> no one has bothered to release their training data

If the FOSS community sets this as the benchmark for open source in respect of AI, they're going to lose control of the term. In most jurisdictions it would be illegal for the likes of Meta to release training data.

Regardless of the training data, the license even heavily restricts how you can use the model.

Please read through their "acceptable use" policy before you decide whether this is really in line with open source.

> Please read through their "acceptable use" policy before you decide whether this is really in line with open source

I'm not taking a specific posiion on this license. I haven't read it closely. My broad point is simply that open source AI, as a term, cannot practically require the training data be made available.

the training data is the source.

> the training data is the source

Sure. But that's not going to be released. The term open source AI cannot be expected to cover it because it's not practical.

Of course it could be practical - provide the data. The fact of that society is a dystopian nightmare controlled by a few megacorporations that don't want free information does not justify outright changing the meaning of the language.

> provide the data

Who? It's not their data.

why are they using it?

And why legislation allows them to use the data to train their LLM and release that, but not release the data?

So because it's really hard to do proper Open Source with these LLMs, means we need to change the meaning of Open Source so it fits with these PR releases?

> because it's really hard to do proper Open Source with these LLMs, means we need to change the meaning of Open Source so it fits with these PR releases?

Open training data is hard to the point of impracticality. It requires excluding private and proprietary data.

Meanwhile, the term "open source" is massively popular. So it will get used. The question is how.

Meta et al would love for the choice to be between, on one hand, open weights only, and, on the other hand, open training data, because the latter is impractical. That dichotomy guarantees that when someone says open source AI they'll mean open weights. (The way open source software, today, generally means source available, not FOSS.)

source available is absolutely not the same as open source

you are playing very loosely with terms that have specific, widely accepted definitions (e.g. https://opensource.org/osd )

I don't get why you think it would be useful to call LLMs with published weights "open source"

> terms that have specific, widely accepted definitions

OSF's definition is far from the only one [1]. Switzerland is currently implementing CH Open's definition, the EU another one, et cetera.

> I don't get why you think it would be useful to call LLMs with published weights "open source"

I don't. I'm saying that if the choice is between open weights or open weights + open training data, open weights will win because the useful definition will outcompete the pristine one in a public context.

[1] https://en.wikipedia.org/wiki/Open-source_software#Definitio...

For the EU, I'm guessing you're talking about the EUPL, which is FSF/OSI approved and GPL compatible, generally considered copyleft.

For the CH Open, I'm not finding anything specific, even from Swiss websites, could you help me understand what you're referring to here?

I'm guessing that all these definitions have at least some points in common, which involves (another guess) at least being able to produce the output artifacts/binaries by yourself, something that you cannot do with Llama, just as an example.

> For the CH Open, I'm not finding anything specific, even from Swiss websites, could you help me understand what you're referring to here

Was on the HN front page earlier [1][2]. The definition comes strikingly close to source on request with no use restrictions.

> all these definitions have at least some points in common

Agreed. But they're all different. There isn't an accepted defintiion of open source even when it comes to software; there is an accepted set of broad principles.

[1] https://news.ycombinator.com/item?id=41047172

[2] https://joinup.ec.europa.eu/collection/open-source-observato...

> Agreed. But they're all different. There isn't an accepted defintiion of open source even when it comes to software; there is an accepted set of broad principles.

Agreed, but are we splitting hairs here and is it relevant to the claim made earlier?

> (The way open source software, today, generally means source available, not FOSS.)

Do any of these principles or definitions from these orgs agree/disagree with that?

My hypothesis is that they generally would go against that belief and instead argue that open source is different from source available. But I haven't looked specifically to confirm if that's true or not, just a guess.

> are we splitting hairs here and is it relevant to the claim made earlier?

I don't think so. Take the Swiss definition. Source on request, not even available. Yet being branded and accepted as open source.

(To be clear, the Swiss example favours FOSS. But it also permits source on request and bundles them together under the same label.)

diluting open source into a marketing term meaning "you can download something" would be a sad result

> specific, widely accepted definitions

Realistically, nobody outside of Hacker News commenters have ever cared about the OSD. It's just not how the term is used colloquially.

who says open source colloquially? ime anyone who doesn't care about software licenses will just say free (per free beer)

and (strong personal opinion) any software developer should have a firm grip on the terminology and details for legal reasons

> who says open source colloquially?

There is a large span of people between gray beard programmer and lay person, and many in that span have some concept of open-source. It's often used synonymously with visible source, free software, or in this case, open weights.

It seems unfortunate - though expected - that over half of the comments in this thread are debating the OSD for the umpeenth time instead of discussing the actual model release or accompanying news posts. Meanwhile communities like /r/LocalLlama are going hog wild with this release and already seeing what it can do.

