While I agree that open-weight is better than nothing, it is not the same as open-source.
Having a truly open-source model would be wonderful and it would also allow us to verify what data it is trained on and whether it has the right to use that data.

The Open Source Initiative's definition of a fully open-source AI model includes the training data as a key element. Some developers wonder, however, how feasible it is to distribute the enormous datasets required.

• https://www.pbs.org/newshour/science/whats-the-difference-between-closed-open%E2%80%91source-and-open-weight-ai-a-researcher-explains

He says Stanford HAI has staked out a specific bar: alignment with the Linux Foundation’s Model Openness Framework at its top tier, the “Open Science” class – meaning the code, the training data (or a thoroughly documented, auditable account of it), and the tooling are all released, not just the weights, alongside a real way for outside researchers and communities to download, run, study, contribute to, and modify the work. Anything short of that, in his view, forecloses entire lines of research: You can’t, for example, study how a model’s training data shapes its cultural assumptions, audit it for how fairly it represents different populations, or test new architectures at the scale where genuinely new behavior shows up. You can only observe a finished system from the outside and guess at why it does what it does.

• https://hai.stanford.edu/news/open-weight-models-arent-enough-we-need-truly-open-source-ai-models-for-science-and-society