[verdverm]

They are more than opaque blobs, some examples:

- One can load them up in a model explorer to see the layers and other components, how it is designed

- One can fine tune the models, which requires adding LoRA to the model and then running some training iterations

[nwiswell]

You can make parallel arguments for binary executables.

[verdverm]

model weights are more like a video file which has metadata about the format so any video player can play it

we run and change llm models with a variety of tools

[nwiswell]

Here's my thinking:

Cloud:

- 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.

---

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.

[rstuart4133]

> 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.

[don_esteban]

Imho this is still wishful thinking ... care to provide examples?