This repository contains official and community contributed sample applications, model recipes, agent skills and utilities for LiteRT (formerly known as TensorFlow Lite), Google's open source, high-performance on-device machine learning framework and LiteRT-LM, a specialized orchestration layer for running LLMs with LiteRT, unlocking maximum performance and efficiency.
Note Please access the interactive web page with a collections of demos there at: https://google-ai-edge.github.io/litert-samples/
The samples demonstrate different API paradigms (LiteRT CompiledModel API and legacy Interpreter API, Tensor API, LiteRT-LM) and provide end-to-end model conversion and deployment pipelines.
- 🐱 Streaming TTS (KittenTTS nano): Added a tiny (15M-param, 32 MB) streaming text-to-speech Android sample — dynamic-length LiteRT graphs, sentence-level streaming playback, live TTFA/RTF metrics (
samples/litert/text_to_speech_streaming/). - 🎙️ Speech Recognition (ASR): Added end-to-end Automatic Speech Recognition sample using the CompiledModel API.
- 📸 PhotoTalk Sample App: Added multimodal sample app combining LiteRT vision processing with LiteRT-LM audio/text generation (
samples/litert/phototalk_sample_app/). - 🗣️ Qwen3-TTS & Qwen3 ASR: Added model recipes, conversion scripts, and Tensor API implementations for Qwen3-TTS and Qwen3 ASR.
- 🎨 Bonsai Image 4B: Added text-to-image diffusion model sample with Python inference and conversion tools (
models/bonsai/bonsai_image_4b/). - 🤖 Agent Skills & Utilities: Added four lifecycle agent skills (
skills/: conversion, quantization, on-device verification, app scaffolding), a GPU conversion toolkit (utilities/litert_gpu_toolkit/), and shared Kotlin helpers (utilities/common/).
All runnable sample applications and interactive playgrounds are organized under samples/:
samples/litert/: Standard samples using the LiteRT CompiledModel API. Designed for modern hardware acceleration (GPU/NPU) and asynchronous execution.- Samples: Speech Recognition, PhotoTalk, Text-to-Speech, Image Generation (text-to-image), Image Segmentation, Image Classification, Digit Classification, Qualcomm NPU acceleration (Gemma, MobileNet, Fast VLM), Google TPU sample app.
samples/litert_interpreter/: Legacy samples using the Interpreter API.- Samples: Broad compatibility examples for Android, iOS, and Python (Image Classification, Object Detection, Image Segmentation, Audio Classification).
samples/litert_lm/: High-level Engine samples for Large Language Models (LLM/SLM).samples/end_to_end/: Complete full-system pipelines (e.g. ImageNet model conversion, preprocessing, and classification).samples/tensor_api_playground/: Interactive Web/WASM playground demonstrating LiteRT Tensor API capabilities directly in the browser (Gemma 3, Image Segmentation, Mandelbrot, Game of Life).
Contains standalone model conversion scripts, export recipes, and model-specific utilities. Many are working in process.
utilities/common/: Shared Kotlin helpers for Android samples (camera pipeline, audio capture, CompiledModel runner, image/tensor and math helpers).utilities/litert_gpu_toolkit/: Pre-conversion patches that rewrite common PyTorch patterns into forms the LiteRT GPU delegate accepts, plus a post-conversion checker.
Custom AI agent skills that carry a model through the LiteRT deployment lifecycle, in order — see skills/README.md for the full index:
gpu-clean-conversion/: PyTorch / Hugging Face model → GPU-resident LiteRT model.accuracy-safe-quantization/: Quantize (fp16 / int8 / int4) without losing accuracy.on-device-verification/: Prove the converted model on the actual device.compiled-model-app-scaffolding/: Build an Android app around the verified model.litert-compiled-model-migration/: Android TFLite → LiteRT CompiledModel V2 with NPU JIT acceleration and zero-copy buffers.
- Android: Android Studio (latest stable version).
- iOS: Xcode (latest version).
- Python: Python 3.9+ and
pip install ai-edge-litert. - Web / WASM: Modern browser with WebGPU / WebAssembly support.
- Navigate to
samples/litert/<sample_name>. - Ensure you have a device with a supported NPU/GPU (e.g., modern Pixel, Samsung, or Qualcomm/MediaTek devices).
- Follow the specific setup instructions in the sample's
README.md.
Visit "Interactive Web" tab, or open
samples/tensor_api_playground/index.html or run index.html at the repository root
via a local HTTP server.
- LiteRT Overview: ai.google.dev/edge/litert
- LiteRT-LM Overview: ai.google.dev/edge/litert-lm
- CompiledModel API Guide: LiteRT for Android
- Model Conversion: Convert models to LiteRT
- LiteRT CLI: CLI for end-to-end journey
Contributions are welcome!
- Read CONTRIBUTING.md.
- Fork the repo and create a branch.
- Submit a Pull Request.
Apache License 2.0. See LICENSE for details.
Disclaimer: This is a sample repository maintained by Google. It is provided "as is" without warranty of any kind.