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The Illustrated Transformer

jalammar.github.io

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Re: The Illustrated Transformer

#41

Post by miki123211 »

libraryofbabel wrote: post #30

I read this article back when I was learning the basics of transformers; the visualizations were really helpful. Although in retrospect knowing how a transformer works wasn't very useful at all in my day job applying LLMs, except as a sort of deep background for reassurance that I had some idea of how the big black box producing the tokens was put together, and to give me the mathematical basis for things like contex…

> Most of us confidently claimed even back in 2023 that, based on LLM architecture and training algorithms, LLMs would never be able to perform well on novel coding or mathematics tasks.

I feel like there are three groups of people:

1. Those who think that LLMs are stupid slop-generating machines which couldn't ever possibly be of any use to anybody, because there's some problem that is simple for humans but hard for LLMs, which makes them unintelligent by definition.

2. Those who think we have already achieved AGI and don't need human programmers any more.

3. Those who believe LLMs will destroy the world in the next 5 years.

I feel like the composition of these three groups is pretty much constant since the release of Chat GPT, and like with most political fights, evidence doesn't convince people either way.

Re: The Illustrated Transformer

#42

Post by libraryofbabel »

miki123211 wrote: post #41
libraryofbabel wrote: post #30

I read this article back when I was learning the basics of transformers; the visualizations were really helpful. Although in retrospect knowing how a transformer works wasn't very useful at all in my day job applying LLMs, except as a sort of deep background for reassurance that I had some idea of how the big black box producing the tokens was put together, and to give me the mathematical basis for things like contex…

> Most of us confidently claimed even back in 2023 that, based on LLM architecture and training algorithms, LLMs would never be able to perform well on novel coding or mathematics tasks. I feel like there are three groups of people: 1. Those who think that LLMs are stupid slop-generating machines which couldn't ever possibly be of any use to anybody, because there's some problem that is simple for humans but hard for…

Those three positions are all extreme viewpoints. There are certainly people who hold them, and they tend to be loud and confident and have an outsize presence in HN and other places online.

But a lot of us have a more nuanced take! It's perfectly possible to believe simultaneously that 1) LLMs are more than stochastic parrots 2) LLMs are useful for software development 3) LLMs have all sorts of limitations and risks (you can produce unmaintainable slop with them, and many people will, there are massive security issues, I can go on and on...) 4) We're not getting AGI or world-destroying super-intelligence anytime soon, if ever 5) We're in a bubble and it's going to pop and cause a big mess 6) This tech is still going to be transformative long term, on a similar level to the web and smartphones.

Don't let the noise from the extreme people who formed their opinions back when ChatGPT came out drown out serious discussion! A lot of us try and walk a middle course with this and have been and still are open to changing our minds.

Re: The Illustrated Transformer

#43

Post by roadside_picnic »

amelius wrote: post #40
roadside_picnic wrote: post #34

Earlier quoted context omitted.

What "actual reasoning" are you referring to? I believe you're making my point for me. Speculative decoding requires the implementer to understand: - How the initial prompt is processed by the LLM - How to retrieve all the probabilities of previously observed tokens in the prompt (this also help people understand things like the probability of the entire prompt itself, the entropy of the prompt etc). - Details of how…

But I can make a similar argument about a simple multiplication: - You have to know how the inputs are processed. - You have to left-shift one of the operands by 0, 1, ... N-1 times. - Add those together, depending on the bits in the other operand. - Use an addition tree to make the whole process faster. Does not mean that knowing the above process gives you a good insight in the concept of A*B and all the related ma…

I'm still confused by what you meant by "actual reasoning", which you didn't answer.

I also fail to understand how building what you described would not help your understanding of multiplication, I think it would mean you understand multiplication much better than most people. I would also say that if you want to be a "multiplication engineer" then, yes you should absolutely know how to do what you've described there.

I also suspect you might have lost the main point. The original comment I was replying to stated:

> Don't really see why you'd need to understand how the transformer works to do LLMs at work.

I'm not saying implementing speculative decoding is enough to "fully understand LLMs". I'm saying if you can't at least implement that, you don't understand enough about LLMs to really get the most out of them. No amount of twiddling around with prompts is going to give you adequate insight into how an LLMs works to be able to build good AI tools/solutions.

Re: The Illustrated Transformer

#45

Post by brcmthrowaway »

libraryofbabel wrote: post #30

I read this article back when I was learning the basics of transformers; the visualizations were really helpful. Although in retrospect knowing how a transformer works wasn't very useful at all in my day job applying LLMs, except as a sort of deep background for reassurance that I had some idea of how the big black box producing the tokens was put together, and to give me the mathematical basis for things like contex…

How was reinforcement learning used as a gamechanger?

