LLM Visualization (opens in new tab)

You might also like this interactive 3D walk through explainer from PyTorch :

https://pytorch.org/blog/inside-the-matrix/

That matches the name of default model selected in the right pane, "nano-gpt". I missed the "bigger picture" at first before I noticed the other models in the right pane header.

I was thinking 42 ;-)

What a nice comment!! This has been a big failing of my mental model. I always believed if I was smart enough I should understand things without effort. Still trying to unlearn this....

99% persperation, 1% inspiration, as the addage goes...and I completely agree.

The frustration for the curious is that there is more than you can ever learn. You encounter something new and exciting, but then you realize that to really get to the spot where you can contribute will take at least a year or six, and that will require dropping other priorities.

which video specifically?

I wish it could integrate other open source LLMs in the backend, but this is already an amazing viz.

I always thought the verb was "weigh" not "weight", but apparently the latter is also in the dictionary as a verb.

Oh well... it seems like it's more confusing than I thought https://www.merriam-webster.com/wordplay/when-to-use-weigh-a...

I think in most deployments, they're not fp32 by the time you're doing inference no them, they've been quantized, possibly down to 4 bits or even fewer.

On the training side I wouldn't be surprised if they were bf16 rather than fp32.

I think a good way of explaining #2 is “weight” in the sense of a weighted average

The audience of this paper are other researchers who already know the concept of attention, which was very well known already in the field. In such research papers, such things are never explained again, as all the researchers already know this or can read other sources, which are cited, but focus on the actual research questions. In this case, the research question was simply: Can we get away by just using attention and not using the LSTM anymore? Before that, everyone was using both together.

I think learning it following it more this historical development can be helpful. E.g. in this case here, learn the concept of attention, specifically cross attention first. And that is this paper: Bahdanau, Cho, Bengio, "Neural Machine Translation by Jointly Learning to Align and Translate", 2014, https://arxiv.org/abs/1409.0473

That paper introduces it. But even that is maybe quite dense, and to really grasp it, it helps to reimplement those things.

It's always dense, because those papers already have space constraints given by the conferences, max 9 pages or so. To get a better detailed overview, you can study the authors code, or other resources. There is a lot now about those topics, whole books, etc.

It’s definitely not obvious no matter how smart you are! The common metaphor used is it’s like a conversation.

Imagine you read one comment in some forum, posted in a long conversation thread. It wouldn’t be obvious what’s going on unless you read more of the thread right?

A single paper is like a single comment, in a thread that goes on for years and years.

For example, why don’t papers explain what tokens/vectors/embedding layers are? Well, they did already, except that comment in the thread came 2013 with the word2vec paper!

You might think wth? To keep up with this some one would have to spend a huge part of their time just reading papers. So yeah that’s kind of what researchers do.

The alternative is to try to find where people have distilled down the important information or summarized it. That’s where books/blogs/youtube etc come in.

I found these notes very useful. They also contain a nice summary of how LLMs/transformers work. It doesn't help that people can't seem to help taking a concept that has been around for decades (kernel smoothing) and giving it a fancy new name (attention).

http://bactra.org/notebooks/nn-attention-and-transformers.ht...

I struggled to get an intuition for this, but on another HN thread earlier this year saw the recommendation for Sebastian Raschka's series. Starting with this video: https://www.youtube.com/watch?v=mDZil99CtSU and maybe the next three or four. It was really helpful to get a sense of the original 2014 concept of attention which is easier to understand but less powerful (https://arxiv.org/abs/1409.0473), and then how it gets powerful with the more modern notion of attention. So if you have a reasonable intuition for "regular" ANNs I think this is a great place to start.

Turns out Attention is all you need isn't all you need!

(I'm sorry)

softmax(QK) gives you a probability matrix of shape [seq, seq]. Think of this like an adjacency matrix with edges with flow weights that are probabilities. Hence semantic routing of parts of X reduced with V.

where

- Q = X @ W_Q [query]

- K = X @ W_K [key]

- V = X @ V [value]

- X [input]

hence

attn_head_i = (softmax(Q@K/normalizing term) @ V)

Each head corresponds to a different concurrent routing system

The transformer just adds normalization and mlp feature learning parts around that.

