Comment by seizethecheese
3 days ago
Since there are many comments here arguing that this is not open weight, I looked at the license. Basically, you're free to use this except this clause:
> If the Licensee or any of its affiliates provide a third party with access to artificial intelligence models as a product or service, via API or a similar interface, where the third party receives outputs of such models (“Model as a Service”), and the aggregate gross revenue of the Licensee and its affiliates exceeds twenty (20) million US dollars (or the equivalent in other currencies) during any consecutive twelve (12) month period, the Licensee must notify Meta and enter into a separate written agreement with Meta before offering the Software or any Derivatives as part of that Model as a Service business. This Section 3 does not apply to (a) internal use, research, or any commercial activity that is not Model as a Service, or (b) the Licensee's use of the Software as a component to power features of the Licensee's own product or service that is not a Model as a Service.
They're allowing the use of this in commercial products, just not selling the model directly.
Still not an open-source license. Means no restrictions on "fields of endeavor", a.k.a. the free software definition's "freedom 0": the freedom to run the software for any purpose.
I'm sick to death of these shameless dilutions (including the notion that open-weight is open-source; the OSI is totally captured at this point)
Neither the person you’re replying to nor the blog post call it open source
But it compares itself to Qwen (Apache 2), GLM (MIT), Beam (Apache 2), Inklink (Apache 2)...
Although to be fair, Kimi contains an identical carve out.
1 reply →
Open-weights means weights are released under an open-source license.
I would, contra the OSI, not consider the model to be open-source even if the weights were under an open-source license. But that is the minimum condition under which it could be considered open-weight.
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I think you're just wrong about the OSI's definition of "open source AI"?
You can see https://opensource.org/ai/open-source-ai-definition
Specifically:
> A precondition to exercising these freedoms is to have access to the preferred form to make modifications to the system.
and
> The preferred form of making modifications to a machine-learning system must include all the elements below:
> Data Information: ...
> Code: ...
> Parameters: ...
Does this address your issues with the OSI definition?
The "data information" section ultimately says that a mere description of the training data is sufficient, which is the dilution on the part of the OSI that the FSF and SFC have criticized. I'm aligned with that criticism.
As for the "open source license" issue, you need the four fundamental software freedoms with whatever is distributed, just like with conventional open-source software. The OSAID enumerates a formulation of each of all four, but this is the one that this release fails to meet:
> An Open Source AI is an AI system made available under terms and in a way that grant the freedoms to¹ [...] [u]se the system for any purpose and without having to ask for permission.
> 1: These freedoms are derived from the Free Software Definition (https://www.gnu.org/philosophy/free-sw.en.html)
(The footnote is theirs, not mine.)
So the OSI agree with me that these weights cannot be considered, in their terms, "open-source weights". Our disagreement is that I think the training data itself should be considered a necessary part of a bona fide open-source ML model.
I think the answer to ethical concerns about releasing training data that involves data that carries privacy restrictions, like medical data, is that such models cannot be free/open-source. I think that's okay; it's okay to acknowledge that not every machine learning model in the world can be open-source. Eve the FSF, who are generally hardliners about using, let alone producing, non-free software, admit the ethical use of non-free models under just such circumstances. They just so so without blurring the distinction between free and non-free, which is the tack I wish the OSI had taken.
The OSI is an American organization founded in 1998, long after the term "Open Source" already had a clear meaning.
They have no right to make new and limited definitions for what Open Source means in other areas like LLMs. They can write and publish a definition if they like, but whether the rest of the world accepts it is another matter.
https://en.wikipedia.org/wiki/Open_Source_Initiative
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Are you? Not me. $19.9mm annual revenue would do fine for me. If anyone else is considering opening up their thing with a clause like this, you've got a friendly supporter in me.
And it’s annual revenue for serving their model as a model. Not for any product.
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This is close to GLM terms
I think it is a fair license. It’s a bit of “Open”-washing but reasonable to the model trainer for their investment and to the public for having more base model options with novel architecture.
