June 7, 2024, 4:42 a.m. | Ruiyang Qin, Dancheng Liu, Zheyu Yan, Zhaoxuan Tan, Zixuan Pan, Zhenge Jia, Meng Jiang, Ahmed Abbasi, Jinjun Xiong, Yiyu Shi

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.03777v1 Announce Type: new
Abstract: The scaling laws have become the de facto guidelines for designing large language models (LLMs), but they were studied under the assumption of unlimited computing resources for both training and inference. As LLMs are increasingly used as personalized intelligent assistants, their customization (i.e., learning through fine-tuning) and deployment onto resource-constrained edge devices will become more and more prevalent. An urging but open question is how a resource-constrained computing environment would affect the design choices for …

abstract arxiv assistants become computing computing resources cs.ai cs.lg customization designing devices edge edge devices fine-tuning guidelines inference intelligent language language models large language large language models laws llms personalized resources scaling through training type

Senior Machine Learning Engineer

@ GPTZero | Toronto, Canada

ML/AI Engineer / NLP Expert - Custom LLM Development (x/f/m)

@ HelloBetter | Remote

Senior Research Engineer/Specialist - Motor Mechanical Design

@ GKN Aerospace | Bristol, GB

Research Engineer (Motor Mechanical Design)

@ GKN Aerospace | Bristol, GB

Senior Research Engineer (Electromagnetic Design)

@ GKN Aerospace | Bristol, GB

Associate Research Engineer Clubs | Titleist

@ Acushnet Company | Carlsbad, CA, United States