Refresher on how LoRA works

As the scale of deep learning models grows, so does the computational and memory cost associated with fine-tuning these models for specific tasks. Fine-tuning a large model can involve adjusting billions of parameters, which is resource-intensive and often impractical. Parameter-efficient fine-tuning (PEFT) techniques have been developed to address this issue, allowing for model adaptation with…