Download Hygiene: Model Provenance, Licenses, and Checksums

Local model work starts before inference: students need to know where the model came from and whether they are allowed to use it. In local AI, the model family is only one part of the system. The runtime, file format, serving path, hardware budget, evaluation set, and safety policy decide whether the model becomes useful.

Current source signal

Build the small version

Create a model intake checklist before adding any new local model to a classroom or company machine.

1. 1Define the user task in one sentence.
2. 2Choose the smallest model and runtime that might pass that task.
3. 3Run one happy-path prompt and one failure-path prompt.
4. 4Record speed, memory pressure, output quality, and the exact reason for any failure.
5. 5Write the operating rule you would give a non-expert user.

model_intake_checklist: source: official_org_or_trusted_quantizer license: reviewed model_card: read checksum_or_release_hash: recorded chat_template: confirmed safety_notes: copied eval_status: pending_before_use

The big idea: model intake checklist. A local model app is not done when the model answers once; it is done when the whole workflow can be installed, measured, trusted, and recovered.