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---
base_model: lerobot/smolvla_base
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- robotics
- smolvla
---
# Model Card for smolvla_box_in_bin_so101_test
<!-- 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
python lerobot/scripts/train.py \
--dataset.repo_id=<user_or_org>/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=<user_or_org>/<desired_policy_repo_id> \
--wandb.enable=true
```
*Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`.*
### Evaluate the policy
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<user_or_org>/eval_<dataset> \
--policy.path=<user_or_org>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval_** and supply `--policy.path` pointing to a local or hub checkpoint.
--- |