Regarding the temperature explanation:

> "Instead of picking the highest-probability token, we can use different selection strategies to balance safety and creativity in the generated text".

Safety is definitely the wrong word here.

Temperature 0 generated text actually has a weird "lack of surprise" character that makes it seem artificial. [1]

> "high-probability texts can be dull or repetitive. Humans use language as a means of communicating information, aiming to do so in a simultaneously efficient and error-minimizing manner; in fact, psycholinguistics research suggests humans choose each word in a string with this subconscious goal in mind."

I'd completely drop the dropout explanation. It's just not part of the modern recipe anymore, AFAICT.

As for the ambitious goal of explaining transformers with a single interactive visualization, I just have a hard time imagining a person is going to newly understand both word embeddings (word2vec blew my mind in 2014) and also gain an understanding of attention.

I am making my own visualizations for a presentation on "Full Bandwidth Transformers"[2] that I am giving tomorrow at the Deep Learning Study Group (SF) (on zoom for the non-locals)[3]. It's not meant to be stand alone/context free, but I'd love some feedback.

https://rrenaud.github.io/fullbandwidth_transformer_viz/

[1] https://arxiv.org/abs/2202.00666 [2] https://arxiv.org/abs/2608.08888 [3] https://www.meetup.com/deep-learning-sf/events/316601593/

Nicely done. For me the most fascinating thing about attention heads is the place where Attention matrix is already computed and is getting multiplied by Value vector. It behaves exactly like pushing Value vector through Dense layer of ordinary network where Attention matrix forms weights of that layer. So attention head is trained to construct this small single layer network dynamically during inference from Key and Query. And that's the point. That's rarely underlined in explanations of LLMs architecture and for me it's quite amazing that it works so well. This mechanism easy to observe in this particular visualization if you click through it.

As someone with an EE degree (though a sysadmin), this use of the term "transformer" is constantly confusing. :)

(Also "cryto" for cryptocurrency rather than cryptography.)

I get that this is for explaining GPT-2, but I really hope laymen don't use it as an example of how modern models work (ex. absolute positional encoding is no longer used)

edit: I know that it mentions its not modern, but these kinds of details have major implications in terms of the representations a model can learn, which is in many ways the most important part!

Great site, intuitive description. I also found [1] very useful in the past.

[1] https://bbycroft.net/llm

I never understood the thinking behind the separate key query value matrixes? What are they doing exactly?

Damn that page took down my Chromebook, never happened before..

Nice work. I really appreciate this tool for enhancing my limited understanding the mechanism(s) behind attention and LLMs.

This is not at all what I was hoping for. Expected a lot more Unicron.