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
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.