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DepthMuun/gfn-isn-shakespeare-char
gfn-isn-shakespeare-char is a text generation model from DepthMuun. Use it when you need the model to write or continue text. It is set up for gfn. The card lists the license as apache-2.0.
[](https://doi.org/10.5281/zenodo.19141132) [](https://huggingface.co/DepthMuun) [](https://github.com/DepthMuun/gfn)
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From the Hugging Face model README
This repository contains the weights and configuration for a Shakespeare-style language model built using the Inertial State Network (ISN) architecture.
To run inference locally, you need the GFN Framework installed.
pip install gfn==v2.7.0
git lfs install
git clone https://huggingface.co/DepthMuun/gfn-isn-shakespeare-char
cd gfn-isn-shakespeare-char
The repository includes an inference.py script for interactive generation:
python inference.py
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
If you use this work, please cite:
@article{sturtz2026gfn,
title = {Geometric Flow Networks: A Physics-Informed Paradigm for Sequential Intelligence},
author = {St{\"u}rtz, Joaqu{\'i}n},
journal = {GFN Research Preprint},
year = {2026},
publisher = {DepthMuun Research},
doi = {10.5281/zenodo.19141132},
url = {https://doi.org/10.5281/zenodo.19141132}
}