Add model card

README.md

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+---
+license: mit
+tags:
+  - world-model
+  - dreamerv3
+  - binary-arithmetic
+  - mechanistic-interpretability
+---
+
+# A World Model That Learned Perfect Binary Arithmetic
+
+DreamerV3 world model trained on a 4-bit binary counting environment (500K steps). The model learned to simulate carry cascades autonomously — 100% completion rate under full observation ablation.
+
+**Paper**: [GitHub](https://github.com/major-scale/anim-binary-counting)
+
+## Files
+
+| File | Description | Size |
+|------|-------------|------|
+| `latest.pt` | Full DreamerV3 checkpoint (PyTorch) | 136 MB |
+| `exported/dreamer_weights.bin` | Extracted weight matrices for numpy RSSM | 23 MB |
+| `exported/dreamer_manifest.json` | Weight name mapping | 4 KB |
+| `battery.npz` | Pre-collected hidden states from 15 episodes | 25 MB |
+| `metrics.jsonl` | Training metrics log | 79 KB |
+
+## Usage
+
+The analysis scripts use the exported weights (no PyTorch required):
+
+```bash
+git clone https://github.com/major-scale/anim-binary-counting
+cd anim-binary-counting
+
+# Download exported weights
+mkdir -p checkpoints/exported
+wget https://huggingface.co/major-scale/anim-binary-counting/resolve/main/exported/dreamer_weights.bin -O checkpoints/exported/dreamer_weights.bin
+wget https://huggingface.co/major-scale/anim-binary-counting/resolve/main/exported/dreamer_manifest.json -O checkpoints/exported/dreamer_manifest.json
+wget https://huggingface.co/major-scale/anim-binary-counting/resolve/main/battery.npz -O data/battery.npz
+
+# Run analysis
+pip install -r code/requirements.txt
+python code/analysis/verify_dual_mode.py
+```
+
+## Training
+
+Trained with [DreamerV3-torch](https://github.com/NM512/dreamerv3-torch) on a single GPU (~4 hours). See `code/training/` in the GitHub repo for configs and launcher.
+
+## License
+
+MIT