Amazon’s latest guide explains how developers can fine-tune its Nova AI models using the Nova Forge SDK. The second part of the series focuses on data mixing during training, providing a structured approach from data preparation to model evaluation.
The document follows a practical workflow. It begins with data curation, where developers select and format datasets for supervised fine-tuning. The guide then demonstrates how to configure data mixing ratios, a key feature of the SDK that blends multiple datasets during training to improve model performance. This method allows customization without requiring entirely new datasets for each task.
Training steps are detailed next. The SDK supports distributed training across multiple GPUs, reducing time needed for large-scale experiments. Developers can adjust hyperparameters directly in the configuration files provided in the SDK. The guide includes sample scripts to replicate the process.
Evaluation is the final phase. The SDK includes built-in metrics to assess model accuracy, response quality, and bias. Amazon recommends comparing results against baseline models to measure improvements. The guide emphasizes reproducibility, encouraging developers to log experiments for future reference.
This installment builds on the first part of the series, which introduced the Nova Forge SDK and initial customization steps. Both parts target developers working with Amazon’s Nova models, aiming to simplify model adaptation for specific use cases.
Source: aws.amazon.com