Fine-tuning (ajuste fino)

A second, task-specific training pass performed on a pre-trained model to refine its parameters for a specific use case. For example, the full training sequence for some large language models is as follows: 1. Pre-training: Train a large language model on a vast general dataset, such as all the English language Wikipedia pages. 2. Fine-tuning: Train the pre-trained model to perform a specific task, such as responding to medical queries. Fine-tuning typically involves hundreds or thousands of examples focused on the specific task. As another example, the full training sequence for a large image model is as follows: 1. Pre-training: Train a large image model on a vast general image dataset, such as all the images in Wikimedia commons. 2. Fine-tuning: Train the pre-trained model to perform a specific task, such as generating images of orcas. Fine-tuning can entail any combination of the following strategies: - Modifying all of the pre-trained model's existing parameters. This is sometimes called full fine-tuning. - Modifying only some of the pre-trained model's existing parameters (typically, the layers closest to the output layer), while keeping other existing parameters unchanged (typically, the layers closest to the input layer). See parameter-efficient tuning. - Adding more layers, typically on top of the existing layers closest to the output layer. Fine-tuning is a form of transfer learning. As such, fine-tuning might use a different loss function or a different model type than those used to train the pre-trained model. For example, you could fine-tune a pre-trained large image model to produce a regression model that returns the number of birds in an input image. Compare and contrast fine-tuning with the following terms: - distillation - prompt-based learning See Fine-tuning in Machine Learning Crash Course for more information.

‘Model fine-tuning involves adjusting the parameters of foundation models or training models with small datasets for a specific task. This process adapts and enhances the model's performance for particular business needs’.

Adapting a model on domain-specific data to improve performance. Requires careful governance of data rights, privacy and overfitting risks.