7 citations · 18 across the 7 of their papers we have counts for
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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.LG2022★ 1 cited
Tempo: Accelerating Transformer-Based Model Training through Memory Footprint Reduction
Muralidhar Andoorveedu, Zhanda Zhu, Bojian Zheng +1
Training deep learning models can be computationally expensive. Prior works have shown that increasing the batch size can potentially lead to better overall throughput. However, th…