Leveraging model distillation to fine-tune a model

# Leveraging model distillation to fine-tune a model OpenAI **Distillation** uses the outputs of a larger model to fine-tune a smaller one. Moving to the smaller model can reduce latency and cost for specific tasks. In this cookbook, we'll distill `gpt-4o` outputs into `gpt-4o-mini` and compare the result with a generic, non-distilled `gpt-4o-mini` model. We'll also use **Structured Outputs** for classification with an enum. We'll measure how structured outputs affect the fine-tuned model and show that they work with each model, including the distilled one. We'll first analyze the dataset, get the output of both 4o and 4o mini, highlighting the difference in performance of both models, then proceed to the distillation and analyze the performance of this distilled model.