Open Weight Definition
A standard for AI model weight distribution that bridges open source and proprietary approaches
Overview
The Open Weight Definition (OWD) is a standardized framework that defines how [open weights] can be shared and used while balancing openness with practical constraints. Unlike traditional open source software that requires complete source code access, the OWD focuses specifically on making model weights freely available while allowing some elements (like training data) to remain proprietary.
The Definition
The OWD specifies ten critical requirements for AI models to be considered "open weight":
| Criterion | Description |
|---|---|
| 1. Free Redistribution | No restrictions on selling or giving away weights; no royalty fees required |
| 2. Model Weights | Must include actual, unobfuscated weights with clear distribution channels; transformed versions must be clearly labeled |
| 3. Derived Works | Must allow modifications and derived works under the same license terms |
| 4. Author Integrity | Must permit distribution of modified weights while protecting original attribution |
| 5. No Personal Discrimination | Must not discriminate against any person or group |
| 6. No Usage Discrimination | Must not restrict use in any field (e.g., business, research) |
| 7. License Distribution | Rights must apply to all recipients without additional licensing |
| 8. License Independence | Rights must not depend on being part of a specific distribution |
| 9. No Other Restrictions | Must not restrict other distributed components |
| 10. Technology Neutral | Must not be tied to specific technologies or interfaces |
Key Requirements
Distribution and Access
- Free sharing and redistribution of weights
- Inclusion in larger model collections permitted
- Easy access through well-publicized channels
- Clear labeling of optimized or quantized versions
- Reasonable reproduction costs, preferably free
Rights and Permissions
- Modifications and derived works allowed
- No discrimination against users or use cases
- Technology-neutral implementation
- Protection of original author attribution
- No additional licensing requirements
Important Trade-offs
Benefits
- Independent deployment of advanced AI technologies
- No permission required for use
- Clear labeling requirements
- Responsible use guidelines
- Accessibility for vendors not ready for full open source
For Developers
- Clear distribution guidelines
- Simplified licensing terms
- Standardized sharing practices
- Freedom to modify and adapt
- Protected intellectual property
For the Community
- Free access to model weights
- Unrestricted commercial use
- Enhanced collaboration
- Educational opportunities
- Accelerated AI innovation
Limitations
- Training data may remain proprietary
- Limited ability to study internal workings
- Restricted capability to address bias at data level
- Cannot fully modify model architecture
- Fine-tuning may be the main modification option