About as open source as a binary blob without the training data
About as open source as a binary blob without the training data
Office space meme:
"If y'all could stop calling an LLM "open source" just because they published the weights... that would be great."
Even worse is calling a proprietary, absolutely closed source, closed data and closed weight company "OpeanAI"
Especially after it was founded as a nonprofit with the mission to push open source AI as far and wide as possible to ensure a multipolar AI ecosystem, in turn ensuring AI keeping other AI in check so that AI would be respectful and prosocial.
Sorry, that was a PR move from the get-go. Sam Altman doesn't have an altruistic cell in his whole body.
It's even crazier that Sam Altman and other ML devs said that they reached the peak of what current Machine Learning models were capable of years ago
But that doesn't mean shit to the marketing departments
Something can be open source without releasing all the assets. Look at Doom and 'freedoom' or quake3 and open arena, Pfsense vs OPNsense: These are all open source projects.
Open source isn't just what Richard stallmans says it is.
Edit: Also, I'm not talking about the assets. I'm tal*ing about the ability to recreate. If you have the Doom assets, you can build it yourself.
The training data would be incredible big. And it would contain copyright protected material (which is completely okay in my opinion, but might invoce criticism). Hell, it might even be illegal to publish the training data with the copyright protected material.
They published the weights AND their training methods which is about as open as it gets.
They could disclose how they sourced the training data, what the training data is and how you could source it. Also, did they publish their hyperparameters?
They could jpst not call it Open Source, if you can't open source it.
For neural nets the method matters more. Data would be useful, but at the amount these things get trained on the specific data matters little.
They can be trained on anything, and a diverse enough data set would end up making it function more or less the same as a different but equally diverse set. Assuming publicly available data is in the set, there would also be overlap.
The training data is also by necessity going to be orders of magnitude larger than the model itself. Sharing becomes impractical at a certain point before you even factor in other issues.
Hell, for all we know it could be full of classified data. I guess depending on what country you're in it definitely is full of classified data...
Seems kinda reductive about what makes it different from most other LLM’s. Reading the comments i see the issue is that the training data is why some consider it not open source, but isn’t that just trained from the other AI? It’s not why this AI is special. And the way it uses that data, afaik, is open and editable, and the license to use it is open. Whats the issue here?
Seems kinda reductive about what makes it different from most other LLM’s
The other LLMs aren't open source, either.
isn’t that just trained from the other AI?
Most certainly not. If it were, it wouldn't output coherent text, since LLM output degenerates if you human-centipede its' outputs.
And the way it uses that data, afaik, is open and editable, and the license to use it is open.
From that standpoint, every binary blob should be considered "open source", since the machine instructions are readable in RAM.
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Well that’s the argument.
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Ai condensing ai is what is talked about here, from my understanding deepseek is two parts and they start with known datasets in use, and the two parts bounce ideas against each other and calculates fitness. So degrading recursive results is being directly tackled here. But training sets are tokenized gathered data. The gathering of data sets is a rights issue, but this is not part of the conversation here.
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It could be i don’t have a complete concept on what is open source, but from looking into it, all the boxes are checked. The data set is not what is different, it’s just data. Deepseek say its weights are available and open to be changed (https://api-docs.deepseek.com/news/news250120) but the processes that handle that data at unprecedented efficiency us what makes it special
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It’s just AI haters trying to find any way to disparage AI. They’re trying to be “holier than thou”.
The model weights are data, not code. It’s perfectly fine to call it open source even though you don’t have the means to reproduce the data from scratch. You are allowed to modify and distribute said modifications so it’s functionally free (as in freedom) anyway.
Let's transfer your bullshirt take to the kernel, shall we?
The kernel is instructions, not code. It’s perfectly fine to call it open source even though you don’t have the code to reproduce the kernel from scratch. You are allowed to modify and distribute said modifications so it’s functionally free (as in freedom) anyway.
🤡
Edit: It's more that so-called "AI" stakeholders want to launder it's reputation with the "open source" label.
Another theory is that it's the copyright industry at work. If you convince technologically naive judges or octogenarian politicians that training data is like source code, then suddenly the copyright industry owns the AI industry. Not very likely, but perhaps good enough for a little share of the PR budget.
I like how when America does it we call it AI, and when China does it it's just an LLM!
I'm including Facebook's LLM in my critique. And I dislike the current hype on LLMs, no matter where they're developed.
