Document model licensing before using a checkpoint

“Open model” does not specify what you may do with a checkpoint. A repository tag is a starting point; approval depends on exact files, revision, license text, restrictions, intended use and distribution path.

Keep unresolved work visible

The adaptation lab validates fields in a synthetic review card.

from adaptation_cases import license_case

result = license_case()
assert result["complete_fields"] is True
assert result["unresolved"] == ["training_data_terms_reviewed"]
assert result["approved"] is False
assert result["legal_advice"] is False
assert result["card"]["reviewer"] == "pending"

Complete fields do not mean complete review. The unresolved provenance question keeps status pending, and the fixture is not legal advice.

Review the artifact and use

Record repository, exact commit/revision, file digests, publisher, model card, full license text and date retrieved. Check commercial/internal use, modification, fine-tuning, hosting, output terms, redistribution of weights/adapters, attribution, notices, naming rules, use restrictions and termination clauses.

Trace base and derivative dependencies. An adapter may require a separately licensed base; a merged artifact may trigger distribution obligations. Review training-data disclosures, privacy and intellectual-property risks even when the model license is clear. Verify code, tokenizer and dataset licenses separately.

Describe the intended product, geography, users, data, deployment and whether weights leave the organization. Assign an accountable legal/compliance reviewer where needed. Store the decision, conditions and reevaluation triggers rather than copying “approved” between projects.

The Generative & Agentic AI course includes licensing in the model experiment and deployment evidence.

Exercise

Choose one candidate checkpoint and build a dependency/license inventory. Identify one unresolved term, keep approval pending, and write the question an authorized reviewer must answer.

Continue learning

This article is part of the Model adaptation and multimodal tasks sequence. Use the neighbouring tasks when you need the prerequisite or the next application.

• Review the prerequisite or neighbouring task in Run an open model locally with explicit resource limits.
• Continue with Compare prompt caching and fine-tuning from workload economics.

Reference: Hugging Face model cards.