QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models

• URL: http://arxiv.org/abs/2502.12346v1
• Date: Mon, 17 Feb 2025 22:20:31 GMT
• Title: QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models
• Authors: Jiajun Zhou, Yifan Yang, Kai Zhen, Ziyue Liu, Yequan Zhao, Ershad Banijamali, Athanasios Mouchtaris, Ngai Wong, Zheng Zhang,
• Abstract summary: Language Models (LLMs) are often quantized to lower precision to reduce the memory cost and latency in inference. Traditional fine-tuning methods require backpropagation, which are error-prone in the low-precision settings. We propose the Quantized Zeroth-Order (QuZO) framework, specifically designed for fine-tuning LLMs through low-precision forward passes.
• Score: 27.730213115659986
• License:
• Abstract: Language Models (LLMs) are often quantized to lower precision to reduce the memory cost and latency in inference. However, quantization often degrades model performance, thus fine-tuning is required for various down-stream tasks. Traditional fine-tuning methods such as stochastic gradient descent and Adam optimization require backpropagation, which are error-prone in the low-precision settings. To overcome these limitations, we propose the Quantized Zeroth-Order (QuZO) framework, specifically designed for fine-tuning LLMs through low-precision (e.g., 4- or 8-bit) forward passes. Our method can avoid the error-prone low-precision straight-through estimator, and utilizes optimized stochastic rounding to mitigate the increased bias. QuZO simplifies the training process, while achieving results comparable to first-order methods in ${\rm FP}8$ and superior accuracy in ${\rm INT}8$ and ${\rm INT}4$ training. Experiments demonstrate that low-bit training QuZO achieves performance comparable to MeZO optimization on GLUE, Multi-Choice, and Generation tasks, while reducing memory cost by $2.94 \times$ in LLaMA2-7B fine-tuning compared to quantized first-order methods.

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