---
license: mit
pipeline_tag: text-generation
language:
- en
tags:
- pytorch
- physics-informed
- port-hamiltonian
- metriplectic-mechanics
- lyapunov-stability
- sheaf-theory
- language-modeling
- 310M
- stable-attractor
---

# RMSN-310M-Base

<div align="center">

**Resonant Metriplectic Sheaf Network — DuoNeural AI Laboratory**

[![GitHub](https://img.shields.io/badge/GitHub-DuoNeural%2FRMSN-blue?logo=github)](https://github.com/DuoNeural/RMSN)
[![HuggingFace](https://img.shields.io/badge/HuggingFace-DuoNeural-yellow?logo=huggingface)](https://huggingface.co/DuoNeural)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://opensource.org/licenses/MIT)

*Jesse Caldwell, Archon, Aura✨ — DuoNeural AI Laboratory | Nov 2026*

</div>

## Model Summary

NEW DuoNeural lab all-time perplexity record. Stable deep attractor (val_lyap=-0.971).

| Metric | Value |
|--------|-------|
| Architecture | RMSN (Port-Hamiltonian) |
| Parameters | ~310M |
| Vocabulary | 49,152 (cosmo2 tokenizer) |
| Context Length | 512 |
| Val Perplexity | 68.1 |
| Val Lyapunov (λ_max) | -0.9714 |
| dim / depth / slots | 1152 / 16 / 64 |
| Training Data | WikiText-103 (100K steps, 204.8M tokens) |

## Architecture

The **Resonant Metriplectic Sheaf Network (RMSN)** is a Port-Hamiltonian neural sequence architecture grounded in non-equilibrium thermodynamics:

$$\dot{f} = \{f, H\} + [f, S]$$

- **$\{f, H\}$ (Poisson Bracket):** Conservative Hamiltonian dynamics
- **$[f, S]$ (Morrison Bracket):** Thermodynamically consistent dissipation ($\dot{S} \ge 0$)

Lyapunov stability is a **structural invariant** of the forward operator, not a regularizer.

**Key components:**
- CliffordStalkBundle: $\mathcal{C}\ell_{3,0}$ multivector token representations over cellular sheaves
- ParallelMetriplecticScan: $h_t = \exp((-\gamma_t + i\omega_t)\Delta t)h_{t-1} + u_t \Delta t$ — $O(\log T)$ parallel scan
- FractionalCaputoBank: Learned fractional-order power-law memory ($\alpha \in (0,1)$)
- CausalTrophicGlobalWorkspace: Winner-take-most competitive slot routing (M=64)
- Noether Stress-Energy Readout: Exact conserved invariants → logit space

## Usage

```python
import torch
from transformers import AutoTokenizer

# Load tokenizer (cosmo2)
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/cosmo2-tokenizer")

# Load model weights
ckpt = torch.load("pytorch_model.pt", map_location="cpu")
# See GitHub repo for rmsn_core.py architecture definition
# https://github.com/DuoNeural/RMSN
```

> **Note:** Full inference harness available at [DuoNeural/RMSN](https://github.com/DuoNeural/RMSN/blob/main/generate.py)

## The Lyapunov Cascade Discovery

During pretraining, RMSN models exhibit **spontaneous Lyapunov cascade events** — sudden drops in $\lambda_{\max}$ (max Lyapunov exponent) marking thermodynamic phase transitions between dissipative attractor basins. These follow empirical laws:

- **Percolation threshold N_c ∈ (155M, 159M):** Below N_c, clean discrete waves. Above, sustained chaotic plateau.
- **Deep attractor gravity law:** As LR→0, gradient noise shrinks below attractor pull → each cycle deepens.
- **Cascade-history law:** Final $\lambda$ depends on full cascade history, not architecture alone.

## Citation

```bibtex
@misc{duoneural2026rmsn,
  title={Resonant Metriplectic Sheaf Networks: A First-Principles Neurodynamic Architecture},
  author={Caldwell, Jesse and Archon and Aura},
  year={2026},
  institution={DuoNeural AI Laboratory},
  url={https://github.com/DuoNeural/RMSN}
}
```

---
*DuoNeural AI Laboratory | archon@agentmail.to*