Open-weight usually means that a model’s trained parameters have been released. Open-source AI, under the Open Source Initiative’s Open Source AI Definition (OSAID) 1.0, requires more: terms that grant the freedoms to use, study, modify and share, plus access to the materials needed to modify the system, including training and inference code, parameters and detailed information about training data. A download page or a model card calling something “open” is not enough to establish either the legal permissions or the practical ability to reproduce and change it.
For a real decision, inspect the exact release, its license or terms, the downloadable components, training-data documentation, safety evidence, performance, hardware requirements and privacy implications. “Open” describes particular rights and materials; it does not certify quality, safety or responsible development.
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The short distinction
Think of a model as a stack rather than a single file. The architecture describes how the system is organized. The parameters, often called weights, are the learned numerical values produced by training. Inference code runs those parameters to generate outputs. Training code and settings describe how a new or modified model can be produced. Information about training data explains what went into the process and how it was selected or filtered.
Releasing weights exposes one important layer of that stack. It may let another developer download a model, run it on their own hardware or adapt it with additional training. It does not, by itself, disclose the training recipe, the source material, the filtering decisions or the code required to reproduce the result.
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OSAID 1.0 uses a broader test. An AI system is open source when its terms support use, study, modification and sharing, and when the preferred form for modification is available. OSI identifies training code, inference code, model parameters and detailed information about training data as parts of that preferred form. The label therefore concerns both permissions and practical access to the materials needed to exercise them.
Neither expression is a universal legal category. “Open-weight” is common industry language, while the Open-Weights project publishes its own proposed definition and permissive license. That project’s approach is separate from OSI’s definition. Always identify which terms govern the particular release.
What model weights are—and what releasing them reveals
Weights are learned parameters
During training, an AI system adjusts a very large set of numerical values. Those learned values, together with the model architecture and software that performs inference, determine how the model responds to inputs. They are often stored in downloadable files that can be loaded into compatible software.
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Weights are valuable because they can make local or private operation possible. A team may be able to run the model without sending prompts to a hosted service, integrate it into an internal application or continue training it for a narrower task. The exact hardware, software and permitted uses still depend on the release.
Weights are not a training record
A weights file normally does not tell you which data was collected, what was removed, how examples were labeled, which hyperparameters were used or how many failed experiments preceded the published checkpoint. It may also omit the original training code, data-processing scripts and evaluation harnesses. A developer can inspect the numerical parameters without being able to reproduce the model or understand every source of its behavior.
That gap matters for provenance, copyright and privacy review. Information about training data is not the same as access to the underlying dataset. A release can document sources, selection and filtering in detail while withholding the original files because of licensing, privacy or security constraints.
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What “open-source AI” means under OSAID 1.0
Four freedoms
OSAID frames openness around four practical freedoms:
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- Study: people can inspect enough of the system and its documentation to understand how it works.
- Modify: people can change the system and create a modified version.
- Share: people can redistribute the original or modified version under the applicable terms.
These freedoms are assessed for the actual model and version, not for a company’s general policy or a headline description. A restriction on a particular field of use, a limit on redistribution or a requirement that prevents practical modification can change the result.
The preferred form for modification
OSI’s definition names four categories of material that should be available in the preferred form for modification:
- Training code and relevant settings, so another party can understand and repeat the training process.
- Inference code, including the software needed to run the released system.
- Model parameters, in a usable form rather than only as output demonstrations or an inaccessible hosted endpoint.
- Detailed information about training data, including useful information about sources, selection and processing.
The standard is not satisfied merely because a model can be queried through an API. An API may provide use without providing study, modification or sharing rights, and it does not supply the preferred modification materials.
Why the labels can mislead
“Open” on a model card is not a legal conclusion
A publisher can call a model “open” while releasing only weights, or while imposing terms that restrict commercial use, redistribution or particular applications. Conversely, a release may provide extensive code and documentation but use a license whose conditions require careful legal analysis. Treat the label as a claim to investigate, not as proof.
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Open weights and open-source AI answer different questions
Open weights primarily answers, “Can I obtain the learned parameters?” OSAID asks a larger set of questions: “Can I use, study, modify and share this system, and do I have the materials needed to modify it?” A model can be open-weight without meeting OSAID’s preferred form. A model described as open source still needs to be checked against the actual terms and materials.
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Different definitions coexist
The Open-Weights project maintains a proposed definition and license focused on neural-network weights. That is a distinct project and should not be presented as OSI’s standard. When comparing releases, name the definition or license you are applying and quote the operative conditions from the release itself.
A practical comparison checklist
Use the same checklist for every model under consideration. Record the exact version, publication date, license version and the region or jurisdiction relevant to your deployment.
| Question | What to inspect | Why it matters |
|---|---|---|
| What may users do? | License or terms covering use, modification, redistribution, derivatives and restrictions | Determines whether the four freedoms are actually granted |
| Are the parameters available? | Download location, file format, access conditions and checksum or version identifier | Separates a runnable release from a hosted-only service |
| Can the system be studied? | Architecture description, inference implementation, documentation and evaluation code | Shows whether behavior can be examined rather than merely observed through an API |
| Can it be modified? | Training code, configuration, preprocessing and fine-tuning instructions | Indicates whether a developer can create a changed version in practice |
| What is known about data? | Sources, collection method, filtering, licensing, provenance and known gaps | Supports copyright, privacy, bias and reproducibility review |
| Is deployment suitable? | Independent safety evidence, performance evaluations, hardware needs, cost and privacy characteristics | These determine operational risk but are not certified by an openness label |
Do not mark a criterion as satisfied merely because a publisher uses similar words. Save the relevant clause, file name and version so a later audit can reproduce your conclusion.
