Keeping an open AI project approachable
Which examples, tests and model documentation make an open AI codebase easier for a new contributor to understand?
A focused place for local models, retrieval systems, evaluation, open agents and reproducible deployment discussions.
Which examples, tests and model documentation make an open AI codebase easier for a new contributor to understand?
Which model pinning, hardware notes, evaluation fixtures and rollback practices work well for small production deployments?