The network architecture itself is not source code, but a rough specification constraining the optimizer, which searches for possible program descriptions that within the specified constraints, minimize some loss function with respect to the data.

Neither data nor network architecture are the actual source, they are better seen as recipes which if followed (will at great expense), allow finding behaviorally similar programs. As you can see, the standard ideas of open source don’t quite carry over because the actual "source-code" is not human interpretable.

Thats totally fair. And you're correct in that I was making an argument for positive outcomes being orthogonal to the semantics distinction.

> I also believe that actual open source models have the near-term opportunity to make an impact and shape the future landscape, with red pajamas and others in the works. The distinction could be very important in the near term, at the rate this field is developing at.

I think Falcon and MPT support your point as well, but those are still models that were trained on very small budgets relative to llama or gpt-3/4. There's a clear quality delta, albeit that gap is closing. Through that lens, I think having a large, well-funded org doing the pre-training work for the OSS community and releasing the weights permissively is a net positive.