Exploration of Low-Rank Adaptation (LoRA), a crucial technique for efficiently fine-tuning large language models (LLMs).
It explains LoRA's theoretical foundation, based on the hypothesis that weight changes during adaptation have a low intrinsic rank, and details its architectural implementation using trainable "adapter" matrices.
The document also provides a practical guide for implementing LoRA using the Hugging Face PEFT library, covering prerequisites, data preparation, and hyperparameter optimization.
Furthermore, it surveys the LoRA ecosystem by discussing advanced variants like QLoRA and other Parameter-Efficient Fine-Tuning (PEFT) methods, addresses challenges such as catastrophic forgetting, and highlights real-world applications across various industries, concluding with a look into future research directions.
