Search Results for author: Xuchen Pan

Found 6 papers, 5 papers with code

EE-Tuning: An Economical yet Scalable Solution for Tuning Early-Exit Large Language Models

1 code implementation1 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).

EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism

1 code implementation8 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).

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

1 code implementation1 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

FS-Real: Towards Real-World Cross-Device Federated Learning

no code implementations23 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.

Federated Learning

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