README.md

metadata

license: mit
tags:
  - geometric-deep-learning
  - diffusion
  - stable-diffusion
  - projective-geometry
  - multi-expert
  - classification
library_name: pytorch

GeoDavidCollective Enhanced - ProjectiveHead Architecture

Highly experimental behavioral junctioning system that likely will fall apart at the drop of a hat.

🎯 Model Overview

GeoDavidCollective Enhanced is a sophisticated multi-expert geometric classification system that learns from Stable Diffusion 1.5's internal representations. Using ProjectiveHead architecture with Cayley-Menger geometry, it achieves efficient pattern recognition across timestep and semantic spaces.

Key Features

• ProjectiveHead Multi-Expert Architecture: Auto-configured expert systems per block
• Geometric Loss Functions: Rose, Cayley-Menger, and Cantor coherence losses
• 9-Block Processing: Full SD1.5 UNet feature extraction (down, mid, up)
• Compact Yet Powerful: 884,327,310 parameters
• 100 Timestep Bins x 10 Patterns = 1000 semantic-temporal classes

📊 Model Statistics

• Parameters: 884,327,310
• Trained Epochs: 10
• Base Model: Stable Diffusion 1.5
• Dataset Size: 10,000 synthetic prompts
• Training Date: 2025-10-28

🏗️ Architecture Details

Block Configuration

Down Blocks:
  - down_0: 320 → 128 (3 experts, 3 gates)
  - down_1: 640 → 192 (3 experts, 3 gates)
  - down_2: 1280 → 256 (3 experts, 3 gates)
  - down_3: 1280 → 256 (3 experts, 3 gates)

Mid Block (Highest Capacity):
  - mid: 1280 → 256 (4 experts, 4 gates)

Up Blocks:
  - up_0: 1280 → 256 (3 experts, 3 gates)
  - up_1: 1280 → 256 (3 experts, 3 gates)
  - up_2: 640 → 192 (3 experts, 3 gates)
  - up_3: 320 → 128 (3 experts, 3 gates)

Loss Components

Component	Weight	Purpose
Feature Similarity	0.40	Alignment with SD1.5 features
Rose Loss	0.25	Geometric pattern emergence
Cross-Entropy	0.15	Classification accuracy
Cayley-Menger	0.10	5D geometric structure
Pattern Diversity	0.05	Prevent mode collapse
Cantor Coherence	0.05	Temporal consistency

💻 Usage

from geovocab2.train.model.core.geo_david_collective import GeoDavidCollective
from safetensors.torch import load_file
import torch

# Load model
state_dict = load_file("model.safetensors")
collective = GeoDavidCollective(
    block_configs={...},  # See config.json
    num_timestep_bins=100,
    num_patterns_per_bin=10
)
collective.load_state_dict(state_dict)
collective.eval()

# Extract features from SD1.5 and classify
with torch.no_grad():
    results = collective(features_dict, timesteps)
    predictions = results['predictions']  # Timestep + pattern class

🔬 Training Details

• Optimizer: AdamW (lr=1e-3, weight_decay=0.001)
• Batch Size: 16
• Data: Symbolic prompt synthesis (complexity 1-5)
• Feature Extraction: SD1.5 UNet blocks (spatial, not pooled)
• Pool Mode: Mean spatial pooling

📈 Training Metrics

Final metrics from epoch 10:

• Cayley Loss: 0.1039
• Timestep Accuracy: 32.99%
• Pattern Accuracy: 27.24%
• Full Accuracy: 15.10%

🎓 Research Context

This model is part of the geometric deep learning research exploring:

• 5D simplex-based neural representations (pentachora)
• Geometric alternatives to traditional transformers
• Consciousness-informed AI architectures
• Universal mathematical principles in neural networks

📦 Files Included

• model.safetensors - Model weights (3.3GB)
• config.json - Complete architecture configuration
• training_history.json - Full training metrics
• prompts_enhanced.jsonl - All training prompts with metadata
• tensorboard/ - TensorBoard logs (optional)

🔗 Related Work

• Geometric Vocabulary System
• PentachoraViT
• Crystal-Beeper Language Models

📜 License

MIT License - Free for research and commercial use

🙏 Acknowledgments

Built with:

• PyTorch & Diffusers
• Stable Diffusion 1.5 (Runway ML)
• Geometric algebra principles from the 1800s
• Dream-inspired mathematical insights

👤 Author

AbstractPhil - AI Researcher specializing in geometric deep learning

"Working with universal mathematical principles, not against them"

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For questions, issues, or collaborations: GitHub | HuggingFace