3 papers
cs.LG2026
DiaBlo: Diagonal Blocks Are Sufficient For Finetuning
Selcuk Gurses, Aozhong Zhang, Yanxia Deng +5
Fine-tuning is a critical step for adapting large language models (LLMs) to domain-specific downstream tasks. To mitigate the substantial computational and memory costs of full-mod…
cs.LG2025
CLoQ: Enhancing Fine-Tuning of Quantized LLMs via Calibrated LoRA Initialization
Yanxia Deng, Aozhong Zhang, Selcuk Gurses +3
Fine-tuning large language models (LLMs) using low-rank adaptation (LoRA) has become a highly efficient approach for downstream tasks, particularly in scenarios with limited comput…
cs.LG2024
MagR: Weight Magnitude Reduction for Enhancing Post-Training Quantization
Aozhong Zhang, Naigang Wang, Yanxia Deng +3
In this paper, we present a simple optimization-based preprocessing technique called Weight Magnitude Reduction (MagR) to improve the performance of post-training quantization. For…