安全自助服务的机构平台 大型语言模型探索
V K Cody Bumgardner1, Mitchell A Klusty1, W Vaiden Logan1
1University of Kentucky, Lexington, KY, USA.
概括
本研究提出了一个新的平台,用于使用多LoRA推理创建定制的大型语言模型 (LLM). 它为科学发现和生物医学信息学提供安全,负担得起的AI服务.
科学领域:
- 人工智能的人工智能
- 生物医学信息学 生物医学信息学
- 计算机科学 计算机科学
背景情况:
- 大型语言模型 (LLM) 为科学发现和生物医学信息学提供了重大潜力.
- 目前的LLM可访问性受到复杂性和成本的限制.
- 在专门的研究应用中,LLM的定制是至关重要的.
研究的目的:
- 引入一个用户友好的平台,用于创建定制的大型语言模型 (LLM).
- 提高研究人员对先进的人工智能模型的可访问性.
- 提供安全和负担得起的LLM服务.
主要方法:
- 开发一个利用多个LoRA推理的平台,以实现高效的定制适配器.
- 使用基于代理的方法实现租户意识的计算网络.
- 集成数据集策划,模型培训,安全推理和基于文本的特征提取.
- 强调过程和数据隔离,端到端加密和基于角色的资源身份验证以确保安全.
主要成果:
- 一个系统架构,使LLMs的高效定制.
- 为LLM服务提供安全和孤立的计算资源.
- 从孤立的资源中展示一个统一的系统.
- 促进安全,负担得起的LLM服务.
结论:
- 开发的平台简化了对尖端人工智能,特别是大型语言模型的访问.
- 它通过可访问的人工智能支持科学发现和生物医学信息学的进步.
- 该系统为定制的LLM部署提供了安全和经济有效的解决方案.
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