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sakshi7502/PEFT

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App README

SVFT: Singular Vector guided Fine Tuning

[Optimized version coming soon]

Installing Required Packages

bash

pip install -r requirements.txt

Setting up Commonsense Reasoning

Once the requirements are installed, download the eval datasets i.e the "dataset" folder from https://github.com/AGI-Edgerunners/LLM-Adapters into the LLM-Adapters directory.

./run_commonsense.sh

Is configured to run Gemma-2B models on Commonesense-15K dataset.

Evaluation is done by running,

python3 multi_dataset_eval.py

Setting up Mathematical Reasoning

First, download the MetaMathQA dataset into the ``data/train`` directory. Then download the MetaMathQA-40K dataset

cd ./data/train

wget https://huggingface.co/datasets/meta-math/MetaMathQA-40K/resolve/main/MetaMathQA-40K.json

To run experiments on Pythia models,

./run_pythia.sh

For other models, run,

./run_math.sh

which is currently configured to run Gemma-2B with SVFT.

run_math.sh``` also contains an example to run evaluation on GSM-8K and MetaMath-40K.


### Vision Experiments

For the vision experiments, see the ReadMe file in the vision experiments folder

## Citation

@misc{lingam2024svft, title={SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors}, author={Vijay Lingam and Atula Tejaswi and Aditya Vavre and Aneesh Shetty and Gautham Krishna Gudur and Joydeep Ghosh and Alex Dimakis and Eunsol Choi and Aleksandar Bojchevski and Sujay Sanghavi}, year={2024}, eprint={2405.19597}, archivePrefix={arXiv}, primaryClass={cs.LG} }