Welcome to the Model Converter Toolkit documentation! This guide will walk you through how to seamlessly convert machine learning models between different formats using our powerful tools. Whether you're migrating models or integrating them into new workflows, you're in the right place.

🔧 Key Features

  • Multi-format support: Convert between ONNX, TensorFlow, PyTorch, and more
  • Auto-optimization: Intelligent optimizations during conversion process
  • Version compatibility: Works with models from v1.0 to latest releases

📝 Conversion Steps

  1. Prepare your model
    Ensure your model is saved in the source format (e.g., .pt for PyTorch)

    Neural Network Architecture

  2. Use the converter CLI
    Run: model_converter --input_model your_model.pt --target_format onnx
    For advanced options, check our command-line reference

  3. Verify the output
    Use the model_validator tool to confirm compatibility

⚠️ Best Practices

  • Always validate the converted model before deployment
  • For custom layers, use the --preserve_custom flag
  • Monitor conversion logs for warnings:
    Conversion Logs

🌐 Extend Your Knowledge

Need help with specific frameworks? Explore our:

Happy converting! 🚀

Model Conversion Flow