LoRA (Low Rank Adaptation) Fine-Tuning for LLMs

Large Language Models (LLMs) have proven incredibly useful for a variety of applications in our day-to-day lives. But to create and train a new model from scratch becomes very expensive as models scale in size and often requires supercomputers to do so.

To tackle this, rather than build a new model every time we’re faced with a new problem, we can tweak an existing one. This technique is called fine-tuning, and it adapts a model to new tasks at a fraction of the cost of full training.

Fine-tuning an existing model allows us to change only certain features to our necessity. Although fine-tuning is a comparatively more efficient way to make practical models, it can also become far too computationally expensive when dealing with large-scale models that house hundreds of billions of parameters.

This leads us to Low-Rank Adaptation (LoRA), a popular technique that makes fine-tuning even more efficient. It was introduced in a 2021 paper titled “LoRA: Low-Rank Adaptation of Large Language Models.”