Open weights

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 artificial intelligence

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.

Comparison of open weights and open-source artificial intelligence^[1]
Component	Open weights	Open-source AI
Weights and biases	Released	Released
Training code	Not shared	Fully shared
Intermediate checkpoints	Withheld	Desirable but not required
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]

References

1 2 "Open Weights: not quite what you've been told". Open Source Initiative. Retrieved 28 July 2026.
↑ "Neural networks: Nodes and hidden layers". Google for Developers. Google. 3 December 2025. Retrieved 28 July 2026.
↑ "Machine Learning Glossary: ML Fundamentals". Google for Developers. Google. Retrieved 28 July 2026.
↑ 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].
↑ "Model Cards". Hugging Face. Retrieved 28 July 2026.
↑ 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.
↑ "Dual-Use Foundation Models with Widely Available Model Weights Report". National Telecommunications and Information Administration. Retrieved 28 July 2026.

External links

[OSI-weights-1] 1 2 "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.

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v t e Large language models (LLMs)
List of LLMs AI Companies Benchmarks List of chatbots Foundation model Generative AI
Concepts	Language model NLP NLG Computational linguistics Foundation model Small language model Reasoning model Generative pre-trained transformer Transformer Attention KV cache Context window Tokenization Word embedding Parameter Hyperparameter Autoregression Mixture of experts (MoE) Inference Model compression Knowledge distillation Speculative decoding PagedAttention Neural scaling law Multimodality Open weights
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Models	Apertus BERT BLOOM Chinchilla Claude DBRX Gemini Gemma GloVe GLM GPT GPT-J PanGu Granite Jais LaMDA Llama MiMo Minerva Mistral and Mixtral Nemotron PaLM Phi Qwen Seq2seq T5 Vicuna Word2vec XLNet
Chatbots and assistants	Amazon Q ChatGPT Character.ai Claude DeepSeek Doubao Ernie Bot GLM Google Gemini Grok Kimi Microsoft Copilot Meta AI Perplexity AI Sparrow You.com
Agents, coding, and applications	AI agent Intelligent agent AutoGPT CrewAI LangChain Manus Model Context Protocol Agent2Agent OpenAI Codex Vibe coding Code generation Question answering Machine translation Text summarization Chatbot Virtual assistant LLMs in higher education
Software	PyTorch TensorFlow Hugging Face Inference engine llama.cpp Ollama SGLang TensorRT-LLM vLLM ONNX OpenVINO Vector database ChromaDB Deep learning software Open-source AI software
Hardware and infrastructure	AI data center AI accelerator GPU CUDA TPU High-bandwidth memory
Benchmarks, evaluation, and detection	Language model benchmark MMLU Humanity's Last Exam LMArena LLM-as-a-Judge Perplexity metric GPTZero Artificial intelligence content detection Undetectable.ai
Datasets and data	Data set Text corpus Common Crawl The Pile Web scraping Synthetic data Training, validation, and test data sets
Organizations	AI21 Labs Alibaba Anthropic Baidu Cohere DeepSeek EleutherAI Google DeepMind Hugging Face Meta AI Microsoft AI Mistral AI MiniMax Moonshot AI Nvidia OpenAI Sarvam AI StepFun Technology Innovation Institute Thinking Machines Lab xAI
People	Sam Altman Dario Amodei Yoshua Bengio Aidan Gomez Demis Hassabis Geoffrey Hinton Andrej Karpathy Yann LeCun Percy Liang Liang Wenfeng Christopher D. Manning Arthur Mensch Mira Murati Alec Radford Noam Shazeer Ilya Sutskever Ashish Vaswani Andrew Ng
Social, economic, and governance	AI boom AI bubble AI slop AI anthropomorphism AI arms race Chatbot psychosis Competition Copyright Deaths linked to chatbots Dependency (GAID) Environmental impact Regulation Ethics Existential risk In education In healthcare Workplace impact
Category:Large language models