Institutional Platform for Secure Self-Service Large Language Model Exploration

• Published in: AMIA Summits on Translational Science proceedings Vol. 2025; pp. 105 - 114
• Main Authors: Bumgardner, V K Cody, Klusty, Mitchell A, Logan, W Vaiden, Armstrong, Samuel E, Leach, Caroline N, Hickey, Caylin, Talbert, Jeff
• Format: Journal Article
• Language: English
• Published: United States American Medical Informatics Association 2025
• ISSN: 2153-4063; 2153-4063

This paper introduces a user-friendly platform developed by the University of Kentucky Center for Applied AI, designed to make customized large 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, affordable 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 and the development of biomedical informatics.