- You cannot directly execute a remotely-hosted program.
- You cannot run inference on an API-served model.
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Closed-source:
- You can execute a program with the binary. You cannot generate a new binary, but you could try to reverse-engineer it or (painfully) modify its execution.
- You can run inference on a model with the weights. You cannot re-produce a new set of weights from scratch, but you can fine-tune.
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Truly open:
- You can freely modify the source and produce new binaries.
- You can use the original training data and model architecture to independently re-produce the weights (assuming you've got the compute). You can modify the model architecture to get the weights that would've resulted from training the model that way.
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To me these are pretty clear parallels... I don't think the weights provided in a vacuum are in the spirit of open source, historically speaking.
The policy argument is totally separate, of course, and I fully understand why none of the frontier labs are truly open.
> Closed-source: You can execute a program with the binary. You cannot generate a new binary, but you could try to reverse-engineer it or (painfully) modify its execution.
Time have changed. This should be:
Closed-source: You point an LLM at it, and get back source that's often easier to understand than the original.
we run and change llm models with a variety of tools