The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence

The AI landscape is evolving rapidly, with new models being released almost daily. But how can we evaluate their true openness and usefulness for research and innovation? The Model Openness Framework (MOF), developed by researchers at the Linux Foundation and the Generative AI Commons, provides a structured approach to assess and classify AI models based on their completeness and openness.

Why Model Openness Matters

The past year has seen remarkable progress in AI, from breakthrough language models to revolutionary image generators. However, many of these models operate as “black boxes,” making it difficult to understand their inner workings or verify their behavior. While companies increasingly release their models publicly, there’s often confusion about what “open” really means.

True openness in AI goes beyond just sharing model weights. It encompasses the entire development lifecycle — from training data and code to documentation and evaluation results. This transparency is crucial for:

• Scientific reproducibility and verification
• Understanding model behavior and limitations
• Enabling collaborative improvement
• Fostering trust in AI systems
• Democratizing access to AI technology

The Three-Tier Classification System

The MOF introduces a clear, three-tier system for classifying AI models:

Class III — Open Model

The entry point, requiring:

• Model architecture
• Final model parameters
• Basic documentation
• Evaluation results
• Model and data cards

Class II — Open Tooling

Builds on Class III by adding:

• Training and testing code
• Inference code
• Evaluation code and data
• Supporting libraries

Class I — Open Science

The gold standard, including everything from Class II plus:

• Complete research paper
• Training datasets
• Data preprocessing code
• Intermediate model checkpoints

The Reality of “Open Weights” Models

It’s worth noting that even models that don’t meet the full criteria for openness can still contribute significantly to AI progress. Take Meta’s LLaMA or DeepSeek’s models — while they may not be fully open source in the strictest sense, their release with publicly available weights has enabled:

• Widespread research and experimentation
• Development of specialized variants
• Testing of safety and alignment techniques
• Innovation in model deployment and optimization

The goal isn’t to discourage such releases but to provide clarity about different levels of openness and encourage movement toward greater transparency.

Introducing the Model Openness Tool

To help the AI community evaluate and track model openness, the Generative AI Commons has launched the Model Openness Tool (MOT). This web-based platform allows:

• Checking a model’s MOF classification
• Evaluating new models against the framework
• Tracking changes in model openness over time
• Comparing different models’ levels of transparency

Looking Forward

The MOF and MOT represent important steps toward standardizing how we think about and measure AI model openness. As the field continues to advance, having clear frameworks for assessing transparency and completeness becomes increasingly crucial.

For model developers, the framework provides a roadmap toward greater openness. For researchers and practitioners, it offers a way to make informed decisions about which models to use and build upon.

Getting Involved

The MOF is an open initiative, and the AI community’s participation is crucial for its success. You can:

• Read the full research paper
• Try out the Model Openness Tool
• Contribute to the framework’s development
• Apply the framework to your own models and submit them to the MOT

As we continue to push the boundaries of what’s possible with AI, let’s ensure we do so with transparency and openness as core principles. The Model Openness Framework gives us a practical way to work toward that goal.