This is amazing, such a nice presentation. It reminds me of the Neural Network Zoo [1], which was also a nice visualization of different architectures.
Interesting collection. The architecture differences show up in surprising ways when you actually look at prompt patterns across models. Longer context windows don't just let you write more, they change what kind of input structure works best.
Is there a sort order? Would be so nice to understand the threads of evolutions and revolution in the progression. A bit of a family tree and influence layout? It would also be nice to have a scaled view so you can sense the difference in sizes over time.
Thanks! This is cool. Can you tell me if you learnt anything interesting/surprising when pulling this together? As in did it teach you something about LLM Architecture that you didn't know before you began?
[1] https://www.asimovinstitute.org/neural-network-zoo/
Is there a sort order? Would be so nice to understand the threads of evolutions and revolution in the progression. A bit of a family tree and influence layout? It would also be nice to have a scaled view so you can sense the difference in sizes over time.