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README.md

@@ -1,52 +1,52 @@
-  ---
-  license: apache-2.0
-  library_name: lerobot
-  base_model: lerobot/smolvla_base
-  datasets:
-    - lerobot/svla_so101_pickplace
-  tags:
-    - robotics
-    - lerobot
-    - smolvla
-    - vision-language-action
-    - so-101
-    - imitation-learning
-  pipeline_tag: robotics
-  ---
-
-  # SmolVLA · SO-101 Pick-and-Place (fine-tuned)
-
-  Fine-tune of [`lerobot/smolvla_base`](https://huggingface.co/lerobot/smolvla_base) (450M VLA)
-  on the real-robot SO-101 dataset
-  [`lerobot/svla_so101_pickplace`](https://huggingface.co/datasets/lerobot/svla_so101_pickplace)
-  (50 teleoperated episodes, 2 cameras `up`/`side`, 6-DoF state).
-  [`lerobot/svla_so101_pickplace`](https://huggingface.co/datasets/lerobot/svla_so101_pickplace)
-  [`lerobot/svla_so101_pickplace`](https://huggingface.co/datasets/lerobot/svla_so101_pickplace)
-  [`lerobot/svla_so101_pickplace`](https://huggingface.co/datasets/lerobot/svla_so101_pickplace)
-  (50 teleoperated episodes, 2 cameras `up`/`side`, 6-DoF state).
-
-  ## Training
-
-  | | |
-  |---|---|
-  | Base | `lerobot/smolvla_base` |
-  | Dataset | `lerobot/svla_so101_pickplace` (50 ep / 11,939 frames) |
-  | Steps | 2,000 (batch size 8) |
-  | GPU | single T4 (~78 min) |
-  | Camera mapping | `up to camera1`, `side to camera2` via `--rename_map` |
-  | Loss | 0.410 to 0.141 (monotonic) |
-
-  ## Intended use & honest limitations
-
-  This is a **pipeline-validation / learning run**, not a production policy.
-
-  - Demonstrates the full real-robot imitation-learning loop: load a real teleoperation dataset, fine-tune a pretrained VLA, converge, ship a checkpoint.
-  - Only 2,000 steps (~1.3 epochs). The SmolVLA paper uses ~20k steps; expect this checkpoint to under-perform a fully trained one.
-  - No closed-loop success rate. Evaluation on the physical SO-101 arm (`lerobot-record`) was not run (no hardware). Reported signal is training-loss
-  convergence only, which proves the model is learning, not real-world task success.
-
-  ## Load
-
-  ```python
-  from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
-  policy = SmolVLAPolicy.from_pretrained("Kaminoikari/smolvla-so101-pickplace-ft")
+---
+license: apache-2.0
+library_name: lerobot
+base_model: lerobot/smolvla_base
+datasets:
+  - lerobot/svla_so101_pickplace
+tags:
+  - robotics
+  - lerobot
+  - smolvla
+  - vision-language-action
+  - so-101
+  - imitation-learning
+pipeline_tag: robotics
+---
+
+# SmolVLA · SO-101 Pick-and-Place (fine-tuned)
+
+Fine-tune of [`lerobot/smolvla_base`](https://huggingface.co/lerobot/smolvla_base) (450M VLA)
+on the real-robot SO-101 dataset
+[`lerobot/svla_so101_pickplace`](https://huggingface.co/datasets/lerobot/svla_so101_pickplace)
+(50 teleoperated episodes, 2 cameras `up`/`side`, 6-DoF state).
+[`lerobot/svla_so101_pickplace`](https://huggingface.co/datasets/lerobot/svla_so101_pickplace)
+[`lerobot/svla_so101_pickplace`](https://huggingface.co/datasets/lerobot/svla_so101_pickplace)
+[`lerobot/svla_so101_pickplace`](https://huggingface.co/datasets/lerobot/svla_so101_pickplace)
+(50 teleoperated episodes, 2 cameras `up`/`side`, 6-DoF state).
+
+## Training
+
+| | |
+|---|---|
+| Base | `lerobot/smolvla_base` |
+| Dataset | `lerobot/svla_so101_pickplace` (50 ep / 11,939 frames) |
+| Steps | 2,000 (batch size 8) |
+| GPU | single T4 (~78 min) |
+| Camera mapping | `up to camera1`, `side to camera2` via `--rename_map` |
+| Loss | 0.410 to 0.141 (monotonic) |
+
+## Intended use & honest limitations
+
+This is a **pipeline-validation / learning run**, not a production policy.
+
+- Demonstrates the full real-robot imitation-learning loop: load a real teleoperation dataset, fine-tune a pretrained VLA, converge, ship a checkpoint.
+- Only 2,000 steps (~1.3 epochs). The SmolVLA paper uses ~20k steps; expect this checkpoint to under-perform a fully trained one.
+- No closed-loop success rate. Evaluation on the physical SO-101 arm (`lerobot-record`) was not run (no hardware). Reported signal is training-loss
+convergence only, which proves the model is learning, not real-world task success.
+
+## Load
+
+```python
+from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
+policy = SmolVLAPolicy.from_pretrained("Kaminoikari/smolvla-so101-pickplace-ft")