Also, IANAL, but under U.S. copyright law, the weights of machine learning models are excluded from copyright as they are the product of an automated optimization process (e.g., stochastic gradient descent, expectation maximization, genetic algorithms) rather than human authorship. Granted, this has yet to be fully tested in court and going to court is expensive, so it's likely that your employer would prefer to err on the side of caution and respect such attempts at model licensing anyway, similar to how one might treat a ToS agreement. Nonetheless, this was partially tested last year in Thaler v. Perlmutter which affirmed that copyright requires human authorship, reading the Copyright Act's provisions on ownership, duration, and transferability as presupposing a human author[1].
If you want to assess the position of the U.S. Copyright Office for yourself, the relevant text can be found in the Compendium of U.S. Copyright Office Practices § 313.2, "Works That Lack Human Authorship"[2], which states:
> […] the Copyright Act protects “original works of authorship.” 17 U.S.C. § 102(a) (emphasis added). To qualify as a work of “authorship” a work must be created by a human being. See Burrow-Giles Lithographic Co., 111 U.S. at 58. Works that do not satisfy this requirement are not copyrightable.
> […] the Office will not register works produced by a machine or mere mechanical process that operates randomly or automatically without any creative input or intervention from a human author. The crucial question is “whether the ‘work’ is basically one of human authorship, with the computer [or other device] merely being an assisting instrument, or whether the traditional elements of authorship in the work (literary, artistic, or musical expression or elements of selection, arrangement, etc.) were actually conceived and executed not by man but by a machine.” U.S. COPYRIGHT OFFICE, REPORT TO THE LIBRARIAN OF CONGRESS BY THE REGISTER OF COPYRIGHTS 5 (1965).
Oh, and there's also a bit in the following Section 313.3, "Works That Do Not Constitute Copyrightable Subject Matter"[2], which explicitly excludes mathematical principles, formulas, algorithms, and equations, along with DNA sequences and other genetic or chemical compounds, regardless of whether they are produced by humans or by nature. If one takes the perspective that machine learning models are algorithms, the conclusions on copyrightability are pretty clear.
[1] https://media.cadc.uscourts.gov/opinions/docs/2025/03/23-523...
[2] https://www.copyright.gov/comp3/chap300/ch300-copyrightable-...
Just distill what you need into an SLM, strips any legal claims right off because that’s the same thing they did.
It's pointless to make this claim without case law stating this for LLMs specifically this is merely speculation on how a judge might rule.
If the weights were produced by a purely mechanical process without any input, then I would be more inclined to believe the distinction there is significant. But as the optimization processes you've mentioned are merely tools that operate with a dataset, the provenance of that dataset becomes a factor towards copyright.
I mean, not according to the lawyers for every AI lab… they do want to have it both ways though.
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> Granted, this has yet to be fully tested in court and going to court is expensive, so it's likely that your employer would prefer to err on the side of caution and respect such attempts at model licensing anyway, similar to how one might treat a ToS agreement.
You may be conflating 2 things - the Muse model weights themselves, and what the models output when run. The former is prima facie the product of human authorship and hence has full protection of copyright law, the latter fits your characterization of likely not having copyright coverage. Meta can use the copyright on the model weights to dictate licensing terms. IANAL too.
Model weights are facts. Facts are not copyrightable.
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Unless the total (!) revenue of the provider is under 20 MM USD, eh? Not exactly fully open, but not _that_ harsh.
Even if your revenue exceeds 20MM you can still use this model from inside a commercial product but you can't expose it directly as a "model as a service"
Yup, I rather meant to point out that even if all you want to do is sell access to the model on your hardware, you can _still_ do so up until a non-trivial scale.
if I have a Sass platform where the user pays per seat $8 and assess to the llm is a listed feature, the clause do apply.
I'm pretty sure DigitalOcean was blatantly doing it anyway, and nobody even noticed.