What happens when you use the 'Fine-Tune Models' capability?

When you use the 'Fine-Tune Models' capability, you adjust the model parameters to enhance its performance on specific tasks. Fine-tuning involves taking a pre-trained model that has learned general features from a vast dataset and then training it further on a smaller, task-specific dataset. This process allows the model to adapt its knowledge to the nuances of the new data, leading to improved accuracy and relevancy for the specific use case.

This approach enables users to leverage the power of existing models, making it faster and more efficient to achieve high levels of performance without needing to build a model from scratch. By fine-tuning, the model retains its generalization while becoming more adept at the specifics of the new task, which is particularly useful in scenarios where data is limited or highly specialized.