24 citations · 26 across the 7 of their papers we have counts for
4 papers · 1 filter
SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training
Jinda Jia, Cong Xie, Hanlin Lu +8
Recent years have witnessed a clear trend towards language models with an ever-increasing number of parameters, as well as the growing training overhead and memory usage. Distribut…
Distributed Sign Momentum with Local Steps for Training Transformers
Shuhua Yu, Ding Zhou, Cong Xie +4
Pre-training Transformer models is resource-intensive, and recent studies have shown that sign momentum is an efficient technique for training large-scale deep learning models, par…
MoE-Pruner: Pruning Mixture-of-Experts Large Language Model using the Hints from Its Router
Yanyue Xie, Zhi Zhang, Ding Zhou +6
Mixture-of-Experts (MoE) architectures face challenges such as high memory consumption and redundancy in experts. Pruning MoE can reduce network weights while maintaining model per…
MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
Ziheng Jiang, Haibin Lin, Yinmin Zhong +29
We present the design, implementation and engineering experience in building and deploying MegaScale, a production system for training large language models (LLMs) at the scale of…