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You still don't get it. For LLMs a "transformer architecture" only means one that:

- Tokenizes sequences

- Converts tokens to vectors

- Performs vector/matrix transformations

- Converts back to tokens

The matrix transformation part is why it's called a "transformer". Do some reading yourself https://en.wikipedia.org/wiki/Transformer_(deep_learning_arc...

> how silly you look

You'll look twice as silly after thinking vectors are unique to LLMs, or that the word "transformer" has anything to do with LLMs rather than lower-level array math.

Consider that a "vector database" is a very specific technology - yet the word "vector" is not off limits in other database related libraries, especially if dealing with vectors.

In any case - if you think I'm trying to pass it off as something else, what I call "transformer" does tokenize lots of text (breaks it down by ~word, ~pixel) and derives semantic values (AKA trains) to produce real-time completions to inputs by way of math, not lookups. It fits the definition even in that sense where "transformer" meant something more abstract than the mathematical term.




You guys both broke the site guidelines badly in this thread. We have to ban accounts that post like this, so please don't.

If you'd please review https://news.ycombinator.com/newsguidelines.html and stick to the rules when posting here, we'd appreciate it.


I didn't know it was that strict, no offense to the other poster, it was just a little disagreement :)




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