CPSVD: Enhancing Large Language Model Compression via Column-Preserving Singular Value Decomposition

This repository provides the official implementation of CPSVD, a novel method for compressing large language models (LLMs) by combining column selection and SVD-based low-rank approximation. CPSVD is designed to preserve important structural information in the weight matrices, enabling efficient compression without sacrificing performance.

🚀 Quick Start

1. Installation

Create and activate a conda environment:

conda create -n cpsvd python=3.9
conda activate cpsvd

Clone the repository:

git clone https://github.com/Pupu792/CPSVD.git
cd CPSVD

Install the required dependencies:

pip install -r requirements.txt

📖 Example

You can run the main script with the following command:

python CPSVD.py --model /models/Llama-7b --ratio 0.4 --step -1 --trunc_rank_method cos --t 0.1 --matrices_optimized --eval_ppl --eval_zero_shot --cuda_devices 0

By default, the results are saved in a format compatible with the transformers library.

Note: If you need to save the decomposed version, set the --step argument to 0 and modify the transformers library to version 4.35.2. For detailed instructions, please refer to the original project: SVD-LLM.

📊 Evaluation

For evaluating zero-shot tasks, we adopt the methods used in the wanda repository.

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🙏 Acknowledgments

This repository is built upon the foundational work of SVD-LLM.