modelsfrom

Modelsfrom is a software tool and metadata framework designed to catalog machine learning models and their provenance. It provides a standardized representation for model artifacts, enabling teams to capture origin, licensing, training data, and performance metrics across diverse frameworks and storage backends.

At its core, modelsfrom generates and validates manifest files that describe each model entry. A typical manifest includes fields such as name, version, framework, source repository or publication, license, training data description, training and evaluation metrics, dependencies, and a cryptographic hash of the artifact to ensure integrity. The tool supports models from PyTorch, TensorFlow, ONNX, and other formats, and can extract metadata from common model registries or file metadata.

Formats and pipelines: It exports to JSON, YAML, or CSV and can integrate with continuous integration workflows to produce a catalog as part of model release processes. It offers a CLI and a programmatic API, plus a pluggable adapter system to fetch models from repositories, artifact stores, or cloud buckets. It also provides validation checks for required fields, license compatibility, and provenance traceability.

Usage and impact: Teams use modelsfrom to improve governance, traceability, and reproducibility. By consolidating model sources and metadata in a single catalog, organizations can audit licenses, reproduce experiments, and compare models while maintaining compliance with internal policies or external regulations.

History and governance: The project originated in 2023 as an open-source initiative by an independent developer community. It has since evolved through community contributions and partnerships with model governance initiatives. The project uses a permissive license and maintains a public repository with issues, discussions, and release notes.