@@ -762,6 +762,47 @@ print(output)`);
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return snippets ;
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} ;
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+ export const lerobot = ( model : ModelData ) : string [ ] => {
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+ if ( model . tags . includes ( "smolvla" ) ) {
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+ const smolvlaSnippets = [
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+ // Installation snippet
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+ `# See https://github.com/huggingface/lerobot?tab=readme-ov-file#installation for more details
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+ git clone https://github.com/huggingface/lerobot.git
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+ cd lerobot
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+ pip install -e .[smolvla]` ,
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+ // Finetune snippet
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+ `# Launch finetuning on your dataset
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+ python lerobot/scripts/train.py \\
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+ --policy.path=${ model . id } \\
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+ --dataset.repo_id=lerobot/svla_so101_pickplace \\
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+ --batch_size=64 \\
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+ --steps=20000 \\
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+ --output_dir=outputs/train/my_smolvla \\
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+ --job_name=my_smolvla_training \\
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+ --policy.device=cuda \\
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+ --wandb.enable=true` ,
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+ ] ;
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+ if ( model . id !== "lerobot/smolvla_base" ) {
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+ // Inference snippet (only if not base model)
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+ smolvlaSnippets . push (
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+ `# Run the policy using the record function
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+ python -m lerobot.record \\
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+ --robot.type=so101_follower \\
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+ --robot.port=/dev/ttyACM0 \\ # <- Use your port
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+ --robot.id=my_blue_follower_arm \\ # <- Use your robot id
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+ --robot.cameras="{ front: {type: opencv, index_or_path: 8, width: 640, height: 480, fps: 30}}" \\ # <- Use your cameras
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+ --dataset.single_task="Grasp a lego block and put it in the bin." \\ # <- Use the same task description you used in your dataset recording
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+ --dataset.repo_id=HF_USER/dataset_name \\ # <- This will be the dataset name on HF Hub
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+ --dataset.episode_time_s=50 \\
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+ --dataset.num_episodes=10 \\
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+ --policy.path=${ model . id } `
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+ ) ;
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+ }
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+ return smolvlaSnippets ;
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+ }
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+ return [ ] ;
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+ } ;
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+
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export const tf_keras = ( model : ModelData ) : string [ ] => [
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`# Note: 'keras<3.x' or 'tf_keras' must be installed (legacy)
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# See https://github.com/keras-team/tf-keras for more details.
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