A fine-tuning pipeline is the repeatable set of steps that prepares example data, trains an existing model on it, and checks whether the new version is actually better.
In plain words
It usually runs in a loop. First, write evals that measure the current model on realistic test data, to get a baseline. Next, build a dataset of examples, often prompt and answer pairs or chat conversations. Then train, for instance with supervised fine-tuning, which learns from input and output pairs. Finally, run the same evals on the new model and adjust the data or prompt based on what they show.
A quick example
Hugging Face's TRL docs train a small Qwen 3 model on the Capybara dataset of multi-turn chats in a few lines of Python. A full pipeline would wrap that step with a before-and-after evaluation on the team's own test questions.
Where you'll meet it
- Teaching a model a house style or output format.
- Specialising open models for one task.
Related
Sources
- DocsModel optimization, OpenAI API docs · read 29 Sept 2026
- DocsSFT Trainer, Hugging Face TRL docs · read 29 Sept 2026
Checked by WhatIsAI against the sources above · 29 Sept 2026. Spotted an error? Report it.