Instructions to use 2chan/beverage-smolvla-40k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use 2chan/beverage-smolvla-40k 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=2chan/beverage-smolvla-40k \ --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=2chan/beverage-smolvla-40k - Notebooks
- Google Colab
- Kaggle
Beverage SmolVLA (40,000 steps)
SmolVLA fine-tuned from lerobot/smolvla_base for bimanual beverage preparation.
- Training run:
20261103_024412_232249(Asia/Seoul). - Checkpoint step: 40,000.
- Selected task IDs: 0, 1, 2, 4 (stir, pour water, pour green tea latte powder, pour coffee powder).
- Robot state and action: 12 dimensions, left arm followed by right arm.
- Cameras: top, left wrist, right wrist; mapped to camera1, camera2, camera3.
- Action chunk size: 50.
Usage
from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
from lerobot.policies import make_pre_post_processors
repo_id = "2chan/beverage-smolvla-40k"
policy = SmolVLAPolicy.from_pretrained(repo_id)
preprocessor, postprocessor = make_pre_post_processors(
policy.config, pretrained_path=repo_id
)
Pass camera observations, robot state, and a task string through the preprocessor before action selection. Apply the postprocessor to recover actions in the original scale.
This repository contains the pretrained model, configuration, and normalization processors. Optimizer/scheduler/RNG state is not included. This upload does not establish physical robot performance.
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Model tree for 2chan/beverage-smolvla-40k
Base model
HuggingFaceTB/SmolLM2-360M Quantized
HuggingFaceTB/SmolLM2-360M-Instruct Quantized
HuggingFaceTB/SmolVLM-500M-Instruct Finetuned
lerobot/smolvla_base