ISN Shakespeare: Character-level Language Model

DOI: 10.5281/zenodo.19141133 Models: Hugging Face GitHub: GFN Framework

This repository contains the weights and configuration for a Shakespeare-style language model built using the Inertial State Network (ISN) architecture.

πŸš€ Highlights

  • Architecture: Inertial State Network (ISN).
  • Parameters: 363,329 (Equivalent to nanoGPT baseline).
  • Inference Efficiency: Constant $O(1)$ VRAM and $O(1)$ per-step time scaling.
  • Performance: 2.48 PPL on TinyShakespeare (Official).
  • Train seq_lenght: 128

πŸ’» Technical Usage (Inference)

To run inference locally, you need the GFN Framework installed.

1. Install GFN Framework

pip install gfn

2. Clone this repository

git lfs install
git clone https://huggingface.co/DepthMuun/gfn-isn-shakespeare-char
cd gfn-isn-shakespeare-char

3. Run Inference Script

The repository includes an inference.py script for interactive generation:

python inference.py

Python API Example (Manual Assembly)

As shown in the audited inference.py, the official way to load ISN models is:

import torch
import json
from gfn import isn

# 1. Load Config
with open("config.json", "r") as f:
    config = json.load(f)

# 2. Instantiate Model (Manual Schema)
model = isn.create(
    vocab_size=65,  # Unique characters in tinyshakespeare.txt
    d_model=config['model']['d_model'],
    d_embedding=config['model']['d_embedding'],
    d_properties=config['model']['d_properties'],
    scanner_cls=isn.GFNScanner,
    world_cls=isn.GFNWorld,
    emitter_cls=isn.GFNEmitter
).to("cpu")

# 3. Load State Dict
checkpoint = torch.load("best_model.pt", map_location="cpu")
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()

# 4. Generate Text
# Use model.generate() or the provided inference.py CLI

πŸ“œ Citation

If you use this work, please cite:

@article{sturtz2026geometry,
  title={Geometric Flow Networks: A Physics-Informed Paradigm for Sequential Intelligence},
  author={StΓΌrtz, JoaquΓ­n},
  journal={Zenodo Preprints},
  year={2026},
  doi={10.5281/zenodo.19141133},
  url={https://doi.org/10.5281/zenodo.19141133}
}

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