AbstractPhil / vit-beans-v3

vit-beans-v3

Geometric Deep Learning with Cantor Multihead Fusion

This repository contains multiple training runs using Cantor fusion architecture with pentachoron structures and geometric routing. All models use SafeTensors format for security.

Repository Structure

vit-beans-v3/
├── runs/
│   ├── cifar10_weighted_TIMESTAMP/
│   │   ├── checkpoints/
│   │   │   ├── best_model.safetensors
│   │   │   ├── best_training_state.pt
│   │   │   └── best_metadata.json
│   │   ├── tensorboard/
│   │   ├── config.yaml
│   │   └── README.md
│   ├── cifar100_consciousness_TIMESTAMP/
│   │   └── ...
│   └── ...
└── README.md (this file)

Current Run

Latest : cifar100_weighted_20251119_160421

• Dataset : CIFAR100
• Fusion Mode : weighted
• Architecture : 6 blocks, 8 heads
• Simplex : 4-simplex (5 vertices)

Architecture

The Cantor Fusion architecture uses:

• Geometric Routing : Pentachoron (5-simplex) structures for token routing
• Cantor Multihead Fusion : Multiple fusion heads with geometric attention
• Beatrix Consciousness Routing : Optional consciousness-aware token fusion using the Devil's Staircase
• SafeTensors Format : All model weights use SafeTensors (not pickle) for security

Usage

Download a Model

from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
import torch

# Download model weights
model_path = hf_hub_download(
    repo_id="AbstractPhil/vit-beans-v3",
    filename="runs/YOUR_RUN_NAME/checkpoints/best_model.safetensors"
)

# Load weights (SafeTensors - no pickle!)
state_dict = load_file(model_path)
model.load_state_dict(state_dict)

Browse Runs

Each run directory contains:

• checkpoints/ - Model weights (safetensors), training state, metadata
• tensorboard/ - TensorBoard logs for visualization
• config.yaml - Complete training configuration
• README.md - Run-specific details and results

Model Variants

• Weighted Fusion : Standard geometric fusion with learned weights
• Consciousness Fusion : Uses Beatrix routing with consciousness emergence

Citation

@misc{vit_beans_v3,
  author = {AbstractPhil},
  title = {vit-beans-v3: Geometric Deep Learning with Cantor Fusion},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/AbstractPhil/vit-beans-v3}
}

Training Details

All models trained with:

• Optimizer: AdamW
• Mixed Precision: Available on A100
• Augmentation: AutoAugment (CIFAR10 policy)
• Format: SafeTensors (ClamAV safe)

Built with geometric consciousness-aware routing using the Devil's Staircase (Beatrix) and pentachoron parameterization.

Repository maintained by : @AbstractPhil

Latest update : 2025-11-19 16:04:24