L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models
This work addresses efficiency challenges for users of large language models, offering an incremental improvement over existing quantization-aware parameter-efficient fine-tuning methods.
The paper tackles the problem of high memory and computational costs in large language models by proposing L4Q, a method that integrates quantization-aware training with LoRA to reduce training overhead while maintaining accuracy, achieving superior results in 4-bit and 3-bit quantization on models like LLaMA and Mistral.
Due to the high memory and computational costs associated with large language models (LLMs), model compression techniques such as quantization, which reduces inference costs, and parameter-efficient fine-tuning (PEFT) methods like Low-Rank Adaptation (LoRA), which reduce training costs, have gained significant popularity. This trend has spurred active research into quantization-aware PEFT techniques, aimed at maintaining model accuracy while minimizing memory overhead during both inference and training. Previous quantization-aware PEFT methods typically apply post-training quantization (PTQ) to pre-trained LLMs, followed by PEFT to recover accuracy loss. Meanwhile, this approach has limitations in recovering the accuracy loss. In this paper, we propose L4Q, a method that integrates Quantization-Aware Training (QAT) with LoRA. By employing a memory-optimized layer design, L4Q significantly reduces QAT's memory overhead, making its training cost comparable to LoRA, while preserving the advantage of QAT in producing fully quantized LLMs with high accuracy. Our experiments demonstrate that this combined approach to quantization and fine-tuning achieves superior accuracy compared to decoupled fine-tuning schemes, particularly in 4-bit and 3-bit quantization, positioning L4Q as an efficient QAT solution. Using the LLaMA and Mistral models with instructional datasets, we showcase L4Q's capabilities in language tasks and few-shot learning.