most citedLoRAFusion: Efficient LoRA Fine-Tuning for LLMs

2 citations · 3 across the 6 of their papers we have counts for

collaborators
Showing cs.LGShow all

3 papers · 1 filter

cs.LG2025★ 2 cited

LoRAFusion: Efficient LoRA Fine-Tuning for LLMs

Zhanda Zhu, Qidong Su, Yaoyao Ding +3

Low-Rank Adaptation (LoRA) has become the leading Parameter-Efficient Fine-Tuning (PEFT) method for Large Language Models (LLMs), as it significantly reduces GPU memory usage while…

cs.LG2025

Hexcute: A Compiler Framework for Automating Layout Synthesis in GPU Programs

Xiao Zhang, Yaoyao Ding, Bolin Sun +3

Efficient GPU programming is crucial for achieving high performance in deep learning (DL) applications. The performance of GPU programs depends on how data is parallelized across t…

cs.LG2025

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Yaoyao Ding, Bohan Hou, Xiao Zhang +5

Serving Large Language Models (LLMs) is critical for AI-powered applications, yet it demands substantial computational resources, particularly in memory bandwidth and computational…