Institutional Platform for Secure Self-Service Large Language Model Exploration
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912242884149248 |
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| author | Bumgardner, V. K. Cody Klusty, Mitchell A. Logan, W. Vaiden Armstrong, Samuel E. Leach, Caroline N. Calvert, Kenneth L. Hickey, Caylin Talbert, Jeff |
| author_facet | Bumgardner, V. K. Cody Klusty, Mitchell A. Logan, W. Vaiden Armstrong, Samuel E. Leach, Caroline N. Calvert, Kenneth L. Hickey, Caylin Talbert, Jeff |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_00913 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Institutional Platform for Secure Self-Service Large Language Model Exploration Bumgardner, V. K. Cody Klusty, Mitchell A. Logan, W. Vaiden Armstrong, Samuel E. Leach, Caroline N. Calvert, Kenneth L. Hickey, Caylin Talbert, Jeff Cryptography and Security Artificial Intelligence Computation and Language 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. |
| title | Institutional Platform for Secure Self-Service Large Language Model Exploration |
| topic | Cryptography and Security Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2402.00913 |