What happens to an LLM without reinforcement learning?

Re: The Illustrated Transformer

#46

Post by sakesun »

throw310822 wrote: post #14
profsummergig wrote: post #2

Haven't watched it yet... ...but, if you have favorite resources on understanding Q & K, please drop them in comments below... (I've watched the Grant Sanderson/3blue1brown videos [including his excellent talk at TNG Big Tech Day '24], but Q & K still escape me). Thank you in advance.

Have you tried asking e.g. Claude to explain it to you? None of the usual resources worked for me, until I had a discussion with Claude where I could ask questions about everything that I didn't get.

Perhaps we have already reached ASI. :)

Re: The Illustrated Transformer

#47

Post by lugu »

libraryofbabel wrote: post #30

I read this article back when I was learning the basics of transformers; the visualizations were really helpful. Although in retrospect knowing how a transformer works wasn't very useful at all in my day job applying LLMs, except as a sort of deep background for reassurance that I had some idea of how the big black box producing the tokens was put together, and to give me the mathematical basis for things like contex…

Nice video o mechanical interpretability from Welch Labs:

https://youtu.be/D8GOeCFFby4?si=2rWnwv4M2bjkpEoc

Re: The Illustrated Transformer

#48

Post by libraryofbabel »

brcmthrowaway wrote: post #45
libraryofbabel wrote: post #30

I read this article back when I was learning the basics of transformers; the visualizations were really helpful. Although in retrospect knowing how a transformer works wasn't very useful at all in my day job applying LLMs, except as a sort of deep background for reassurance that I had some idea of how the big black box producing the tokens was put together, and to give me the mathematical basis for things like contex…

How was reinforcement learning used as a gamechanger? What happens to an LLM without reinforcement learning?

The essence of it is that after the "read the whole internet and predict the next token" pre-training step (and the chat fine-tuning), SotA LLMs now have a training step where they solve huge numbers of tasks that have verifiable answers (especially programming and math). The model therefore gets the very broad general knowledge and natural language abilities from pre-training and gets good at solving actual problems (problems that can't be bullshitted or hallucinated through because they have some verifiable right answer) from the RL step. In ways that still aren't really understood, it develops internal models of mathematics and coding that allow it to generalize to solve things it hasn't seen before. That is why LLMs got so much better at coding in 2025; the success of tools like Claude Code (to pick just one example) is built upon it. Of course, the LLMs still have a lot of limitations (the internal models are not perfect and aren't like how humans think at all), but RL has taken us pretty far.

Unfortunately the really interesting details of this are mostly secret sauce stuff locked up inside the big AI labs. But there are still people who know far more than I do who do post about it, e.g. Andrej Karpathy discusses RL a bit in his 2025 LLMs Year in Review: https://karpathy.bearblog.dev/year-in-review-2025/

Re: The Illustrated Transformer

#49

Post by prashant418 »

This guide is such a beast, Try pairing this guide with say claude code and ask it to generate sample mini pytorch pesudo-code and you can spend hours just learning/re-learning and mentally visualize a lot of these concepts. I am a big fan

Re: The Illustrated Transformer

#50

Post by krackers »

throw310822 wrote: post #20
ActorNightly wrote: post #18

People need to get away from this idea of Key/Query/Value as being special. Whereas a standard deep layer in a network is matrix * input, where each row of the matrix is the weights of the particular neuron in the next layer, a transformer is basically input* MatrixA, input*MatrixB, input*MatrixC (where vector*matrix is a matrix), then the output is C*MatrixA*MatrixB*MatrixC. Just simply more dimensions in a layer. A…

I might be completely off road, but I can't help thinking of convolutions as my mental model for the K Q V mechanism. Attention has the same property of a convolution kernel of being trained independently of position; it learns how to translate a large, rolling portion of an input to a new "digested" value; and you can train multiple ones in parallel so that they learn to focus on different aspects of the input ("ker…

I think there are two key differences though: 1) Attention doesn't doesn't use fixed distance-dependent weight for the aggregation but instead the weight becomes "semantically-dependent", based on association between q/k. 2) A single convolution step is a local operation (only pulling from nearby pixels), whereas attention is a "global" operation, pulling from the hidden states of all previous tokens. (Maybe sliding window attention schemes muddy this distinction, but in general the degree of connectivity seems far higher).

There might be some unifying way to look at things though, maybe GNNs. I found this talk [1] and at 4:17 it shows how convolution and attention would be modeled in a GNN formalism

[1] https://www.youtube.com/watch?v=J1YCdVogd14

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