Watch: Fine-tuning
Continuing training of a pre-trained model on a smaller, task-specific dataset to specialise its behaviour — the standard way DEJAN adapts base models into production classifiers.
Transcript
Fine-tuning is the process of taking a pre-trained model—one already trained on a massive, general dataset—and training it a bit further on a much smaller, task-specific dataset. Because the model already understands the basics of language, we don't have to start from scratch. We simply redirect that existing knowledge toward a specific job, like classifying sentiment or detecting AI-generated text.
This approach is highly efficient. While pre-training requires billions of words and weeks of computing power, fine-tuning can deliver production-quality results in just a few hours, using only thousands of labeled examples.
At Dejan, most production models are built this way. Models like LinkBERT, the AI Content Detection tool, and the Query Intent Classifier all start as strong, pre-trained encoders that are fine-tuned on high-quality data for specific SEO tasks. Success during this process relies heavily on hyperparameters like learning rate, batch size, and epoch count, alongside a well-balanced dataset. In fact, data quality and a carefully tuned learning rate matter far more than the sheer number of training steps.
When it comes to execution, you can perform a full fine-tuning, which updates all of the model's weights, or you can use parameter-efficient methods. A popular approach is LoRA, which freezes the original weights and only trains small adapter matrices. This drastically reduces the memory and computing power needed, making it the most practical option for very large models.