Aditya Bhaskara

An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruning

This work develops a new SpFT framework, based on ideas from neural network pruning, that improves SpFT's memory efficiency by 20-50\% while matching the accuracy of state-of-the-art methods like LoRA's variants.