4 citations · 4 across the 3 of their papers we have counts for
4 papers
STAPLE: Static Sparse Upcycling of Pretrained Checkpoints via Embedding Modules
Ranajoy Sadhukhan, Euijin Hong, Sheng Cao +9
Sparse computation can scale the parametric capacity of LLMs without proportionally increasing active computation, but pretraining sparse architectures remains expensive and data-h…
MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training
Juntao Zhao, Qi Lu, Wei Jia +13
Modern frameworks for training large foundation models (LFMs) employ dataloaders in a data-parallel manner, with each loader processing a disjoint subset of training data. When pre…
ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs
Hao Ge, Junda Feng, Qi Huang +6
Scaling long-context ability is essential for Large Language Models (LLMs). To amortize the memory consumption across multiple devices in long-context training, inter-data partitio…
ByteCheckpoint: A Unified Checkpointing System for Large Foundation Model Development
Borui Wan, Mingji Han, Yiyao Sheng +9
Checkpointing to preserve training states is crucial during the development of Large Foundation Models (LFMs), for training resumption upon various failures or changes in GPU resou…