What is fine-tuning
Fine-tuning is the process of taking a pre-trained machine learning model and further training it on a specialized, task-specific dataset to adapt it to a particular application. It leverages the general knowledge the model has already learned while specializing its capabilities for a distinct objective.
This technique builds upon large foundational models that have been trained on vast, general datasets. Fine-tuning requires a smaller, high-quality dataset specifically curated for the target task. The model's weights are adjusted during this additional training phase, but the core architecture typically remains unchanged. It significantly reduces the need for massive computational resources compared to training a model from scratch.
Fine-tuning enables the practical adaptation of powerful general models to solve specific business problems effectively and efficiently. Key applications include customizing chatbots for customer service, improving sentiment analysis for product reviews, enhancing diagnostic accuracy from medical images, and generating tailored content like marketing copy. This delivers cost-effective, high-performance solutions faster.
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