You know what, I think the panning being pinned to a specific plane is actually great, it means you actually pan across the surface of the object instead of mostly moving it out of the viewport like in this example:

https://connectivity.brain-map.org/3d-viewer?v=1&types=PLY&P...

This pinning to one single plane works really well in this particular case because what you are showing is mostly a flat thing anyway, so you don't have much reason to put the view direction close to the plane. A straightforward extension of this behaviour would be to add a few more planes, like one for each of the the cardinal directions and just switch which plane is the one the panning happens in, might be interesting to try for a more rounded object.

To me the zoom seems to do what is expected, zooming around the cursor position tends to be disorientating in 3D anyway, though maybe I didn't understand what problem you complained about.

Should add I'm using keyboard and mouse, haven't tested it with a touch interface.

Is it a brain, though? As far as I understand it it's mostly stochastic calculations based on language or image patterns whose rule sets are static and immutable. Every conceivable idea of plasticity (and with that: a form of fake consciousness) is only present during training.

Add to that the fact that a model is being trained actively and the weights are given by humans and the possible realm of outputs is being heavily moderated by an army of faceless low paid workers I don't see any semblance of a brain but a very high maintenance indexing engine sold to the marketing departments of the world as a "brain".

Works fine on Firefox on Linux for me.

Or that you've blocked it (some sources recommend this to avoid fingerprinting so various extensions and privacy configuration receipes do it).

Thanks, I assumed that was the case, but they didn't make that explicit. Then the question is, is the world simulation running at the highest order attention layer or is it an emergent property of the interaction cycle between the attention layers.

Not using brave.

I get the same fail in both firefox and (chromium-based) qutebrowser.

The web is where useful error messages go to die.

It's an incentives problem. At research universities, promotion is contingent on research output, and teaching is often seen as a distraction. At so-called teaching universities, promotion (or even survival) is mainly contingent on throughput, and not measures of pedagogical outcome.

If you are a teaching faculty at a university, it is against your own interests to invest time to develop novel teaching materials. The exception might be writing textbooks, which can be monetized, but typically are a net-negative endeavor.

Have you considered the considerably greater breadth of content required for a full course, as well as the other responsibilities of the people teaching them such as testing, public speaking, etc.

This is the result of a single person on the internet, who was not chosen randomly. it's not a fair characterization to call this the product of some random person on the internet. You can't just choose anyone on the internet at random and get results this good.

Also, according to his home page, Mr. Bycroft earned a BSc in Mathematics and Physics from the University of Canterbury in 2012. It's true that this page isn't the direct result of a professor or a university course, and it's also true that it's not a completely separate thing. It seems clear that his university education had a big role to play in creating this.

I second the reply about incentives. Funding curriculum materials and professional curriculum development is often seen as more of a K-12 thing. There is not even enough at the vocational level.

If big competitive grants and competitive salaries went to people with demonstrated ability like the engineer of this viz, there would be less stem dropouts in colleges and more summer learning! Also, in technical trades like green construction, solar, hvac, building retrofits, data center operations and the like, people would get farther and it would be a more diverse bunch.

You're betting on the hundreds of top university cs professors to produce better content than the hundreds of thousands of industry veterans or hobbyists...

Why does YouTube sometimes have better content than professionally produced media? It's a really long tail of creators and educators

Except for this man: Professor Robert Ghrist

https://www.youtube.com/c/ProfGhristMath

This person is amazing.

The person who made this went to university for maths education (if I found the right profile).

Because Faculty are generally paid very poorly, have many courses to teach, and what takes up more and more of their time are the broken bureaucratic systems they have to deal with.

Add that at research universities, they have to do research.

Also add in that at many schools, way too many students are just there to clock in, clock out and get a piece of paper that says they did it. Way too few are there to actually get an education. This has very real consequences on the moral of the instructors. When your students don't care, it's very hard for you to care. If your students aren't willing to work hard, why are you willing to work hard? Because you're paid so well?

I know plenty of instructors who would love to do things like this, but when are they going too? When are they going to find the time to learn the skills necessary to build an interactive web app? You think everyone outside of comp sci and like disciplines just naturally know how to build these types of apps?

I could go on, but the tl;dr of it is: Educators are over worked, underpaid and don't have enough time in the day.