Instructions to use cagataydev/smolvla_tictactoe_vision_unfrozen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use cagataydev/smolvla_tictactoe_vision_unfrozen 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=cagataydev/smolvla_tictactoe_vision_unfrozen \ --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=cagataydev/smolvla_tictactoe_vision_unfrozen - Notebooks
- Google Colab
- Kaggle
SmolVLA TicTacToe -- Vision Unfrozen (60K steps)
Fine-tuned SmolVLA with vision encoder unfrozen (393M trainable params) on TicTacToe manipulation data.
Training Recipe
- Base: lerobot/smolvla_base
- Steps: 60,000 / 60,000
- Trainable params: 392,904,096 (393M) -- full model including vision
- Samples seen: 480,000 (~3.32 epochs)
- Duration: 640.4 min (~10h40m)
- Final loss: 0.078
- Final gradient norm: 2.175
- LR (final): 2.5e-06
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Notes
Vision encoder was not frozen during fine-tuning, allowing full-model adaptation. Compare with frozen-vision baseline for ablation.
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