoRA is easily implemented with a few lines of code, and it equips the original LoRA with two-fold abilities: (i) during fine-tuning, the LLM's weights are quantized (e.g., into INT4) to reduce time and memory usage; (ii) after fine-tuning, the LLM and auxiliary weights are naturally integrated into a quantized model without loss of accuracy.

Installation

conda create -n qalora python=3.8
conda activate qalora
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
git clone -b peft_integration https://github.com/PanQiWei/AutoGPTQ.git && cd AutoGPTQ
pip install .[triton]
cd ..
git clone https://github.com/timdettmers/bitsandbytes.git
cd bitsandbytes
# CUDA_VERSIONS in {110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 120}
# make argument in {cuda110, cuda11x, cuda12x}
# if you do not know what CUDA you have, try looking at the output of: python -m bitsandbytes
CUDA_VERSION=117 make cuda11x
python setup.py install
cd ..
pip install git+https://github.com/huggingface/transformers.git
pip install git+https://github.com/huggingface/peft.git
pip install git+https://github.com/huggingface/accelerate.git
pip install -r requirements.txt
pip install protobuf==3.20.*

Change the peft_utils.py in your own auto-gptq path(python path/auto_gptq/utils/peft_utils.py) with the new one. For the users of GPTQLORA, you only need to change the peft_utils.py file.

Quantization

We use GPTQ for quantization. bits=4, group-size=32, act-order=False If you change the group-size, you need to change the group_size in peft_utils.py and merge.py accordingly.

Training

python qalora.py --model_path <path>

The file structure of the model checkpoint is as follows:

config.json             llama7b-4bit-32g.bin  special_tokens_map.json  tokenizer_config.json
generation_config.json  quantize_config.json      tokenizer.model

Merge

Note that our trained LoRA modules can be perfectly merged into the quantized model. We offer a simple merged script in this repo.

Acknowledgements

Our code is based on QLoRA, GPTQLORA, Auto-GPTQ