Examples
Worked examples
- Is an instance
A fine-tuned medical-coding model declaring base = Llama-3 70B and fine-tune = institutional 12M-record corpus.
- Is an instance
A model lineage graph linking 14 community fine-tunes back to a single Mistral base release.
Counter-examples
Looks similar, but isn't
- Not an instance
A model with no declared base.
- Not an instance
A version history of a single training run with no derivative chain.
Editorial commentary
Model lineage is the complete provenance graph for an AI model: which base model it derives from, what fine-tuning, merging, distillation, or quantisation operations produced it, and what further derivatives exist downstream. A full lineage record lets an institution trace a deployed model back through every training operation that shaped it — the same audit function a data-management plan performs for a dataset, or a version-control history performs for code.
Why it matters
Licence terms, security patches, and known biases propagate along the lineage graph. If a base model carries a non-commercial restriction, every fine-tune inherits it; if a base model has a documented vulnerability or bias, every downstream derivative should be flagged until independently re-evaluated. For a research office reviewing a proposal to deploy or publish results from a fine-tuned model, lineage is the first thing to ask for: without it, there is no reliable way to know what the model actually is.
How this differs from its close siblings
Model lineage is the broadest of four closely related terms on this site. Model fine-tune lineage is one specific record type within it — the sequence of fine-tuning operations (dataset, method, hyperparameters) applied to a base model, rather than the full derivation graph across all operation types. Model versioning is a different axis entirely: it identifies which specific revision you are looking at, not how that revision was derived. A model checkpoint is narrower still — a single saved parameter snapshot at one point in training, the kind of artefact a lineage record points to rather than describes. In practice these overlap heavily, and a mature registry usually tracks all four as facets of one underlying record rather than as four separate documents.
Emerging mechanisms
Hugging Face’s model-card finetuned_from field and draft SPDX AI/ML bill-of-materials profiles are the two most concrete mechanisms currently in use for recording lineage in a machine-readable way; neither is yet a mandatory or universally adopted standard.
References
- Hugging Face Model Card metadata schema (
finetuned_fromfield). - SPDX AI/ML profile (draft), Linux Foundation.
- See also: Training data provenance, Persistent Identifiers and Citation for AI Models and Training Datasets.
Also known as
model provenance · model ancestry
Machine-readable encodings
Use in your systems
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