Harnessing Model Distillation for Superior Fine-Tuning

OpenAI's Distillation empowers you to train compact models like gpt-4o-mini using gpt-4o outputs, slashing costs and latency. This guide distills a dataset for top results, explores Structured Outputs for classification, and proves compatibility across models.

OpenAI has launched Distillation, a powerful method that uses predictions from a larger model to fine-tune a smaller one. This technique dramatically lowers costs and latency for targeted tasks by transitioning to a more efficient model. In this cookbook, we'll examine a dataset, distill gpt-4o outputs into gpt-4o-mini, and demonstrate how it achieves far better performance than the standard, non-distilled gpt-4o-mini.

We'll also utilize Structured Outputs for a classification task based on an enum list. You'll discover how the fine-tuned model gains from structured outputs, the resulting performance improvements, and confirmation that Structured Outputs function seamlessly with all these models, including the distilled version.

We'll begin by analyzing the dataset, generating outputs from both gpt-4o and gpt-4o-mini to underscore their performance differences, then advance to the distillation process and evaluate the distilled model's capabilities.