finetuning

Fine-tuning is the process of taking a pre-trained model and adapting it to a specific task or domain by continuing training on a task-specific dataset. It leverages representations learned from large corpora or datasets, enabling improved performance with limited labeled data compared to training from scratch.

Typical workflow involves selecting a pre-trained model, assembling labeled data for the target task, deciding which layers to update (full fine-tuning vs. partial fine-tuning), configuring the optimization setup (learning rate, optimizer, regularization), and training while monitoring performance on a validation set. Often a smaller learning rate is used, and certain layers may be frozen to preserve generic features.

Variants and techniques include using adapters or low-rank adaptation to insert trainable modules while keeping the base model fixed, as well as prompting or prefix-tuning in natural language processing as a lightweight alternative. The process may require substantial computational resources for large models, sometimes necessitating distributed training.

Data quality and distribution alignment are important considerations. Risks include overfitting on small datasets, catastrophic forgetting of previously learned tasks, data leakage, and high compute costs. Regularization, data augmentation, and careful data splitting help mitigate these issues.

Evaluation relies on task-appropriate metrics such as accuracy, F1, BLEU, or ROUGE, comparing against baselines and prior fine-tuning attempts. Fine-tuning is a central component of transfer learning, enabling adaptable, task-specific models without full retraining.