There are systems programming languages since JOVIAL in 1958, enough of them to chose from if C had never existed, and what syntaxes would be favoured instead.
It's an interesting thought experiment, but honestly for all its flaws, C did have one large advantage regarding syntax: braces. Even if C had never been invented, at some point people would have noticed that Fortran/Pascal/Whatever style BEGIN ... END blocks are annoying and just a waste of tokens.
Nah, the advantage was called UNIX, with source code being available for a symbolic price with a C compiler, at least until AT&T got allowed to charge for it, a decade later.
You mean the same people that now write piles of Markdown tokens in plain English?
The OSI actually published an "open-source" definition for model weights, which requires the training code and training data composition to be published. The exact training corpus can remain private though.
Calling this open weight is misleading. That's why we had huge backlash when Redis, Elasticsearch, and Terraform went source-available, right?
Weight-available is a better adjective for this kind of licensing. Same for Qwen, GLM, Kimi, MiniMax, etc. If they really want to be open source, they should pick an OSI-approved license.
> 2. If the Licensee or any of its affiliates operates a Model as a Service business, the Licensee must enter into a separate commercial license with Mistral before using the Model or its derivative works for any commercial purpose. The Licensee may contact Mistral, using this form: https://mistral.ai/contact/, to request a commercial license. [0]
Calling this "open weight" is misleading. That's why we had huge backlash when Redis, Elasticsearch, and Terraform went source-available, right?
Weight-available is a better adjective for this kind of licensing. Same for Qwen, GLM, Kimi, MiniMax, etc. If they really want to be open source, they should pick an OSI-approved license.
Training a frontier model is prohibitively expensive, so it’s understandable that open weights aren’t truly open source. In fact, I worry whether open weights will even survive[1] once the current AI cycle cools down. An even bigger issue is that as long as model development remains this capital intensive, a genuinely open-source community around frontier AI is virtually impossible. That will have massive implications for our careers. The more capital concentrates at the top, the less leverage we’ll have in the job market.
[1] Arguably hardware companies like Nvidia have incentives to subsidize open models to drive GPU demand. That said, I'm not sure how sustainable and sufficient that incentive can be.
No, they're still capable of calling it something different, instead of implying open source licenses that they aren't actually using. That's a valid complaint that's kind of unrelated to any of this other stuff.
Whether it's feasible to release everything under open terms is actually a different issue from whether they're being truthful in how they're advertising it.
> Arguably hardware companies like Nvidia have incentives to subsidize open models to drive GPU demand
Not speaking on behalf of my employer and I have no insight into this strategy at all, but I feel like the sale of consumer GPUs is so small by comparison to data centers (more than 10x from what I can tell online) that more users of open weights wouldn't likely make much difference in the earnings report.
There’s no reason to believe that companies running open weights models would use consumer hardware. I can speak on behalf of my employer and say that we’d vastly prefer to have enterprise grade servers running inference on premises.
Not consumer demand. I was assuming that it's Nvidia's interest that intelligence is not only in a couple of frontier lab's hands, or they can choke the demand to suit their needs. If any company can access a frontier model without going through the frontier labs, the ecosystem will be larger and healthier for everyone, including Nvidia.
Open weights models in the data center are more important than on consumer devices.
In the future, even consumers will launch weights and fine tunes they own in the cloud.
People don't own the fiber to their home, so why should they expect to run the GPUs? If they want smart coding and video models, they'll need H200s and B200s, and they'll need to rent them.
counterpoint: I have smart coding at this very second, and I am running it on 128GB of ddr4 with a 16GB gpu. (Qwen 3.8 Flash Next).
I expect to continue to have this, and more, as smaller models become even more capable. I also expect hardware prices to resume going down once the supply crunch ends.
I see no need to stop owning things or change my expectations around ownership, nor will I accept something like that.
Someone’s surely doing it atm we just don’t know yet. And then we scan all books but the stuff stays local. Perhaps possible, perhaps not. But I don’t see why everyone could not run given the epoch on his content only, reporting back findings (loss).
> Arguably hardware companies like Nvidia have incentives to subsidize open models to drive GPU demand.
This does not mean that they would necessarily subsidise consumer demand. They may be very happy to sell palette-loads of GPUs to datacenter and large corporations.
Well as long as we are all aware we’re hurdling towards techno communism. Better not fuck it up and end up ruled by fascists. Not looking great on that front so far.
