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README.md

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+---
+license: mit
+task_categories:
+  - video-classification
+  - reinforcement-learning
+tags:
+  - world-model
+  - nes
+  - super-mario-bros
+  - game-ai
+size_categories:
+  - 100K<n<1M
+---
+
+# SMB World Model Training Data
+
+Training data for a Super Mario Bros world model using the Titan memory architecture.
+
+## Dataset Description
+
+- **118,166 frames** from 8 TAS (Tool-Assisted Speedrun) playthroughs
+- **Frame size:** 224x256x3 (RGB)
+- **Action space:** 8 buttons [Up, Down, Left, Right, A, B, Start, Select]
+- **Format:** Compressed `.npz` files (each contains frame + action bundled together)
+- **Total size:** ~293MB (compressed)
+
+## TAS Files Used
+
+| TAS File | Description |
+|----------|-------------|
+| smb_all_items | Collects all items |
+| smb_low_percent | Minimal item collection |
+| smb_max_coins | Maximum coins |
+| smb_max_score | Maximum score |
+| smb_min_a_presses | Minimal A button presses |
+| smb_scoreless | Zero score run |
+| smb_warpless | No warp zones |
+| smb_warps | Using warp zones |
+
+## Data Format
+
+Each `.npz` file contains:
+```python
+data = np.load("frame_000000.npz")
+frame = data['frame']   # shape: (224, 256, 3), dtype: uint8
+action = data['action'] # shape: (8,), dtype: float32
+```
+
+Action order: `[Up, Down, Left, Right, A, B, Start, Select]`
+
+## Usage
+
+```python
+from huggingface_hub import hf_hub_download
+import zipfile
+
+# Download
+path = hf_hub_download(
+    repo_id="DylanRiden/smb-worldmodel-data",
+    filename="smb_frames.zip",
+    repo_type="dataset"
+)
+
+# Extract
+with zipfile.ZipFile(path, 'r') as z:
+    z.extractall("./nes_data")
+```
+
+## Collection Method
+
+- Emulator: FCEUX with Lua scripting
+- Frame skip: Every 4th frame (15fps from 60fps)
+- Menu skip: First 250 frames skipped
+- Real-time conversion to bundled `.npz` format
+
+## License
+
+MIT