kyegomez/OpenMythos

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14805 stars · 3293 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

OpenMythos is an open-source, theoretical PyTorch implementation of a Recurrent-Depth Transformer (RDT) architecture inspired by speculation about Anthropic's Claude models. It features a Prelude/looped Recurrent Block/Coda structure with switchable MLA or GQA attention and sparse MoE feed-forward layers.

Use cases

• implement a looped recurrent-depth transformer from scratch
• experiment with compute-adaptive depth-variable reasoning in transformers
• compare MLA vs GQA attention implementations
• train a sparse mixture-of-experts transformer with routed and shared experts
• study theoretical reconstructions of proprietary LLM architectures
• prototype adaptive inference loops in PyTorch

When to choose

• you want to experiment with recurrent-depth or looped transformer architectures in PyTorch
• you need a configurable MoE transformer with switchable attention backends
• you are researching compute-adaptive reasoning and want a hackable codebase

When to avoid

• you need a production-ready, officially supported Claude model or API
• you require guaranteed fidelity to Anthropic's proprietary architecture
• you need a battle-tested framework for training large models at scale

Facets

library · maturity experimental

machine-learning deep-learning llm-training llm-inference deep-learning large-language-models machine-learning python transformer recurrent-depth mixture-of-experts pytorch jax attention research-implementation looped-transformers gpu

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