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.

本文介绍了由肯塔基大学应用人工智能中心开发的一种用户友好的平台，旨在提高大型定制化语言模型（LLMs）的可用性。通过利用最近在多 LoRA 推理方面的进展，该系统有效地适应了各种用户和项目的定制适配器。该论文概述了系统的体系结构和主要特点，包括数据集筛选、模型训练、安全推理和基于文本的特征提取。