manage-local-models

You can cache local open-weight models by pulling signed snapshots, verifying SHA256 hashes, and recording metadata in a .understudy-snapshot.json file for reproducible, disk-conscious offline inference.

The best way to manage disk usage for cached local models is to curate, prune, and relocate weights to optimize storage while preserving provenance and maintaining a structured snapshot for auditing.

You verify the integrity and provenance of pulled model weights by validating downloads with SHA256 sums and generating a structured snapshot file to capture provenance for reproducibility and auditing.

Yes, you can inventory and verify exactly which open-weight models are cached and what remains on disk, allowing you to track your local model assets and optimize your storage footprint.

Local model onboarding for offline inference enforces safety gates, integrity checks, provenance capture, and metadata generation to ensure secure, reproducible, and disk-conscious private inference.