[lordswork]

When it comes to how "open" LLMs are, there are basically three categories:

- Open Source: The architecture of the model is available, so you can bring your own data and compute to train a similar model.

- Open Weights: The trained model itself is available for you to use. You can download it and run it on your own hardware without needing to train anything from scratch.

- Open Data: You get access to the data that was used to train the model. With this, you can completely reproduce the training process, which is super helpful if you want to fine-tune the model or see exactly how it was trained.

There are some other nuances around hyperparams, training methodology, etc. but that's basically the 3 main categories.

[bick_nyers]

I would probably refer to category 1 as "Open Architecture". I wouldn't want to give anyone the false impression that category 1 is comparable in the slightest to Open Weights, which is vastly more useful.

[lordswork]

I like that better too, but open source is often used synonymously with open architecture, so it's good to be aware of.

[perching_aix]

I see, thanks! I didn't know being open source referred to so little when it came to ML.