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title: README
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# 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://ai.google.dev/edge/litert-lm) 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, IoT and Web allow easy deployment and scaling across a diverse device landscape.
# Trying it Live
Not sure where to start? We recommend first trying our models in our Google AI Edge Gallery app on [Android](https://play.google.com/store/apps/details?id=com.google.ai.edge.gallery&pli=1) and [iOS](https://apps.apple.com/us/app/google-ai-edge-gallery/id6749645337).
# Community Contributions
Are we missing your favorite model? You can convert and run [PyTorch](https://github.com/google-ai-edge/litert-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!
## Submission Guidelines
* **Model Conversion:** Your model must be converted using the LiteRT-Torch or Generative API in `.tflite` or `.litertlm` formats.
* **Location:** All contributions must be made in the [community contribution folder](https://huggingface.co/collections/litert-community/community-contribution) (e.g., `LiteRT-Community/community-contribution/mobile-bert`).
* **Licensing:** If your model has a license, it must be either an Apache 2.0 or MIT open-source license. Models with no license are also accepted.
* **Data Provenance and Privacy:** If applicable, you must include a summary of the model's training data in your submission. Additionally, you must provide confirmation that all Personally Identifiable Information (PII) has been scrubbed from the data.
## Policies & Support
* **Content Policy:** All uploaded models must fully comply with the [Hugging Face content guidelines](https://huggingface.co/collections/litert-community/community-contribution).
* **Community Moderation:** The Google AI Edge team will not actively moderate community contributions to this community, except for reactive removal in clear cases of spam or abuse.
* **Conversion Requests:** In the event that you cannot successfully convert a `.tflite` or `.litertlm` model, you may submit a PR requesting conversion support from the LiteRT team.