kyegomez/OpenMythos ? reverse-engineered prompt

I want to build a model based on the theoretical design of Claude Mythos. The main idea is a "looped transformer" where the model can 'think deeper' by reusing a set of layers multiple times for a single request. More loops should equal more reasoning power.

This isn't about chain-of-thought; all the reasoning should happen silently, inside a single forward pass. The overall architecture should have three stages: a 'Prelude' for the input, a main 'Recurrent Block' that gets looped, and a final 'Coda' for the output. This structure should help it solve complex problems with multi-step logic without having to explicitly write out the steps.

Please build this in Python with PyTorch. Make sure it's possible to control the number of loops during inference. Also, include a simple example showing how to initialize the model and use it to generate some output.