Customize DeepSeek-R1 distilled models using Amazon SageMaker HyperPod recipes – Part 1

Increasingly, organizations across industries are turning to generative AI foundation models (FMs) to enhance their applications. To achieve optimal performance for specific use cases, customers are adopting and adapting these FMs to their unique domain requirements. This need for customization has become even more pronounced with the emergence of new models, such as those released by DeepSeek.

However, customizing DeepSeek models effectively while managing computational resources remains a significant challenge. Tuning model architecture requires technical expertise, training and fine-tuning parameters, and managing distributed training infrastructure, among others. This often forces companies to choose between model performance and practical implementation constraints, creating a critical need for more accessible and streamlined model customization solutions.

In this two-part series, we discuss how you can reduce the DeepSeek model customization complexity by using the pre-built fine-tuning workflows (also called “recipes”) for both DeepSeek-R1 model and its distilled variations, released as part of Amazon SageMaker ...