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There was a paper posted in some thread here a while ago. Basically instead text based llm you turn the text into an image and use that as input and have the model work with the resulting matrices. This ended up as you'd guess, faster/more efficient/generally better in all their benchmarks compared to text string based llm.

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It's a totally different technique though than what parent is referring to. The one you are referring to is used to take advantage of image and video compression algorithms

what would be the benefit of turning it into an image rather than some arbitrary representation?

I'm not sure exactly. Maybe its just easier to work with matrix data. That's all an image is anyhow. The imaging is just to convert the text to some matrix that's tied to the text structure.

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