fine-tuning API
An API endpoint where you upload training data and request the model to fine-tune on it, returning a new model variant optimized for your task.
Think of it like
Sending your generic car to a shop and saying 'make it handle better on mountain roads'—you get a custom variant back.
Example
Upload 500 examples of customer emails + correct support-ticket categories. Call the fine-tuning API. In an hour, get back a model that's better at categorization than the base model.
How it actually works
Fine-tuning APIs run the training loop on their hardware, charge by token or example, and give you a model checkpoint to call. Tradeoff: you don't see training loss curves, you can't customize loss functions, but you're not managing GPUs. Popular for task-specific optimization when in-context examples don't cut it.
For product teams
Improves model quality on domain tasks; more cost-effective than prompt engineering beyond a certain threshold.
For engineers
Managed training; no GPU setup; token-based pricing.
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