Comparison
Fine-tuning vs Foundation Model
Fine-tuning and Foundation Model 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 Foundation Model
Foundation Model comes up when the question is fundamentally about foundations.
GPT-4 used by tens of thousands of applications via API.
Frequently asked
What is the difference between Fine-tuning and Foundation Model?
Fine-tuning: Fine-tuning continues training a pretrained model on a smaller, task-specific dataset, adjusting its weights to specialize behavior or knowledge. Foundation Model: A foundation model is a single large model pretrained on broad data that can be adapted to many downstream tasks. LLMs are the most common type.
When should I use Fine-tuning vs Foundation Model?
After you've exhausted prompting and retrieval, and you have a few hundred to thousands of clean labeled examples. Foundation Model applies when you are focused on foundations.
Are Fine-tuning and Foundation Model the same thing?
No. Fine-tuning is training; Foundation Model is foundations. They are related but address different parts of the AI stack.