It is a family of multimodal models based on pretrained Qwen2-72B-Instruct LLM and InterViT vision encoder. There are three variants differentiated by the way the vision tokens are used: decoder-only (like the majority of existing VLM), using cross-attention, and a hybrid. Only the first seems to be on huggingface at the moment.
Also they seem to only train on publically available data, concluding that quality is more important than scale.
It has a non-commercial cc-by-nc-4.0 license, I would guess the only way to use this in production is to use Nvidias data centers to host it? Or are there other ways?
Not a lawyer, not legal advice, but... the legal status quo is that neural network outputs are not copyrightable. They are currently considered not made by humans nor considered a derivative work from the training material / network weights (assuming it's not regurgitating copyrighted material verbatim).
The cc-by-nc-4.0 license applies to the network weights. The only thing non-commercial about the license is that it restricts how you may reproduce the licensed material:
> reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and
As long as you are not selling the network weights themselves, nothing in the license prevents you from evaluating the neural network for commercial purposes and selling the outputs. In 'production' you will have to directly download the weights from Nvidia themselves (or another 3rd party which is distributing the network weights non-commercially in good faith) though, you can't share the network weights onto your commercial inference server from another one of your commercial deployment servers. Or at least, it gets more dicy there and may be considered commercial reproduction so better avoid it.
For similar reasons you may 3D print a CC-BY-NC model of a tool and use that tool in your commercial workshop, you may use a CC-BY-NC compiler of a language to compile commercial programs, etc.
> the legal status quo is that neural network outputs are not copyrightable.
Can't this flip on a dime and a billion dollar company lose billions?
Not a lawyer, but work with lawyers a lot, and this type of rules-lawyering doesn't tend to work in the legal profession. Consult a lawyer before trying any of this.
It's an interesting question indeed!
Creative Commons themselves write at https://creativecommons.org/faq/#can-i-apply-a-creative-comm... :
"Can I apply a Creative Commons license to software? We recommend against using Creative Commons licenses for software. Instead, we strongly encourage you to use one of the very good software licenses which are already available."
Of course, LLM weights aren't traditional software...
First time I read this interpreation regarding CC-BY-NC model weights, are there any sources to back it?
Even selling the network weights shouldn't matter, since there's no copyright.
The problem is if you happen to sign any agreement with NVIDIA in order to get the weights. The problem is whatever contracts you may be bound by.
How much GPU RAM would be needed to run this with just one GPU?
I haven't tested it, but likely around 170GB, regardless of if you're using only one GPU or spreading it out over several ones.
I think the only relevant part to note here is that this model showed improved text-only performance after multimodal training. Wonder if this translates to Llama models also ? Is it possible to extend Llama 3.1 405b with multi-modal training to create another SOTA large model ?
Llama-3-V models do that, but are not published.
Reminder that Nvidia is still the only company making any money out of the "AI revolution".
That's natural given that they mostly produce hardware several layers of abstraction distant from the end user value, companies need to buy the hardware before they can start delivering their own value. AI model training is not value by itself if there's no use-case for the model that can be charged for.
I see it playing out one of two ways. Either Nvidia are selling shovels in a gold rush, the rush will end, and the business will dry up (after they have made a lot of money!). Or AI sticks/takes off, and Nvidia are selling a commodity too far from the value, like most electronic component manufacturers, and they'll maintain significant market share but have their margins reduced to a fraction of what they were before (after they made a lot of money!).
The human value doesn't come from ML training or inference, it comes from taking a better photo. The business value comes from drafting a better email. Those companies closer to that value will likely do better in the long run, as they always have done.
I'm pretty sure https://www.topazlabs.com/ is also making money with the AI revolution.
Also Klarna threw out 700 people, they probably make money with AI.
And i found this article: https://www.ft.com/content/a9a192e3-bfbc-461e-a4f3-112e63d0b...
"When there is a gold rush, sell shovels"
They started the gold rush.
I'm pretty sure OpenAI started it, they just used NVIDIA shovels to dig the first mines.
My analogy still holds. NVIDIA just created good shovels that are useful in both the garden and in a gold mine.
AMD and Intel insisted on selling only flimsy garden shovels.
Wrong
Midjourney is profitable. All the acquired startups (i.e. Streamlit or MosaicML) who made millions per employee "made money" for the people who cared.
Midjourney is one, but the others are not. Plenty of people “made money” at Twitter, but the company is a money pit.
OP was likely talking about profitability.
FWIW I wouldn’t really count streamlit as an ai company
Twitter was (mildly) profitable.
That's not true, there are plenty of companies that make a profit, Midjourney, for example, an obvious one.
Are there others?
i have yet to hear of anyone actually using AI for something properly
only exception im excited about is the non-main characters from video games, where a lot of the random NPCs, can now actually bring some more fun to the game.
I have seen plenty of very good internal AI Demos which we are adding to our products. From GenAI stuff, to image analysis, lightweight agents who answer proper questions.
I used chatgpt 3 days ago to generate a script for me. Saved me probably an hour too.
We use it also in my startup for tasks which we wouldn't even tried without ML models because the quality of old libraries were to bad. Like pdf catalog to text, image classification and segmentation.
I run in production a system that uses LLM translation and summerization from hundreds of sources in dozens of languages. Users are extremely satisfied by the results that are far cheaper and far higher quality than what was available before
Vision models are a godsent for blind user. I use a vision model to sort my laundry, for instance...
And translation and grammar/spell checking is also at a level which was unthinkable before LLMs hit.
But thats it, really. The "talking machine" aspect of it is more and more uncovered as totally useless.
> I use a vision model to sort my laundry
you built a robot that sorts laundry? Tell us more!
Is that faster than just determining by touch what type of garment something is? Or is this about sorting by color?
Claiming no one is using MLMs “properly” despite the various scientific and industrial use cases (vision systems, robots, protein folding, drug simulation, etc) while being “excited” for something as pathetically trivial as a text generator with a text-to-speech tacked on for your mass-produced open world games. Truly peak HN.
Its an revolution. Don't undersell this.
There was never ever any technology like LLMs close to what chatgpt and co can do in regards of understanding random human input.
My startup doesn't need to make money with it directly, but for us it increased our data quality on text and images.
I'm also quite happy to pay 10-20$ per month for random things LLMs do quite well for different use cases like creating some scripts etc.
I love how they include a helpful chart that shows this model scores worse than everything else.
Am I looking at the wrong table? It dominates everything on visual interpretation benchmarks.
Edit: specifically ocrbench and VQAv2
All jokes aside (and that did make me laugh) at least they're not training just to hit the benchmarks, which seem to be more meaningless as a quality indicator with each passing day.
I see at a few models (3 models in MMMU) that score lower than Nvidia's. But putting that aside, they at least get points for apparent objectivity. At least they probably aren't fudging numbers.
It's not that bad, and I'd much rather that they be honest instead of lying like everyone else does.
Well but it actually doesn't, unless you're looking only at MMMU.