README.md

---
license: cc-by-nc-4.0
tags:
- sudoku
- reasoning
- pytorch
- rhan
---

# PotatoAGI (RHAN-Sudoku)

This is the official weight repository for the **Recurrent Hybrid Attention Network (RHAN)** trained on Sudoku.

It uses a **Universal Linear Attention** mechanism combined with **Recursive Memory** and was trained using **Adversarial Erasure**.

## Stats
- **Parameters:** ~150k
- **Architecture:** 12-Loop Recurrent CNN + Linear Attention
- **Accuracy:** 99% Cell Accuracy / 90%+ Perfect Solve Rate
- **License:** CC BY-NC 4.0 (Non-Commercial Research Use Only)

## Files in this Repository

model.py # Model architecture (UniversalPotato)
model.safetensors # Trained weights
local_test_sudoku.py # Dataset-based local evaluation
README.md

## Usage
### 1️⃣ Install dependencies

```bash
pip install torch safetensors
```

Python ≥ 3.10 recommended.

2️⃣ Load the model and weights

import torch
from safetensors.torch import load_file
from model import UniversalPotato, HIDDEN_DIM

device = "cuda" if torch.cuda.is_available() else "cpu"

model = UniversalPotato().to(device)
model.load_state_dict(load_file("model.safetensors"), strict=True)
model.eval()

3️⃣ Run inference on a single Sudoku puzzle

Sudoku grids are represented as a flat tensor of length 81,
with 0 indicating empty cells.

# Example puzzle (0 = empty)
puzzle = [
    5,3,0,0,7,0,0,0,0,
    6,0,0,1,9,5,0,0,0,
    0,9,8,0,0,0,0,6,0,
    8,0,0,0,6,0,0,0,3,
    4,0,0,8,0,3,0,0,1,
    7,0,0,0,2,0,0,0,6,
    0,6,0,0,0,0,2,8,0,
    0,0,0,4,1,9,0,0,5,
    0,0,0,0,8,0,0,7,9,
]

clues = torch.tensor(puzzle, dtype=torch.long).unsqueeze(0).to(device)
board = clues.clone()
memory = torch.zeros(1, HIDDEN_DIM, 9, 9, device=device)

with torch.no_grad():
    for _ in range(24):  # reasoning steps
        logits, memory = model(
            clues=clues,
            current_board=board,
            memory=memory,
            blindfold=False,
        )
        board = logits.argmax(dim=-1)

solution = board.view(9, 9).cpu()
print(solution)

4️⃣ Dataset-based evaluation

To evaluate the model on a real Sudoku dataset:

    Download sudoku.csv from Kaggle
    👉 https://www.kaggle.com/datasets/rohanrao/sudoku

    Place it in the repository root

    Run:

python local_test_sudoku.py

This script:

    runs multi-step inference

    compares predictions against ground truth

    reports solve success rate

Notes

    This model does not use Hugging Face Transformers

    model.py is the authoritative architecture definition

    Inference requires multiple recurrent steps for best results

    Designed for reasoning research, not commercial deployment

License

This project is released under CC BY-NC 4.0.

You may:

    use

    modify

    redistribute
    for non-commercial research purposes only, with attribution.

Commercial use is not permitted.