Merging models has become a powerful way to compress information and build powerful models for cheap. Right now, the process is still quite experimental: which models to merge? which parameters should I use? We have some intuition but no principled approach.
I made a little tool to make things a little clearer. It allows you to visualize the family tree of any model on the Hub. It also displays the type of license they use: permissive (green), noncommercial (red), and unknown (gray). It should help people select the right license based on the parent models.
In addition, I hope it can be refined to extract more information about these models: do models from very different branches work better when merged? Can we select them based on the weight difference? There are a lot of questions to explore in this new space. :)
Here's a link to the colab notebook I made: https://colab.research.google.com/drive/1s2eQlolcI1VGgDhqWIANfkfKvcKrMyNr
If you want to know more about model merging or build you own merges, here's the article I wrote about this topic: https://huggingface.co/blog/mlabonne/merge-models