OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models

• URL: http://arxiv.org/abs/2306.02272v4
• Date: Wed, 24 Jan 2024 02:53:27 GMT
• Title: OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models
• Authors: Changhun Lee, Jungyu Jin, Taesu Kim, Hyungjun Kim, Eunhyeok Park
• Abstract summary: outlier-aware weight quantization (OWQ) method minimizes large language models' footprint through low-precision representation. OWQ prioritizes a small subset of structured weights sensitive to quantization, storing them in high-precision, while applying highly tuned quantization to the remaining dense weights. Experiments demonstrate that 3.1-bit models using OWQ perform comparably to 4-bit models optimized by OPTQ.
• Score: 15.461748851931588
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) with hundreds of billions of parameters require powerful server-grade GPUs for inference, limiting their practical deployment. To address this challenge, we introduce the outlier-aware weight quantization (OWQ) method, which aims to minimize LLM's footprint through low-precision representation. OWQ prioritizes a small subset of structured weights sensitive to quantization, storing them in high-precision, while applying highly tuned quantization to the remaining dense weights. This sensitivity-aware mixed-precision scheme reduces the quantization error notably, and extensive experiments demonstrate that 3.1-bit models using OWQ perform comparably to 4-bit models optimized by OPTQ. Furthermore, OWQ incorporates a parameter-efficient fine-tuning for task-specific adaptation, called weak column tuning (WCT), enabling accurate task-specific LLM adaptation with minimal memory overhead in the optimized format. OWQ represents a notable advancement in the flexibility, efficiency, and practicality of LLM optimization literature. The source code is available at https://github.com/xvyaward/owq

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