Their existence is the only thing keeping costs down across the board. If they had gotten open-weight model importing banned in the US a year ago, I'm sure they'd have 100% increased token cost relative to the advertised 100%+ intelligence improvements.
Open weight models are kind of weird because they're usually just a bunch of weights and maybe a chat template, not even test vectors or anything. Quite odd, and probably one of the reasons why vllm, sglang, the llamas etc. so often produce buggy output. It's actually quite strange how little attention is given to verifying these setups work.
Why cant the community piece together the open-weights using open community-based like, implementation or whatever magic sauce integrates those weights
> Either way, Mistral is a failing company with little relevance in the AI space and that's not going to save them.
Already ? I was wondering when some US based company will buy them including all that "soverignity" prompts :)
Local llms are what we need and to get that we need memory so we need computer builders from outside of US. Why ? Because when something external appears US companies also allows US citizens to have that too at lower price :) Not before external forces force them to be suddenly good hearted to their own peoples...
Can we also have coarser MoE's ? If I want to code I do not need llm be trained on past and present capitol names and chemistry and everything unrelevant to the task ! Watch Matrix again ! We need somewhat "inteligent" llms and knowledge can be uploaded just like Neo learned kung-fu :) - prompts and light tr^h^hweek of learning and some exams, we need kind of university for llms :) So llms finally fit into users computers... And when some grandmas or snowflakes start to whine tell them to learn - llms usage can be a skill finally ;)
Edit:
Wait, can we have something like interchengable weights so knowledge can be just downloaded to model folder ? Common weights format ? Reproducible builds ?
It's not understandable for them to lie or mislead. Calling something open weights and not being remotely open in the commonly understood sense is misleading and not ok.
I don't think "you can do whatever you want other than just turn around and sell a chat wrapper for this" makes it immediately not open weights. Could it be more open? Sure. That doesn't mean it's misleading or a lie and I would strong disagree that it's "not being remotely open."
Open source doesn't mean "anyone can use the source code commercially". Open weights similarly doesn't mean the weights can be used for anything. It just means the weights are open for anyone to download and inspect.
> Open source doesn't mean "anyone can use the source code commercially".
Yes it does. It might have requirements that you release the code for any changes you make to it or link with it. But no OSI (or FSF) approved license prohibits you from using it commercially.
I'm a little confused by your distinction between vendor and lab. AI2 (Allen Institute for AI) is a western lab and they did release a fully open source model, with open weights, data, and training code (Olmo3).
We are now at the mercy of labs to provide frontier models as open-weight. Now that even some Chinese labs are releasing their models as weight-available, what stops them from prohibiting certain internal uses of new models? What about public chatbots?
Open models we have now will become obsolete within a few years. Imagine a world where our last open models were from the GPT-2 era.
I believe we need truly sovereign models, not in the Western sense, but from a community perspective. We need a model line that stays open-source across generations.
The problem is the massive amount of money, data, and talent to produce anything remotely competitive. Ideas have been floated about distributed training, but training requires insanely fast interconnect between nodes making it impossible with the current methodologies we have. Now if you mean funded at a national level, that seems more doable so long as the training runs are centralized in big DCs.
Great idea, let’s counter the power of a handful of large private organisations by putting that power in the hands of a single large government. What could possibly go wrong?
There are lots of governments around the world - some big, some small. Lots of discussions around digital sovereignty are framed in this manner - a model for Brazil, a model for Japan, etc. There certainly could be coalitions of governments or governments could strike out on their own. Whatever floats your boat.
There's always going to be some nerds out there that want to make and release a model as open, and share their knowledge along the way. I'm not too concerned.
The fear you're sharing has been shared since day 1 of open-weight models and it has yet to come to fruition.
Further, regardless of what any of these labs license their model as, the end user who has downloaded the weights can still use them for virtually any purpose (synthetic data, classification, etc) as much as they want and there is no way to reasonably enforce a license on those outputs.
So even if you can't run a service using this model's weights (without a license), the weights still do provide utility for training new models with any other license.
