Institutional Platform for Secure Self-Service Large Language Model Exploration

V. K. Cody Bumgardner, Mitchell A. Klusty, W. Vaiden Logan, Samuel E. Armstrong, Caylin Hickey, Jeff Talbert

Research output: Working paperPreprint

7 Downloads (Pure)

Abstract

This paper introduces a user-friendly platform developed by the University of Kentucky Center for Applied AI, designed to make large, customized language models (LLMs) more accessible. By capitalizing on recent advancements in multi-LoRA inference, the system efficiently accommodates custom adapters for a diverse range of users and projects. The paper outlines the system's architecture and key features, encompassing dataset curation, model training, secure inference, and text-based feature extraction. We illustrate the establishment of a tenant-aware computational network using agent-based methods, securely utilizing islands of isolated resources as a unified system. The platform strives to deliver secure LLM services, emphasizing process and data isolation, end-to-end encryption, and role-based resource authentication. This contribution aligns with the overarching goal of enabling simplified access to cutting-edge AI models and technology in support of scientific discovery.
Original languageUndefined/Unknown
StatePublished - Feb 1 2024

Bibliographical note

10 pages 11 figures, 5 listings, 4 tables

Keywords

  • cs.CR
  • cs.AI
  • cs.CL

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