Open-weight means a model’s learned parameters are available to download or otherwise access. That can let people run or adapt the model, but it does not by itself make the model open source under the Open Source Initiative’s Open Source AI Definition (OSAID) v1.0. That standard also considers the information and code needed to study, modify, and share the system.
What are a model’s weights?
Weights are numerical parameters learned during training. They help determine how a model responds to an input. A trained model’s weights are important, but they are not the whole system: the Open Source Initiative (OSI) describes an AI model as including its architecture, parameters (including weights), and inference code.
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As a result, access to weights can be useful without giving someone everything needed to understand how the model was made, reproduce its training, or make substantial changes. OSI explains this distinction in its overview of open weights.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsWhat does “open source AI” mean under OSI’s definition?
OSI’s Open Source AI Definition v1.0 sets out four freedoms: use, study, modify, and share. Its stated scope is an AI system made available under terms and in a way that grants those freedoms. The definition was adopted by the OSI board on October 27, 2024.
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“An Open Source AI is an AI system made available under terms and in a way that grant the freedoms to:”
— Open Source Initiative, The Open Source AI Definition v1.0
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The definition also addresses the preferred form for making modifications. For an AI system, that includes parameters, sufficiently detailed information about training data, and the code used to train and run the system. The goal is to make meaningful study and modification possible, not merely to provide a ready-to-run set of weights.
Open weights vs. open source AI
Under OSAID v1.0, the key difference is scope: “open-weight” describes the availability of parameters, while “open source AI” describes a system, its accompanying materials, and the freedoms granted by its terms. Weights alone do not establish whether a release meets the definition.
| What to check | Open-weight label | OSAID v1.0 assessment |
|---|---|---|
| Parameters or weights | Signals that learned parameters are available. | Parameters are part of the preferred materials for modification, but they are not sufficient by themselves. |
| Architecture and inference code | The label does not establish whether these are available. | Relevant to understanding and running the model; check the release materials. |
| Training code and data information | The label does not establish whether these are available. | The preferred form for modification includes code used to train and run the system, plus sufficiently detailed training-data information. |
| Permissions | The label alone does not establish what users may legally do. | Terms must grant the freedoms to use, study, modify, and share. |
OSI says “Open Source models” and “Open Source weights” must include the data information and code used to derive the parameters. Its definition does not mandate one specific legal mechanism for making parameters available. Read the release terms rather than assuming a particular license or permission from the label.
How to assess a specific model release
Assess the particular checkpoint or release you plan to use, not just the model family name. OSI notes that AI systems involve more than weights, including data, configuration, documentation, and legal terms; the available materials and permissions can vary between releases.
- Identify the exact release. Record the model name, version or checkpoint, and the terms attached to that release.
- Inventory the materials. Look for parameters, architecture details, inference code, training code, and sufficiently detailed training-data information, including provenance and methods where provided.
- Read the terms. Check whether they permit use, study, modification, and sharing, including sharing modified versions.
- Judge whether modification is practical. Ask whether the materials are the preferred form needed to make meaningful changes, rather than weights alone.
OSI’s OSAID FAQs explain the definition and its application. Because releases and terms can change, verify the materials and permissions for the version you intend to use.
What OSI’s 2024 model examples do—and do not—show
In its December 17, 2024 year-end review, OSI reported that its evaluation found OLMo (AI2), Pythia (EleutherAI), CrystalCoder (LLM360), and T5 (Google) met OSAID criteria. It reported that Llama 2 (Meta), Phi-2 (Microsoft), Mixtral (Mistral), and Grok (X/Twitter) fell short.
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Those are findings from that dated review, not permanent judgments on entire model families or evaluations of later releases. For a current decision, examine the exact version and terms in question.
Why the label can be confusing
In industry discussion, “open source” is sometimes used more loosely than OSI’s formal definition. When comparing claims, ask what the speaker means and whether they are using OSAID v1.0. A release may offer accessible weights while leaving important details about training data, code, or permissions unavailable; that fact alone does not establish that it is closed or proprietary, only that the open-weight label does not settle the OSAID question.
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