> any software developer should have a firm grip on the terminology and details for legal reasons

They'd simply need to review the terms of the license to see if it fits their usage. It doesn't really matter if the license satisfies the OSD or not.

> Open training data is hard to the point of impracticality. It requires excluding private and proprietary data.

Right, so the onus is on Facebook/Meta to get that right, then they could call something Open Source, until then, find another name that already doesn't have a specific meaning.

> (The way open source software, today, generally means source available, not FOSS.)

No, but it's going in that way. Open Source, today, still means that the things you need to build a project, is publicly available for you to download and run on your own machine, granted you have the means to do so. What you're thinking of is literally called "Source Available" which is very different from "Open Source".

The intent of Open Source is for people to be able to reproduce the work themselves, with modifications if they want to. Is that something you can do today with the various Llama models? No, because one core part of the projects "source code" (what you need to reproduce it from scratch), the training data, is being held back and kept private.

>Meanwhile, the term "open source" is massively popular. So it will get used. The question is how.

Here's the source of the disagreement. You're justifying the use of the term "open source" by saying it's logical for Meta to want to use it for its popularity and layman (incorrect) understanding.

Other person is saying it doesn't matter how convenient it is or how much Meta wants to use it, that the term "open source" is misleading for a product where the "source" is the training data, and the final product has onerous restrictions on use.

This would be like Adobe giving Photoshop away for free, but for personal use only and not for making ads for Adobe's competitors. Sure, Adobe likes it and most users may be fine with it, but it isn't open source.

>The way open source software, today, generally means source available, not FOSS.

I don't agree with that. When a company says "open source" but it's not free, the tech community is quick to call it "source available" or "open core".

> You're justifying the use of the term "open source" by saying it's logical for Meta to want to use it for its popularity and layman (incorrect) understanding

I'm actually not a fan of Meta's definition. I'm arguing specifically against an unrealistic definition, because for practical purposes that cedes the term to Meta.

> the term "open source" is misleading for a product where the "source" is the training data, and the final product has onerous restrictions on use

Agree. I think the focus should be on the use restrictions.

> When a company says "open source" but it's not free, the tech community is quick to call it "source available" or "open core"

This isn't consistently applied. It's why we have the free vs open vs FOSS fracture.

No, we need to adapt an existing term into the new context that it is being deployed in.

Meta can call it something else other than open source.

Synthetic part of the training data could be released.

I don’t think it’s that simple. The source is “the preferred form of the work for making modifications to it” (to use the GPL’s wording).

For an LLM, that’s not the training data. That’s the model itself. You don’t make changes to an LLM by going back to the training data and making changes to it, then re-running the training. You update the model itself with more training data.

You can’t even use the training code and original training data to reproduce the existing model. A lot of it is non-deterministic, so you’ll get different results each time anyway.

Another complication is that the object code for normal software is a clear derivative work of the source code. It’s a direct translation from one form to another. This isn’t the case with LLMs and their training data. The models learn from it, but they aren’t simply an alternative form of it. I don’t think you can describe an LLM as a derivative work of its training data. It learns from it, it isn’t a copy of it. This is mostly the reason why distributing training data is infeasible – the model’s creator may not have the license to do so.

Would it be extremely useful to have the original training data? Definitely. Is distributing it the same as distributing source code for normal software? I don’t think so.

I think new terminology is needed for open AI models. We can’t simply re-use what works for human-editable code because it’s a fundamentally different type of thing with different technical and legal constraints.

Given a well behaved training setup, you will give an equivalently powerful model, given the same dataset and training scripts, and training settings. At least if you are willing to run it several times, an pick the best one - a process that is commonly used for large models.

No the preferred way to make modifications is using the the training code. One may also input a snapshot weighs to start from, but the training code is definitely what you would modify to make a change.

how do you train it in a different language by changing the training code?

By selecting different dataset. Of course this dataset does need to exist. In practice building and curating datasets also involves a lot of code.

sounds like you need the data to train the model.

No. It's an asset used in the training process, the source code can process arbitrary training data.

We've had a similar debate before, but the last time it about whether Linux device drivers based on non-public datasheets under NDA were actually open source. This debate occurred again over drivers that interact with binary blobs.

I disagree with the purists - if you can legally change the source or weights - even without having access to the data used by the upstream authors - it's open enough for me. YMMV.

I don’t think even that is true. I conjecture that Facebook couldn’t reproduce the model weights if they started over with the same training data, because I doubt such a huge training run is a reproducible deterministic process. I don’t think anyone has “the” source.

numpy.random.seed(1234)

> In most jurisdictions it would be illegal for the likes of Meta to release training data.

How come releasing an LLM trained on that data is not illegal then? I think it should be.

AI2 has released training data in their OLMo model: https://blog.allenai.org/hello-olmo-a-truly-open-llm-43f7e73...