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How to evaluate a model step by step
- Fix the object of evaluation. Write down the model family, exact checkpoint, quantized or full-precision variant, release date and intended geography. A later checkpoint may have different terms or documentation.
- Read the governing terms first. Identify the license, acceptable-use policy, additional restrictions, attribution requirements, notice obligations and rules for derivatives or redistribution. Check whether separate terms apply to the weights, code and data documentation.
- Verify parameter access. Confirm that the files are actually downloadable or otherwise available under those terms. Record whether access requires an application, authentication or agreement, and whether the files correspond to the advertised version.
- Locate inference code. Establish which implementation is authorized or supported, what dependencies it needs and whether the code is complete enough to run the released parameters.
- Locate training materials. Look for training scripts, configuration, tokenizer or preprocessing code, fine-tuning instructions and evaluation harnesses. Distinguish a recipe that can be followed from a marketing description of training.
- Review data information. Check whether the publisher identifies sources, selection, filtering, dates, licensing and known exclusions. Note explicitly when the documentation provides information but not the original data.
- Map each freedom to evidence. Make four separate findings for use, study, modification and sharing. A positive finding in one category does not prove the others.
- Run independent deployment checks. Test safety controls, accuracy on your own representative tasks, latency, memory consumption, hardware compatibility, logging and privacy. These are separate engineering and governance decisions.
- Freeze the record. Store copies or hashes of the terms and files you relied on, because model pages and repositories can change.
Permissions, code and data: common edge cases
Weights available, code unavailable
You may be able to run or fine-tune the released checkpoint with third-party software, but you cannot inspect the publisher’s original inference implementation or reproduce its training process. Describe that release as providing weights, not as automatically meeting the OSAID preferred form.
Code available, weights gated
Public training and inference code can improve study and reproducibility, yet a gated checkpoint may limit use and sharing. Check whether the access agreement permits redistribution or derivative models.
Detailed data summary, no dataset download
Detailed information about training data does not require handing over every source file. Evaluate the quality and completeness of the information separately from whether the original data can be accessed.
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Permissive wording with hidden conditions
Terms may look permissive in a headline while adding restrictions in an acceptable-use policy, a separate click-through agreement or a clause for high-risk applications. Read all documents incorporated by reference and determine which one controls in your jurisdiction.
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A derivative checkpoint can have different training data, code, accuracy and license obligations from its base model. Evaluate the derivative itself and verify whether the base model’s terms flow through to redistribution.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Openness is not a safety or quality certification
OSI’s FAQ states: “The Open Source AI Definition does not specifically guide or enforce ethical, trustworthy, or responsible AI development practices.” That scope limit is important. Openness can improve inspectability and give more people the ability to test or fix a system, but it does not establish that the model is safe, unbiased, secure, accurate or appropriate for a sensitive decision.
Evaluate safety and provenance independently. Review documented evaluations, red-team findings, known failure modes, content safeguards, privacy behavior and the ability to monitor or disable the system. Test the model on the languages, domains and user populations that matter to your application. Consider whether local deployment reduces data exposure or instead creates new obligations for patching, access control and incident response.
Performance and cost also remain separate. A model with complete source materials may require more memory, specialized accelerators or substantial engineering work. A smaller weights-only release may be easier to deploy but harder to audit or adapt. Choose based on the operational requirement, not on the label alone.
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Openness is not a permanent property of an entire model family. Licenses can change, documentation can be expanded, and a new checkpoint can add or remove restrictions. Record the exact release and date whenever you publish an assessment or approve a deployment.
OSI’s final board report named Llama 2, Grok, Phi-2 and Mixtral as failing criteria it evaluated at that time. That historical assessment should not be extended automatically to later releases, variants or revisions. Recheck the current terms and materials for the particular version you plan to use.
Geography can matter as well. A license may interact with local copyright, privacy, export-control or consumer-protection rules. A permission granted in the release terms is not a guarantee that every proposed use is lawful in every country.
Choosing the right level of openness for a project
Choose weights access when deployment is the priority
A weights release may be sufficient when your primary need is local inference, predictable latency or adaptation with an existing toolchain. Before adopting it, confirm that the terms permit your industry and redistribution model, and budget for the missing training and provenance information in your risk review.
Prefer OSAID-style materials when reproducibility matters
Research teams, auditors and organizations building long-lived derivatives benefit from training and inference code, parameters and detailed data information. Those materials make it easier to investigate behavior, reproduce results, apply fixes and explain how a derivative was produced.
Use a hosted service when neither label solves the operational problem
If your requirements center on managed uptime, rapid updates or a provider’s support obligations, an API may be the practical choice. It should be evaluated as a service agreement, with attention to data retention, processing location, security and exit options—not described as open source merely because the underlying model is discussed publicly.
A decision record you can hand to legal, security and engineering
For each candidate, produce a one-page record containing:
- Model name, exact version, variant and acquisition date.
- Every applicable license, policy and click-through term, with the document versions.
- Evidence for use, study, modification and sharing, listed separately.
- Whether weights, inference code, training code, settings and data information are available, and under what access conditions.
- Known gaps in provenance, licensing, privacy and documentation.
- Independent test results for accuracy, safety, robustness, latency and resource consumption.
- Deployment controls: access permissions, logging, monitoring, update process, rollback plan and incident owner.
- A review date, because terms and model artifacts can change.
This record prevents a common failure: treating a convenient download as proof that every downstream activity is allowed and reproducible.
Bottom line for readers comparing two models
Ask two separate questions. First, what can I legally and practically do with these weights? Second, do the terms and materials meet the open-source AI definition I am applying? Then make a third, independent assessment of safety, provenance, performance, cost, hardware and privacy. A model earns a more meaningful openness description through documented permissions and available materials—not through the word “open” on a landing page.
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