# Launch finetuning on your dataset
python lerobot/scripts/train.py \
--policy.path=DecentVLA/smolvla_cubestack_fl_3client \
--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=DecentVLA/smolvla_cubestack_fl_3clientSmolVLA CubeStack — FedAvg 3-client (global)
Federated global model: FedAvg over 3 color-pair clients, 50 rounds x 250 local steps, full fine-tune. Shared pooled-6-repo normalizer. The FL method point vs the centralized ceiling. Final train loss 0.0108.
lerobot-native export (portable config, real 6-D action/state).
SO-101 CubeStack, cameras camera1 (front) / camera2 (wrist), camera3 zero-filled.
Trained on Isambard-AI (GH200) with decent-vla.
Part of the 3-client SmolVLA CubeStack federated study — SAME color-pair non-IID
partition as the pi0.5 3-client study (c0=harry Green/Orange, c1=zhekai Green/Blue,
c2=kevin Orange/Blue; each client blind to the 3rd color). TRUE full fine-tune
(unfrozen VLM, no LoRA), SmolVLA LIBERO recipe (lr 1e-4, cosine, batch 32, grad-clip 10).
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Model tree for DecentVLA/smolvla_cubestack_fl_3client
Base model
lerobot/smolvla_base
# 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]