Fine-tuning data
Fine-tuning data supplies the examples or signals used during an additional training stage on a pre-trained model. It might contain instruction-response pairs, labeled inputs, demonstrations, or other task-specific material. Fine-tuning is the adaptation process; fine-tuning data is the material used in that process.
How it works
Define the desired behavior and choose a representation compatible with the training method. Examples should provide the context and targets the model is expected to learn from. Preparation can include label checks, formatting, deduplication, and rights review.
Hold out suitable evaluation examples and avoid placing near-identical cases in both partitions. A dataset that reproduces the test answers may produce impressive scores without demonstrating generalization.
Why it matters for licensing
A license should address whether model adaptation is permitted and what can be retained afterward. A recipient also needs to know the dataset’s collection method, quality checks, and limitations. Existing business records may require substantial work before they become useful fine-tuning examples.
Example
Fictional example: A team prepares approved examples of routing service requests to the correct department. It records the input available at routing time, checks labels, and reserves separate cases for evaluation before fine-tuning a model.
Limitations and misconceptions
Small or biased datasets can teach narrow behavior or reinforce errors. More examples are not automatically better, and a well-formed file does not guarantee improvement. Sensitive or restricted material remains a concern even when embedded in otherwise useful examples.
Questions to ask
- Which target behavior and training method determine the example format?
- How were labels, permissions, and duplicates checked?
- How is final evaluation kept independent?
Sources
Explore whether your business data could be a fit.
Start with a description of your systems—not a data upload.