Because typically adapting or improving traditional code to your needs is very difficult without access to the source code and build files.
For an LLM you can finetune and enhance, distill and embed given just the model weights, the runtime, and a permissive license. Having more is better. Well written detailed model release papers help a lot. Training code and training data are a great bonus.
However, I find the purity contest a bit too dismissive of the great contributions to the AI dev ecosystem that Meta and Deepseek have brought us. Without these, there wouldn't be the open ecosystem we have today.
It's not a purity contest, it's a clarity contest. If Meta and Deepseek want to operate the way they have been, where they release baked models and whitepapers, that's fine - and you're right, it's certainly more than they're obligated to release. They just shouldn't be calling it "open source" when the source is literally not open.
Eh, I can kinda see it. It depends on your definitions of words. People have been muddying the waters with what "open source" means anyway. I have known it to mean code released under an open source license. Other people use it to mean programs where the source is available regardless of license. I would use "source available" to describe that, but some people strongly disagree with my definitions.
If I write a program, then obfuscate it and then release the obfuscated code under an open source license, would you consider it open source(I would)? That's kind of the case here, they are releasing the model weights under an open source license.
Personally, I think it's fine to shorten it to "open source model" instead of "a model with the weights released under an open source license". What I would object to is releasing model weights under a restrictive license and calling that open source.
> If I write a program, then obfuscate it and then release the obfuscated code under an open source license, would you consider it open source(I would)?
I wouldn't. Most definitions of open source say something like "in the form used for editing". You can release a built binary under an unrestrictive license, but that does not mean that you've opened the source. It's literally the plain meaning of the words: the source, as in where the thing comes from, needs to be open for it be meaningful.
For an LLM you can finetune and enhance, distill and embed given just the model weights, the runtime, and a permissive license. Having more is better. Well written detailed model release papers help a lot. Training code and training data are a great bonus.
However, I find the purity contest a bit too dismissive of the great contributions to the AI dev ecosystem that Meta and Deepseek have brought us. Without these, there wouldn't be the open ecosystem we have today.