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Upload policy weights, train config and readme

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  1. README.md +69 -0
  2. config.json +96 -0
  3. model.safetensors +3 -0
  4. train_config.json +215 -0
README.md ADDED
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+ ---
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+ base_model: lerobot/smolvla_base
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+ datasets:
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+ - shuohsuan/grasp_pos_000_009
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+ - shuohsuan/grasp_pos_010_019
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+ - shuohsuan/grasp_pos_020_029
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+ - shuohsuan/grasp_pos_030_039
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+ - shuohsuan/grasp_pos_040_049
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+ - shuohsuan/grasp_pos_050_059
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+ library_name: lerobot
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+ license: apache-2.0
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+ model_name: smolvla
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+ pipeline_tag: robotics
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+ tags:
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+ - smolvla
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+ - robotics
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+ - lerobot
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+ ---
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+
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+ # Model Card for smolvla
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+
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+ [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.
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+
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+
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+ This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
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+ See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
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+
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+ ---
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+
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+ ## How to Get Started with the Model
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+
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+ For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
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+ Below is the short version on how to train and run inference/eval:
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+
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+ ### Train from scratch
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+
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+ ```bash
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+ python -m lerobot.scripts.train \
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+ --dataset.repo_id=${HF_USER}/<dataset> \
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+ --policy.type=act \
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+ --output_dir=outputs/train/<desired_policy_repo_id> \
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+ --job_name=lerobot_training \
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+ --policy.device=cuda \
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+ --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
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+ --wandb.enable=true
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+ ```
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+
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+ *Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`.*
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+
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+ ### Evaluate the policy/run inference
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+
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+ ```bash
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+ python -m lerobot.record \
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+ --robot.type=so100_follower \
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+ --dataset.repo_id=<hf_user>/eval_<dataset> \
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+ --policy.path=<hf_user>/<desired_policy_repo_id> \
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+ --episodes=10
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+ ```
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+
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+ Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
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+
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+ ---
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+
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+ ## Model Details
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+
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+ * **License:** apache-2.0
config.json ADDED
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+ {
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+ "type": "smolvla",
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+ "n_obs_steps": 1,
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+ "normalization_mapping": {
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+ "VISUAL": "IDENTITY",
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+ "STATE": "MEAN_STD",
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+ "ACTION": "MEAN_STD"
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+ },
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+ "input_features": {
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+ "observation.state": {
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+ "type": "STATE",
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+ "shape": [
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+ 6
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+ ]
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+ "type": "VISUAL",
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+ "shape": [
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+ ]
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+ "type": "ACTION",
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+ "shape": [
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+ },
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+ "device": "cuda",
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+ "use_amp": false,
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+ "push_to_hub": true,
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+ "repo_id": "shuohsuan/svla_multi_grasp_pos",
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+ "max_action_dim": 32,
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+ "resize_imgs_with_padding": [
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+ 512,
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+ 512
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+ ],
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+ "empty_cameras": 0,
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+ "adapt_to_pi_aloha": false,
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+ "use_delta_joint_actions_aloha": false,
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+ "tokenizer_max_length": 48,
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+ "num_steps": 10,
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+ "use_cache": true,
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+ "freeze_vision_encoder": true,
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+ "train_expert_only": true,
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+ "train_state_proj": true,
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+ "optimizer_lr": 0.0001,
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+ 0.9,
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+ "optimizer_eps": 1e-08,
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+ "optimizer_weight_decay": 1e-10,
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+ "optimizer_grad_clip_norm": 10.0,
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+ "scheduler_warmup_steps": 1000,
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+ "scheduler_decay_steps": 30000,
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+ "scheduler_decay_lr": 2.5e-06,
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+ "vlm_model_name": "HuggingFaceTB/SmolVLM2-500M-Video-Instruct",
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+ "load_vlm_weights": true,
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+ "add_image_special_tokens": false,
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+ "attention_mode": "cross_attn",
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+ "prefix_length": 0,
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+ "pad_language_to": "max_length",
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+ "num_vlm_layers": 16,
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+ "expert_width_multiplier": 0.75,
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+ "min_period": 0.004,
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+ "max_period": 4.0
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+ }
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+ {
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+ "dataset": {
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+ "shuohsuan/grasp_pos_000_009",
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+ "shuohsuan/grasp_pos_010_019",
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+ }