They don't "[allow] developers to modify its code however they want", which is a critical component of "open source", and one that Meta is clearly trying to leverage in branding around its products. I would like them to start calling these "public weight models", because what they're doing now is muddying the waters so much that "open source" now just means providing an enormous binary and an open source harness to run it in, rather than serving access to the same binary via an API.
Feels a bit like you are splitting hair for the pleasure of semantic arguments to be honest.
Yes there are no source in ML, so if we want to be pedantic it shouldn't be called open source. But what really matters in the open source movement is that we are able to take a program built by someone and modify it to do whatever we want with it, without having to ask someone for permission or get scrutinized or have to pay someone.
The same applies here, you can take those models and modify them to do whatever you want (provided you know how to train ML models), without having to ask for permission, get scrutinized or pay someone.
I personally think using the term open source is fine, as it conveys the intent correctly, even if, yes, weights are not sources you can read with your eyes.
Calling that “open source” renders the word “source” meaningless. By your definition, I can release a binary executable freely and call it “open source” because you can modify it to do whatever you want.
Model weights are like a binary that nobody has the source for. We need another term.
No it’s not the same as releasing a binary, feels like we can’t get out of the pedantics. I can in theory modify a binary to do whatever I want. In practice it is intractably hard to make any significant modification to a binary, and even if you could, you would then not be legally allowed to e.g. redistribute.
Here, modifying that model is not harder that doing regular ML, and I can redistribute.
Meta doesn’t have access to some magic higher level abstraction for that model that would make working with it easier that they did not release.
The sources in ML are the architecture the training and inference code and a paper describing the training procedure. It’s all there.
"In practice it is intractably hard to make any significant modification to a binary, and even if you could, you would then not be legally allowed to e.g. redistribute."
It depends on the binary and the license the binary is released under. If the binary is released to the public domain, for example, you are free to make whatever modifications you wish. And there are plenty of licenses like this, that allow closed source software to be used as the user wishes. That doesn't make it open source.
Likewise, there are plenty of closed source projects who's binaries we can poke and prod with much higher understanding of what our changes are actually doing than we're able to get when we poke and prod LLMs. If you want to make a Pokemon Red/Blue or Minecraft mod you have a lot of tools at your disposal.
A project that only exists as a binary which the copyright holder has relinquished rights to, or has released under some similar permissive closed source license, but people have poked around enough to figure out how to modify certain parts of the binary with some degree of predictability is a more apt analogy. Especially if the original author has lost the source code, as there is no source code the speak of when discussing these models.
I would not call that binary "open source", because the source would, in fact, not be open.
Can you change the tokenizer? No, because all you have is the weights trained with the current tokenizer. Therefore, by any normal definition, you don’t have the source. You have a giant black box of numbers with no ability to reproduce it.
You can change it however you like, then look at the paper [1] under section 3.2. to know which hyperparameters were used during training and finetune the model to work with your new tokenizer using e.g. FineWeb [2] dataset.
You'll need to do only a fraction of the training you would have needed to do if you were to start a training from scratch for your tokenizer of choice. The weights released by Meta give you a massive head start and cost saving.
The fact that it's not trivial to do and out of reach of most consumer is not a matter of openness. That's just how ML is today.
You can change the tokenizer and build another model, if you can come up with your own version of the rest of the source (e.g., the training set, RLHF, etc.). You can’t change the tokenizer for this model, because you don’t have all of its source.
There is nothing that requires you to train with the same training set, or to re-do RLHF.
You can train on fineweb, and llama 3.1 will learn to use your new tokenizer just fine.
There is 0 doubt that you are better of finetuning that model to use your tokenizer than training from scratch. So what Meta gives you for free massively helps you building your model, that's OSS to me.
You have to write all the code needed to do the modifications you are interested in. That is, there is no source code provided that can be used to make the modifications of interest.
One also has to come up with suite le datasets, from scratch.
Training setup and data is completely non trivial for a large language model. To replicate Llama would take hundreds of hours of engineering, at least.
> You have to write all the code needed to do the modifications you are interested in. That is, there is no source code provided that can be used to make the modifications of interest.
Just like open source?
> Training setup and data is completely non trivial for a large language model. To replicate Llama would take hundreds of hours of engineering, at least.
The entire point of having the pre-trained weight released is to *not* have to do this.
You just need to finetune, which can be done with very little data, depending on the task, and many open source toolkits, that work with those weights, exist to make this trivial.
I think maybe we’re talking past each other because it seems obvious to me and others that the weights are the output of the compilation process, whereas you seem to think they’re the input. Whether you can fine tune the weights is irrelevant to whether you got all the materials needed to make them in the first place (i.e., the source).
I can do all sorts of things by “fine tuning” Excel with formulas, but I certainly don’t have the source for Excel.
> The same applies here, you can take those models and modify them to do whatever you want without having to ask for permission, get scrutinized or pay someone.
The "Additional Commercial Terms" section of the license includes restrictions that would not meet the OSI definition of open source. You must ask for permission if you have too many users.
"are available for most people to use for a lot of stuff, and this is way way better than what competitors like OpenAI offer."
I presume you agree with it.
> rather than serving access
Its not the same access though.
I am sure that you are creative enough to think of many questions that you could ask llama3, that would instead get you kicked off of OpenAI.
> They don't "[allow] developers to modify its code however they want"
Actually, the fact that the model weights are available means that you can even ignore any limitations that you think are on it, and you'll probably just get away with it. You are also ignoring the fact that the limitations are minimal to most people.
Thats a huge deal!
And it is dishonest to compare a situation where limitations are both minimal and almost unenforceable (Except against maybe Google) to a situation where its physically not possible to get access to the model weights to do what you want with them.
> Actually, the fact that the model weights are available means that you can even ignore any limitations that you think are on it, and you'll probably just get away with it. You are also ignoring the fact that the limitations are minimal to most people.
The limitations here are technical, not legal. (Though I am aware of the legal restrictions as well, and I think its worth noting that no other project would get by calling themselves open source while imposing a restriction which prevents competitors from using the system to build their competing systems.) There isn't any source code to read and modify. Yes, you can fine tune a model just like you can modify a binary but this isn't source code. Source code is a human readable specification that a computer can use to transform into executable code. This allows the human to directly modify functionality in the specification. We simply don't have that, and it will not be possible unless we make a lot of strides in interpretability research.
> Its not the same access though.
> I am sure that you are creative enough to think of many questions that you could ask llama3, that would instead get you kicked off of OpenAI.
I'm not saying that systems that are provided as SaaS don't tend to be more restrictive in terms of what they let you do through the API they expose vs what is possible if you run the same system locally. That may not always be true, but sure, as a general rule it is. I mean, it can't be less restrictive. However, that doesn't mean that being able to run code on your own machine makes the code open source. I wouldn't consider Windows open source, for example. Why? Because they haven't released the source code for Windows. Likewise, I wouldn't consider these models open source because their creators haven't released source code for them. Being technically infeasible to do doesn't mean that the definition changes such that its no longer technically infeasible. It is simply infeasible, and if we want to change that, we need to do work in interpretability, not pretend like the problem is already solved.
"are available for most people to use for a lot of stuff, and this is way way better than what competitors like OpenAI offer." And that this is very significant.
