index.md

Documentation

Complexity Framework is a PyTorch research stack for deterministic TR-Hash Mixture-of-Experts (TR-MoE) language and multimodal models.

Start here

1. Architecture and naming
2. Getting started
3. TR-Hash execution engine
4. TokenRoutedMLP (removed) and migrating to TR-Hash
5. Training
6. Run configurations
7. GPU and dispatch paths
8. API reference

Architecture vocabulary

Public name	Attention	FFN
TR-GQA	GQA	TR-MoE
TR-MHA	MHA	TR-MoE

TR-GQA and TR-MHA share the same TRHashEngineMLP. They differ only in the attention head layout. The framework is currently scoped to TR-Hash MoE only — dense and learned-router baselines were removed and will return later as explicit comparisons.

The registry values tr_mha and tr_mha_v2 refer to experimental routed residual adapters inside attention. They are documented separately in ../TR_MHA.md to avoid conflating them with the main MHA + TR-MoE architecture.

Additional guides

• MoE comparison
• Custom models and registries
• Efficient training
• Multimodal prototypes
• Historical Mu-Guidance control

Multimodal / image

• TR-Hash image editor
• TR-Hash image-text-to-text
• TR-Hash text-to-image
• TR-Hash object detection and serving

Operations

• 200M o200k B200 runbook
• Hugging Face org card

Evidence policy

A configuration file or model implementation is not a completed experiment. Documentation distinguishes:

• implemented: represented in code and tests;
• pilot: bounded evidence, usually short or single-seed;
• controlled comparison: matched protocol with tracked metrics;
• planned: a launch or cluster plan without completed metrics.

Claims should point to the tracked configuration, metrics, or artifact that supports them.