9 citations · 10 across the 3 of their papers we have counts for
3 papers
cs.LG2024
EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models
Xuchen Pan, Yanxi Chen, Yaliang Li +2
This work introduces EE-Tuning, a lightweight and economical solution to training/tuning early-exit large language models (LLMs). In contrast to the common approach of full-paramet…
cs.LG2023★ 9 cited
FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning
Weirui Kuang, Bingchen Qian, Zitao Li +7
LLMs have demonstrated great capabilities in various NLP tasks. Different entities can further improve the performance of those LLMs on their specific downstream tasks by fine-tuni…
cs.LG2023★ 1 cited
FS-Real: Towards Real-World Cross-Device Federated Learning
Daoyuan Chen, Dawei Gao, Yuexiang Xie +5
Federated Learning (FL) aims to train high-quality models in collaboration with distributed clients while not uploading their local data, which attracts increasing attention in bot…