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Open-weight AI usually means that a model’s trained weights—the numerical parameters it learned during training—are available for people to download or use. That access alone does not tell you whether the model’s training data or code is available, whether you can redistribute or modify the weights, or whether use is restricted. Those details depend on the model’s terms and disclosures.
What are a model’s weights?
A trained AI model contains parameters, often called weights, that shape how it responds to inputs. In ordinary usage, a model is called open-weight when those trained parameters are made available rather than being accessible only through a provider’s hosted service. The label describes access to the weights; it does not, on its own, describe everything needed to build, study, or modify the model.
Open weights, the Open Weight Definition, and open-source AI are not interchangeable
“Open weight” is often used as a practical description of publicly available model parameters. The Open Weight Definition (OWD) gives the term more specific criteria: its version 0.3 calls for free redistribution, permission to distribute modified or derived weights, and no restrictions based on who uses them or the field of endeavor. It does not require the source, such as training data, to be distributed. See the Open Weight Definition (version 0.3, last modified January 21, 2025).
The Open Source Initiative’s Open Source AI Definition (OSAID) v1.0 takes a broader approach. It applies the same requirements whether an offering is described as an AI system, model, weights, or parameters, and sets out freedoms to use, study, modify, and share. Its preferred form for modifying a machine-learning system includes data information, code, and model parameters. Read the OSAID v1.0 and the OSI FAQs.
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As a result, a model can have downloadable weights without meeting OSAID’s requirements for open-source AI. Likewise, a provider’s use of “open” is not a substitute for checking the release against a named standard and its actual terms.
What information does OSAID require?
OSAID calls for enough information about training data for a skilled person to build a substantially equivalent system. Its requirements include a full description of the data’s provenance, scope and characteristics; how it was obtained, selected, labeled, processed, or filtered; lists of publicly available and third-party obtainable data; the complete source code used to prepare data, train, and run the system; and the model parameters.
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This does not mean every raw training example must be redistributed. OSI’s FAQ recognizes that legal or privacy reasons can prevent data from being shared. The emphasis is on sufficiently detailed data information and materials to understand the system and support downstream work. OSI announced OSAID v1.0 on October 28, 2024; its release announcement explains the training-data information requirement.
How to assess a specific open-weight model
Use the model’s own release terms and documentation rather than relying on its label. Check these separate questions:
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- Weights access: Are usable weights actually available, and how can they be obtained?
- Rights: Do the terms permit use, redistribution, and sharing modified weights? Are there restrictions on users or fields of use?
- Training-data information: Does the release describe data provenance and preparation? Is the data itself public, obtainable from third parties, or unshareable?
- Code and modification materials: Are data-processing, training, and inference code and relevant model configuration available?
- Other constraints: Are there separate usage policies, infrastructure requirements, or proprietary tools that affect what you can do?
For example, OpenAI describes its gpt-oss weights as publicly available under Apache 2.0 and its usage policy, while noting that surrounding tooling or infrastructure may remain proprietary. That is OpenAI’s description of its own release—not a universal definition of open-weight AI. Check the current gpt-oss information alongside the model’s specific terms.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the label does—and does not—tell you
“Open-weight” is useful when the question is whether trained parameters can be obtained. To decide whether a particular model is open source under OSAID, or whether you can use, adapt, and redistribute it for your purpose, inspect its license, policies, and available development materials separately. Availability is one part of openness, not a complete account of a model’s rights or how it was made.
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