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README.md

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+# LiteRT Community 
+
+A community org for developers to discover models that are ready for deployment to edge platforms. [LiteRT](https://ai.google.dev/edge/litert), formerly known as TensorFlow Lite, is a high-performance runtime for on-device AI. 
+
+Models in the organization are pre-converted and ready to be used on [Android](https://ai.google.dev/edge/litert/android) and [iOS](https://ai.google.dev/edge/litert/ios/quickstart). For more information on how to run these models see our [LiteRT Documentation](https://ai.google.dev/edge/litert).
+
+## LLMs 
+
+To make LLMs as simple as possible, LiteRT models can be bundled into .task files compatible with [MediaPipe LLM Inference API](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference). MediaPipe LLM Inference API wraps LiteRT to provide an easy prompt in -> response out interface on [Android](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference/android), [iOS](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference/ios), and [Web](https://ai.google.dev/edge/mediapipe/solutions/genai/llm_inference/web_js).
+
+## How to Convert and Contribute Models
+
+Follow the instructions for converting from [TensorFlow](https://ai.google.dev/edge/litert/models/convert_tf), [PyTorch](https://github.com/google-ai-edge/ai-edge-torch), or [JAX](https://ai.google.dev/edge/litert/models/convert_jax).  
+
+For LLMs specifically, use the [LiteRT Torch Generative API](https://github.com/google-ai-edge/ai-edge-torch/tree/main/ai_edge_torch/generative). 
+
+Once converted, join the LiteRT community org and add the model yourself. 
+