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.
Welch Labs has a beautiful visualization in their YouTube video about Vision Language Action Models, where the attention of the prompt can be traced to the exact origin pixels in the image from one of those matrices.
Look at this poster [1] (its low-res, full res is paid). Also see this video for context [2] - it talks about deepseek's innovation, but explains attention well.
Above I was pointing to the moment where multiplication AV happens. In normal Dense layer in MLPs when you go through the layer you compute y=Wx, where x is an input and y is output (before gactivation) and W is a weight matrix. This W is usually what's produced through training process. This multiplication AV looks like Wx. If we take one column from matrix V in the poster and call it v, we can pretend for one moment that W=A and x=v [3]. So this multiplication Av works like linear transformation in ordinary network Wx. But in case of attention heads A is not trained directly but produced during inference, and is not trained directly like W is in ordinary network. In this case network is trained to produce A during inference.
> "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.
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!
Well the embedding itself is in some latent space.
Wq projects it to the space of queries. I.E What questions is this token asking?
Wk projects it to the space of keys. I.E What questions does this token answer.
Wk projects it to the space of values. I.E What are those answers?
Of course this explanation is prescribed onto the matrixes after the fact.
You can in fact do weird stuff like construct weights so attention calculates least squares, or sorts numbers, or other weird constructions like a transformer that calculates gradient descent steps. It seems to be very flexible in terms of what functions on data it can encode.
For the uninitiated, I can't recommend enough, The Illustrated Transformer:
https://jalammar.github.io/illustrated-transformer/
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.
Welch Labs has a beautiful visualization in their YouTube video about Vision Language Action Models, where the attention of the prompt can be traced to the exact origin pixels in the image from one of those matrices.
Can you explain this more, maybe dumb it down a little? Sounds important. I have t quite been able to get the attention section to click for me.
Look at this poster [1] (its low-res, full res is paid). Also see this video for context [2] - it talks about deepseek's innovation, but explains attention well. Above I was pointing to the moment where multiplication AV happens. In normal Dense layer in MLPs when you go through the layer you compute y=Wx, where x is an input and y is output (before gactivation) and W is a weight matrix. This W is usually what's produced through training process. This multiplication AV looks like Wx. If we take one column from matrix V in the poster and call it v, we can pretend for one moment that W=A and x=v [3]. So this multiplication Av works like linear transformation in ordinary network Wx. But in case of attention heads A is not trained directly but produced during inference, and is not trained directly like W is in ordinary network. In this case network is trained to produce A during inference.
[1] https://www.welchlabs.com/store/mladeepseek-attention-poster...
[2] https://www.youtube.com/watch?v=0VLAoVGf_74
[3] When multiplying A by V, we perform the same linear transform Av_i for each i-th column of V.
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.)
Seriously. I was expecting more Optimus Prime.
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/
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
this is awesome, thanks for sharing
I never understood the thinking behind the separate key query value matrixes? What are they doing exactly?
Well the embedding itself is in some latent space.
Wq projects it to the space of queries. I.E What questions is this token asking?
Wk projects it to the space of keys. I.E What questions does this token answer.
Wk projects it to the space of values. I.E What are those answers?
Of course this explanation is prescribed onto the matrixes after the fact.
You can in fact do weird stuff like construct weights so attention calculates least squares, or sorts numbers, or other weird constructions like a transformer that calculates gradient descent steps. It seems to be very flexible in terms of what functions on data it can encode.
Damn that page took down my Chromebook, never happened before..
Text under the "Examples" section...
"Try examples while GPT-2 model is being downloaded (600MB)"
That's a hefty chunk of download and likely compute too.
twice...
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.