1 code implementation • 21 Feb 2024 • Dawei Gao, Zitao Li, Xuchen Pan, Weirui Kuang, Zhijian Ma, Bingchen Qian, Fei Wei, WenHao Zhang, Yuexiang Xie, Daoyuan Chen, Liuyi Yao, Hongyi Peng, Zeyu Zhang, 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.
1 code implementation • 1 Feb 2024 • Xuchen Pan, Yanxi Chen, Yaliang Li, Bolin Ding, Jingren Zhou
This work introduces EE-Tuning, a lightweight and economical solution to training/tuning early-exit large language models (LLMs).
1 code implementation • 8 Dec 2023 • Yanxi Chen, Xuchen Pan, Yaliang Li, Bolin Ding, Jingren Zhou
We present EE-LLM, a framework for large-scale training and inference of early-exit large language models (LLMs).
1 code implementation • 5 Sep 2023 • Daoyuan Chen, Yilun Huang, Zhijian Ma, Hesen Chen, Xuchen Pan, Ce Ge, Dawei Gao, Yuexiang Xie, Zhaoyang Liu, Jinyang Gao, Yaliang Li, Bolin Ding, Jingren Zhou
A data recipe is a mixture of data from different sources for training LLMs, which plays a vital role in LLMs' performance.
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.
no code implementations • 23 Mar 2023 • Daoyuan Chen, Dawei Gao, Yuexiang Xie, Xuchen Pan, Zitao Li, Yaliang Li, Bolin Ding, Jingren Zhou
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 both academia and industry.