Performance Efficiency Trade-offs in LoRA and QLoRA-Based Fine-Tuning of Large Language Models
The role of pre-trained language models is fundamental for NLP; however, fine-tuning them for a particular task becomes increasingly computationally costly as the size of the model increases. Approaches like LoRA and QLoRA are more cost-effective because they train just a handful of parameters, leaving the whole pre-tr...
Lakshya Santani, Eashan Salian, Harshika Masand
· Zenodo (CERN European Organi... · 0 citations