I would consider the "source code" for artwork to be the project file, with all of the layers intact and whatnot. The Photoshop PSD, the GIMP XCF or the Krita KRA. The "compiled" version would be the exported PNG/JPG.
You can license a compiled binary under CC BY if you want. That would allow users to freely decompile/disassemble it or to bundle the binary for their purposes, but it's different from releasing source code. It's closed source, but under a free license.
The situation is somewhat different and nuanced. With weights there are tools for fine-tuning, LoRA/LoHa, PEFT, etc., which presents a different situation as with binaries for programs. You can see that despite e.g. LLaMA being “compiled”, others can significantly use it to make models that surpass the previous iteration (see e.g. recently WizardLM 2 in relation to LLaMA 2). Weights are also to a much larger degree architecturally independent than binaries (you can usually cross train/inference on GPU, Google TPU, Cerebras WSE, etc. with the same weights).
it is only open source if i can build it myself. Which I can't if you just give me the weights.
The weights are the "compiled" version of the dataset. It's the dataset that's the source, not the weights
So the cover art I made for a friend's album isn't open source, even though I released it as CC BY-SA... because you can't make it yourself?
I would consider the "source code" for artwork to be the project file, with all of the layers intact and whatnot. The Photoshop PSD, the GIMP XCF or the Krita KRA. The "compiled" version would be the exported PNG/JPG.
You can license a compiled binary under CC BY if you want. That would allow users to freely decompile/disassemble it or to bundle the binary for their purposes, but it's different from releasing source code. It's closed source, but under a free license.
you released it under a non open source license. So very clearly: no it is not
Wut. That license is literally compatible with the GPL
CC BY-SA is considered open source. CC BY-NC is not.
The situation is somewhat different and nuanced. With weights there are tools for fine-tuning, LoRA/LoHa, PEFT, etc., which presents a different situation as with binaries for programs. You can see that despite e.g. LLaMA being “compiled”, others can significantly use it to make models that surpass the previous iteration (see e.g. recently WizardLM 2 in relation to LLaMA 2). Weights are also to a much larger degree architecturally independent than binaries (you can usually cross train/inference on GPU, Google TPU, Cerebras WSE, etc. with the same weights).