dinglx/smolvla_stacking_model

1---2base_model: lerobot/smolvla_base3datasets: lerobot/svla_so100_stacking4library_name: lerobot5license: apache-2.06model_name: smolvla7pipeline_tag: robotics8tags:9- smolvla10- lerobot11- robotics12---13 14# Model Card for smolvla15 16<!-- Provide a quick summary of what the model is/does. -->17 18 19[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.20 21 22This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).23See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).24 25---26 27## How to Get Started with the Model28 29For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).30Below is the short version on how to train and run inference/eval:31 32### Train from scratch33 34```bash35python -m lerobot.scripts.train \36  --dataset.repo_id=${HF_USER}/<dataset> \37  --policy.type=act \38  --output_dir=outputs/train/<desired_policy_repo_id> \39  --job_name=lerobot_training \40  --policy.device=cuda \41  --policy.repo_id=${HF_USER}/<desired_policy_repo_id>42  --wandb.enable=true43```44 45*Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`.*46 47### Evaluate the policy/run inference48 49```bash50python -m lerobot.record \51  --robot.type=so100_follower \52  --dataset.repo_id=<hf_user>/eval_<dataset> \53  --policy.path=<hf_user>/<desired_policy_repo_id> \54  --episodes=1055```56 57Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.58 59---60 61## Model Details62 63* **License:** apache-2.0