6 citations · 8 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 6 cited
Liger Kernel: Efficient Triton Kernels for LLM Training
Pin-Lun Hsu, Yun Dai, Vignesh Kothapalli +7
Training Large Language Models (LLMs) efficiently at scale presents a formidable challenge, driven by their ever-increasing computational demands and the need for enhanced performa…
cs.LG2024
Enhancing Stability for Large Language Models Training in Constrained Bandwidth Networks
Yun Dai, Tejas Dharamsi, Byron Hsu +2
Training extremely large language models (LLMs) with billions of parameters is a computationally intensive task that pushes the limits of current data parallel training systems. Wh…
cs.DC2021★ 2 cited
Apache Submarine: A Unified Machine Learning Platform Made Simple
Kai-Hsun Chen, Huan-Ping Su, Wei-Chiu Chuang +12
As machine learning is applied more widely, it is necessary to have a machine learning platform for both infrastructure administrators and users including expert data scientists an…