Comparison
Fine-tuning vs Pretraining
Fine-tuning and Pretraining 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 Pretraining
Pretraining comes up when the question is fundamentally about training.
GPT-3 pretrained on ~300B tokens.
Frequently asked
What is the difference between Fine-tuning and Pretraining?
Fine-tuning: Fine-tuning continues training a pretrained model on a smaller, task-specific dataset, adjusting its weights to specialize behavior or knowledge. Pretraining: Pretraining is the initial training phase where an LLM learns to predict the next token on trillions of tokens of general text. It produces a base model that can be adapted later.
When should I use Fine-tuning vs Pretraining?
After you've exhausted prompting and retrieval, and you have a few hundred to thousands of clean labeled examples. Pretraining applies when you are focused on training.
Are Fine-tuning and Pretraining the same thing?
No. Fine-tuning is training; Pretraining is training. They are related but address different parts of the AI stack.