Paper ID: 2402.04902

L4Q: Parameter Efficient Quantization-Aware Fine-Tuning on Large Language Models

Hyesung Jeon, Yulhwa Kim, Jae-joon Kim

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 follow a two-step approach: first, applying post-training quantization (PTQ) to model weights, followed by PEFT on the quantized model. However, recovering from the quantization error introduced by PTQ through fine-tuning has proven challenging. Additionally, most PTQ-based PEFT methods result in a mixture of low-precision quantized weights and high-precision adapter weights, limiting the efficiency of full quantization during inference. While a previous method attempted to address these issues, it still suffers from limited adaptability due to the constrained LoRA parameter structure required to produce fully-quantized models. To overcome these challenges, we propose L4Q, a method that integrates Quantization-Aware Training (QAT) with LoRA to effectively reduce quantization error. By employing a memory-optimized layer design, L4Q significantly reduces QAT's memory overhead while producing fully-quantized weights, enabling effective adaptation to downstream tasks. Our experiments demonstrate that this combined approach to quantization and fine-tuning achieves superior accuracy compared to decoupled fine-tuning schemes, particularly in sub-4-bit quantization, positioning L4Q as an efficient QAT solution. Using the LLaMA model families and instructional datasets, we showcase L4Q's capabilities in language tasks and few-shot learning.

Submitted: Feb 7, 2024