Fine-tuning
By HubAI editorial team · Source checked: 24 October 2026What does it mean?
Further training a pre-trained model on selected data to adapt its behaviour or task performance. This changes model parameters, unlike simply adding examples to a prompt.
Picture this
A team trains on carefully reviewed examples of its own document labels to make classification more consistent, then tests on documents not used in training.
Where you’ll come across it
Repeated specialised tasks, tone or output patterns when simpler approaches are not enough. Start with a baseline so you can tell whether training actually improved anything.
What it doesn’t mean
Fine-tuning is not automatically the right way to add frequently changing facts. Retrieval may be more suitable, and weak training examples can bake mistakes into the model.
Compare on a job, not a label
A short exercise · use fictional or non-sensitive material.
- Choose a small set of non-sensitive tasks, including an awkward case and a missing-information case.
- Keep the inputs and scoring checklist the same when you compare two model setups.
- Record useful completions, corrections and elapsed time. Keep the model name and settings with the results.
Is the improvement still useful after you include checking and correction time?
Did that make it clearer?
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Written and explained by HubAI. The reference below lets you go deeper; it is not an endorsement of a particular product.
Technical reference: huggingface.co ↗