README.md

---
base_model: lerobot/smolvla_base
datasets:
- imstevenpmwork/thanos_picking_power_gem_1749731584242992
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- robotics
- smolvla
model_summary: '[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.'
---

# 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:

### 1 Train from scratch

```bash
python lerobot/scripts/train.py \
  --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/`.*

### 2 Evaluate the policy

```bash
python -m 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