Even if you did, I doubt training is bit-for-bit reproducible, so you will always have to take someone’s word for the final artifact.
- 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
we run and change llm models with a variety of tools
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.
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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.
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.
Given the USA companies have been loudly claiming the Chinese models are distillations of their models, also claiming "no access to source materials" seems dubious. As it was dubious anyway with because the Chinese publish lots of papers on how their models are designed, I'm left feeling I'm looking at the south end of a north bound bull.
That aspect is probably more important for an open model then the source materials