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What does “open-source AI” mean?
Under OSAID 1.0, an AI system must let people use it for any purpose without permission, study how it works, modify it for any purpose, and share it with or without changes. OSI applies these freedoms to an AI system and to its components, such as a model and its parameters. The key question is not just whether a file is downloadable, but whether people have the rights and materials needed to inspect, change, and redistribute the system.
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For machine-learning systems, OSI describes the preferred form for modification in three broad parts: detailed information about training data, complete source code used to train and run the system, and model parameters such as weights. Those materials must be available under appropriate terms that preserve the relevant freedoms. Read the Open Source AI Definition 1.0.
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“Open weights” usually means that a model’s trained parameters are available. This can enable local use, analysis, or fine-tuning, but it does not establish that the training-data information, full code, or permissions to modify and share are also available. Similarly, “publicly available” or “open access” describes access, not necessarily rights.
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| Term | What it indicates | What it does not establish by itself |
|---|---|---|
| Publicly available or open access | Users can access a model or its materials. | Rights to modify and redistribute. |
| Open weights | Model parameters are accessible. | Availability of training information or complete code, or permission to use, modify, and share for any purpose. |
| Open-source AI under OSAID | Relevant freedoms and preferred modification materials are supplied under appropriate terms. | That the model is safe, responsible, or suitable for every use. |
These labels are not used consistently across the field. An OSI-affiliated 2025 analysis examined metadata for about 20,000 Hugging Face models surfaced through “open” or “open source” tags. Apache 2.0 was the most common OSI-approved license in that tagged sample, followed by MIT; the analysis also found substantial use of custom terms and models without a license. The author cautioned that the tagged results were noisy and not a compliance judgment, so this is a snapshot of that selection method, not a census of AI models. See the 2025 analysis and its caveats.
What materials should an open-source AI release include?
Training-data information
OSAID calls for a sufficiently detailed description of training data to help a skilled person build a substantially equivalent system. Relevant information includes provenance, scope and characteristics; how data was obtained and selected; labeling procedures; processing and filtering; and listings of public or third-party datasets with information about where to obtain them.
Code
The preferred form includes complete source code for training and running the system. That can encompass data processing and filtering, training settings, validation and testing, supporting libraries such as tokenizers, hyperparameter-search code, inference code, and model architecture.
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Parameters and configuration
Weights are central, but the relevant parameters can include other configuration settings. OSI gives intermediate checkpoints and the final optimizer state as examples of material that may matter to modification.
Having only one of these categories may still be useful, but it does not provide the same basis for study and modification as a release containing the full set of relevant materials and rights. The exact materials needed depend on the system and its structure.
Does open-source AI require public training data?
No. OSAID asks for detailed information about training data; it does not require redistribution of every raw training example. Privacy, copyright, and jurisdictional constraints may prevent raw data from being shared. OSI’s FAQ explains that information about sources, scope, selection, labeling, and processing can support study and downstream work without publishing all underlying data. See OSI’s FAQ on the definition.
This distinction matters when assessing reproducibility. Data descriptions can help others scrutinize a system and build a substantially equivalent one, but they do not necessarily let them repeat the identical training run from the same raw examples. OSI describes the definition as enabling reproducibility without requiring full reproducibility.
How can you compare two claims of openness?
Check both the permissions and the release artifacts. A model can publish extensive technical materials yet impose terms that limit use or redistribution; another can grant broad permissions while disclosing little about how it was made. Neither access nor documentation alone answers both questions.
- Read the license and use terms. Determine whether use, study, modification, and sharing are permitted for any purpose. Look for extra conditions, acceptable-use rules, and requirements that apply to modified versions.
- Inventory the components. Check whether the release includes weights, architecture, training and inference code, evaluation code, and configuration materials.
- Assess training-data information. Look for provenance, scope, selection, labeling, and processing details sufficient for meaningful study and downstream work.
- Check inspection and reproducibility artifacts. Research documentation, key datasets, checkpoints, evaluation materials, and logs provide different levels of insight; a release limited to weights and basic documentation offers less.
- Evaluate safety and deployment separately. Openness is not evidence that a system is safe, responsible, or fit for a particular application.
The OECD’s 2025 policy primer summarizes a second framework, the Linux Foundation’s Model Openness Framework (MOF), which classifies releases by how many components they provide. It is a component-completeness framework, not the same thing as OSI’s definition of rights and terms. Read the OECD primer.
| MOF class | Materials described in the OECD summary | What the added materials help with |
|---|---|---|
| Class III – Open Model | Core model materials such as architecture, parameters, and basic documentation, under open licenses. | Use and analysis, with less insight into development. |
| Class II – Open Tooling | Class III materials plus training, evaluation, and run-time code and key datasets. | Stronger validation and reproducibility. |
| Class I – Open Science | Class II materials plus broader research artifacts, such as raw training datasets, a detailed paper, intermediate checkpoints, and logs. | More extensive inspection of the research and development process. |
The OECD also identifies comparison artifacts such as preprocessing and evaluation code, libraries and tools, datasets, model weights, data and model cards, research papers, evaluation results, metadata, and configuration files. A class label does not replace checking the actual release or its license.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the benefits and trade-offs?
Open releases can give developers and users more autonomy, transparency, reuse, and opportunities for collaborative improvement. The more relevant components are available, the easier it can be for others to inspect, modify, validate, and reproduce aspects of a model.
The practical limits are significant: a release may omit components, its terms may restrict certain uses or redistribution, and missing or unclear licensing can make permissions hard to determine. More complete disclosure can improve inspection and potential reproducibility, while sharing raw data may raise privacy, copyright, or other legal concerns. Openness itself is not a safety assessment. OSI says OSAID does not specifically guide or enforce ethical, trustworthy, or responsible AI development practices.
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Do named models meet the definition?
OSI’s FAQ describes examples from its validation phase while developing the definition. It listed Pythia (EleutherAI), OLMo (AI2), Amber and CrystalCoder (LLM360), and T5 (Google) as examples that passed. It listed Llama 2 (Meta), Grok (X), Phi-2 (Microsoft), and Mixtral (Mistral) among analyzed examples that did not pass because components were missing and/or legal agreements were incompatible with the principles.
OSI explicitly said these were not certifications; they describe validation work during the definition process, not a current, exhaustive judgment of every version or release. Model artifacts and terms can change, so check the specific version’s current model card, license, and release materials before making a present-day compliance claim.
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