Add Robotics tag and metadata

README.md

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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 mobile_so100_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.
+
+---