Fine-tuning
Fine-tuning is a method to adapt pretrained AI models by further training on task-specific or domain-specific data. It reduces training effort and improves performance for niche applications. The process includes data preparation, hyperparameter tuning and evaluation, and requires careful overfitting control and validation strategies. Use cases span classification, QA, and generative modeling.
This block bundles baseline information, context, and relations as a neutral reference in the model.
Definition · Framing · Trade-offs · Examples
What is this view?
This page provides a neutral starting point with core facts, structure context, and immediate relations—independent of learning or decision paths.
Baseline data
Context in the model
Structural placement
Where this block lives in the structure.
No structure path available.
Relations
Connected blocks
Directly linked content elements.