The development of Large Language Models (LLMs) has primarily been driven by resource-rich research groups and industry partners. Due to the lack of on-premise computing resources required for increasingly complex models, many researchers are turning to cloud services like AWS SageMaker to train Hugging Face models. However, the steep learning curve of cloud platforms often presents a barrier for researchers accustomed to local environments. Existing documentation frequently leaves knowledge gaps, forcing users to seek fragmented information across the web. This demo paper aims to democratize cloud adoption by centralizing the essential information required for researchers to successfully train their first Hugging Face model on AWS SageMaker from scratch.

翻译：大型语言模型（LLMs）的发展主要由资源丰富的研究团队和行业合作伙伴推动。由于日益复杂的模型需要大量本地计算资源，许多研究者开始转向使用AWS SageMaker等云服务来训练Hugging Face模型。然而，云平台陡峭的学习曲线往往对习惯本地环境的研究者构成障碍。现有文档常存在知识断层，迫使用户从网络碎片化信息中寻求解决方案。本演示论文旨在通过整合核心知识，帮助研究者从零开始在AWS SageMaker上成功训练首个Hugging Face模型，从而推动云服务的普及应用。