Honest feedback - I was really excited when I read the opening. However, I did not come away from this without a greater understanding than I already had.
For reference, my initial understanding was somewhat low: basically I know a) what embedding is basically b) transformers work by matrix multiplication, and c) it's something like a multi-threaded Markov chain generator with the benefit of prior-trained embeddings
Similarly, I was really excited when I read the headline here on HN and thought this would be about the electrical device. I wonder if the LLM meaning has eclipsed the electrical meaning at this point, as a default in the absence of other qualifiers, in communities like this.
here’s a basic impl that i trained on tinystories to decent effect: https://gist.github.com/nikki93/f7eae83095f30374d7a3006fd5af... (i used claude code a lot to help with the above bc a new field for me) (i did this with C and mlx before but ultimately gave into the python lol)
but overall it boils down to:
- tokenize the text
- embed tokens (map each to a vector) with a simple NN
- apply positional info so each token also encodes where it is
- do the attention. this bit is key and also very interesting to me. there are three neural networks: Q, K, V – that are applied to each token. you then generate a new sequence of embeddings where each position has the Vs of all tokens added up weighted by the Q of that position dot’d with the K of the other position. the new embeddings are /added/ to the previous layer (adding like this is called ‘residual’)
- also do another NN pass without attention, again adding the output (residual)
there’s actually multiple ‘heads’ each with a different Q, K, V – their outputs are added together before that second NN pass
there’s some normalization at each stage to keep the numbers reasonable and from blowing up
you repeat the attention + forward blocks many times, then the last embedding in the final layer output is what you can sample based on
i was surprised by how quickly this just starts to generate coherent grammar etc. having the training loop also do a generation step to show example output at each stage of training was helpful to see how the output qualitatively improves over time, and it’s kind of cool to “watch” it learn.
this doesn’t cover MoE, sparse vs dense attention and also the whole thing about RL on top of these (whether for human feedback or for doing “search with backtracking and sparse reward”) – i haven’t coded those up yet just kinda read about them…
now the thing is – this is a setup for it to learn some processes spread among the weights that do what it does – but what those processes are seems still very unknown. “mechanistic interpretability” is the space that’s meant to work on that, been looking into it lately.
Honest feedback - I was really excited when I read the opening. However, I did not come away from this without a greater understanding than I already had.
For reference, my initial understanding was somewhat low: basically I know a) what embedding is basically b) transformers work by matrix multiplication, and c) it's something like a multi-threaded Markov chain generator with the benefit of prior-trained embeddings
Similarly, I was really excited when I read the headline here on HN and thought this would be about the electrical device. I wonder if the LLM meaning has eclipsed the electrical meaning at this point, as a default in the absence of other qualifiers, in communities like this.
Have you checked out this video from 3Blue1Brown that talks bit about transformers?
https://youtu.be/wjZofJX0v4M
I've seen it but I don't believe I've watched it all the way through. I will now
Pasting a comment I posted elsewhere:
Resources I’ve liked:
Sebastian Raschka book on building them from scratch
Deep Learning a Visual Approach
These videos / playlists:
https://youtube.com/playlist?list=PLoROMvodv4rOY23Y0BoGoBGgQ... https://youtube.com/playlist?list=PLoROMvodv4rOwvldxftJTmoR3... https://youtube.com/playlist?list=PL7m7hLIqA0hoIUPhC26ASCVs_... https://www.youtube.com/live/uIsej_SIIQU?si=RHBetDNa7JXKjziD
here’s a basic impl that i trained on tinystories to decent effect: https://gist.github.com/nikki93/f7eae83095f30374d7a3006fd5af... (i used claude code a lot to help with the above bc a new field for me) (i did this with C and mlx before but ultimately gave into the python lol)
but overall it boils down to:
- tokenize the text
- embed tokens (map each to a vector) with a simple NN
- apply positional info so each token also encodes where it is
- do the attention. this bit is key and also very interesting to me. there are three neural networks: Q, K, V – that are applied to each token. you then generate a new sequence of embeddings where each position has the Vs of all tokens added up weighted by the Q of that position dot’d with the K of the other position. the new embeddings are /added/ to the previous layer (adding like this is called ‘residual’)
- also do another NN pass without attention, again adding the output (residual) there’s actually multiple ‘heads’ each with a different Q, K, V – their outputs are added together before that second NN pass
there’s some normalization at each stage to keep the numbers reasonable and from blowing up
you repeat the attention + forward blocks many times, then the last embedding in the final layer output is what you can sample based on
i was surprised by how quickly this just starts to generate coherent grammar etc. having the training loop also do a generation step to show example output at each stage of training was helpful to see how the output qualitatively improves over time, and it’s kind of cool to “watch” it learn.
this doesn’t cover MoE, sparse vs dense attention and also the whole thing about RL on top of these (whether for human feedback or for doing “search with backtracking and sparse reward”) – i haven’t coded those up yet just kinda read about them…
now the thing is – this is a setup for it to learn some processes spread among the weights that do what it does – but what those processes are seems still very unknown. “mechanistic interpretability” is the space that’s meant to work on that, been looking into it lately.
I'd also recommend another article on this topic of LLMs discussed a few days ago. I read it to the finish line and understood everything fully:
> How can AI ID a cat?
https://news.ycombinator.com/item?id=44964800
So glad you shared this. Super accessible without diluting. Thank you!
very cool!