HighNoon Language Framework — Lite Edition

Important

CPU-Native Architecture: HighNoon is purpose-built for pure CPU execution. No GPU required. Run production AI on commodity hardware—from data centers to edge deployments.

Note

Binary Distribution: The Lite Edition includes pre-compiled, hardened binaries. Users run the setup script and immediately start building models through the WebUI—no compilation required.

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🔗 Community & Resources

• 🤗 HuggingFace: huggingface.co/versoindustries — Pre-trained models and datasets
• 📖 Documentation: docs/ — Architecture guides and API reference
• 💬 Contact: versoindustries.com/messages — Enterprise inquiries and support

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🚀 What is HighNoon?

HighNoon is the Lite Edition of the HSMN (Hierarchical State-Space Model Network) Architecture—a radical departure from GPU-dependent Transformer models. Built on Hyperdimensional Computing and quantum-inspired algorithms, HighNoon delivers:

• O(L) Linear Complexity — Process 5 million token contexts without quadratic scaling
• Zero GPU Dependency — Full-stack execution on commodity CPUs (Intel Xeon, AMD EPYC)
• 100–200x Lower Energy — Run inference on 100W servers instead of 700W GPU clusters
• 50–100x Faster Generation — Quantum Superposition Generation (QSG) parallel decoding

The CPU-Native Paradigm

Traditional AI demands H100 GPUs at $25,000–40,000 each, megawatt data centers, and 12+ month procurement timelines. HighNoon runs on hardware you already own:

Aspect	Traditional (GPU)	HighNoon (CPU)
Hardware Cost	$350,000/node (8×H100)	$30,000/node (Dual EPYC)
Power Draw	10kW per node	~500W per node
Procurement Time	12–52 weeks	Available now
Export Restrictions	Subject to ITAR/EAR	Commodity hardware

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⚡ Quick Start

1. Clone & Setup

git clone https://github.com/versoindustries/HighNoon-Language-Framework.git
cd HighNoon-Language-Framework

# Run the setup script (creates venv, installs dependencies)
./scripts/setup.sh

2. Launch the WebUI

# Start the React Dashboard + FastAPI Backend
./scripts/launch_webui.sh

Access the WebUI at http://localhost:5173

3. Build Your First Curriculum

The WebUI provides an integrated workflow for training language models:

1. Select a Template — Choose from pre-built curriculum presets (General Pre-training, Coding, Math/Reasoning, etc.)
2. Browse HuggingFace Datasets — Search and add datasets directly from the HuggingFace Hub
3. Configure Training — Set model architecture, hyperparameters, and optimization settings
4. Launch Training — Start QAHPO (Quantum Adaptive HPO) to automatically optimize your model

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🖥️ The WebUI: Curriculum Builder

HighNoon's WebUI is the primary interface for building and training models. It features deep HuggingFace Hub integration for seamless dataset access.

Core Features

Feature	Description
Template Gallery	Pre-built curriculum presets with curated HuggingFace datasets
Dataset Browser	Search, preview, and add any HuggingFace dataset
Curriculum Builder	Drag-and-drop stage management with dataset mixing
QAHPO Dashboard	Real-time hyperparameter optimization with importance analysis
Cockpit HUD	Live training metrics, loss charts, and QULS health indicators
Model Export	Save and export trained models

Pre-Built Curriculum Templates

Template	Focus	Example Datasets
General Pre-training	Broad knowledge	FineWeb, Cosmopedia, OpenWebMath
Code Expert	Programming	The Stack v2, CodeContests, CodeSearchNet
Math & Reasoning	STEM skills	FineMath, GSM8K, NuminaMath
Instruction Following	Assistant behavior	OpenAssistant, UltraChat, Orca
Sovereign Defense	Air-gapped deployments	Custom classified data

HuggingFace Integration

The WebUI connects directly to the HuggingFace Hub API:

• Search datasets by name, task type, or license
• Preview samples before adding to curriculum
• Stream training data directly from HuggingFace servers
• Automatic format detection for text, chat, and instruction formats

