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

Would be curious if https://spreadsheets-are-all-you-need.ai/gpt2/ works on your Chromebook. You need to download the weights and then drag and drop them back into the page but maximum compatibility was one of the design goals.

All good I’ll check on my main!

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

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

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

Definitely more than meets the eye

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.

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.

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.

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!

This is how pedagogy works. You must simplify something.

Yeah it's a bit odd, especially since RoPe is a lot more conceptually simple imo.

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.

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/

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.

For the uninitiated, I can't recommend enough, The Illustrated Transformer:

https://jalammar.github.io/illustrated-transformer/

His book is also very good: https://www.amazon.com/Hands-Large-Language-Models-Understan...

seconded, the book is very good.

Why did the inner EE in me think this was going to be a power transformer lol

Would have been very interested. I know there are genius/insane people out there winding their own transformers.

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.

It seem that everyone is getting into details of how transformers work, but I am more interested in why other setups didn't work.

Or is it?

how transformers work,

most people in ML have no idea what transformers actually are.

Traditional networks, at every layer, used to be output = [weights matrix][input], where input is a vector, and weights matrix is the weights, where each row corresponds to the set of weights for each neuron.

Transformers upscale the dimension of the data. Instead of the above, transformers do [output] = [input]

There is nothing magical about K/Q/V. There is nothing about any one doing any querying or any one representing some keys. The naming is just a carry over from how they that selection process is used in pre llm data science fields where you manually define the key and query matricies to define relationships between components.

The reason of why it works is because is an extension of something called kernel tricks from pre LLM machine learning days - you map a lower dimensional space to an extra dimension based on some equation, and it lets you apply some classifier on the combination of existing values and new value. Thats what transformers are doing - they are mapping the individual token to the dk x n_heads latent space, which allows for a higher dimensional representation of the data, capturing complex relationships.

You can do Transformers with 5 matricies instead of 3, you can do this with 4-dimentional tensors, and so on. The thing is, there really isn't any way to tell if any of that gives you more advantage - it certainly would give you more granularity, but as of right now, in terms of training to generate a specific token given previous ones before it, it seems that you don't need any more dimentions than dk x n_heads. Interestingly enough, you also can mathematically represent any such transformer including the starting one with a sequence of linear layers like in traditional networks, the only thing is that it becomes computationally inefficient due to having duplicates of data.

The reason why RNNs and others and others didn't work is because RNN training is effectively trying to linearly regress on chaotic effects - i.e what set of starting conditions would evolve with a given process into what you want. This is an NP hard problem, and you can't really do it linearly.

Transformer models on the other hand, use breadth instead of compute to capture interactions. In those learned weight matrices, you have a latent space of a bunch of "knowledge" compressed, and an algorithm to search on that "knowledge".

But, its very possible that an RNN can be smarter than a frontier model while being much smaller in size - in the same way that its very possible that you can have the right set of prompts for an existing local inference smaller model that can basically be very close to AGI in terms of being able to solve any problem across any domain. Right now, the space is about exploring those prompts, which is the frameworks and harnesses, to get to there, as well as making the compute portion more efficient so you can explore that space faster.

And the thing that comes after harnesses/efficiency in terms of progress should be obvious if you understand all of the above.

Thank you sir for this lengthy explaination.

You can do this with 5 matricies instead of 3, you can do this with 4-dimentional tensors, and so on

As a outsider I have many dumb quesion like these. I am trying to understand transformers in a Occam's razor way. It's a complicated machinary after all.

This is not about Optimus Prime and the gang, is it?