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Start by separating open-source AI from the broader category of open models. A downloadable model is not automatically open source under the Open Source Initiative’s AI-specific definition: that definition also addresses the code used to derive the model and sufficiently detailed information about its training data. The reading list below explains the distinction, points to representative releases and publisher terms, and gives you a practical way to assess a model for your use case.
First, learn what “open-source AI” means
Read the Open Source AI Definition 1.0
The Open Source Initiative’s Open Source AI Definition 1.0 is the starting point for using the label precisely. It addresses model parameters, including weights; the code used to derive the model; and sufficiently detailed information about training data. Availability of weights alone does not establish that a release meets all of those criteria.
Use the FAQ to understand the definition
The OSI’s FAQ provides context for applying the definition. Read the definition and FAQ together rather than treating “open source” as a synonym for “downloadable.” In this article, “open models” is the broader, practical term for models whose weights or other artifacts are made available, whether or not the release meets every OSI criterion.
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These examples illustrate different release and licensing approaches; they are not a complete catalog, legal advice, or a benchmark ranking. A license named by a publisher is only one part of the terms to check: access conditions, acceptable-use rules, and version-specific provisions can matter too.
| Example | What the publisher says | What to check before use |
|---|---|---|
| Meta Llama | Meta says Llama models are governed by the applicable Community License and Acceptable Use Policy. Its FAQ describes broad commercial use and the ability to create and redistribute additional work, subject to the terms and restrictions. | Identify the exact model and version, read its Community License and AUP, and confirm whether access is gated. Meta’s Llama organization on Hugging Face lists multiple families and requires agreement to terms for gated access. |
| Google Gemma 4 | Google announced Gemma 4 under Apache 2.0. Google describes variants spanning edge devices to 31 billion parameters. | Verify the exact variant and its current terms. The stated parameter range is not a promise that a particular device can run a model: deployment requirements depend on configuration and workload. |
| DeepSeek R1 | DeepSeek’s R1 announcement identifies MIT as the license for its code and models. | Check the terms for the exact R1 release and any associated artifacts or hosting service you plan to use; do not assume another model family’s license applies. |
For all three examples, the release-specific materials are more authoritative than a general model catalog. A catalog can help you discover a repository, but it does not replace reviewing the license, policy, and access terms linked to that particular version.
Use this checklist to decide whether a model fits
- Assess openness and disclosure. Check whether parameters, derivation code, and sufficiently detailed training-data information are available under the OSI definition. If you find only downloadable weights, describe the release as an open model rather than assuming it qualifies as open-source AI.
- Read the exact license and acceptable-use policy. Confirm the terms for the version you intend to use, including commercial use, redistribution, attribution, and restrictions. Do not transfer permissions from one publisher or family to another.
- Confirm access conditions. Determine whether the model is directly downloadable, gated behind acceptance of terms, or offered through a hosting provider with separate service terms.
- Match capability and modality to the task. Establish whether the specific release supports the text, image, or other task you need. The examples here do not have a common evaluation, so they cannot support a universal ranking or winner.
- Check size and deployment needs. Compare the model variant with your hardware and workload. A parameter count alone does not establish real-world memory, speed, or suitability for a deployment.
- Review output-reuse rules if you plan to train another model. Meta says Llama 3.1 and later allow outputs to be used to train or improve other models if attribution requirements are met, while it describes restrictions for Llama 2 and Llama 3. Check the terms for the precise version rather than generalizing across generations.
Put model activity in context
Hugging Face’s Summer 2026 analysis examines activity on its Hub during the first seven months of 2026. It can help readers understand activity on that platform, but it is not a census of all models or the entire AI ecosystem. Treat platform activity as context for discovery, not proof of openness, quality, or suitability.
Quick Recap
A practical reading order
- Read the OSI definition and FAQ to set a clear standard for the word “open.”
- Choose a model family that appears relevant, then locate the exact version’s publisher terms and access requirements.
- Use the checklist above to assess disclosures, license, policy, task fit, deployment needs, and output reuse.
- Recheck the publisher’s current materials before adopting a model. Releases, access gates, and terms can change.
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