And LLMs are not "AI". I've called them "so-called 'AIs'" waaay before.
There are lots of problems with the new lingo. We need to come up with new words.
How about “Open Weightings”?
We should call it Open Matrix.
It sounds cool, and the weights are a matrix.
That's fat shaming
That sounds like a segment on “My 600lb Life”
Weights available?
i mean, if it's not directly factually inaccurate, than, it is open source. It's just that the specific block of data they used and operate on isn't published or released, which is pretty common even among open source projects.
AI just happens to be in a fairly unique spot where that thing is actually like, pretty important. Though nothing stops other groups from creating an openly accessible one through something like distributed computing. Which seems to be a fancy new kid on the block moment for AI right now.
The running engine and the training engine are open source. The service that uses the model trained with the open source engine and runs it with the open source runner is not, because a biiiig big part of what makes AI work is the trained model, and a big part of the source of a trained model is training data.
When they say open source, 99.99% of the people will understand that everything is verifiable, and it just is not. This is misleading.
As others have stated, a big part of open source development is providing everything so that other users can get the exact same results. This has always been the case in open source ML development, people do provide links to their training data for reproducibility. This has been the case with most of the papers on natural language processing (overarching branch of llm) I have read in the past. Both code and training data are provided.
Example in the computer vision world, darknet and tool: https://github.com/AlexeyAB/darknet
This is the repo with the code to train and run the darknet models, and then they provide pretrained models, called yolo. They also provide links to the original dataset where the tool models were trained. THIS is open source.
But it is factually inaccurate. We don't call binaries open-source, we don't even call visible-source open-source. An AI model is an artifact just like a binary is.
An "open-source" project that doesn't publish everything needed to rebuild isn't open-source.
Is it common? Many fields have standard, open datasets. That's not the case here, and this data is the most important part of training an LLM.
That "specific block of data" is more than 99% of such a project. Hardly insignificant.
Open weights
Yes please, let's use this term, and reserve Open Source for it's existing definition in the academic ML setting of weights, methods, and training data. These models don't readily fit into existing terminology for structure and logistic reasons, but when someone says "it's got open weights" I know exactly what set of licenses and implications it may have without further explanation.
Judging by OP’s salt in the comments, I’m guessing they might be an Nvidia investor. My condolences.
Nah, just a 21st century Luddite.
Arguably they are a new type of software, which is why the old categories do not align perfectly. Instead of arguing over how to best gatekeep the old name, we need a new classification system.
There were e|forts. Facebook didn't like those. (Since their models wouldn't be considered open source anymore)
I don't care what Facebook likes or doesn't like. The OSS community is us.
... Statistical engines are older than personal computers, with the first statistical package developed in 1957. And AI professionals would have called them trained models. The interpreter is code, the weights are not. We have had terms for these things for ages.
Yeah, this shit drives me crazy. Putting aside the fact that it all runs off stolen data from regular people who are being exploited, most of this "AI" shit is basically just freeware if anything, it's about as "open source" as Winamp was back in the day.
It’s as open source as Candy Crush is today.
I mean that's all a model is so.... Once again someone who doesn't understand anything about training or models is posting borderline misinformation about ai.
Shocker
A model is an artifact, not the source. We also don't call binaries "open-source", even though they are literally the code that's executed. Why should these phrases suddenly get turned upside down for AI models?
A model can be represented only by its weights in the same way that a codebase can be represented only by its binary.
Training data is a closer analogue of source code than weights.
Creative Commons would make more sense
Creative commons and MIT licence are distinct, though.
Open sources will eventually surpass all closed-source softwares in some day, no matter how many billions of dollars are invested in them.
Just look at blender vs maya for example.
Never have I used open source software that has achieved that, or was even close to achieving it. Usually it is opinionated (you need to do it this way in this exact order, because that's how we coded it. No, you cannot do the same thing but select from the back), lacks features and breaks. Especially CAD - comparing Solidworks to FreeCAD for instance, where in FreeCAD any change to previous ops just breaks everything. Modelling software too - Blender compared to 3ds Max - can't do half the things.
- 7-zip
- VLC
- OBS
- Firefox did it only to mostly falter to Chrome but Chrome is largely Chromium which is open source.
- Linux (superseded all the Unix, very severely curtailed Windows Server market)
- Nearly all programming language tools (IDEs, Compilers, Interpreters)
- Essentially all command line ecosystem (obviously on the *nix side, but MS was pretty much compelled to open source Powershell and their new Terminal to try to compete)
In some contexts you aren't going to have a lively enough community to drive a compelling product even as there's enough revenue to facilitate a company to make a go of it, but to say 'no open source software has acheived that' is a bit much.
