This is an excellent tool to realize how an LLM actually works from the ground up!
For those reading it and going through each step, if by chance you get stuck on why 48 elements are in the first array, please refer to the model.py on minGPT [1]
It's an architectural decision that it will be great to mention in the article since people without too much context might lose it
Wow, I love the interactive wizzing around and the animation, very neat! Way more explanations should work like this.
I've recently finished an unorthodox kind of visualization / explanation of transformers. It's sadly not interactive, but it does have some maybe unique strengths.
First, it gives array axis semantic names, represented in the diagrams as colors (which this post also uses). So sequence axis is red, key feature dimension is green, multihead axis is orange, etc. This helps you show quite complicated array circuits and get an immediate feeling for what is going on and how different arrays are being combined with each-other. Here's a pic of the the full multihead self-attention step for example:
It also uses a kind of generalization tensor network diagrammatic notation -- if anyone remembers Penrose's tensor notation, it's like that but enriched with colors and some other ideas. Underneath these diagrams are string diagrams in a particular category, though you don't need to know (nor do I even explain that!).
Are you referring specifically to line 141, which sets the number of embedding elements for gpt-nano to 48? That also seems to correspond to the Channel size C referenced in the explanation text?
That matches the name of default model selected in the right pane, "nano-gpt". I missed the "bigger picture" at first before I noticed the other models in the right pane header.
The visualization I've been looking for for months. I would have happily paid serious money for this... the fact that it's free is such a gift and I don't take it for granted.
What a nice comment!! This has been a big failing of my mental model. I always believed if I was smart enough I should understand things without effort. Still trying to unlearn this....
99% persperation, 1% inspiration, as the addage goes...and I completely agree.
The frustration for the curious is that there is more than you can ever learn. You encounter something new and exciting, but then you realize that to really get to the spot where you can contribute will take at least a year or six, and that will require dropping other priorities.
Andrej Karpathy twisting his hands as he explains it is also a great device. Not being sarcastic, when he explains it I understand it for a good minute it two. Then need to rewatch as I forget (but that is just me)!
Could as well be titled 'dissecting magic into matmuls and dot products for dummies'. Great stuff. Went away even more amazed that LLMs work as well as they do.
Another visualization I would really love would be a clickable circular set of possible prediction branches, projected onto a Poincare disk (to handle the exponential branching component of it all). Would take forever to calculate except on smaller models, but being able to visualize branch probabilities angularly for the top n values or whatever, and to go forwards and backwards up and down different branches would likely yield some important insights into how they work.
Good visualization precludes good discoveries in many branches of science, I think.
(see my profile for a longer, potentially more silly description ;) )
Readit News
For those reading it and going through each step, if by chance you get stuck on why 48 elements are in the first array, please refer to the model.py on minGPT [1]
It's an architectural decision that it will be great to mention in the article since people without too much context might lose it
[1] https://github.com/karpathy/minGPT/blob/master/mingpt/model....
I've recently finished an unorthodox kind of visualization / explanation of transformers. It's sadly not interactive, but it does have some maybe unique strengths.
First, it gives array axis semantic names, represented in the diagrams as colors (which this post also uses). So sequence axis is red, key feature dimension is green, multihead axis is orange, etc. This helps you show quite complicated array circuits and get an immediate feeling for what is going on and how different arrays are being combined with each-other. Here's a pic of the the full multihead self-attention step for example:
https://math.tali.link/raster/052n01bav6yvz_1smxhkus2qrik_07...
It also uses a kind of generalization tensor network diagrammatic notation -- if anyone remembers Penrose's tensor notation, it's like that but enriched with colors and some other ideas. Underneath these diagrams are string diagrams in a particular category, though you don't need to know (nor do I even explain that!).
Here's the main blog post introducing the formalism: https://math.tali.link/rainbow-array-algebra
Here's the section on perceptrons: https://math.tali.link/rainbow-array-algebra/#neural-network...
Here's the section on transformers: https://math.tali.link/rainbow-array-algebra/#transformers
https://pytorch.org/blog/inside-the-matrix/
https://github.com/karpathy/minGPT/blob/master/mingpt/model....
The frustration for the curious is that there is more than you can ever learn. You encounter something new and exciting, but then you realize that to really get to the spot where you can contribute will take at least a year or six, and that will require dropping other priorities.
Really nice stuff.
Since X now hides replies for non-logged in user here is a nitter link for those without an account (like me) that might want to see the full thread.
https://nitter.net/BrendanBycroft/status/1731042957149827140
Good visualization precludes good discoveries in many branches of science, I think.
(see my profile for a longer, potentially more silly description ;) )