# Inspect the graph with Model Explorer

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/)
- [Explore PTQ and QAT for ExecuTorch INT8 deployment](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/1-introduction/)
- [Set up your environment for ExecuTorch quantization](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/2-set-up-your-environment/)
- [Apply PTQ and export a quantized VGF model](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/3-run-ptq-and-export-vgf/)
- [Apply QAT and export a quantized VGF model](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/4-run-qat-and-export-vgf/)
- [Inspect the graph with Model Explorer](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/5-validate-and-choose-a-quantization-strategy/)
- [Next Steps](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/quantize-neural-upscaling-models/_next-steps/)

## Install the Model Explorer

If you use `.vgf` as an intermediate artifact, it helps to inspect the exported graph before you integrate it into your runtime.

Install and launch Model Explorer with the VGF adapter:

```bash
pip install vgf-adapter-model-explorer
pip install torch ai-edge-model-explorer
model-explorer --extensions=vgf_adapter_model_explorer
```

Open the `.vgf` file from `./output/` and `./output_qat/`.

When you review the graph, look for unexpected layout conversions (for example, extra transpose operations), operators that you did not intend to run on your GPU path, and model I/O shapes that do not match your integration.

## Advanced: connect the model to an ML Extensions for Vulkan workflow

The fastest way to understand the integration constraints is to start from a known-good sample and then replace the model.

Use the Learning Path [Get started with neural graphics using ML Extensions for Vulkan](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/) and focus on how the sample loads and executes `.vgf` artifacts. This is where you validate assumptions about input and output tensor formats and where any required color-space or layout conversions happen.

## Choose your quantization strategy

You now have a complete reference workflow for quantizing an image-to-image model with TorchAO and exporting INT8 `.vgf` artifacts using the ExecuTorch Arm backend. You also have a practical baseline you can use to debug export issues before you switch to your production model and data.

When you move from the CIFAR-10 proxy model to your own model, keep these constraints in mind:

- Treat calibration data as part of your model contract. If PTQ quality drops, start by fixing the representativeness of calibration inputs.
- Use QAT when PTQ introduces visible artifacts or regressions that matter to your visual quality bar.
- Validate early by inspecting the exported graph so you can catch unexpected layouts, operators, or tensor shapes before runtime integration.