Training runs are not deterministic. Not just in the case of floating point not being associative, but nodes have hardware issues and go down and back up at various points through training. Problems get encountered and then various parameters get changed during the middle of a training run.
Making the process of creating the source code repeatable goes beyond the idea of open source. Open source is about being able to work with the code, not recreate it. It doesn't require domain experts to document every single thing they know. For example look at some of the GPU drivers in the Linux kernel. The GPU is not properly documented and there is trust that vendors are implementing things correctly.
> Training runs are not deterministic.
I agree which is why I said you could make a similarly capable model.
> Open source is about being able to work with the code, not recreate it.
You can also use a hex editor to modify compiled binaries, but no one would consider that "open source".
>You can also use a hex editor to modify compiled binaries
It would be like if someone gave you a prompt you could pass into ChatGPT to produce the entire Linux kernel. While yes you could modify that prompt and then spend a ton of money on inference to generate millions of lines of code which hopefully are equivalent to the original Linux kernel it would be easier if you just had the source code to work with where you can make a simple extension. If you want to end a model you want the weights, and not the entire setup to generate it. The weights are the starting point that you can work off for training a new model similar to how the source code is the starting point people want to work off of.