Supervised Fine-Tuning

Supervised Fine-Tuning (SFT) is a machine learning technique where a pre-trained model is further trained on a labeled dataset to adapt it to a specific task. It leverages transfer learning by starting from a model that has already learned general features from a large dataset, then refining it with task-specific data. This approach is commonly used in natural language processing and computer vision to improve performance on downstream applications like text classification or image recognition.

Also known as: SFT, Supervised Finetuning, Fine-Tuning, Task-Specific Fine-Tuning, Transfer Learning with Fine-Tuning

Why learn Supervised Fine-Tuning?

Developers should use Supervised Fine-Tuning when they have a limited amount of labeled data for a specific task but want to achieve high accuracy by building on a pre-trained model's general knowledge. It is particularly valuable in domains like NLP for tasks such as sentiment analysis or named entity recognition, where pre-trained models like BERT or GPT provide a strong foundation. This method reduces training time and computational costs compared to training from scratch while enhancing model performance on specialized applications.

Learning Resources

• tutorial: Hugging Face Fine-Tuning Tutorial
• docs: Stanford CS224N Lecture on Fine-Tuning
• course: Fast.ai Practical Deep Learning for Coders
• tutorial: PyTorch Fine-Tuning Guide