Export and quantize SmolVLA for ONNX Runtime on Arm
Introduction
Set up the SmolVLA environment
Export and validate the SmolVLA ONNX model
Quantize SmolVLA to INT4 and compare it with FP32
Next Steps

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Download the scripts

Download the scripts for this Learning Path by copying and pasting the following commands into your terminal:

    

        
        
mkdir -p scripts
cd scripts
base_url="https://raw.githubusercontent.com/ArmDeveloperEcosystem/arm-learning-paths/main/content/learning-paths/cross-platform/smolvla-onnx-conversion/scripts"
for f in check_assets.py compare_onnx_outputs.py export_onnx.py quantize_onnx_torchao.py setup.sh workspace.py; do
    wget -q "$base_url/$f"
done
cd ..

Check the system requirements

You’ll run the exported models on an Arm Linux CPU.

Review the processor, Python version, and available space on the system you’ll use to run the project:

    

        
        
lscpu
python3 --version
df -h .

Install Git and Python 3.12 if they aren’t already available on your system.

If python3 -m venv fails, install the venv module for your distribution. On Ubuntu or Debian, run the following command:

    

        
        
sudo apt install python3.12-venv

Create the environment

Run the setup script:

    

        
        
bash scripts/setup.sh

The script downloads model weights, clones the LeRobot source, and installs PyTorch and other Python dependencies.

Note

The setup might take 30 minutes or more, depending on your network speed.

The script:

  • Creates work/venv
  • Checks out the pinned LeRobot source
  • Installs the conversion and runtime dependencies
  • Downloads the SmolVLA policy and its SmolVLM2 dependency
  • Records the installed Python packages in work/environment.freeze.txt and the source and model revisions in work/revisions.json

Activate the virtual environment so you can use python directly in later commands:

    

        
        
source work/venv/bin/activate

Verify the downloaded assets

Check the downloaded files and revisions:

    

        
        
python scripts/check_assets.py

The expected output ends with:

    

        
        PASS: public policy, base model, LeRobot source, and environment are ready

What you’ve accomplished and what’s next

You’ve prepared an Arm Linux environment with the pinned SmolVLA checkpoint, source, and Python dependencies.

Next, you’ll export SmolVLA as an FP32 ONNX model and validate it with ONNX Runtime.

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