Improving LoRA: Implementing Weight-Decomposed Low-Rank Adaptation (DoRA) from Scratch

Low-rank adaptation (LoRA) is a machine learning technique that modifies a pretrained model (for example, an LLM or vision transformer) to better suit a specific, often smaller, dataset by adjusting only a small, low-rank subset of the model's parameters.

adjusting dataset example llm lora low low-rank adaptation machine machine learning parameters scratch small transformer vision