[lxgr]

> open model weights fulfill much of the intent / "soul" of the open source movement

Absolutely not. The intent of the open source movement is sharing methods, not just artifacts, and that would require training code and methodology.

A binary (and that's arguably what weights are) you can semi-freely download and distribute is just shareware – that's several steps away from actual open source.

There's nothing wrong with shareware, but calling it open source, or even just "source available" (i.e. open source with licensing/usage restrictions), when it isn't, is disingenuous.

[MoonGhost]

> The intent of the open source movement is sharing methods, not just artifacts, and that would require training code and methodology.

That's not enough. The key point was trust. When executable can be verified by independent review and rebuild. It it cannot be rebuilt it can be virus, troyan, backdoor, etc. For LLMs there is no way to reproduce, thus no way to verify them. So, they cannot be trusted and we have to trust producers. It's not that important when models are just talking, but with tools use it can be a real damage.

[lxgr]

Hm, I wouldn't say that that's the key point of open software. There are many open source projects that don't have reproducible builds (some don't even offer any binary builds), and conversely there is "source available" software with deterministic builds that's not freely licensed.

On top of that, I don't think it works quite that way for ML models. Even their creators, with access to all training data and training steps, are having a very hard time reasoning about what these things will do exactly for a given input without trying it out.

"Reproducible training runs" could at least show that there's not been any active adversarial RHLF, but seem prohibitively expensive in terms of resources.

[MoonGhost]

Well, 'open source' is interpreted in different ways. I think the core idea is it can be trusted. You can get Linux distribution and recompile every component except for the proprietary drivers. With that being done by independent groups you can trust it enough to run bank's systems. The other options are like Windows where you have to trust Microsoft and their supply chain.

There are different variations, of course. Mostly related to the rights and permissions.

As for big models even their owners, having all the hardware and training data and code, cannot reproduce them. Model may have some undocumented functionality pretrained or added in post process, and it's almost impossible to detect without knowing the key phrase. It can be a harmless watermark or something else.

[anonym29]

But there is also no publicly known way to implant unwanted telemetry, backdoors, or malware into modern model formats either (which hasn't always been true of older LLM model formats), which mitigates at least one functional concern about trust in this case, no?

It's not quite like executing a binary in userland - you're not really granting code execution to anyone with the model, right? Perhaps there is some undisclosed vulnerability in one or more of the runtimes, like llama.cpp, but that's a separate discussion.

[lxgr]

The biggest problem is arguably at a different layer: These models are often used to write code, and if they write code containing vulnerabilities, they don't need any special permissions to do a lot of damage.

It's "reflections on trusting trust" all the way down.

[anonym29]

If people who cannot read code well enough to evaluate whether or not it is secure are using LLM's to generate code, no amount of model transparency will solve the resulting problems. At least not while LLM's still suffer from the the major problems they have, like hallucinations, or being wrong (just like humans!).

Whether the model is open source, open weight, both, or neither has essentially zero impact on this.

[Manabu-eo]

I saw the argument that the source code is the preferred base to make changes and modifications in software, but in the case of those large models, the weights themselves are the preferred way.

It's much easier and cheap to make a finetune or LoRA than to train from scratch to adapt it to your use case. So it's not quite like source vs binary in software.