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cs.LG2026
POET-X: Memory-efficient LLM Training by Scaling Orthogonal Transformation
Zeju Qiu, Lixin Liu, Adrian Weller +2
Efficient and stable training of large language models (LLMs) remains a core challenge in modern machine learning systems. To address this challenge, Reparameterized Orthogonal Equ…
cs.LG2024
QuickLLaMA: Query-aware Inference Acceleration for Large Language Models
Jingyao Li, Han Shi, Xin Jiang +3
The capacity of Large Language Models (LLMs) to comprehend and reason over long contexts is pivotal for advancements in diverse fields. Yet, they still stuggle with capturing long-…
cs.LG2024
AlgoFormer: An Efficient Transformer Framework with Algorithmic Structures
Yihang Gao, Chuanyang Zheng, Enze Xie +6
Besides natural language processing, transformers exhibit extraordinary performance in solving broader applications, including scientific computing and computer vision. Previous wo…