2 citations · 2 across the 9 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2024
MoS: Unleashing Parameter Efficiency of Low-Rank Adaptation with Mixture of Shards
Sheng Wang, Liheng Chen, Pengan Chen +5
The rapid scaling of large language models necessitates more lightweight finetuning methods to reduce the explosive GPU memory overhead when numerous customized models are served s…
cs.LG2024
PRoLoRA: Partial Rotation Empowers More Parameter-Efficient LoRA
Sheng Wang, Boyang Xue, Jiacheng Ye +4
With the rapid scaling of large language models (LLMs), serving numerous low-rank adaptations (LoRAs) concurrently has become increasingly impractical, leading to unaffordable cost…