Fine-tuning

Adapting a pre-trained model to a specific task by continuing training on labelled task data with a small learning rate. Updates all model weights (not just the classification head).

The second phase of BERT's training. The pre-trained model is loaded and trained further on a small labelled dataset for a specific task (e.g. sentiment analysis). All parameters — including the Transformer encoder — are updated, but the learning rate is kept very small (2e-5 to 5e-5) to avoid catastrophic forgetting.

Training a pre-trained model on labeled data specific to a particular task. In traditional NLP (before GPT-3), every new task required fine-tuning. GPT-3 replaced fine-tuning with prompt engineering + in-context learning.

Training a pre-trained model on smaller amounts of labeled data specific to a downstream task. The scaling laws focus on pre-training; fine-tuning uses a different compute profile.

Further training that nudges some of a model's weights on focused examples; changes behavior/style/format, not facts.

Continuing to train a pretrained model on a smaller, focused dataset to specialize its behavior, skill, or style. (M00, M02)

Further training an existing model on your own examples to specialize it (your domain, tone, or to make a small model good enough to replace a big one). (Mod 5, 10)