Instructions to use HelloCephalopod/my_smolvla_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use HelloCephalopod/my_smolvla_2 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=HelloCephalopod/my_smolvla_2 \ --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=HelloCephalopod/my_smolvla_2 - Notebooks
- Google Colab
- Kaggle
SmolVLA Fine-tuned Model
This model is a fine-tuned version of HuggingFaceTB/SmolVLA trained using the LeRobot library.
Model Details
- Base Model: HuggingFaceTB/SmolVLA
- Training Steps: 20,000
- Batch Size: 10
- Checkpoints Available: 4
Available Checkpoints
This repository contains multiple checkpoints from the training process:
checkpoint-020000/- Step 020000checkpoint-040000/- Step 040000checkpoint-060000/- Step 060000checkpoint-last/- Step last
Usage
from lerobot import LeRobotPolicy
# Load the final checkpoint
model = LeRobotPolicy.from_pretrained("HelloCephalopod/my_smolvla_2/checkpoint-last")
# Or load a specific checkpoint
model = LeRobotPolicy.from_pretrained("HelloCephalopod/my_smolvla_2/checkpoint-10000")
Training Details
- Training Data: Custom dataset from Hugging Face Hub
- Training Steps: 60,000
- Batch Size: 10
- Save Steps: 20,000
- Hardware: Google Colab GPU
Checkpoint Selection
- checkpoint-40000: Mid-training checkpoint
- checkpoint-60000: Final checkpoint
- checkpoint-last: Latest checkpoint (recommended)
Citation
If you use this model, please cite the original SmolVLA paper and the LeRobot library.