A large model performs a narrow task well but has unacceptable serving cost. The team considers training a smaller supported model using outputs or other teaching signals from the larger model. What is this technique called?

1. Token truncation.
2. Prompt caching.
3. Model distillation.
4. Retrieval Augmented Generation.