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README.md

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+---
+license: mit
+library_name: pytorch
+tags:
+  - rubiks-cube
+  - reinforcement-learning
+  - deepcubea
+  - pytorch
+pipeline_tag: other
+---
+
+# DeepCube — Cube3 (3×3×3) cost-to-go network
+
+PyTorch weights for a DeepCubeA-style cost-to-go network that solves the 3×3×3 Rubik's cube via weighted A\*.
+
+- **Input**: one-hot encoded cube state (324 dims = 54 stickers × 6 colors)
+- **Output**: scalar cost-to-go estimate (predicted moves to solved state)
+- **Architecture**: MLP — see `deepcube/model.py` in the source repo
+- **Training**: Approximate Value Iteration on random scrambles (see `train.ipynb`)
+- **Source code**: https://github.com/ac1982/deepcube
+
+## Files
+
+- `deepcube_cube3.pt` — final weights (keys: `net`, `cfg`, `loss_hist`, `elapsed`)
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+from deepcube.model import load_checkpoint
+
+ckpt_path = hf_hub_download("alexever/deepcube-cube3", "deepcube_cube3.pt")
+model, cfg = load_checkpoint(ckpt_path)
+```
+
+Or via the bundled server, which auto-loads from `checkpoints/deepcube_cube3.pt`:
+
+```bash
+huggingface-cli download alexever/deepcube-cube3 deepcube_cube3.pt \
+    --local-dir checkpoints
+python -m deepcube.server
+```
+
+## License
+
+MIT — see source repository.