# SORSA: Singular Values and Orthonormal Regularized Singular Vectors Adaptation of Large Language Models

[![arXiv](https://img.shields.io/badge/arXiv-2409.00055-b31b1b.svg)](https://arxiv.org/abs/2409.00055) [![PyPI - Version](https://img.shields.io/pypi/v/sorsa)](https://pypi.org/project/sorsa/)

This repository contains the codes of experiments of the paper *SORSA: Singular Values and Orthonormal Regularized Singular Vectors Adaptation of Large Language Models*.

![Architecture of SORSA](./assets/SORSA.png)

The rapid advancement in large language models (LLMs) comes with a significant increase in their parameter size, presenting challenges for adaptation and fine-tuning. Parameter-efficient fine-tuning (PEFT) methods are widely used to adapt LLMs for downstream tasks efficiently. In this paper, we propose Singular Values and Orthonormal Regularized Singular Vectors Adaptation, or SORSA, a novel PEFT method. Each SORSA adapter consists of two main parts: trainable principal singular weights $W_p = U_p \text{diag}(S_p) V^\top_p$, and frozen residual weights $W_r = U_r \text{diag}(S_r) V^\top_r$. These parts are initialized by performing SVD on pre-trained weights. Moreover, we implement and analyze an orthonormal regularizer. SORSA adapters could be merged during inference, thus eliminating any inference latency.

## Empirical Experiments

![Exp](./assets/Exp.png)


## Reproduce the Experiments

First, install `sorsa` package from pip:
```bash
pip install sorsa
```

Then, create `.env` file in the root directory of the project and add your [Hugging Face Access Token](https://huggingface.co/settings/tokens):
```bash
hf=Your_Hugging_Face_Access_Token
```

### Llama 2 7B, Mistral v0.1 7B and Gemma 7B

First, install the packages via anaconda

```bash
conda env create -f environment.yml
```

Run scripts from `./scripts/train_sorsa.sh` to train the model.

After training, run the ``./scripts/merge_sorsa.sh`` to merge the adapter to the base model:

Run following command to evaluate on GSM-8K:

```bash
python3 run.py --name llama2_sorsa_r128 \
  --test \
  --test-dataset gsm-8k \
  --test-precision bf16
```

Run following command to evaluate on MATH:

```bash
python3 run.py --name llama2_sorsa_r128 \
  --test \
  --test-dataset math \
  --test-precision bf16
```

Run following command to evaluate on HumanEval:

```bash
python3 run.py --name llama2_sorsa_r128 \
  --test \
  --test-dataset humaneval \
  --test-precision bf16
```

### RWKV6

If you are training, merging or testing RWKV6 model, please add `--rwkv` flag to `run.py`.

## Cite the work

You could cite the work by using the BibTeX code as follows:
```bibtex
@article{cao2024sorsa,
  title={SORSA: Singular Values and Orthonormal Regularized Singular Vectors Adaptation of Large Language Models},
  author={Cao, Yang},
  journal={arXiv preprint arXiv:2409.00055},
  year={2024}
}
```
