Parameter-Efficient Fine-Tuning (PEFT): A Hands-On Guide with LoRA

Parameter-Efficient Fine-Tuning (PEFT) enables the fine-tuning of complex AI models without needing significant computational resources. Techniques like Low-Rank Adaptation (LoRA) focus on tweaking crucial parameters to adapt models for specific tasks efficiently. This method reduces memory requirements and speeds up training while preserving the original model's weights. A guide on implementing LoRA from scratch in PyTorch is provided, illustrating its practical benefits using an MNIST digit recognition example.