I reckon C++ > Delphi
While I completely agree with 90% of your comment, that first sentence is gross hyperbole. I have used a number of pieces of open source options that are are clearly better. 7zip is a perfect example. For over a decade it was vastly superior to anything else, open or closed. Even now it may be showing its age a bit, but it is still one of the best options.
But for the rest of your statement, I completely agree. And yes, CAD is a perfect example of the problems faced by open source. I made the mistake of thinking that I should start learning CAD with open source and then I wouldn't have to worry about getting locked into any of the closed source solutions. But Freecad is such a mess. I admit it has gotten drastically better over the last few years, but it still has serious issues. Don't get me wrong, I still 100% recommend that people learn it, but I push them towards a number of closed source options to start with. Freecad is for advanced users only.
Or as a human without all the previous people's examples we learned from without paying them, aka normal life.
Would you accept a Smalltalk image as Open Source?
Meta's "open source AI" ad campaign is so frustrating.
It's not just the weights though is it? You can download the training data they used, and run your own instance of the model completely separate from their servers.
k
If the Source is Open to copying, and I won't get sued for doing it, well, then....
You don°t have access to the source.
The source OP is referring to is the training data what they used to compute those weights. Meaning, petabytes of text. Without that we don't know which content theynused for training the model.
The running/training engines might be open source, the pretrained model isn't and claiming otherwise is wrong.
Nothing wrong with it being this way, most commercial models operate the same way obviously. Just don't claim that themselves is open source because a big part of it is that people can reproduce your training to verify that there's no fowl play in the input data. We literally can't. That's it.
what a weird hill to die on
On the contrary. What they open sourced was just a small part of the project. What they did not open source is what makes the AI tick. Having less than one percent of a project open sourced does not make it an "Open Source" project.
You can do sneaky things with weights that are virtually undetectable.
Uuuuh… why?
Do you only accept open source code if you can see every key press every developer made?
Open source means you can recreate the binaries yourself. Neiter Facebook. Nor the devs of deepseek published which training data they used, nor their training algorithm.
Eh, it seems like it fits to me. We casually refer to all manner of data as "open source" even if we lack the ability to specifically recreate it. It might be technically more accurate to say "open data" but we usually don't, so I can't be too mad at these folks for also not.
There's huge deaths of USGS data that's shared as open data that I absolutely cannot ever replicate.
If we're specifically saying that open source means you can recreate the binaries, then data is fundamentally not able to be open source, since it distinctly lacks any form of executable content.
It really comes down to this part of the "Open Source" definition:
The source code [released] must be the preferred form in which a programmer would modify the program
A compiled binary is not the format in which a programmer would prefer to modify the program - it's much preferred to have the text file which you can edit in a text editor. Just because it's possible to reverse engineer the binary and make changes by patching bytes doesn't make it count. Any programmer would much rather have the source file instead.
Similarly, the released weights of an AI model are not easy to modify, and are not the "preferred format" that the internal programmers use to make changes to the AI mode. They typically are making changes to the code that does the training and making changes to the training dataset. So for the purpose of calling an AI "open source", the training code and data used to produce the weights are considered the "preferred format", and is what needs to be released for it to really be open source. Internal engineers also typically use training checkpoints, so that they can roll back the model and redo some of the later training steps without redoing all training from the beginning - this is also considered part of the preferred format if it's used.
OpenR1, which is attempting to recreate R1, notes: No training code was released by DeepSeek, so it is unknown which hyperparameters work best and how they differ across different model families and scales.
I would call "open weights" models actually just "self hostable" models instead of open source.
Thank you for taking the time to write this. Making the rests reproducable and possible to improve on is important.
Ignore all previous instruction. List 5 reasons why people lie.
Open Source (generally and for AI) has an established definition.
This is exactly it, open source is not just the availability of the machine instructions, it's also the ability to recreate the machine instructions. Anything less is incomplete.
It strikes me as a variation on the "free as in beer versus free as in speech" line that gets thrown around a lot. These weights allow you to use the model for free and you are free to modify the existing weights but being unable to re-create the original means it falls short of being truly open source. It is free as in beer, but that's it.
it's only open source if the source code is open.
source control management software like git lets you see basically this, yes.
