Step-by-Step Guide to Fine-Tuning GPT-4o Mini Chat Models

Boost your model's capabilities with fine-tuning on massive datasets beyond prompt limits for top results across tasks. This hands-on notebook demos entity extraction from recipes using RecipeNLG—a go-to NER dataset—with GPT-4o mini. Get started today and deploy your custom model!

Fine-tuning enhances model performance by training on extensive examples that exceed prompt capacities, delivering superior outcomes across diverse tasks. This notebook offers a detailed walkthrough for fine-tuning our latest GPT-4o mini model. We'll tackle entity extraction with the RecipeNLG dataset, featuring assorted recipes alongside lists of parsed generic ingredients. It's a popular choice for named entity recognition (NER) applications.

Note: GPT-4o mini fine-tuning is available to developers in our Tier 4 and 5 usage tiers. Kick off your GPT-4o mini fine-tuning by heading to your fine-tuning dashboard, selecting "create", and choosing “gpt-4o-mini-2024-07-18” from the base model dropdown.

Here's the roadmap we'll follow:

• Setup: Load the dataset and narrow it to a single domain for targeted fine-tuning.

• Data preparation: Format your data for fine-tuning by crafting training and validation examples, then upload them via the Files endpoint.

• Fine-tuning: Build your custom fine-tuned model.

• Inference: Run your fine-tuned model on fresh inputs.

Upon completion, you'll be equipped to train, assess, and launch a fine-tuned gpt-4o-mini-2024-07-18 model.

For deeper insights into fine-tuning, check our documentation guide or API reference.