Definition
An open-weight model makes learned parameters available for download and inference. Availability may permit local deployment, evaluation, quantization or fine-tuning, but the exact freedoms depend on the license and supplied components.
Open source distinction
The Open Source AI Definition requires freedoms to use, study, modify and share, together with the preferred form for modification. A weight release may omit training data information or training code and therefore remain open-weight rather than fully open source.
Applications
Open-weight models support private local assistants, offline tools, edge deployments, reproducible experiments and specialized creative systems.
Electronic Artefacts position
Local and open-weight models are relevant where archives, unreleased audio, project documents or private graphs should remain under operator control. Deployment choices must still account for provenance, security, performance and licensing.
Limitations
Running weights locally does not remove model bias, hallucination, data rights concerns or maintenance cost. Hardware requirements, context limits and update procedures remain operational constraints.
References
See the OSI Open Source AI Definition, llama.cpp, Open Source and Provenance.