I think these endless debates about whether open-weights models qualify for a particular piece of terminology are... tiring. That said, I think the debates would benefit from discussing model training and model inference as two separate systems, because that's what they are. It's possible for model training to be closed-source while model inference is open-source, and vice versa.
Consider recent Mistral-Small release. The model training is almost totally closed-source. You can't replicate it. However, the model inference is fully open source: the code and weights are Apache licensed. Not only that, but Mistral released both the base model and the instruction-tuned model, so you have a good foundation to work from (the base model) should you prefer to do your own instruction tuning. In fact, Mistral has also open-sourced code to aid in the fine-tuning process as well. So you really have everything you need* to use and customize this inference system. And for most practical purposes, even if you had the original training data, it would be of no use to you.
It's also worth considering the inverse scenario. Suppose Meta were to release a big blob of pre-training data and scripts for Llama 405B, but no weights. This clearly qualifies as open source, but it is basically useless unless you have many millions of dollars to do something with it. It would do very little to democratize access to AI.
* Asterisk: There is one situation where having access to the original training data would be really, really useful -- model distillation. Nobody can match Meta's ability to distill Llama 405B into an 8B size, because that process works best when you can do it on identically distributed data.
For me, the attacks on ML that are possibly by poisoning the training data preclude considering models without freely distributable and modifiable training as open-source or libre models.
Consider recent Mistral-Small release. The model training is almost totally closed-source. You can't replicate it. However, the model inference is fully open source: the code and weights are Apache licensed. Not only that, but Mistral released both the base model and the instruction-tuned model, so you have a good foundation to work from (the base model) should you prefer to do your own instruction tuning. In fact, Mistral has also open-sourced code to aid in the fine-tuning process as well. So you really have everything you need* to use and customize this inference system. And for most practical purposes, even if you had the original training data, it would be of no use to you.
It's also worth considering the inverse scenario. Suppose Meta were to release a big blob of pre-training data and scripts for Llama 405B, but no weights. This clearly qualifies as open source, but it is basically useless unless you have many millions of dollars to do something with it. It would do very little to democratize access to AI.
* Asterisk: There is one situation where having access to the original training data would be really, really useful -- model distillation. Nobody can match Meta's ability to distill Llama 405B into an 8B size, because that process works best when you can do it on identically distributed data.