Can small models also be fine-tuned?

Small models can absolutely be fine-tuned. This process is both feasible and widely practiced to enhance their performance for specific tasks.

Fine-tuning a small model requires labeled data relevant to the target task. While computationally less demanding than training large models, sufficient resources are still needed. The model's pre-existing knowledge provides a foundation, which is then refined. Careful hyperparameter tuning and avoiding overfitting through methods like early stopping are crucial, especially given smaller models' potentially lower capacity.

Implementation involves preparing a task-specific dataset, selecting a pre-trained small model architecture, and adjusting its final layers or parameters. This is cost-effective for deployment on edge devices, faster prototyping, or specific applications like text classification, sentiment analysis, or moderate complexity tasks. Fine-tuning enables significant performance gains over using the model out-of-the-box while remaining resource-efficient.