Under the Open Source Initiative’s Open Source AI Definition 1.0 (OSAID), an open-source language model lets anyone use, study, modify, and share the system for any purpose—and provides the materials needed to make meaningful changes. Downloadable weights alone are not enough: the definition also calls for detailed information about training data and the code used to build and run the model.
What does open source mean for a language model?
OSAID 1.0 adapts open-source principles to AI systems. A trained model is more than source code: its data, configuration, parameters and training procedures all affect whether someone can study or modify it. The definition therefore looks at the materials needed to understand and reproduce a substantially equivalent system, as well as the freedoms its terms grant.
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The Open Source Initiative announced OSAID 1.0 on October 28, 2024, as a standard for community-led, public evaluation of whether an AI system qualifies as open source: Open Source AI Definition and OSI announcement.
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Information about training data
The release should describe the training data in enough detail that a skilled person can build a substantially equivalent system. The definition calls for information such as data provenance, scope and characteristics; how data was obtained and selected; labeling; processing and filtering; and where publicly available or third-party-obtainable data can be found. See the definition’s data-information requirements.
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This does not mean every raw training record must be published. Some data may not be legally or reasonably shareable. In those cases, the required information should still describe it sufficiently. OSI’s FAQ distinguishes open, public, obtainable and unshareable nonpublic data.
Complete training and running code
The code should cover the steps needed to train and run the system, not merely a small inference demo. OSAID’s examples include data processing and filtering, training settings, validation and testing, supporting libraries such as tokenizers, hyperparameter-search code, inference code and model architecture.
Parameters and configuration
Model parameters, including weights, and relevant configuration settings must be available under terms that preserve the required freedoms. OSAID also says that a release described as “Open Source models” or “Open Source weights” must include the data information and code used to derive those parameters. A weights download, by itself, does not meet that standard.
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No. “Open weights” describes access to model parameters; it does not establish that the training-data information and necessary code are available, or that the terms allow use, study, modification and sharing for any purpose. Treat the label as a claim to check, not proof that a model meets OSAID 1.0.
When evaluating a particular model release, check these points together:
- Is the training-data information specific enough to understand how the data was assembled and to build a substantially equivalent system?
- Are the training, data-processing and inference code, along with relevant supporting components, available?
- Are the weights and relevant configuration available?
- Do the legal terms preserve the freedoms to use, study, modify and share for any purpose?
A model card or permissive-sounding label may help explain a release, but neither substitutes for checking its materials and terms against the definition.
Does an open-source model have to publish its training data?
Not necessarily as raw records. OSAID requires sufficiently detailed information about the data, including when some underlying data cannot legally or reasonably be shared. The practical test is whether the release explains the data and methods well enough for a skilled person to build a substantially equivalent system, rather than whether every training example is downloadable.
What the definition does—and does not—establish
OSAID evaluates openness and modifiability. It does not, by itself, certify a model as safe, accurate, ethical or responsible to deploy; OSI says the definition does not guide or enforce those practices: OSI FAQ.
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OSI has published examples from a validation phase in which some models passed and others did not, but it explicitly says those results are not certifications. They should not be treated as a permanent certification roster. Assess the specific version and release materials you intend to use against the definition: OSI FAQ.
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