Instructions to use fbsh96/rebot_smolvla_flipbread_44eps_20260425_6000steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use fbsh96/rebot_smolvla_flipbread_44eps_20260425_6000steps with LeRobot:
# See https://github.com/huggingface/lerobot?tab=readme-ov-file#installation for more details git clone https://github.com/huggingface/lerobot.git cd lerobot pip install -e .[smolvla]
# Launch finetuning on your dataset python lerobot/scripts/train.py \ --policy.path=fbsh96/rebot_smolvla_flipbread_44eps_20260425_6000steps \ --dataset.repo_id=lerobot/svla_so101_pickplace \ --batch_size=64 \ --steps=20000 \ --output_dir=outputs/train/my_smolvla \ --job_name=my_smolvla_training \ --policy.device=cuda \ --wandb.enable=true
# Run the policy using the record function python -m lerobot.record \ --robot.type=so101_follower \ --robot.port=/dev/ttyACM0 \ # <- Use your port --robot.id=my_blue_follower_arm \ # <- Use your robot id --robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \ # <- Use your cameras --dataset.single_task="Grasp a lego block and put it in the bin." \ # <- Use the same task description you used in your dataset recording --dataset.repo_id=HF_USER/dataset_name \ # <- This will be the dataset name on HF Hub --dataset.episode_time_s=50 \ --dataset.num_episodes=10 \ --policy.path=fbsh96/rebot_smolvla_flipbread_44eps_20260425_6000steps - Notebooks
- Google Colab
- Kaggle
reBot SmolVLA Flip Bread 44 Episodes - 6000 Steps
SmolVLA checkpoint fine-tuned for seeed_b601_dm_follower on merged LeRobot flip-bread-to-pot demonstrations collected on 2026-04-25.
This model continues training from the 3000-step checkpoint:
fbsh96/rebot_smolvla_flipbread_44eps_20260425_3000steps
Schema
observation.state: 7Dobservation.images.front:(3, 480, 640)observation.images.wrist:(3, 480, 640)action: 7D- action chunk:
50 x 7
Joint/action order:
shoulder_pan.pos
shoulder_lift.pos
elbow_flex.pos
wrist_flex.pos
wrist_yaw.pos
wrist_roll.pos
gripper.pos
Training
Merged dataset:
phi-media-lab/rebot_flipbreadtopot_20260425_44eps
44 episodes, 18426 frames, 30 FPS
Training stages:
20 steps: smoke test
1000 steps: 10-episode overfit
3000 steps: 44-episode run
3000 additional steps: continued from 3000-step checkpoint
Effective total on the 44-episode run: 6000 steps.
Final continued-training loss: about 0.036.
Validation
The checkpoint reloads with SmolVLAPolicy.from_pretrained(...) and outputs (1, 50, 7) action chunks.
Observed L20 latency:
- first call: about
535 ms - steady calls: about
149 ms/chunk
Example first actions from three merged-dataset frames:
[2.954437, -14.011684, -24.574974, 9.224384, -3.540721, -13.049671, -92.255066]
[-38.249802, -123.130707, -110.822876, 83.909637, -2.882618, -23.338799, -99.232513]
[2.840845, 0.345646, -4.611893, 11.636082, 1.457067, -3.227002, 5.661499]
Safety
This is a reBot-native overfit validation model, not a certified autonomous control policy. Before real actuator execution, use logging-only validation, clipping, rate limits, joint limits, and emergency stop handling.
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