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.
View on arXiv@article{bumgardner2025_2402.00913,
title={ Institutional Platform for Secure Self-Service Large Language Model Exploration },
author={ V. K. Cody Bumgardner and Mitchell A. Klusty and W. Vaiden Logan and Samuel E. Armstrong and Caroline N. Leach and Kenneth L. Calvert and Caylin Hickey and Jeff Talbert },
journal={arXiv preprint arXiv:2402.00913},
year={ 2025 }
}