[flossly]

Exactly.

And the article specifically talks on restricting hardware for the China and restricting China's open source models for the west. All while leading us on with "we're all for competition (but...)"

I think China did great by releasing AI innovation as open source, thereby limiting or sooner-bursting the AI bubble; which is clearly in their interest.

[petcat]

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.

[0cf8612b2e1e]

Do 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.

[purpleflashing]

Sure. The models are still not open source and should not be called open source.

[rstuart4133]

> 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-]

Distillation 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.

[gbalduzzi]

Didn'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

[matheusmoreira]

Which 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.

[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.

---

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]

> 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.