*Edit, responding to the replies: Constraints give rise to ingenuity. Who knows how we'll be training models in the future. Hopefully in a distributed fashion, maybe with some kind of consensus-driven training dataset selection. Crowd funding, etc. There's too many people that want this, and the knowledge on how to do it is out there (many times by now)
I think this is a quite optimistic view... releasing a SOTA model is a collosal capital investment, and unlike free software, it's not iterative - you can't make a small model and improve it one step at a time, you need to risk the whole load of cash, expertise and GPU time up front.
It's only happening now because labs have bucket tons of VC cash to burn and need a big public flex to raise more cash, or because they're subsidised by the Chinese government. I'm not massively confident either of those will stay true forever.
I think the problem is up to date knowledge isn't really the difficult or useful bit. We're sort of moving to a world where the real value in training comes from tool calling and performance over long context - your models shouldn't really be relying on their internal knowledge, but instead calling various tools. But the performance gap between now and six months for that is pretty huge.
Unless "some nerds" means "billionaires" - they may want to release new models as open source, but they will not have enough money to train a worthwhile model.
If it is capital intensive and we require the work be done in the public benefit, the only options are international consortia or nationalization. Non-profits will be guided by the interests of the rich and are unaccountable to the public. Worker cooperatives are unlikely to be well capitalized (because what rich person would want to invest and not have control) and will still have private profit incentives.
Peer production processes (like open source) require that the inputs be specialized knowledge and relatively small amounts of capital.
A completely different direction is to remove the fair use allowance for training on copyrighted information (unless you e.g. open source your weights).
We already had that world, the axis of evil stopped releasing open source models years ago. It’s only because of the Chinese that we kept getting them.
Of course they can. They're expensive to host, and if they're legally scrubbed off of places like huggingface then they are for all practical purposes 'closed' as in unavailable to people who don't already use them and if they're illegal or outpaced by Sota the ones who have copies can't even use them.
Thousands or more people already have copies and the license explicitly is irrevocable and allows redistribution. So if they take it off HF and HF is ok with that, people will just upload to anywhere else.
"outpaced by SOTA" doesn't matter at all, the models we have are plenty good enough for most real tasks.
I’m pretty sure they’re covered by the so-called sui generis protection of databases within the EU/EEA at least. That one is about protecting the effort and investment, not originality of expression.
"In the meaning of the European Union Directive 96/9/EC on the legal protection of databases,the term database refers to a collection of independent works, data or other materials, which have been arranged in a systematic or methodical way, and have been made individually accessible by electronic or other means. In the meaning of the Directive the data or materials:
must not be linked, or must be capable of separation without losing their informative content;
must be organised according to specific criteria, which means that only planned collections are covered;
must be individually accessible – mere storage of data is not covered by the term database."
It seems like model weights does not clear the definition of a database in EU law- notably, they are not a collection of individual works that are individually accessible.
However, there is a more straightforward solution. The allows general use inside a company. The license blocks the "Model-as-a-Service".
So instead, any GPU holder can provide a 'Infrastructure-as-a-service' and have clients provide the model, and then tell the clients that as an optimization they can also provide just the hash and they'll load it from their memory.
I think its good that this license pushes the ecosystem towards such a generic-utility framework, but as a moat to protect a business its banking on most companies being lazy and wasteful.
Unless considerable amount of lobbying reverses it in a new directive/regulation, that's the case on model outputs - Originally I learnt of it from briefing prepared by lawyers regarding EU laws on AI.
It seems basically that LLM output is public domain, but a bit of human-generated content can be mixed in (whatever is enough to pass the "threshold of originality") and then the overall work can be copyrighted. Much like republishing a public domain book and adding an introduction or a few footnotes (but harder to extract the public domain component.)
But this all says nothing about the original question, about whether the weights can be copyrighted.
Unfortunately I can't link to IRL discussion with lawyer (wish it was so easy!)
We got guidance that:
a) weights should be considered to be public domain due to "blender" aspect of training on otherwise copyrighted data (similar argument as to why stable diffusion weights can't be infringing - because any specific copyrighted work provides less impact on process than RNG)
b) model outputs should be considered public domain as far as "work done by model" counts - prompts/inputs done by human are copyrightable.
However, model output can infringe - essentially same framework as if you used a ton of Disney stuff in a drawing you made by yourself - since the drawing would remain what it is without the copyrighted information, it is infringing even if you did it from memory.
Similarly any human-made addition to model output are copyrightable, but to make an useful distinction there would need to be some significant work. So let's say you made an image that incorporated model outputs for parts of it - going back to "prompts" it also counts if you made a sketch/wireframe/etc. and then used a model to "fill in" or postprocess it.
