Instructions to use maedmatt/DREAM_SmolVLA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use maedmatt/DREAM_SmolVLA with LeRobot:
# See https://github.com/huggingface/lerobot?tab=readme-ov-file#installation for more details git clone https://github.com/huggingface/lerobot.git cd lerobot pip install -e .[smolvla]
# Launch finetuning on your dataset python lerobot/scripts/train.py \ --policy.path=maedmatt/DREAM_SmolVLA \ --dataset.repo_id=lerobot/svla_so101_pickplace \ --batch_size=64 \ --steps=20000 \ --output_dir=outputs/train/my_smolvla \ --job_name=my_smolvla_training \ --policy.device=cuda \ --wandb.enable=true
# Run the policy using the record function python -m lerobot.record \ --robot.type=so101_follower \ --robot.port=/dev/ttyACM0 \ # <- Use your port --robot.id=my_blue_follower_arm \ # <- Use your robot id --robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \ # <- Use your cameras --dataset.single_task="Grasp a lego block and put it in the bin." \ # <- Use the same task description you used in your dataset recording --dataset.repo_id=HF_USER/dataset_name \ # <- This will be the dataset name on HF Hub --dataset.episode_time_s=50 \ --dataset.num_episodes=10 \ --policy.path=maedmatt/DREAM_SmolVLA - Notebooks
- Google Colab
- Kaggle
Upload policy
Browse files- config.json +102 -0
- model.safetensors +3 -0
config.json
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{
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"type": "smolvla",
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"n_obs_steps": 1,
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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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},
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"observation.images.camera1": {
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"type": "VISUAL",
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"shape": [
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3,
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256,
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256
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]
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},
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"observation.images.camera2": {
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"type": "VISUAL",
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"shape": [
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3,
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256,
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256
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]
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},
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"observation.images.camera3": {
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"type": "VISUAL",
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"shape": [
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3,
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256,
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256
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]
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}
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},
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"output_features": {
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"action": {
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"type": "ACTION",
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"shape": [
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6
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]
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}
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},
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"device": "cuda",
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"use_amp": false,
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"use_peft": false,
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"push_to_hub": true,
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"repo_id": "maedmatt/DREAM_SmolVLA",
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"private": null,
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"tags": null,
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"license": null,
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"pretrained_path": "lerobot/smolvla_base",
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"pretrained_revision": null,
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"chunk_size": 50,
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"n_action_steps": 50,
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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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"max_state_dim": 32,
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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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"optimizer_betas": [
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0.9,
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0.95
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],
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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": 20000,
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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_expert_layers": 0,
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"num_vlm_layers": 16,
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"self_attn_every_n_layers": 2,
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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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"rtc_config": null,
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"compile_model": false,
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"compile_mode": "max-autotune"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:7db60f40ce74b6c971028ca03a3a9b94e056b3a1901acdb31cc8210a0f229205
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size 906712552
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