Skip to content
ModelVerifiedExternal
Download

Smolvla Cup Push Red

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.

KindText modelBase modellerobot/smolvla_baseVersionmodelPublisher@subhodipsahaCgrade

Model source

Kind
Text model
File size
865 MB
Base model
lerobot/smolvla_base
Version
vmodel
Updated
Sep 26, 2026
Publisher
@subhodipsaha
Downloads
4
Source
Hugging Face

Source excerpt

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.

Sources

1 source
Hugging FacePrimaryVersion 759c7da441b32495fd142674e94332cc88986a64
huggingface.co/subhodipsaha/smolvla_cup_push_red/tree/759c7da441b32495fd142674e94332cc88986a64

Verified Sep 26

Model artifacts

1 artifact

model.safetensors

safetensors · 865 MB · SHA-256 1003e017d446…dee6 · Hugging Face

Download

Source excerpts

3 excerpts

--- base_model: lerobot/smolvla_base datasets: subhodipsaha/so101_cup_push_red library_name: lerobot license: apache-2.0 model_name: smolvla pipeline_tag: robotics tags: - lerobot - smolvla - 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 ## Comments - Trained in the data collected in Hyderabad. Didn't work well. Very inconsistent, sometimes rotated correctly, sometimes not.

Source context: 4 downloads · 0 likes · Pipeline robotics · Library lerobot · Repo subhodipsaha/smolvla_cup_push_red

Source context: 24 downloads · 0 likes · Pipeline robotics · Library lerobot · Repo subhodipsaha/smolvla_cup_push_red