I had ambitious hopes of putting a pipeline of over 1B billion parameters of neural networks into a zkML prover. Turns out I had to scale WAY back!!! I was in both ezkl and zkVM dependency hell, simultaneously reading docs & questioning LLMs to solve my issues. ChatGPT first suggested I trace my models into torchscript. But then I'd have to load libtorch in rust for zkVM and that is IMPRACTICAL. ONNX seems the way. The ezkl docs were nice, although way too sparse, and were using a syntax from a different version from the latest I was running. I had to figure it out painfully. OK so I realized I still need to scale back. I can't do a full music generating DiT, but I could just use very small bytebeat/demoscene style music generation code. The goal is to use, at the end of the net pipeline, a model that does aesthetic scoring (returns a number of how "good"), Meta's Audiobox Aesthetics. Ok but it's 100M params. STILL TOO BIG. Also ONNX is such a pain the ass, Microsoft doesn't support all the opcodes in the runtime the way it should. So I scaled it back more. 10s inputs of audio. Also distilled the model down into 100K param transformer model. I started getting paranoid about time so I distlled it further to 20K params. ezkl setup was STILL getting killed. AHHH. I was downsizing params to 12 logrows, then 5, 2 was too far. OK eventually I got a prover.