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文章探讨了私有化部署大型语言模型（LLM）的潜力和挑战，包括训练和微调方法、开源模型许可问题、评估标准缺乏等，并强调中文数据集的重要性。
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ChatGPT作为代表的大型语言模型服务在2023年开始大规模爆发，私有化部署在众多数据敏感行业有巨大需求。本文讨论了私有化部署LLM的潜力和挑战，介绍了大型语言模型的训练和微调方法， 探讨了开源模型面临的许可问题和评估标准缺乏的挑战，并强调了中文数据集和特性的重要性。最后得出结论 通过利用开源LLM，微调训练特定领域的LLM，并通过模型量化技术，在有限的硬件资源上实现私有化部署。私有化部署ChatGPT：潜力与挑战
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