I disagree. The file I linked to, model.py, contains the Llama 3 model itself.

You can use that model with open data to train it from scratch yourself. Or you can load Meta’s open weights and have a working LLM.

> The file I linked to, model.py, contains the Llama 3 model itself.

That makes it source available ( https://en.wikipedia.org/wiki/Source-available_software ), not open source

Source available means you can see the source, but not modify it. This is kinda the opposite, you can modify the model, but you don't see all the details of its creation.

> Source available means you can see the source, but not modify it.

No, it doesn't mean that. To quote the page I linked, emphasis mine,

> Source-available software is software released through a source code distribution model that includes arrangements where the source can be viewed, and in some cases modified, but without necessarily meeting the criteria to be called open-source. The licenses associated with the offerings range from allowing code to be viewed for reference to allowing code to be modified and redistributed for both commercial and non-commercial purposes.

> This is kinda the opposite, you can modify the model, but you don't see all the details of its creation.

Per https://github.com/meta-llama/llama3/blob/main/LICENSE there's also a laundry list of ways you're not allowed to use it, including restrictions on commercial use. So not Open Source.

Yeah a lot of people here seem to not understand that PyTorch really does make model definitions that simple, and that has everything you need to resume back-propagation. Not to mention PyTorch itself being open-sourced by Meta.

That said the LLama-license doesn't meet strict definitions of OS, and I bet they have internal tooling for datacenter-scale training that's not represented here.