---
title: README
emoji: 🌖
colorFrom: yellow
colorTo: indigo
sdk: static
pinned: false
---

# LiteRT Community

[LiteRT](https://ai.google.dev/edge/litert) is Google's on-device framework for high-performance ML & GenAI deployment on edge platforms. It is the improved successor to TensorFlow Lite. On this community page, you can find ready-to-run LiteRT models for a wide range of ML/AI tasks. 

Within this ecosystem, [LiteRT-LM](https://github.com/google-ai-edge/LiteRT-LM/blob/main/README.md) specializes in cutting edge GenAI. Recognizing that LLMs now function as complex pipelines of related models rather than single standalone models, LiteRT-LM leverages LiteRT to deliver an optimized solution for running LLMs on-device. 

Both LiteRT and LiteRT-LM can run on a variety of devices including Android, iOS, Windows, macOS, Linux and Web allow easy deployment and scaling across a diverse device landscape. 

# Trying it Live

<div align="center">

| [<svg xmlns="http://www.w3.org/2000/svg" height="72px" viewBox="0 -960 960 960" width="72px" fill="currentColor"><path d="M240-40q-50 0-85-34.5T120-159v-641q0-50 35-85t85-35q17 0 32.5 4.5T302-903l555 319q28 17 45 44.5t17 59.5q0 33-17.5 61T855-374L301-56q-14 8-29.5 12T240-40Zm25-59 353-202-116-116q-9-9-21-9t-21 9L179-135q7 27 33.5 39t52.5-3Zm156-357q9-9 9-21.5t-9-21.5L177-743v533l244-246Zm248 125 157-90q16-9 25.5-24.5T861-479q0-17-9.5-32T827-535l-158-92-127 127q-9 9-9 21t9 21l127 127ZM503-539l116-117-354-205q-25-15-51-1.5T180-820l281 281q9 9 21 9t21-9Z"/></svg>](https://play.google.com/store/apps/details?id=com.google.ai.edge.gallery&pli=1) | [<svg xmlns="http://www.w3.org/2000/svg" height="84px" viewBox="0 -960 960 960" width="84px" fill="currentColor"><path d="M160-615v-60h60v60h-60Zm0 335v-275h60v275h-60Zm292 0H347q-24.75 0-42.37-17.63Q287-315.25 287-340v-280q0-24.75 17.63-42.38Q322.25-680 347-680h105q24.75 0 42.38 17.62Q512-644.75 512-620v280q0 24.75-17.62 42.37Q476.75-280 452-280Zm-105-60h105v-280H347v280Zm228 60v-60h165v-114H635q-24.75 0-42.37-17.63Q575-489.25 575-514v-106q0-24.75 17.63-42.38Q610.25-680 635-680h165v60H635v106h105q24.75 0 42.38 17.62Q800-478.75 800-454v114q0 24.75-17.62 42.37Q764.75-280 740-280H575Z"/></svg>](https://github.com/google-ai-edge/mediapipe-samples/tree/main/examples/llm_inference/ios) | [<svg xmlns="http://www.w3.org/2000/svg" height="72px" viewBox="0 -960 960 960" width="72px" fill="currentColor"><path d="M334-120v-60h86v-100H140q-24 0-42-18t-18-42v-440q0-24 18-42t42-18h680q24 0 42 18t18 42v440q0 24-18 42t-42 18H540v100h86v60H334ZM140-340h680v-440H140v440Zm0 0v-440 440Z"/></svg>](https://github.com/google-ai-edge/LiteRT-LM?tab=readme-ov-file#quick-start) | [<svg xmlns="http://www.w3.org/2000/svg" height="72px" viewBox="0 -960 960 960" width="72px" fill="currentColor"><path d="M838-79 710-207v103h-60v-206h206v60H752l128 128-42 43Zm-358-1q-83 0-156-31.5T197-197q-54-54-85.5-126.36T80-478q0-83.49 31.5-156.93Q143-708.36 197-762.68 251-817 324-848.5 397-880 480-880t156 31.5q73 31.5 127 85.82 54 54.32 85.5 127.75Q880-561.49 880-478q0 23-2 44.5t-7 43.5h-63q6-21.67 9-43.33 3-21.67 3-44.47 0-22.8-2.95-45.6-2.94-22.8-8.83-45.6H648q2 23 4 45.5t2 45q0 22.5-1.25 44.5T649-390h-61q3-22 4.5-44t1.5-44q0-22.75-1.5-45.5T588-569H373.42q-3.42 23-4.92 45.5t-1.5 45q0 22.5 1.5 44.5t4.5 44h197v60H384q14 53 34 104t62 86q23 0 45-2.5t45-7.5v60q-23 5-45 7.5T480-80ZM151.78-390H312q-2.5-22-3.75-44T307-478q0-22.75 1-45.5t3-45.5H151.71q-5.85 22.8-8.78 45.6-2.93 22.8-2.93 45.6t2.95 44.47q2.94 21.66 8.83 43.33ZM172-629h149.59q11.41-48 28.91-93.5T395-810q-71 24-129.5 69.5T172-629Zm222 478q-26-41-43.5-86T323-330H172q33 67 91 114t131 65Zm-10-478h193q-13-54-36-104t-61-89q-38 40-61 89.5T384-629Zm255.34 0H788q-35-66-93-112t-129-68q27 41 44.5 86.5t28.84 93.5Z"/></svg>](https://mediapipe-studio.webapps.google.com/studio/demo/llm_inference) |
| :---: | :---: | :---: | :---: |
| [Android](https://play.google.com/store/apps/details?id=com.google.ai.edge.gallery&pli=1) | [iOS](https://github.com/google-ai-edge/mediapipe-samples/tree/main/examples/llm_inference/ios) | [Desktop](https://github.com/google-ai-edge/LiteRT-LM?tab=readme-ov-file#quick-start) | [Web](https://mediapipe-studio.webapps.google.com/studio/demo/llm_inference) |

</div>

Not sure where to start? We recommend first trying our models in our [Google AI Edge Gallery app](https://play.google.com/store/apps/details?id=com.google.ai.edge.gallery&pli=1) on Android.

# Community Contributions

Are we missing your favorite model? You can convert and run [PyTorch](https://github.com/google-ai-edge/ai-edge-torch), [TensorFlow](https://ai.google.dev/edge/litert/models/convert_tf), or [JAX](https://ai.google.dev/edge/litert/models/convert_jax) models to the classic TFLite format using the LiteRT conversion and optimization tools. Or for LLMs, you can use the [LiteRT Torch Generative API](https://github.com/google-ai-edge/ai-edge-torch/tree/main/ai_edge_torch/generative). When your model is ready, join the LiteRT community org and upload the model here for others to try!