Comparison
Fine-tuning vs Supervised Fine-Tuning
Fine-tuning and Supervised Fine-Tuning are both common AI/LLM terms but cover different ideas. Here is a quick side-by-side.
When you would reach for Fine-tuning
After you've exhausted prompting and retrieval, and you have a few hundred to thousands of clean labeled examples.
Fine-tuning Llama 3 on medical Q&A for a clinical assistant.
When you would reach for Supervised Fine-Tuning
Supervised Fine-Tuning comes up when the question is fundamentally about training.
Training Llama-3-Base on Anthropic's HH-RLHF "chosen" responses as a first pass.
Frequently asked
What is the difference between Fine-tuning and Supervised Fine-Tuning?
Fine-tuning: Fine-tuning continues training a pretrained model on a smaller, task-specific dataset, adjusting its weights to specialize behavior or knowledge. Supervised Fine-Tuning: SFT is fine-tuning where each training example has an explicit input and a desired output, supervised by a loss that penalizes deviation from that output.
When should I use Fine-tuning vs Supervised Fine-Tuning?
After you've exhausted prompting and retrieval, and you have a few hundred to thousands of clean labeled examples. Supervised Fine-Tuning applies when you are focused on training.
Are Fine-tuning and Supervised Fine-Tuning the same thing?
No. Fine-tuning is training; Supervised Fine-Tuning is training. They are related but address different parts of the AI stack.