Fine-tuning transforms pre-trained models into specialized ones by training them on domain-specific datasets, enhancing their performance in specific tasks. This can be achieved through supervised or unsupervised methods. Supervised fine-tuning uses labeled data to align models with particular tasks, while unsupervised fine-tuning leverages unlabeled data to adapt models to new domains. The post explores these methods using real-world examples, including adaptations in healthcare and legal sectors.
298 Impressions