add model card

README.md

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+---
+license: apache-2.0
+tags:
+  - robotics
+  - robotwin
+  - pi0.5
+  - vla
+---
+
+# RACE RoboTwin — π0.5 t5k showcase checkpoints
+
+Single-task π0.5 (OpenPI) checkpoints on RoboTwin 2.0 (aloha-agilex, `demo_clean`, 50 demos/task), for 6 tasks:
+`pick_dual_bottles`, `move_can_pot`, `place_dual_shoes`, `place_can_basket`, `blocks_ranking_rgb`, `stack_blocks_three`.
+
+All fine-tunes start from the **5k-step teacher** (weak-teacher regime, "t5k").
+
+## Layout
+
+```
+<task>/
+  teacher_ac50/            # 5k-step teacher (JAX -> PyTorch conversion, float32)
+  vlmfreeze_ac50/{5000,10000}/   # phase-loc fine-tune, VLM frozen, action horizon 50
+  vlmfreeze_ac75/{5000,10000}/   # action horizon 75
+  vlmfreeze_ac100/{5000,10000}/  # action horizon 100
+```
+
+Each checkpoint dir has `model.safetensors`, `assets/` (norm stats), and `metadata.pt`
+(training config + step). Optimizer states are omitted.
+
+## Serving / eval
+
+Serve with OpenPI's `scripts/serve_policy.py` (PyTorch path):
+
+```
+python scripts/serve_policy.py --port <P> policy:checkpoint \
+  --policy.config=st_<task>_phase_loc[_h75|_h100] --policy.dir=<downloaded dir>
+```
+
+`--policy.config` must match the action horizon (`st_<t>_phase_loc` = 50,
+`_h75` = 75, `_h100` = 100); evaluate with RoboTwin 2.0 `script/eval_policy.py`
+(`--pi0_step` = the same horizon, `demo_clean`, seed 0, 50 episodes).
+
+Checkpoints are uploaded progressively as trainings/evals complete.