LoRA (Low-Rank Adaptation) is an efficient method for adapting large AI models to specific tasks without having to retrain the entire model.
LoRA can be thought of as a small, specialized add-on that is applied to the original AI model. This approach is comparable to an expert acquiring additional specialized knowledge without altering their foundational knowledge.
The major advantage of LoRA lies in its resource efficiency: while training a complete AI model requires enormous computing power and storage capacity, LoRA needs only a fraction of these resources. This makes the technology particularly attractive for smaller companies and individual developers who want to adapt AI models to their specific needs.
For example, a general language model can be trained using LoRA to adopt a company’s specific writing style or to produce texts in a particular technical language. LoRA is also frequently used with image generation models like Stable Diffusion to specialize them for specific art styles, characters, or visual concepts.
The term “Low-Rank” refers to the mathematical method used to make these adaptations, which significantly reduces the complexity of the calculations.