Prannesshkva/QU-SSM-60M-MoE

🏛️ QU-SSM-60M-MoE: Continuous Quasi-Unitary State Space Model with Sparse Mixture-of-Experts

### 📄 Official Research Paper (PDF) & Open Verification Title: "Gated Quasi-Unitary Lie-Algebra Recurrent State Space Models" Author: Prannessh K.V.A. (@prannesshkva) 📥 [Download Full Research Paper (PDF)](https://huggingface.co/Prannesshkva/QU-SSM-60M-MoE/resolve/main/Gated_Quasi_QU_SSM_Paper.pdf) | 🏛️ [Zenodo DOI: 10.5281/zenodo.22283431](https://doi.org/10.5281/zenodo.22283431) | 🎮 [Live Interactive Space](https://huggingface.co/spaces/Prannesshkva/QU-SSM-Scientific-Benchmark-Suite)

     

QU-SSM-60M-MoE is the mid-tier foundation model of the QU-SSM family designed and invented by Prannessh K.V.A. (Sole Architect & Inventor). It combines continuous Lie-group unitary recurrence on SO(2) ≅ U(1) with 4 SwiGLU Mixture-of-Experts (MoE) and Top-2 routing (44.64M active parameters per token).

🧬 Base Model Lineage & Technical Notes

• —Core Architecture: Continuous Quasi-Unitary State Space Model (SO(2) phase rotations) coupled with 4 SwiGLU Mixture-of-Experts and Top-2 routing.
• —Tokenizer Lineage: Standard GPT-2 Byte-Pair Encoding (BPE) vocabulary (50,257 tokens).
• —Pre-training & Calibration: Pre-trained on roneneldan/TinyStories (~20M+ tokens) demonstrating sub-millisecond step latency and exact norm preservation.
• —Parameter Footprint: 64.30M Total Parameters, 44.64M Active Parameters per token.
• —Inference Efficiency: Constant O(1) inference state RAM (0.19 MB) regardless of sequence length.
• —Official Research Contact: LinkedIn — Prannesh K. V. A.

🔍 What is QU-SSM?

QU-SSM is a linear-time continuous sequence engine that replaces the monotonic dissipative decay of classical state space models with non-dissipative SO(2) unitary phase rotations (‖R(θ)‖₂ ≡ 1.00000), delivering strictly constant O(1) inference memory and sub-millisecond step latency.

📊 Architecture Specifications

🔒 Intellectual Property & Citation

• —Sole Architect & Inventor: Prannessh K.V.A.
• —Official Research DOI: **`10.5281/zenodo.22283431`**
• —License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• —Official Contact: LinkedIn — Prannesh K. V. A.

🔗 Related QU-SSM Hub Repositories

• —🚀 QU-SSM-130M-MoE (134.89M Flagship Model)
• —⚡ QU-SSM-60M-MoE (64.30M Mid-Tier Model)
• —🔬 QU-SSM-15M (29.80M Foundation Model)
• —⚛️ Liquid QU-SSM-130M (Continuous-Time LNN)
• —🎮 QU-SSM Scientific Benchmark Suite