GSQ-Tuning introduces a groundbreaking framework that enables fully integer-based LLM fine-tuning on resource-constrained devices, addressing both computational efficiency and privacy concerns.

• Eliminates the need for floating-point arithmetic through innovative Group-Shared Exponents quantization
• Achieves comparable accuracy to full-precision training while using only integer operations
• Enables on-device fine-tuning for sensitive data without cloud dependencies
• Significantly reduces computational requirements making LLM adaptation possible on edge devices

This engineering breakthrough creates new possibilities for deploying customizable AI in privacy-sensitive applications and resource-limited environments without sacrificing model quality.

GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training for LLMs On-Device Fine-tuning