this post was submitted on 07 Aug 2024
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@dandi8 But the proof is in your quote. Open source is a license which allows people to study the source code. The source code of a model is a bunch of float numbers, and you can study it as much as you want in Mixtral and others. Clearly a model can be published without the dataset (Mixtral), and also a model can be closed, hosted, unavailable for study (OpenAI). I think you need to find some argument showing how "source code" of a model = the dataset. It just isn't so.
That's like saying the source code of a binary is a bunch of hexadecimal numbers. You can use a hex editor to look at the "source" of every binary but it's not human readable...
Yes, the model can be published without the dataset - that makes it, by definition, freeware (free to distribute). It can even be free for commercial use. That doesn't make it open source.
At best, the tools to generate a model may be open source, but, by definition, the model itself can never be considered open-source unless the training data and the tools are both open-source.
@dandi8 surprise surprise, LLMs are not a classic compiled software, in case you haven't noticed yet. You can't just transfer the same notions between these two. That's like wondering why quantum physics doesn't work the same as agriculture.
Think of it as a database. If you have an open-source social network, all tools and code is published, free to use, but the value of the network is in the posts, the accounts, the people who keep coming back. The data in the database is not the source code
You're trying to change the definition of open source for AI models and your argument is that they're magic so different rules should apply.
No, they're not fundamentally different from other software. Not by that much.
The training data is the source of knowledge for the AI model. The tools to train the model are the compiler for that AI model. What makes an AI model different from another is both the source of knowledge and the compiler of that knowledge.
AFAIK, only one of those things is open source for Mistral - the compiler of knowledge.
You can make an argument that tools to make Mistral models are open source. You cannot make an argument that the model Mistral Nemo is open source, as what makes it specifically that model is the compiler and the training data used, and one of those is unavailable.
Therefore, I can agree on the social network analogy if we're talking about whether the tools to make Mistral models are open-source. I cannot agree if we're talking about the models themselves, which is what everyone's interested in when talking about AI.
@dandi8 I'm not changing the definition of open-source. And I'm not saying models are magic. Please take your strawmen back. You are the one saying that dataset is source code, and you have no backing for this argument. I agree that dataset is the "source for training", but that doesn't make it "source code" as per the open-source licenses. And the tools are not the compiler. Just because something was created from something else, that doesn't turn it into "source code".
We'll have to agree to disagree on pretty much everything, then.