Efficient Fine-Tuning with LoRA: Implementing Low-Rank Adaptation in Python
Introduction
Fine-tuning large pre-trained models can be computationally expensive and resource-intensive, especially with the growing size of models in natural language processing (NLP) and other machine learning fields. LoRA, which stands for Low-Rank Adaptation, is a novel technique designed to address these challenges. By decomposing weight updates into low-rank matrices, LoRA significantly reduces the number of trainable parameters and the computational overhead required for fine-tuning.
In this blog post, we will delve into the concept of LoRA, its benefits, and provide a step-by-step guide on how to implement it in Python.
Understanding LoRA
What is LoRA?
LoRA (Low-Rank Adaptation) is a method to efficiently fine-tune large pre-trained models by introducing low-rank adaptations to their weights. Instead of updating all the parameters of the model, LoRA decomposes the weight updates into two smaller matrices. This approach drastically reduces the number of parameters that need to be trained, making the fine-tuning process more resource-efficient.
