Update litert samples readme

README.md

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-# Google AI Edge LiteRT API Samples
+# **Google AI Edge LiteRT Samples**
 
-This repository has source code for the Android, iOS, and Python AI Edge samples for the Google AI Edge LiteRT API.
+This repository contains official sample applications and code examples for **LiteRT** (formerly known as TensorFlow Lite), Google's high-performance on-device machine learning framework.
+
+The samples are organized into two main versions to demonstrate different API paradigms:
+
+* **`v1/`**: Samples using the standard **Interpreter API** (Classic TensorFlow Lite).  
+* **`v2/`**: Samples using the new **LiteRT CompiledModel API** (Optimized for Accelerators/NPU).
+
+**Note:** For Generative AI and Large Language Models (LLMs), please refer to the [LiteRT-LM repository](https://github.com/google-ai-edge/LiteRT-LM).
+
+## **📂 Repository Structure**
+
+### **1\. `v1/` (Standard Interpreter API)**
+
+This folder contains the classic samples that use the `Interpreter` class. These are best for general-purpose inference on CPU or standard GPU delegates.
+
+* **Key Features:**  
+  * Standard `.tflite` model execution.  
+  * Broad compatibility across all Android/iOS versions.  
+  * Legacy Task Library usage.  
+* **Available Samples:**  
+  * **Image Classification**: Recognize objects in images/video.  
+  * **Object Detection**: Locate and label multiple objects.  
+  * **Image Segmentation**: Separate objects from the background.  
+  * **Audio Classification**: Identify audio events.  
+  * **Digit Classification**: Handwritten digit recognition (MNIST).  
+* **Platforms:** Android (Kotlin/Java), iOS (Swift/Objective-C), Python (Raspberry Pi/Linux).
+
+### **2\. `v2/` (CompiledModel API)**
+
+This folder contains samples using the **LiteRT CompiledModel API**. This new API is designed to maximize performance by compiling models ahead-of-time (AOT) or just-in-time (JIT) for specific hardware accelerators, particularly **NPUs** (Neural Processing Units).
+
+* **Key Features:**  
+  * **Hardware Acceleration**: Specialized for NPU execution.  
+  * **Async Execution**: Improved performance for complex pipelines.  
+  * **Buffer Management**: efficient input/output handling.  
+* **Available Samples:**  
+  * **NPU AOT**: Ahead-of-Time compilation examples.  
+  * **NPU JIT**: Just-in-Time compilation examples.  
+* **Platforms:** Primarily Android (Kotlin/C++).
+
+## **🛠️ Getting Started**
+
+### **Prerequisites**
+
+* **Android**: Android Studio (latest stable version).  
+* **iOS**: Xcode (latest version).  
+* **Python**: Python 3.9+ and `pip install ai-edge-litert`.
+
+### **Running a Sample**
+
+#### **For V1 Samples (Standard)**
+
+1. Navigate to `v1/` directory.  
+2. Open the project in Android Studio or Xcode.  
+3. Build and run on your device.
+
+#### **For V2 Samples (High Performance / NPU)**
+
+1. Navigate to the `v2/` directory.  
+2. Ensure you have a device with a supported NPU (e.g., modern Pixel, Samsung, or devices with MediaTek/Qualcomm chips).  
+3. Follow the specific setup instructions in the sub-folder to enable the specialized hardware delegates.
+
+## **📚 Documentation**
+
+* **LiteRT Overview**: [ai.google.dev/edge/litert](https://ai.google.dev/edge/litert)  
+* **CompiledModel API Guide**: [LiteRT for Android](https://ai.google.dev/edge/litert/android)  
+* **Model Conversion**: [Convert models to LiteRT](https://ai.google.dev/edge/litert/models/convert)
+
+## **🤝 Contributing**
+
+Contributions are welcome\!
+
+1. Read [CONTRIBUTING.md](https://www.google.com/search?q=CONTRIBUTING.md).  
+2. Fork the repo and create a branch.  
+3. Submit a Pull Request.
+
+## **📄 License**
+
+Apache License 2.0. See [LICENSE](https://www.google.com/search?q=LICENSE) for details.
+
+---
+
+*Disclaimer: This is a sample repository maintained by Google. It is provided "as is" without warranty of any kind.*