The art and science of hyperparameter optimization on Amazon Nova Forge | Amazon Web Services
Large language models (LLMs) deliver strong results on general tasks, but they often struggle with specialized work that requires understanding proprietary data, internal processes, or domain-specific terminology. Amazon Nova Forge addresses this by enabling you to build your own frontier models using Amazon Nova. You can start development from early model checkpoints, blend proprietary data with Amazon Nova-curated training data, and host custom models securely on AWS. A key capability is data mixing, which blends your training data with curated datasets. This helps the model absorb your domain while retaining broad reasoning, instruction-following, and language capabilities. This prevents catastrophic forgetting that typically undermines domain customization.
Successful customization requires careful hyperparameter tuning. Learning rate, data mixing ratio, checkpoint selection, and training techniques all interact in ways that can silently undermine a training run. If any of them are wrong, you trade one problem for another. This post covers the art...
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