AgentScope: A Flexible yet Robust Multi-Agent Platform

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.

Federated Fine-tuning of Large Language Models under Heterogeneous Language Tasks and Client Resources

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).

Federated Learning Privacy Preserving

Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes

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.

Federated Learning Large Language Model

FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning

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.

Benchmarking Federated Learning +1