LLMs are amazing zero-shot learners. Still, there are situations which demand either partial or full fine tuning in order to satisfy specific tasks. Adapting large LLMs (Ex. 175 billion parameters) for such a specific task presents a significant challenge: the prohibitive computational cost of fine tuning them for such a task. This limitation restricts the accessibility and applicability of LLMs, hindering their full potential impact. Fortunately researchers have come up with different solutions to address the cost equation.
Low-Rank Adaptation of LLMs (LoRA for short) presents a novel and effective solution to this problem. By decomposing the LLM’s weight matrix into two distinct components – a low-rank matrix capturing the model’s core knowledge and a dense matrix containing task-specific information – LoRA enables efficient and flexible adaptation. LoRA is a specialized form of Parameter-Efficient Fine Tuning (PEFT), both aimed at efficiently adapting pre-trained models to new tasks or domains while conserving resources. LoRA, as part of the PEFT spectrum, focuses on fine-tuning models by integrating low-rank matrices, thus adjusting a minimal number of parameters. In contrast, PEFT encompasses a wider array of techniques, each distinct in its method for achieving parameter efficiency. Crucially, only the low-rank matrix undergoes training for each new task. This drastically reduces the number of parameters to be optimized, leading to significantly faster and more cost-effective adaptation compared to full fine tuning.
This approach offers several compelling benefits:
Enhanced Efficiency: LoRA’s reduced training footprint minimizes computational resources and time required for task adaptation.
Increased Adaptability: By training separate low-rank matrices for different tasks, a single LLM can be readily adapted to a diverse range of applications.
Improved Accessibility: The reduced resource requirements of LoRA enable deployment of LLMs on resource-constrained environments, democratizing access to their capabilities.
LoRA’s ability to overcome the size and computational cost barriers associated with LLMs paves the way for a more accessible and versatile future for these powerful language models.
Reference:
https://arxiv.org/abs/2106.09685
https://huggingface.co/docs/peft/task_guides/clm-prompt-tuning
See Also: Parameter-Efficient Tuning Methods