A fine-tuned model is a machine learning or AI model that has been further trained (or fine-tuned) on a smaller, specialized dataset after its initial training on a broad, general-purpose dataset. This fine-tuning process allows the model to adapt to a specific context, category, or problem set, making it more effective for particular tasks or domains.
For example, a general language model trained on a wide range of texts can be fine-tuned to perform well on specific tasks like medical diagnosis, legal document analysis, or customer support in a particular industry. Fine-tuning typically involves adjusting the model’s parameters or using transfer learning techniques to ensure it understands the nuances and specialized knowledge required for the target domain. This enables the model to provide more accurate and relevant outputs tailored to specific needs.