Fine-Tuning
The process of adapting a pre-trained AI model using additional domain-specific data to improve performance for a particular task or application.
What is Fine-Tuning?
Fine-tuning starts with a model that has already learned general patterns from a large dataset. The model is then trained further using a smaller, task-specific dataset so its parameters adapt to particular requirements. This allows organizations to customize existing models without training a new model entirely from scratch.
Why is Fine-Tuning Important?
Pre-trained models are designed to perform well across broad tasks but may not meet specialized requirements. Fine-tuning can improve performance for specific domains, terminology, behaviors, or tasks while reducing the data, computing resources, and time required compared with building a model from the beginning.
Common use cases
Fine-tuning is commonly used in large language models, chatbots, sentiment analysis, text classification, image recognition, domain-specific AI, and customized generative AI applications.