Supervised fine-tuning (Full FT, PEFT)

Supervised fine-tuning (SFT) is the process of providing a collection of prompt-response pairs to a foundation model to improve the performance of a pre-trained foundation model on a specific task. The labeled examples are formatted as prompt-response pairs and phrased as instructions. This fine-tuning process modifies the weights of the model.

You should use SFT when you have domain-specific data that requires providing specific prompt-response pairs for optimal results. Both full-rank SFT and parameter-efficient SFT are available.

For detailed instructions about using SFT with Amazon Nova model customization, see the Supervised Fine-Tuning (Full FT, PEFT) section from SageMakeruser guide.