>> The emerging consensus across different jurisdictions is that purely AI‑generated content
with minimal to no human creativity does not qualify for copyright protection. By contrast, works
created with the use of AI‑applications may still enjoy copyright protection, where substantial human
creativity has been exercised. Whether this threshold is met must be assessed on a case‑by‑case
basis.
This makes sense, I believe it will eventually become the clear case in the US as well. However, it does leave open the case where a human carefully crafts a series of prompts to produce an creative work that qualifies for copyright.
Further, this does not appear to be binding on courts within the EU?
This doesn't seem like an actual court decision? Also it specifically says "if you use AI as a tool... then you probably own it" which, how else can you use AI? It doesn't do anything without a human wielding it, which has been the basis for all copyright claims so far.
Copyright doesn't care whether it's expensive. The fact that someone else, with the infrastructure and training data could get the same or almost the same weights makes it obvious there's no human originality in the weights, which means they aren't eligible for copyright. Some aspect of the infrastructure or processing of the training data might be copyrightable (or patentable), but the weights themselves? No human originality. No copyright.
To the people who demanded completely open source licensed software, or that complained about "they shove Wayland/systemd down our throats", I've said many times that this is a supply/demand problem. Open source licensed software exists because there is a lot of excess software production capacity, e.g. there are many volunteers. Non-systemd distros are rare because such production capacity is low, e.g., there are few volunteers willing to do this work. To those people I've said: stop complaining about others not doing it for you, if you so want your non-systemd distro or FOSS-licensed whatever, you gotta do it yourself and contribute to the community, or fund an effort.
I got super angry reactions.
Now, the same thing is happening with open weights models, but in on a much more extreme scale. They exist because of excess capacity for whatever reason, and they will cease to exist when this excess capacity reduces. There is no use complaining about something not being open weights. What is productive is funding an effort.
Unfortunately, most complainers want others to do things for them, for free.
That's a strange take when it comes to AI companies who stole their training data in the first place. The AI companies sure wanted others to do things for them for free. What would "funding an effort" look like here? Bribing companies to share what they got from their stolen goods? Funding an effort to steal everyone's work too and then use it to train a truly open model ourselves?
That objection is completely orthogonal to my point. The people who want open weights models are not so concerned about mass data ingestion. You are, but I guess you also don't want any good models to exist (to be clear, which I think is a valid opinion, even if it's one I don't fully agree with) since nobody has figured out how to make a good AI model without trampling on other people's feelings regarding data usage, or violating licenses or copyright.
It doesn't really work like that. There are "keys" where you train in a specific response to a variety of queries. You can't really fine tune that away either.
Releasing weights even with permissive license, doesn't technically make it "open-source", unless all the method, data and technical inner working to do it is also released. Otherwise, it's just a binary floating in the wild, even if it's under MIT.
I would compare them more to media released under the creative commons license.
For example, you can record yourself playing different instruments, singing, and making other sounds. Then you can go combine all these recordings into a song, and you release the song under a CC license. The CC licenses framework does not demand that you also release the component recordings. But the released song can be remixed by someone else.
(Not making a value judgement, just making an equivalence).
This is ancient (2 years ago), all their recent stuff (e.g. [0][1]) are MIT licensed. The only exception i've found from a quick browse of their stuff is [2], which is Apache 2 licensed. Even the update to V3 (which you linked at), V3.2, was MIT licensed.
I don't recall their being a huge backlash to elasticsearch ELv2. Mostly people seemed receptive to blocking the major cloud providers from mooching off the project, and the need to protect their revenue.
People need to make money.
This provider exclusion licenses though.. In a world were someone can point an LLM at your software to launder it.. Not sure what's going to happen.
The weights are open, meaning publicly accessible, but you can't have a business profit off of them without paying for a license. That sounds fair and accurate.
HashiCorp has the same business model for their products: you are free to use them even in a business, except if your business is a competitor of their own cloud solutions.
No, but I might have someone who is subject to the restrictions host it or its distillates for me. That entity is going to end up having some kind of non-public deal with Mistral. And that can affect me.
Contract law covers the use of licensed materials -- The license is the enforcement mechanism. Agreement of the model terms is implied by the use of the model.
