Open weights

Open weights are the publicly released learned parameters of a trained artificial intelligence model, principally its weights and biases. In an artificial neura

Open weights
Not to be confused with Openweight.

Open weights are the publicly released learned parameters of a trained artificial intelligence model, principally its weights and biases. In an artificial neural network, weights are numerical values that determine how strongly inputs contribute to the network's calculations. Publishing these values allows others to download and use the model, while permission to modify, fine-tune or redistribute it depends on its license. The term is commonly applied to large language models and other generative artificial intelligence models.[1][2]

Characteristics

During training, a neural network adjusts its parameters to improve its output. Weights determine the contribution of inputs to weighted sums, while biases are additional learned values. A model described as having seven billion parameters contains roughly seven billion learned values.[3]

Model openness

Releasing model weights may form part of a broader effort to make an artificial intelligence model more transparent and reproducible. The Model Openness Framework evaluates whether components such as the model's parameters, source code, training data, evaluation results, intermediate checkpoints and technical documentation are publicly available under an open license. Under the framework, releasing only the final weights represents a lower degree of openness than releasing the materials needed to study, modify and reproduce the model.[4]

Model cards

A model card is a document accompanying a trained machine learning model that describes a model’s intended uses, limitations, training data and evaluation results. On Hugging Face, model cards are stored as Markdown README.md files with additional metadata in each model repository, helping users evaluate a model before downloading or deploying it.[5][6]

Comparison with open-source AI

The Open Source Initiative distinguishes open weights from open-source artificial intelligence when the materials needed to study and modify the model's development process are not also available.[1]

Comparison of open weights and open-source artificial intelligence[1]
Component Open weights Open-source
Weights and biases Released Released
Training code Not shared Fully shared
Intermediate checkpoints Withheld Optional, but desirable
Training dataset Not shared or disclosed Released where legally permitted
Training-data composition Partially disclosed or undisclosed Fully disclosed

Benefits and concerns

Open weights can support local deployment, customization, independent research and competition without requiring access through the original developer's API. However, widely distributed weights generally cannot be withdrawn and may be modified to remove safeguards. A 2024 report by the National Telecommunications and Information Administration examined these benefits and risks and recommended continued monitoring rather than immediate restrictions.[7][8]

Professors Peter Henderson and Mark Lemley argue that open-weight license terms are largely unenforceable because they presuppose intellectual property rights in ML model weights that do not exist. Whereas computer programs are typically copyrightable because they are "derived from a human-written copyrightable expression of an idea", model weights are generated automatically by a machine learning algorithm. The functional nature of ML model weights likely precludes copyright protection as well.[9] A whitepaper published by the Intellectual Property Owners Association in 2020 proposes a sui generis right in trained AI models.[10]

Open-weight large language models

See also: List of large language models

The following is a list of notable large language models and model families for which at least one version has been released with publicly accessible weights.[11]

See also

References

  1. ^ a b c "Open Weights: not quite what you've been told". Open Source Initiative. Retrieved 28 July 2026.
  2. ^ "Neural networks: Nodes and hidden layers". Google for Developers. Google. 3 December 2025. Retrieved 28 July 2026.
  3. ^ "Machine Learning Glossary: ML Fundamentals". Google for Developers. Google. Retrieved 28 July 2026.
  4. ^ White, Matt; Haddad, Ibrahim; Osborne, Cailean; Liu, Xiao-Yang; Abdelmonsef, Ahmed; Varghese, Sachin Mathew; Le Hors, Arnaud (2024). "The Model Openness Framework: Promoting Completeness and Openness for Reproducibility, Transparency, and Usability in Artificial Intelligence". arXiv:2403.13784 [cs.AI].
  5. ^ "Model Cards". Hugging Face. Retrieved 28 July 2026.
  6. ^ Mitchell, Margaret; Wu, Simone; Zaldivar, Andrew; Barnes, Parker; Vasserman, Lucy; Hutchinson, Ben; Spitzer, Elena; Raji, Inioluwa Deborah; Gebru, Timnit (2019). Model Cards for Model Reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery. pp. 220–229. arXiv:1810.03993. doi:10.1145/3287560.3287596.
  7. ^ "Dual-Use Foundation Models with Widely Available Model Weights Report". National Telecommunications and Information Administration. Retrieved 28 July 2026.
  8. ^ "Open weights and American AI leadership". Microsoft. Retrieved August 6, 2026.
  9. ^ Henderson, Peter; Lemley, Mark A. (June 2025). "The Mirage of Artificial Intelligence Terms of Use Restrictions". Indiana Law Journal. 100 (4): 1327–1387. arXiv:2412.07066.
  10. ^ Tham, Yeen (2020-11-02). "Sui Generis Right for Trained AI Models" (PDF). Intellectual Property Owners Association. Retrieved 2026-08-08.
  11. ^ Anel, Philippe (June 2026). "Open Weight Models". GitHub. Retrieved August 5, 2026.

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