There are ten measures by which a model can/should be open:

1. The model code (pytorch, whatever)

2. The pre-training code

3. The fine-tuning code

4. The inference code

5. The raw training data (pre-training + fine-tuning)

6. The processed training data (which might vary across various stages of pre-training and fine-tuning)

7. The resultant weights blob

8. The inference inputs and outputs (which also need a license; see also usage limits like O-RAIL)

9. The research paper(s) (hopefully the model is also described in literature!)

10. The patents (or lack thereof)

A good open model will have nearly all of these made available. A fake "open" model might only give you two of ten.