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Open weights do not automatically make an AI model open source. A downloadable model’s learned parameters can let people run or adapt it, but the Open Source Initiative’s Open Source AI Definition (OSAID) v1.0 also looks for the information and code needed to study and modify the system. The disagreement is partly about what “open” means—and partly about which parts of an AI system a provider has actually released.
What “open weights” means—and what it leaves out
Weights are the learned parameters produced when a model is trained. They are one artifact in a larger system, alongside its architecture, inference code, training code, data information, and terms of use. The OECD distinguishes weights—the results of training and fine-tuning—from source code, which is a set of instructions. A user may be able to download weights and run or fine-tune a model without being able to inspect how it was trained or reproduce a substantially equivalent system. OECD, Initial Policy Considerations for Generative Artificial Intelligence
That makes “open weights” a useful description of what is available, but not a complete answer about openness. Access may also be subject to a provider’s license, registration, approval, or use restrictions. A free download is not, by itself, proof that commercial use, modification, or redistribution is permitted.
What OSI’s Open Source AI Definition asks for
The Open Source Initiative (OSI) defines open source AI in terms of four freedoms: use the system for any purpose without asking permission; study and inspect it; modify it for any purpose; and share it, with or without modifications, for any purpose. For machine-learning systems, OSI says the preferred form for making modifications includes data information, code, and parameters. Open Source AI Definition, v1.0
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Data information, not necessarily every raw example
OSI calls for a sufficiently detailed account of the training data to enable a skilled person to build a substantially equivalent system. That includes information about data provenance, scope, characteristics, acquisition and selection, labeling, processing, and filtering. It also calls for lists of public data and where to obtain it, as well as third-party data that can be obtained, including paid data.
This is not a blanket requirement to republish every training example. OSI’s FAQ says its approach allows for data that cannot legally be shared, including privacy-sensitive information such as healthcare data. The requirement is meaningful information about the data and its sources, including information about data that cannot be redistributed. OSI FAQ: Open Source AI
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Complete code for training and running the system
The code component includes the source code used to train and run the system—not just the code needed to call a model. OSI’s description includes data processing and filtering, training code and settings, validation and testing, supporting libraries, tokenizers, hyperparameter search, inference code, and architecture.
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Parameters and terms that support modification
Parameters include weights and other configuration settings; the preferred form may also include intermediate checkpoints and the final optimizer state. OSI does not prescribe one legal mechanism for making parameters available. It notes that the legal status of model parameters is not settled by the definition and that qualifying terms may be needed. The definition also permits conditions requiring modified versions to be released under the same terms. Open Source AI Definition, v1.0
Why the label is contested
Software is usually written as instructions. A trained model’s behavior, by contrast, is learned from data, and reproducing a training process can be difficult even for the system’s creator. OSI argues that data information and training code therefore matter to a genuine ability to study and modify an AI system—not only to the ability to use its trained weights. OSI FAQ: Open Source AI
Providers and users may use “open” more narrowly to mean that weights are downloadable and usable under particular terms. That usage describes a real form of access, even when it falls short of OSI’s definition. The OECD describes a spectrum that includes non-gated downloadable models, gated downloadable models, and fully open releases. The dispute is best understood by naming the test: a provider’s own use of “open,” access to weights, or the freedoms and materials required by OSAID are not interchangeable claims. OECD, Initial Policy Considerations for Generative Artificial Intelligence
How to assess an “open” model release
When a provider calls a model open, check the release itself rather than relying on the label. Record the model version and date, because terms and available components can change.
- Parameters: Are weights or other parameters available? Are they directly downloadable, gated, or available only through an API?
- Code: Is inference code available? Is the training code complete enough to cover data processing, training settings, validation, and testing?
- Training-data information: Does the provider explain provenance, selection, processing, and filtering? Are public and obtainable third-party data sources identified? Do not confuse this information with release of raw data.
- Terms: Check permissions and restrictions on use, modification, redistribution, and commercial use. Note any registration, approval, or user or scale limits.
- Reproducibility: Do the published materials let someone meaningfully study and modify the system, or mainly run the released weights?
- Named standard: Ask whether the claim is about OSAID v1.0, a tier in another framework, or the provider’s own definition of “open.”
The Linux Foundation’s Model Openness Framework offers a tiered way to compare releases: Class III “Open Model,” Class II “Open Tooling,” and Class I “Open Science.” Those categories provide another set of comparison axes; they do not replace OSI’s test. The OECD primer discusses both the framework and the range of access configurations. OECD, Initial Policy Considerations for Generative Artificial Intelligence
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What OSI’s model examples do—and do not—show
OSI’s FAQ reports the results of its validation volunteers’ analysis. Pythia (EleutherAI), OLMo (AI2), Amber and CrystalCoder (LLM360), and T5 (Google) passed the validation phase. OSI said BLOOM (BigScience), Starcoder2 (BigCode), and Falcon (TII) might pass if licensing or legal terms changed. It listed Llama 2 (Meta), Grok (X/Twitter), Phi-2 (Microsoft), and Mixtral (Mistral) among systems that did not pass its analysis because components were missing and/or terms conflicted with open-source principles. OSI FAQ: Open Source AI
These are examples reported by OSI, not endorsements, ongoing audits, or certifications. They refer to the systems and analysis described in the FAQ; they should not be generalized to every model from those organizations or treated as a verdict on current versions.
The definition itself is not a government regulation, legal ruling, or universal certification. OSI released OSAID v1.0 on October 28, 2024, after a year-long global co-design process. OSI describes it as a stable first version and a basis for community-led evaluation. OSI announcement: release of the Open Source AI Definition
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OSI explicitly says its definition does not guide or enforce ethical, trustworthy, or responsible AI practices. Whether a model meets an openness standard is a separate question from whether it is safe, reliable, privacy-preserving, or suitable for a particular use. Evaluate those properties on their own evidence rather than treating an openness label as a guarantee. OSI FAQ: Open Source AI
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