The Fine Tuning Process

Fine tuning deep learning models is completely different to fine tuning machine learning models

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Fine-tuning is a process that enhances the performance of neural language models by precisely adjusting them for specific tasks.

Unlike prompt engineering, which directs a model without altering its internal structure, fine-tuning involves a deeper modification, adjusting the model's internal parameters, such as weights, to improve its task-specific performance.

This process not only sharpens the model's abilities, ensuring higher accuracy and reliability but also saves significant time and resources by leveraging pre-existing knowledge from pre-trained models.

Fine-tuning equips models with the flexibility to adapt to a wide range of tasks with minimal adjustments, making it an essential step in the model training pipeline.

After pretraining, fine-tuning refines the broad knowledge base of the model, aligning it with the particularities of a new task, which significantly optimises performance.

The process involves preparing diverse and relevant data, updating model weights through backpropagation, and carefully selecting hyperparameters to guide the model's learning.

Fine-tuning is not just an adjustment but an enhancement that ensures neural language models achieve task-specific precision and efficiency.