That’s not really true. The models have source code defining their architecture and it is open-source.

People keep trying to shoehorn OSS concepts onto model weights, but the concepts don’t fit because the weights aren’t software. They aren’t compiled code. They are learned parameters to use with a (very big) function that itself is expressed in the code.

So they’re a very valuable asset that complements the code, but they are not the code. You could use randomly initialized weights and the software will work - it will output tokens. They just won’t have useful patterns.

I don’t think OSS definitions have ever required that assets have their source included. For example, artwork is very important to a game, but nobody thinks a game is not open source if it doesn’t come with sketches and a copy of Adobe Illustrator to recreate the artwork from scratch.

Edit: The distinction I would draw between models like Deepseek and models like OLMo is whether they are open science. With a model like OLMo, they have published everything you need to replicate the training experiment. Whereas Deepseek does share a lot of knowledge, but keeps a lot proprietary too.

My understanding was that there was a lot of non determinism in the training process because of many factors: how GPUs optimise floating point arithmetic, how the compute is distributed between the thousands of GPUs etc

I'm not sure how static training data is either (or how you'd distribute it considering its size and nevermind the legality of sharing copyrighted things).

You'd likely get a model with very similar behaviour but the weights would be different.

Please someone correct me if I'm wrong.