5 papers · 1 filter
Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild
Xinyu Zhao, Guoheng Sun, Ruisi Cai +13
As Large Language Models (LLMs) excel across tasks and specialized domains, scaling LLMs based on existing models has garnered significant attention, which faces the challenge of d…
On the Surprising Effectiveness of Attention Transfer for Vision Transformers
Alexander C. Li, Yuandong Tian, Beidi Chen +2
Conventional wisdom suggests that pre-training Vision Transformers (ViT) improves downstream performance by learning useful representations. Is this actually true? We investigate t…
Mini-Sequence Transformer: Optimizing Intermediate Memory for Long Sequences Training
Cheng Luo, Jiawei Zhao, Zhuoming Chen +2
We introduce Mini-Sequence Transformer (MsT), a simple and effective methodology for highly efficient and accurate LLM training with extremely long sequences. MsT partitions input…
LoCoCo: Dropping In Convolutions for Long Context Compression
Ruisi Cai, Yuandong Tian, Zhangyang Wang +1
This paper tackles the memory hurdle of processing long context sequences in Large Language Models (LLMs), by presenting a novel approach, Dropping In Convolutions for Long Context…
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Jiawei Zhao, Zhenyu Zhang, Beidi Chen +3
Training Large Language Models (LLMs) presents significant memory challenges, predominantly due to the growing size of weights and optimizer states. Common memory-reduction approac…