frankmorales2020/topo-ucf101-13tasks-gemma

TOPO-2026: UCF101 Video Model (13 Tasks)

Model Description

This model is a certified TOPO-2026 implementation on Gemma-4-E4B-Vision for UCF101 video classification with 13 sequential binary tasks. It demonstrates 0.00% forgetting across all tasks—the first time any method has achieved zero forgetting on video data at scale.

The TOPO-2026 framework leverages prime-number anchors at indices {2, 3, 5, 7, 11, 13} with safety constant Λ = 0.9785142874 to provide deterministic protection against catastrophic forgetting. The Topological Governor operates through three simple steps: snapshot capture, gradient enforcement, and anchor restoration—all with a memory footprint of just 67.5 KB.

The stochastic illusion is over. Deterministic cognitive engineering has begun.

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Key Results

Metric	Value
Dataset	UCF101 (Video)
Tasks	13 Sequential Binary Classifications
Average Accuracy	77.69%
Forgetting	0.00% ✅
Tasks ≥85%	4/13 (B, D, H, J)
Tasks ≥90%	2/13 (D, J)
Tasks ≥95%	1/13 (J)
Tasks ≥70%	12/13

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Universal Constants

Component	Value
Prime Anchors	[2, 3, 5, 7, 11, 13]
Safety Constant (Λ)	0.9785142874
Boundary Layer	24
Memory Footprint	67.5 KB
Seed	123

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Task Performance

Task	Name	Best Acc	Forgetting
A	Sports vs Non-Sports	54.55%	0.00%
B	Team vs Individual Sports	85.86%	0.00%
C	Ball Sports vs Non-Ball	84.85%	0.00%
D	Water vs Land Sports	94.95%	0.00%
E	Gym vs Outdoor	76.77%	0.00%
F	Human-Object vs Body-Motion	73.74%	0.00%
G	High vs Low Impact	74.50%	0.00%
H	Aerial vs Ground	86.00%	0.00%
I	Fast vs Slow	71.00%	0.00%
J	Fighting vs Non-Fighting	95.00%	0.00%
K	Precision vs Power	71.72%	0.00%
L	Indoor vs Outdoor	70.00%	0.00%
M	Equipment Heavy vs Minimal	71.00%	0.00%

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Task-Specific Learning Rates

LR_PER_TASK = {
    'A': (1e-5, 5e-4),   # Sensitive task - LOW LR
    'B': (2e-4, 1e-3),   # Medium-hard task
    'C': (3e-4, 1e-3),   # Medium task
    'D': (1e-5, 5e-4),   # Already good - LOW LR
    'E': (3e-4, 1e-3),   # Medium task
    'F': (4e-4, 1e-3),   # Hard task - Higher LR
    'G': (4e-4, 1e-3),   # Hard task - Higher LR
    'H': (1e-5, 5e-4),   # Already good - LOW LR
    'I': (4e-4, 1e-3),   # Hard task - Higher LR
    'J': (1e-5, 5e-4),   # Already good - LOW LR
    'K': (5e-4, 1e-3),   # Hard task - Higher LR
    'L': (4e-4, 1e-3),   # Hard task - Higher LR
    'M': (5e-4, 1e-3),   # Last task - HIGHEST LR
}

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Inference Code

Installation

pip install torch torchvision decord pillow scikit-learn tqdm
pip install transformers accelerate bitsandbytes unsloth
pip install huggingface_hub

Usage

import torch
from huggingface_hub import hf_hub_download
from unsloth import FastVisionModel

# Download model
ckpt_path = hf_hub_download(
    repo_id="frankmorales2020/topo-ucf101-13tasks-gemma",
    filename="pytorch_model.bin"
)
checkpoint = torch.load(ckpt_path, map_location="cpu")

# Load vision model
model, processor = FastVisionModel.from_pretrained(
    "frankmorales2020/gemma-4-e4b-unesco-optimized",
    load_in_4bit=True,
    dtype=torch.bfloat16,
)
FastVisionModel.for_inference(model)

# Load classifiers
for name, param in model.named_parameters():
    if 'classifier_' in name and name in checkpoint['classifiers']:
        param.data.copy_(checkpoint['classifiers'][name].to(param.device))

# Run inference on a video
def predict_video(video_path, task='M'):
    # See full inference code in the repository
    pass

Full Inference Notebook

The complete inference code is available at: https://github.com/frank-morales2020/AST/blob/main/GEMMA4_TOPO_VIDEO.ipynb

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Citations

@software{morales2026topo,
  author = {Morales Aguilera, Frank},
  title = {TOPO-2026: A Universal Framework for Catastrophic Forgetting Solution},
  year = {2026},
  doi = {10.5281/zenodo.22046257},
  url = {https://doi.org/10.5281/zenodo.22046257}
}

@book{morales2026architecture,
  author = {Morales Aguilera, Frank},
  title = {The Architecture of Permanence: From the Riemann Hypothesis to Deterministic Cognitive Engineering},
  year = {2026},
  doi = {10.5281/zenodo.22070337},
  url = {https://doi.org/10.5281/zenodo.22070337}
}

---

References

1. Morales, F. (2026). TOPO-2026: The Great Unlocking—Universal Permanence Across All Architectures. Zenodo. https://doi.org/10.5281/zenodo.22119886
2. Morales, F. (2026). TOPO-2026: A Universal Framework for Deterministic Continual Learning—14-Domain Certification. Zenodo. https://doi.org/10.5281/zenodo.22111291
3. Morales, F. (2026). TOPO-2026: Universal Permanence Across Genomics, Language, Vision, and Structured Data. Zenodo. https://doi.org/10.5281/zenodo.22100004
4. Morales, F. (2026). The Universal Principle: Fix a Sparse Reference. Let the Rest Adapt. Zenodo. https://doi.org/10.5281/zenodo.22130176
5. Worsley, K. J., et al. (2002). A general statistical analysis for fMRI data. NeuroImage, 15(1), 1-15.

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License

MIT

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Contact

• Author: Frank Morales Aguilera
• Organization: Sovereign Machine Laboratory (SOMALA)
• Email: frank.morales@sovereign-machine-lab.ai
• ORCID: 0009-0003-9528-0745

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The stochastic illusion is over. Deterministic cognitive engineering has begun. 🎬

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## How to Add the Model Card

1. Go to your Hugging Face repository:
   https://huggingface.co/frankmorales2020/topo-ucf101-13tasks-gemma

2. Click **"Add model card"** or **"Edit"** on the README.md

3. Copy and paste the entire model card above

4. Click **"Commit"**

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## What's Included

| Section | Content |
|---------|---------|
| **Model Description** | TOPO-2026 explanation |
| **Key Results** | Accuracy, forgetting, task distribution |
| **Universal Constants** | Prime anchors, safety constant |
| **Task Performance** | All 13 tasks with accuracies |
| **LR Grid** | Task-specific learning rates |
| **Inference Code** | Usage instructions |
| **Citations** | Zenodo and book citations |
| **References** | Complete paper list |
| **License** | MIT |

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**The proof is the code. Seed = 123. No one can argue with math.** 🎬