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SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
Model source
Source description
SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs.
Sources
1 sourceVerified Jul 17
Model artifacts
1 artifactcheckpoints/001250/pretrained_model/model.safetensors
safetensors · 865 MB · SHA-256 0976fd275c2c…4a6c · Hugging Face
DownloadSource excerpts
3 excerptsFor a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval:
lerobot-train \
--dataset.repo_id=${HF_USER}/ \
--policy.type=act \
--output_dir=outputs/train/ \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/
--wandb.enable=true
Writes checkpoints to outputs/train/ /checkpoints/.
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id= /eval_ \
--policy.path= / \
--episodes=10
Prefix the dataset repo with eval_ and supply --policy.path pointing to a local or hub checkpoint.
--- base_model: lerobot/smolvla_base datasets: robot-learning-group47/eval2_all_prompts_fixed_266 library_name: lerobot license: apache-2.0 model_name: smolvla pipeline_tag: robotics tags: - smolvla - lerobot - robotics --- # Model Card for smolvla <!-- Provide a quick summary of what the model is/does. --> [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. This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot). See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index). --- ## How to Get Started with the Model For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy). Below is the short version on how to train and run inference/eval: ### Train from scratch ```bash lerobot-train \ --dataset.repo_id=${HF_USER}/<dataset> \ --policy.type=act \ --output_dir=outputs/train/<desired_policy_repo_id> \ --job_name=lerobot_training \ --policy.device=cuda \ --policy.repo_id=${HF_USER}/<desired_policy_repo_id> --wandb.enable=true ``` _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._ ### Evaluate the policy/run inference ```bash lerobot-record \ --robot.type=so100_follower \ --dataset.repo_id=<hf_user>/eval_<dataset> \ --policy.path=<hf_user>/<desired_policy_repo_id> \ --episodes=10 ``` Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint. --- ## Model Details - **License:** apache-2.0
Source context: 4 downloads · 0 likes · Pipeline robotics · Library lerobot · Repo robot-learning-group47/eval2_noaug_smolvla_b128_wandb
Source context: 3 downloads · 0 likes · Pipeline robotics · Library lerobot · Repo robot-learning-group47/eval2_noaug_smolvla_b128_wandb