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The models are not open source. They are deeply proprietary since we have no access to the source materials and cannot reproduce the model independently. They
by petcat 3mo ago
The models are not open source. They are deeply proprietary since we have no access to the source materials and cannot reproduce the model independently. They are opaque binary blobs that the Chinese labs are just allowing other providers to run directly instead of only access through an API.
- verdverm 3mo agoThey 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 3mo agoYou can make parallel arguments for binary executables.
- verdverm 3mo agomodel 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 3mo agoHere's my thinking: Cloud: - You cannot directly execute a remotely-hosted program. - You cannot run inference on an API-served model. --- 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. --- 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 3mo ago> 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 3mo agoImho this is still wishful thinking ... care to provide examples?
- matheusmoreira 3mo agoWhich is why we need the ability to train our own models. Maybe it will be viable to do it in a distributed computing setup one day. Research's already being done in that direction.
- rstuart4133 3mo ago> we have no access to the source materials and cannot reproduce the model independently. 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.
- usef- 3mo agoDistillation of this kind isn't the only data that is fed into models, it's still a small minority. It might be weighed higher in training to learn the thinking patterns/etc but you need a lot more tokens.
- 0cf8612b2e1e 3mo agoDo you expect any of the labs to have an accompanying data dump with: here’s every book ever written, newspaper article, song lyric, Disney movie, GitHub repo, etc. Oh, and we obviously never paid for any of this. 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.
- deleted 3mo ago[deleted]
- purpleflashing 3mo agoSure. The models are still not open source and should not be called open source.
- gbalduzzi 3mo agoDidn't Deepseek also publish a lot of research material on how they trained their model? That aspect is probably more important for an open model then the source materials