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README.md
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---
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pipeline_tag: robotics
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tags:
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- lerobot
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library_name: lerobot
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datasets:
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- TekbotRobotics/svla_so101_pickplace_flags_sorting
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---
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## SmolVLA: A vision-language-action model for affordable and efficient robotics
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Resources and technical documentation:
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[Train using Google Colab Notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/lerobot/training-smolvla.ipynb#scrollTo=ZO52lcQtxseE)
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[SmolVLA HF Documentation](https://huggingface.co/docs/lerobot/smolvla)
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Designed by Tekbot Robotics and Inspired from Hugging Face.
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This model was finetuned on [hugging Face base model](https://huggingface.co/lerobot/smolvla_base/).
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Before proceeding to the next steps, you need to properly install the environment by following [Installation Guide](https://huggingface.co/docs/lerobot/installation) on the docs.
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Install smolvla extra dependencies:
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```bash
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pip install -e ".[smolvla]"
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```
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Example of finetuning the smolvla pretrained model (`smolvla_base`):
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```bash
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python lerobot/scripts/train.py \
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--policy.path=lerobot/smolvla_base \
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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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Example of finetuning the smolvla neural network with pretrained VLM and action expert
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intialized from scratch:
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```bash
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python lerobot/scripts/train.py \
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--dataset.repo_id=TekbotRobotics/svla_so101_pickplace_flags_sorting \
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--batch_size=64 \
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--steps=200000 \
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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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