Supervised fine-tuning (SFT) - Amazon SageMaker AI
Nova Model Comparison and SelectionWhen to Use Nova 1.0 vs. Nova 2.0
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Supervised fine-tuning (SFT)

The SFT training process consists of two main stages:

  • Data Preparation: Follow established guidelines to create, clean, or reformat datasets into the required structure. Ensure that inputs, outputs, and auxiliary information (such as reasoning traces or metadata) are properly aligned and formatted.

  • Training Configuration: Define how the model will be trained. When using SageMaker HyperPod, this configuration is written in a YAML recipe file that includes:

    • Data source paths (training and validation datasets)

    • Key hyperparameters (epochs, learning rate, batch size)

    • Optional components (distributed training parameters, etc)

Nova Model Comparison and Selection

Amazon Nova 2.0 is a model trained on a larger and more diverse dataset than Amazon Nova 1.0. Key improvements include:

  • Enhanced reasoning abilities with explicit reasoning mode support

  • Broader multilingual performance across additional languages

  • Improved performance on complex tasks including coding and tool use

  • Extended context handling with better accuracy and stability at longer context lengths

When to Use Nova 1.0 vs. Nova 2.0

Choose Amazon Nova 2.0 when:

  • Superior performance with advanced reasoning capabilities is needed

  • Multilingual support or complex task handling is required

  • Better results on coding, tool calling, or analytical tasks are needed

Choose Amazon Nova 1.0 when:

  • The use case requires standard language understanding without advanced reasoning

  • Performance has already been validated on Amazon Nova 1.0 and additional capabilities are not needed