this is very cool indeed that meta has made available more than they need to in terms of model weights. however, the "open-source" narrative is being pushed a

this is very cool indeed that meta has made available more than they need to in terms of model weights. however, the "open-source" narrative is being pushed a bit too much like descriptive ML models were called "AI", or applied statistics "data science". with reinforced examples such as this, we start to lose the original meaning of the term. the current approach of startups or small players "open-sourcing" their platforms and tools as a means to promote network effect works but is harmful in the long run. you will find examples of terraform and red hat happening, and a very segmented market. if you want the true spirit of open-source, there must be a way to replicate the weights through access to training data and code. whether one could afford millions of GPU hours or not, real innovation would come from remixing the internals, and not just fine-tuning existing stuff. i understand that this is not realistically going to ever happen, but don't perform deceptive marketing at the same time.