I'm very interested in Diffusion text models. The concept of taking noise and adding words starting randomly all over the response, and filling in the noise from there on breaks my brain.

I'm sure I have a fundamental misunderstanding of the technology, though.

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embedding-shape 3 hours ago | parent | next [-]

DiffusionGemma goes one step further even, and does this denoising over multiple "canvases" which lets it do reasoning and separate out a "final reply" canvas, looks something like this: https://gist.github.com/embedding-shapes/f4cb46bad704b6d0168...

Diffusion text models for me is the more interesting type of LLMs for local usage, as it really makes good use of single GPUs for single responses, rather than auto-regressive ones, and is a lot faster! Probably the fastest model I've been able to run so far, ending up doing ~670 tok/s (depending on the type of text) on a Pro 6000

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moffkalast 2 hours ago | parent | prev [-]

How does that break your brain? It's how basically every human writes and iterates on text..?

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jermaustin1 2 hours ago | parent [-]

Because my brain thinks through text in a forward motion. Pausing at the end of each word and searching for the next.

My entire brain runs on sentences and words since I have no inner eye or whatever. So my thinking and writing both work kind of forward only.

I wouldn’t have thought that was too unique. But maybe it is?

	▲	pebbly_bread an hour ago \| parent [-]
		Normally people have feelings about things before they are able to put them into words, I would imagine if you were asked a question like "what city would you most like to visit" then unless you've already thought about it a lot, then you would have to do substantial non-verbal thinking before you can come up with an answer, and once you have the answer you may respond "my favorite city is X" and you decided what X would be before you started the sentence.