LFM2.5-VL-3B
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LFM2.5-VL-450M
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View details ↗LLaVA-OneVision-0.5B
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View details ↗Matcha-TTS
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View details ↗MI-GAN-512-Places2-LiteRT
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View details ↗Ministral-3-3B-Instruct-2512
litert-communityLiteRT is Google's on-device runtime, the new name for TensorFlow Lite (Android: `com.google.ai.edge.litert:litert`), and `litert-torch`, the renamed `ai-edge-torch`, is its PyTorch converter: a PyTorch model converted unmodified with `litert_torch.convert` matched the original to 4e-7 on a Galaxy S26 (measured, LiteRT 2.2.0, Android 16, 2026-09-05). Measured on device (edge-compat): Galaxy S26 · LiteRT-LM prebuilt-adac974b · GPU · decode 22.1 tok/s · prefill 338 tok/s · TTFT 3.07 s · all 1210 ops delegated (2026-10-05); Pixel 8a · LiteRT-LM 0.16.0 · GPU · decode 6.8 tok/s · prefill 8 tok/s · TTFT 1.68 s · all 1187 ops delegated (2026-08-17); Mac Studio M4 Max · LiteRT-LM 0.17.1 · GPU · decode 129.5 tok/s · prefill 1340 tok/s · TTFT 201 ms · all 1210 ops delegated (2026-10-05). Record: https://github.com/john-rocky/edge-compat/blob/main/cards/ministral3-3b/CARD.md mistralai/Ministral-3-3B-Instruct-2512 converted to the **LiteRT-LM** (`.litertlm`) format for on-device inference with Google's LiteRT-LM runtime (the engine behind the official `litert-community/*` models).
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View details ↗MobileNet-v2
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View details ↗MobileNet-v3-small
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View details ↗MoGe-2-LiteRT
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View details ↗moonshine-tiny
litert-communityMoonshine Tiny is a 27M-parameter encoder-decoder speech-recognition model from Moonshine AI (formerly Useful Sensors), introduced in Moonshine: Speech Recognition for Live Transcription and Voice Commands. It transcribes English speech and is designed for fast on-device inference. This repository packages the model for LiteRT: a float32 model, an int8 model (dynamic-range-quantized decoder with a float32 encoder), and ahead-of-time compiled variants for a range of MediaTek and Qualcomm SoCs so the model can run on the device NPU. For an on-device Android demo that runs Moonshine (and other ASR models) with hardware acceleration, see the LiteRT speech recognition sample.
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View details ↗NAFNet-SIDD-width32-LiteRT
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View details ↗Nanbeige4.2-3B
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View details ↗Nemotron-3-Nano-4B
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View details ↗Neo_Dragon
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View details ↗NIMA-LiteRT
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View details ↗North-Micro-Vision-Instruct
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View details ↗Ovis2.5-2B
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View details ↗PaddleOCR-VL-1.6
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View details ↗parakeet-ctc-0.6b
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View details ↗parakeet-tdt-0.6b-v3
litert-communityThis is nvidia/parakeet-tdt-0.6b-v3 (600M-parameter FastConformer encoder + Token-and-Duration Transducer, TDT, decoder) packaged as a `.litertlm` bundle for LiteRT-LM, Google's open-source on-device inference runtime, alongside standalone `.tflite` and AOT-compiled NPU variants for LiteRT. Everything needed to transcribe arbitrary-length audio with automatic language detection, punctuation, and capitalization is inside the one `.litertlm` file: INT8 encoder, hybrid stateful/stateless TDT decoder, unified 8,192-token multilingual SentencePiece tokenizer, and `Speech2TextMetadata` (16 kHz resampler and 128-bin Slaney log-mel front-end configuration) — no Python and no network required. All commands below accept mono or multi-channel WAV files at any sample rate (automatically resampled to 16 kHz mono) and stream confirmed and unconfirmed transcript segments to stdout (or `--output_path`). Prerequisites (Bazel, Android NDK) are in the LiteRT-LM build guide. > [!TIP] > If you omit `--model_path` and pass `--model_name=parakeet-tdt-0.6b-v3`, > `omni_runner` automatically downloads > `parakeet_tdt_0.6b_v3_5s_i8_stateful.litertlm` into `--cache_dir` on first > run.
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View details ↗Phi-4-mini-instruct
litert-communityThis model provides a few variants of microsoft/Phi-4-mini-instruct that are ready for deployment on Android using the LiteRT (fka TFLite) stack, MediaPipe LLM Inference API (in maintenance-only mode) and LiteRT-LM. *Disclaimer: The target deployment surface for the LiteRT models is Android/iOS/Web and the stack has been optimized for performance on these targets. Trying out the system in Colab is an easier way to familiarize yourself with the LiteRT stack, with the caveat that the performance (memory and latency) on Colab could be much worse than on a local device.* * Download or build the app from GitHub.
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View details ↗Phi-4-mini-reasoning
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View details ↗PP-OCRv5-LiteRT
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View details ↗PP-OCRv6-Small-LiteRT
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