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I'll plug the project I've started work on along the same lines - a simplified CPU focused embedding model (right now with distillbert) that's coded as a single file with no dependencies and no abstraction. https://github.com/rbitr/ferrite

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Pretty interesting, and rare to see projects in the space written in Fortran. Would be interested to hear what the advantages are, and appreciate if it's simply a case of personal preference too!

I'm mostly a python programmer, but I find a lot of the ML frameworks are overkill for what they actually do, especially for inference. Fortran is pretty close to numpy - it handles arrays natively including slicing and matmul instinsics, you don't have to worry about memory etc. But it compiles into something fast and lightweight much more easily than python. It's nothing you couldn't do in C but I think Fortran is better suited for linear algebra.

See also https://github.com/rbitr/llama2.f90 which is basically the same thing but for running llama models and has 16-bit and 4-bit options and a lot more optimization.

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