[Kubuxu]

The gap between fine-tuning API and weights-available is much more significant than you give it credit for.

You can take the weights and train LoRAs (which is close to fine-tuning), but you can also build custom adapters on top (classification heads). You can mix models from different fine-tunes or perform model surgery (adding additional layers, attention heads, MoE).

You can perform model decomposition and amplify some of its characteristics. You can also train multi-modal adapters for the model. Prompt tuning requires weights as well.

I would even say that having the model is more potent in the hands of individual users than having the dataset.

[thayne]

That still doesn't make it open source.

There is a massive difference between a compiled binary that you are allowed to do anything you want with, including modifying it, building something else on top or even pulling parts of it out and using in something else, and a SaaS offering where you can't modify the software at all. But that doesn't make the compiled binary open source.