9 citations · 31 across the 16 of their papers we have counts for
4 papers · 1 filter
EE-LLM: Large-Scale Training and Inference of Early-Exit Large Language Models with 3D Parallelism
Yanxi Chen, Xuchen Pan, Yaliang Li +2
We present EE-LLM, a framework for large-scale training and inference of early-exit large language models (LLMs). While recent works have shown preliminary evidence for the efficac…
Data-Juicer: A One-Stop Data Processing System for Large Language Models
Daoyuan Chen, Yilun Huang, Zhijian Ma +10
The immense evolution in Large Language Models (LLMs) has underscored the importance of massive, heterogeneous, and high-quality data. A data recipe is a mixture of data from diffe…
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…
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…