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🏗️ Architecture: HSMN

The Hierarchical State-Space Model Network achieves linear complexity through four synergistic pillars:

                      ┌─────────────────────────────────────────┐
                      │          Reasoning Block × N            │
Input Tokens ──►      │  ┌─────────────────────────────────┐   │
                      │  │  HD Spatial Block (O(L·D log D))│   │
   Holographic        │  │  FFT Bundling • CTQW Spreading  │   │
   Embedding  ──►     │  └─────────────────────────────────┘   │
                      │  ┌─────────────────────────────────┐   │
   Floquet            │  │  HD TimeCrystal Block           │   │ ──► Output
   Position   ──►     │  │  Floquet Dynamics • Symplectic  │   │     Logits
                      │  └─────────────────────────────────┘   │
   Superposition      │  ┌─────────────────────────────────┐   │
   BPE        ──►     │  │  LMWT Attention (O(L log L))    │   │
                      │  │  Multi-Scale Wavelet Transform  │   │
                      │  └─────────────────────────────────┘   │
                      │  ┌─────────────────────────────────┐   │
                      │  │  HD-MoE (O(D) per token)        │   │
                      │  │  Holographic Similarity Routing │   │
                      │  └─────────────────────────────────┘   │
                      └─────────────────────────────────────────┘

Core Components

Component	Function	Complexity
SpatialHDblock	Hyperdimensional state-space with FFT bundling	O(L · D log D)
HD TimeCrystal	Floquet Hamiltonian dynamics for 100+ layer stability	O(L · D)
LMWT Attention	Learnable Multi-scale Wavelet Transform	O(L log L)
HD-MoE	Holographic Mixture-of-Experts routing	O(D) per token
QSG Inference	Quantum Superposition parallel decoding	50–100x speedup

Why It Works

• Hyperdimensional Embeddings: Dense holographic vectors replace sparse attention matrices
• Physics-Aware Training: Hamiltonian energy conservation prevents gradient instability
• Quantum Simulation: Superposition, entanglement, and Born-rule sampling—on classical CPUs

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📊 Lite Edition Specifications

Caution

Enforced Limits: The Lite Edition includes tamper-proof compiled binaries with cryptographic integrity checks. These limits cannot be bypassed.

Feature	Lite Edition	Enterprise Edition
Architecture	Full HSMN	Full HSMN
Max Parameters	20B	Unlimited
Reasoning Blocks	24	Unlimited
MoE Experts	12	Unlimited
Superposition Dimension	4	Unlimited
Context Length	5M tokens	Unlimited
Binary Modification	❌ Protected	✅ Configurable
Support	Community	Dedicated

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📈 Performance

Benchmarked on AMD Ryzen 7 2700X (8 cores, 64GB RAM, no GPU):

Metric	Result	Notes
Streaming Inference	174–181 tok/s	O(1) memory across all context lengths
Batch=2 Inference	342–345 tok/s	Near-linear scaling
Context Scaling	✅ Constant	128 → 131K → 5M tokens with no degradation
Memory (1M context)	~3.8 GB	Fits in commodity server RAM
SIMD Optimization	✅ AVX2/FMA	32-bit optimized for cache efficiency

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🏢 Enterprise Edition

For production deployments requiring unlimited scale:

• Unlimited Parameters — Train and deploy models of any size
• Unlimited Context — Beyond 5M tokens for full-document analysis
• Source Code License — Full ownership for sovereign deployments
• Domain Modules — Finance, Healthcare, Legal, Defense
• Technology Transfer — On-premise training with air-gapped security

Contact: versoindustries.com/messages

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📄 License

Apache License 2.0 — See LICENSE for details.

The compiled native binaries are provided as-is under the Lite Edition terms.

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🙏 Acknowledgments

HighNoon is developed by Verso Industries and builds upon research in:

• State-Space Models (Mamba, S4)
• Hyperdimensional Computing
• Quantum-Inspired Machine Learning
• Hamiltonian Neural Networks

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Built with ❤️ by Verso Industries
Sovereign AI for the Post-GPU Era