1 code implementation • 21 Feb 2024 • Dawei Gao, Zitao Li, Weirui Kuang, Xuchen Pan, Daoyuan Chen, Zhijian Ma, Bingchen Qian, Liuyi Yao, Lin Zhu, Chen Cheng, Hongzhu Shi, Yaliang Li, Bolin Ding, Jingren Zhou
With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications.
no code implementations • 18 Feb 2024 • Jiamu Bai, Daoyuan Chen, Bingchen Qian, Liuyi Yao, Yaliang Li
Federated Learning (FL) has recently been applied to the parameter-efficient fine-tuning of Large Language Models (LLMs).
2 code implementations • 11 Dec 2023 • Zhen Qin, Daoyuan Chen, Bingchen Qian, Bolin Ding, Yaliang Li, Shuiguang Deng
Pre-trained large language models (LLMs) need fine-tuning to improve their responsiveness to natural language instructions.
1 code implementation • 1 Sep 2023 • Weirui Kuang, Bingchen Qian, Zitao Li, Daoyuan Chen, Dawei Gao, Xuchen Pan, Yuexiang Xie, Yaliang Li, Bolin Ding, Jingren Zhou
When several entities have similar interested tasks, but their data cannot be shared because of privacy concerns regulations, federated learning (FL) is a mainstream solution to leverage the data of different entities.