most citedFLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations

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

collaborators

2 papers

cs.DC2024★ 2 cited

Domino: Eliminating Communication in LLM Training via Generic Tensor Slicing and Overlapping

Guanhua Wang, Chengming Zhang, Zheyu Shen +2

Given the popularity of generative AI, Large Language Models (LLMs) often consume hundreds or thousands of GPUs for parallelizing and accelerating the training process. Communicati…

cs.LG2024★ 4 cited

FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations

Ziyao Wang, Zheyu Shen, Yexiao He +4

The rapid development of Large Language Models (LLMs) has been pivotal in advancing AI, with pre-trained LLMs being adaptable to diverse downstream tasks through fine-tuning. Feder…