AI researchers and engineers waste time recreating spec formats for each new model. A reusable framework could standardize how parameters, architectures, and training details are defined.
Develop an open-source spec framework with versioning, validation, and rendering support. Focus on extensibility so teams can add custom fields.
AI labs and startups would pay to ensure consistency across their models and simplify onboarding new hires.
Start with a core schema for common model attributes, then add plugins for specific architectures.
The risk is low adoption if major labs prefer their own internal standards.