Amazon SageMaker AI now supports serverless model customization for Muse Glimmer, Qwen 3.8 and Ministral 3 models

Amazon SageMaker AI Model Customization Update

Amazon SageMaker AI now supports serverless model customization for Muse Glimmer, Qwen3.8 27B, and Ministral 3 3B, 8B, and 14B models. You can adapt these models to your specific domains and workflows using supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement fine-tuning (RFT) techniques. You can also adapt these models with either low-rank adaptation (LoRA) or full fine-tuning techniques.

Model customization allows you to tailor foundation models with your proprietary data, improving accuracy on domain-specific tasks, aligning outputs with your organization's tone, and improving performance on new tasks. SageMaker AI handles all infrastructure provisioning and training orchestration, so you can focus on your data and evaluation.

What to do

• Navigate to the Models page in Amazon SageMaker Studio to launch a customization job.
• Use the SageMaker Python SDK or Agent skills with your choice of coding assistant.
• Refer to the Amazon SageMaker AI model customization documentation for more information.

Source: AWS release notes

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