Model distillation transfers behavior from a larger teacher model to a smaller student model. The teacher may generate answers, probability distributions or intermediate supervision for selected tasks. The student learns from those examples instead of relying only on labels provided directly by people.
The attraction is efficiency. A small distilled model may run faster, use less memory and cost less to serve than the original model. That can help with local applications or high-volume tasks. How much ability transfers depends on the teacher, the examples, the student architecture and the training objective.
Distillation does not guarantee that the student matches the teacher on every task. It can inherit mistakes and biases, perform poorly outside the distilled domain and learn a narrower range of responses. Synthetic teacher outputs also require quality checks and the right to use them under applicable licenses and service terms.
Evaluate a distilled model against a held-out set of real tasks, not only the same prompts used to train it. Measure accuracy, latency and cost together, and retain examples where the smaller model fails so that lower running cost does not hide worse outcomes.