Fine-Tuning is the process of adapting a pre-trained machine learning model to perform better for a specific task, industry, dataset, or use case. Instead of training a model entirely from scratch, additional training is performed using carefully selected examples relevant to the desired application.
For example, a general language model can be fine-tuned with specialized legal documents to improve its ability to understand legal terminology and generate relevant responses. Fine-Tuning can improve task-specific accuracy, consistency, and performance while requiring less data and computing resources than training a model from scratch.
