# Examples, Demos, Tutorials

This page introduces various demos, examples, and tutorials currently available with the Ryzen™ AI Software.

## Getting Started Tutorials

### NPU

- [Getting Started Tutorial for INT8 models](https://ryzenai.docs.amd.com/en/latest/getstartex.html) - Uses a custom ResNet model to demonstrate:
  - Pretrained model conversion to ONNX
  - Model Quantization using AMD Quark quantizer
  - Deployment using ONNX Runtime C++ and Python code
- [Getting Started Tutorial for BF16 models](https://github.com/amd/RyzenAI-SW/tree/main/CNN-examples/getting_started_resnet/bf16) - Uses a custom ResNet model to demonstrate:
  - Preparation and compilation of BF16 models
  - Deployment using Python
  - Deployment using C++
- [Hello World Jupyter Notebook Tutorial](https://github.com/amd/RyzenAI-SW/tree/main/CNN-examples/hello_world)
- Additional BF16 model examples:
  - [Image Classification](https://github.com/amd/RyzenAI-SW/tree/main/CNN-examples/image_classification)
  - [Finetuned DistilBERT for Text Classification](https://github.com/amd/RyzenAI-SW/tree/main/Transformer-examples/DistilBERT_text_classification_bf16)
- [Super-Resolution Models on AMD Ryzen AI NPU](https://github.com/amd/RyzenAI-SW/tree/main/CNN-examples/super-resolution)
- LLM examples
  - [LLMs on RyzenAI with ONNX Runtime GenAI API](https://github.com/amd/RyzenAI-SW/tree/main/LLM-examples/oga_api)
  - [ONNX Runtime GenAI(OGA)‑based RAG LLM](https://github.com/amd/RyzenAI-SW/tree/main/LLM-examples/RAG-OGA)
  - [Running Vision Language Model (VLM) on RyzenAI NPU](https://github.com/amd/RyzenAI-SW/tree/main/LLM-examples/VLM)
  - [Running GPT-OSS-20B with chat template](https://github.com/amd/RyzenAI-SW/tree/main/LLM-examples/oga_inference)

### iGPU

- [ResNet50 on iGPU](https://github.com/amd/RyzenAI-SW/tree/main/CNN-examples/iGPU/getting_started)

## Other examples, demos, and tutorials

- Refer to [RyzenAI-SW repo](https://github.com/amd/RyzenAI-SW/tree/main/)

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