If someone were to create a derivative model and distribute that, it would get murky if they licensed it Apache 2.0 or MIT, because users of that derivative would have never agreed to the upstream license (and may not even be aware of it), and the model is unlikely to be copyrightable.
your agreement to the terms when downloading the weights is the enforcement - if you grow too big, you become a target from which money can be extracted if you violated the terms.
I don't understand this demand. Isn't the training data likely petabytes in size? You want that to be available? I imagine just the index (name,date,source) is a terabyte or two.
As the training data is every written thing they can get their hands on, right?
And wouldn't making that "open source" violate every license that the original data has? You can't release someone else's work.
The only way to make sure that a model was trained without hiding weird biases and backdoors is to monitor this process. It also allows anybody (with the hardware resources) to experiment with the model and how it's trained. Will most people want to do that? No, but most people also don't want to recompile their own kernel, that doesn't negate the value of Linux being FOSS.
I hope that, in the long term, I'll be able to use competitive, completely open models in the same way that I can run Linux on my computer.
You're completely right that the licensing of the training data is a big issue though (and I would argue that it should be a much bigger issue right now, even for closed models). I hope that eventually our understanding of the tech will let us build competitive LLMs using purely public domain/Creative Commons-style training data.
I realize that we have a long way to go... but that's a very worthwhile goal IMO.
Even if they did that, you would have no way of verifying that the data they upload and the model they claim to have trained is the weights they have shared. It’s a nonsensical idea.
If you want a trusted public model, we need to start a foundation that does it. The Linux of AI.
I'm aware that in 2026 this is science fiction, but if the rate of computer improvement over the next 40 years matches that of the past 40, this is not going to remain sci-fi for extremely long in the grand scheme of things.
I hope that the long term future of AI is more like Linux and less like Windows.
You don't have access to the compute necessary to rebuild the original model even if all the original training data, custom human interventions, and RNG seeds were documented and released.
It is not a good faith request.
Also, what is this obsession with reproducing? Even if you could reproduce it, down to the RNG seed, that tells you nothing about whether or not some of the training data was malicious. You'd need an AI to go through the data and figure out if there is some malicious needle in a multi-petabyte dataset.
Releasing the model is good enough, because it is the only practical reality that makes sense.
To be clear I was making a wish for the future, I wasn't saying that Mistral were bad for not doing this today, that obviously wouldn't really be technically feasible.
Given the rate of progress of LLMs, I wouldn't be surprised if we had decent fully open and reproducible models within a decade or so, especially once the current spike in hardware price passes and building a local training cluster will be more realistic.
I don't recall scientific studies giving other scientists the original person's used in a study, set back to before the study. Ir the beakers they used.
You might find this comparison stupid, but I think it is rather apt.
Reproducing a work does not mean you get all the exact same things that were originally used.
At best, I would agree releasing the index is valuable for understanding how the model was made.
But frankly, I don't think the people who demand all this effort are arguing in good faith.
You do not have access to the compute necessary to "reproduce" what they made. So it is a pointless request.
You can hardly re-create the binary from a suitable source, just like the weights just fell from the sky. But you can change the binary (patch the machine code) just like you can adjust the weights. The comparison is quite apt.
Modding isn't equivalent to post-training. Modders write new code, replace assets, patch existing instructions etc. With model weights you can continue original training process – directly optimizing existing artifact to acquire new behavior. There's no equivalent general-purpose process for a compiled binaries.
It's usually against the license to redistribute modified binaries. There are few pieces of software (I cannot think of any) that allow redistribution of modified binaries, but aren't open source.
As a result, in practice, the only example I can think of is cracks for pirated software.
I think opening something in the hex editor, changing necessary bytes and saving it is as general purpose as it gets. You usually don't distribute it (I remember they days when it was common however) due to law, so in practice people write code that hot-patches stuff after starting the process.
You can also just open gdb and patch the process on the fly when you need to do things.
At the end of the day there isn't much hard difference between source code and binary code. By design source code is usually easier to read and write, but it's all syntax sugar and transformations all the way down.
You're conflating runtime patching with continuing development.
Post training directly evolves existing model weights using the same optimization process that created them.
Software: source code -> compiler -> binary
Models: weights + training data + optimizer -> updated weights
Ie. binary is output of the development process.
Model weights are both – it's output and it's direct input for further development.
