如何在 AWS 上部署與微調 DeepSeek 模型：Hugging Face 官方指南
Original: How to deploy and fine-tune DeepSeek models on AWS
As DeepSeek-R1 swept through the AI landscape on the strength of its powerful reasoning capabilities, how to safely and efficiently deploy…
本文為 Hugging Face 釋出的實用指南，詳細介紹如何在 AWS 環境中部署與微調熱門的 DeepSeek-R1 及其蒸餾（Distilled）模型。內容涵蓋使用 Hugging Face LLM DLC（深度學習容器）與 TGI 技術在 Amazon SageMaker 上進行低延遲推論部署，以及如何透過 SageMaker 訓練作業與 Hugging Face TRL 庫進行高效微調（如 LoRA），並提供針對不同模型大小的 AWS 硬體配置建議。
As DeepSeek-R1 swept through the AI landscape on the strength of its powerful reasoning capabilities, how to safely and efficiently deploy and fine-tune these models in enterprise environments has become a hot topic. Hugging Face published an official guide detailing how to use AWS — specifically Amazon SageMaker — to bring DeepSeek models into production.
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