PEFT

Traditional fine-tuning methods become increasingly impractical as the model size grows due to the immense computational resources and time required to train and update all model parameters. For most businesses, including larger organizations, a classical approach to fine-tuning is cost-prohibitive and, effectively, a non-starter.

Alternatively, PEFT methods modify only a small subset of a model’s parameters, reducing the computational burden while still achieving state-of-the-art performance. This method is advantageous for adapting large models to specific tasks without extensive retraining.

One such PEFT method is the Low-Rank Adaptation (LoRA) methodology, developed by Hu et al. (2021).

LoRA

The LoRA method focuses on selectively fine-tuning specific components within the Transformer architecture to enhance efficiency and effectiveness in LLMS. LoRA targets the weight matrices found in the self-attention module of the Transformer, which, as discussed in...