Flow Matching model inference in C
As the title and description of the GitHub repo suggest, I’m working on a small project for purely educational purposes, with the goal of implementing generative model inference (small models capable of modeling 2D distributions) based on the Flow Matching paradigm in C. I’ve worked on generative AI models based on Flow Matching from a more “abstract” perspective, using frameworks like PyTorch, and I wanted to understand what goes on behind the scenes. The repository is still a work in progress and is also one of my first "serious" projects in C.
Does the same job
all alternatives →- BABrancher – A user-friendly PyTorch module for probabilistic inference2019 · brancher.org · ▲96
- ISI started a repo for sharing algorithm implementations2013 · github.com · ▲52
Everything that would be here is in the README. I hope this gets big, it has tons of potential.
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I just noticed it takes literally ~5 minutes to train millions parameters on slow CPU...but before you call Yudkowsky that "it's over", an important note: the main bottleneck is the corpus size, params are just 'cleverness' but given limited info it's powerless. Anyway, here is the project: https://github.com/bggb7781-collab/lrnnsmdds/tree/main couple of notes: 1. single C file, no dependencies. Below are literally all the "dependencies", not even custom header (copy paste from the top of the single c file): #define _POSIX_C_SOURCE 200809L #include #include…
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