Institutional Platform for Secure Self-Service Large Language Model Exploration

• URL: http://arxiv.org/abs/2402.00913v2
• Date: Mon, 23 Sep 2024 15:24:09 GMT
• Title: Institutional Platform for Secure Self-Service Large Language Model Exploration
• Authors: V. K. Cody Bumgardner, Mitchell A. Klusty, W. Vaiden Logan, Samuel E. Armstrong, Caylin Hickey, Jeff Talbert,
• Abstract summary: The paper outlines the system's architecture and key features, encompassing dataset curation, model training, secure inference, and text-based feature extraction. The platform strives to deliver secure LLM services, emphasizing process and data isolation, end-to-end encryption, and role-based resource authentication.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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