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What do LLMs see? I wrote a lil' tool that extracts the attention matrices out of open models and creates this typing visual, with each token's opacity changing according to its average attention score as the prompt progresses. Dimmer words are considered less important to the model.

This was done with a relatively small model, LFM2.5 1.2B. In the process of downloading the biggest model my lil laptop can run to see how it looks.

I wrote it because my boss nerd sniped me by saying "recent tokens in context are always much more important than old ones." I wanted to know if that was actually true. So far I think it _might_ be, but more testing required.

> recent tokens in context are always much more important than old ones The key insight to overcome this fallacy is to stop thinking in tokens and accept that LLM "think" in embeddings, which contain multiple tokens plus their attention "score".

Is it a fallacy?

from my own very flawed perception, the very first thing (system prompt / first message) seems to carry a lot of 'weight', and the most recent prompt does as well, but the 'middle stuff' seems to carry 'less weight'. it would be very interesting to see this visualized or the 'mechanics'.

poloclub.github.io/transformer-... is a superb visual of the end-to-end GPT-2 architecture (modern architectures aren't _that_ different to this, so it's a good base).

Interesting that the most consistently attended to tokens are newlines/periods. I think I've heard some folks argue that semantic information is getting stored for those tokens in the KV cache, not sure if that's true but there has to be some reason for that consistent focus.

Here's a version that lets me look at individual layers and heads (a layer just being the mean of all of its heads). Notice some heads really care about specific tokens that others don't.

Where can we try this tool?