Understand open weights
Open weights and open source AI
Use precise language for downloadable model parameters and understand why open weights alone do not reproduce an AI system.
An open-weight model makes its learned parameters available to download under some set of terms.
This can let you run the model locally, keep a fixed version, study it, quantize it, fine-tune it, or redistribute it. The license decides which actions are allowed.
Open weights are not automatically the same as open source AI.
With ordinary software, source code is the preferred form for studying and modifying a program. A compiled binary may be useful, but it does not show everything used to produce it.
Weights have a similar limit. You can run and modify them, but they may not reveal the training data, how the data was filtered, or the complete training code and settings.
The Open Source Initiative definition asks for the freedoms to use, study, modify, and share the system. It also describes the code, parameters, and detailed data information needed to exercise those freedoms.
This does not make an open-weight release unimportant. Downloadable weights can provide enormous practical freedom. It only means we should describe what was released accurately.
My advice is to say open weight when you know the parameters are available, then inspect the rest of the release before calling the complete system open source.
Lesson completed