jasonjmcghee
For the uninitiated, I can't recommend enough, The
Illustrated
Transformer:https://jalammar.github.io/illustrated-transfo
rmer/
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> hangonhn
His book is also very good:
https://www.amazon.com/Hands-Large-Language-Models-Und
erstan...
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andblac
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.
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> bonoboTP
It's definitely part of many intro explanations, but
it sits in a weird position where you have to know
MLPs well but not Transformers for this to be
enlightening, which is a shrinking set of people
nowadays. Either someone is a novice to all this, or
know both, so beginner Transformer explanations can
rarely assume deep familiarity with MLPs.But yes, a
Transformer block can be thought of as basically
input-dependently deciding the weight vector of a
dense layer. In classical MLP-like networks there was
never any multiplication between input-dependent
values (there are exceptions, like
Squeeze-and-Excitation layers). Multiplication is
always between parameters (that don't depend on the
input) and activations that depend on the input. With
Transformers attention provides a lot of
multiplicative interactions between input dependent
activations.Also obligatory: Schmidhuber talked about
this a long time ago.
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> encrux
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.
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> bilsbie
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.
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> > andblac
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-attent
ion-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.
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throw0101a
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.)
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> kQq9oHeAz6wLLS
Seriously. I was expecting more Optimus Prime.
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robrenaud
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_transform
er_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/
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ftumminello
Why did the inner EE in me think this was going to be a
power transformer lol
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> jaggederest
Would have been very interested. I know there are
genius/insane people out there winding their own
transformers.
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> > CTDOCodebases
I didn't realize this was a think until I heard of
people doing it in a a CRT forum.I suspect it will
be getting more popular as flyback transformers
get harder and harder to find.
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> > > jaggederest
I first heard of it here:
https://ludens.cl/paradise/turbine/turbine.htm
l (CTRL+F transformer), a pretty fascinating
DIY medium distance power transmission setup.
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> Vaslo
Could have been the cars that turn into robots for
that matter
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utopcell
Great site, intuitive description. I also found [1] very
useful in the past.[1] https://bbycroft.net/llm
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> gyanchawdhary
this is awesome, thanks for sharing
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E-Reverance
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!
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> foobazgt
This is how pedagogy works. You must simplify
something.
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> > E-Reverance
I'm talking about bringing up a specific outdated
details, not about simplifying it
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jwpapi
Damn that page took down my Chromebook, never happened
before..
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> shagie
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.
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bilsbie
I never understood the thinking behind the separate key
query value matrixes? What are they doing exactly?
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> kingstnap
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.
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tanseydavid
Nice work. I really appreciate this tool for enhancing my
limited understanding the mechanism(s) behind attention
and LLMs.
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esseph
This is not at all what I was hoping for. Expected a lot
more Unicron.
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