This is great, thank you. I have to teach this stuff on Friday so perfect timing. It's hard to explain the attention mechanism in a way that becomes intuitive because the weighting scheme does not help much with the intuition. Having a visualization like this helps a lot. Don't move that page please since I'll link to it!

UX report. I wished to examine attention state step by step, but I found the animation moved along too fast for that. So I tried pausing...

On Chromium/linux, pressing pause doesn't pause, instead resetting the animation to it's pre-play state - the current attention highlighting disappears. Pressing play again, restarts at the beginning. Having a commonplace "pause pauses, and play resumes" UI, could allow more time to look over state. A youtube-like slow playback 0.25? option might similarly help. Or perhaps even better, buttons for single stepping. Tnx for your work.

This is great, I've read multiple books and watched videos about the attention mechanism. Now that I understand it, this is the clearest example I've seen on how attention works.

This is very cool. It's simpler than Bertviz for understanding inference and surface level and a good starting people for new learners as well.

Is the attention explanation of why the model tells like this? I've seen that there are many discussions about this. (Image attention visualizations were not that good I think)

You can also mine attention from image models, it's a lot of fun and very interesting.

I highly question this simplistic idea of high vector magnitude = high influence.

neat, combining info from two phrases is hard to see without such a tool.

are you worried later-layer attention gets drowned out by earlier layers just because there are more of them contributing to the sum?

I like the visualisation. Pretty cool

thanks for making it simple and visualizable

How it works?

I am curious what's the actual formula.

I mean, there so many headers and layers, it is tricky to make a choice that will resonate with our intuition . Is it some weighted average? Or maybe ablation test?

I don't know much about LLMs but does that mean you have N^2 computation with the context size since every token needs to track how it relates to every other token?

INSANE