Model weight snapshots are continuously used all the time in this iterative process from the very beginning, being output of current step and input to the next step.
Hotpatching binaries used to be common. It's less so in the modern age but mainly because we moved software to higher levels of abstractions, virtual machines and interpreters, and also because rebuilding even a massive project from source is achievable on commodity hardware. You can still see various hotpatching schemes being developed, for instance to allow partial recompile of the sources while developping.
If in 2080 we can retrain Opus 129.5 directly on dev machines from scratch in <2h, I doubt many will bother messing with the weights directly.
Many Qwen model weights are licensed with Apache 2.0 (though unfortunately not the latest releases). Unfortunately open-weight is generally the term being used for weights-available, so the battle tends to be on whether the models are "open source", i.e. whether they release training code and data alongside the weights.
I'm tired of people trying to approach every legal/contractual issue as a binary, and treating flexible terminology like the magic words of a spell. They're up front about needing a license to exploit it commercially, but meantime you can use it without restrictions for personal/ experimental/ scientific/ educational/ development purposes, which is a lot. It's not 100% open by your pious definition, but it's clearly much more open than frontier labs who never release weights to the public at all.
If you're running a business, buy a license and be happy. All businesses have expenses, if you're not willing to ever pay for anything you're a freeloader and your business model is probably shit. Complain about whether the license is good value or not if you like. If you're not running a business on top of it, use the model and be happy.
Unless, i am missing something they haven't claimed open source anywhere. none of them have claimed open source. open weights has never meant the same thing as open source.
Open weights was already a weaker term then open source, but this is not even that. "Weights available" feels like an apt description akin to source available.
Open weight is still the correct term because it's not a licensing thing, it's an availability thing. OpenAI/Anthropic are Closed weight because you can't download them at all. Open Source is different because its original use had to do with licensing requirements. Not every phrase with "Open" in it is going to have the same requirements.
When it comes to words, popular usage defines (and redefines) the meaning.
Ha! you can't try to enforce the definition of a new phrase, language evolves naturally. At least with "open source" there was the dubious claim that OSI had gotten their first and had trademarked it. But now you have nothing except wanting free things with no strings attached.
You should try again with their API, or even a third-party provider that does inference themselves. This response is likely just occurring in their web version.
I think we need a different default format than Markdown. Markdown has never had a single standard. Tables were invented by GitHub and were never included in the CommonMark standard. Line breaks are weird for non-Latin languages like Chinese, and for historical reasons, you need two asterisks (**) to bold a word. It is also troublesome to parse.
John MacFarlane, the creator of Pandoc, invented the Djot format to solve many of these issues. It's derived from CommonMark and smooths out the parts of CommonMark's syntax that make it complex and slow to parse. It's not seen huge success so far, but I like it.
AsciiDoc solves a lot of problems Markdown created, but it's got its own problems. At least it doesn't just inject HTML tags all over the place to work around limitations.
I'd settle for just saving CommonMark files with a dedicated file extension, most people think of CommonMark when they say Markdown anyway.
Maybe not now, but in a few years, we might not need frontier models anymore. Open-weight models are only half a year behind the frontier, and both categories are advancing really fast.
Gemini 2.0 Flash, released just 1.5 years ago, could not even repeat a few paragraphs unchanged. Now, we have Mistral, DeepSeek, and MiMo.
> ...open sourcing everything will help growing their economy (as a whole).
Most Chinese models are no longer open source. Qwen, MinMax, and GLM are transitioning to weights-available models, leaving only Xiaomi and DeepSeek in the open-source space.
AFAIK, there's no evidence that Alibaba won't keep releasing more open source models. Before 3.8 there was speculation they wouldn't release the weights, but they finally did - Although I guess the largest model wasn't multi modal.
And Minmax is set to release their latest flash model on October 23rd.
So not sure where you're getting this from, unless I misunderstood something
This is not yet a given, I expect rule-making or copyright law expansion if courts rule otherwise.
Things also change. In the 80s lawyers carefully suggested white pages phone listings came under copyright but "sweat of the brow" was rejected by US supreme court in Feist Publications, Inc. v. Rural Telephone Service Co., 499 U.S. 340 (1991)
Because if you don’t open weight you can dictate pricing as you’re the only party that can serve the model. Also attracts users to your platform rather than inf providers.
Maybe that is worth open-sourcing.
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