{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/12"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/7"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/15"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"Converts machine learning models to LiteRT format for deployment on mobile and embedded devices with low latency and minimal binary size.","skillfed_tags":["model-conversion","mobile-ml","embedded-inference"],"use_cases":["Convert a trained TensorFlow model to LiteRT format for deployment on an Android app.","Optimize a neural network for inference on iOS devices with strict latency and memory constraints.","Prepare a machine learning model for edge deployment on embedded Linux systems.","Automate model conversion in a CI/CD pipeline targeting multiple mobile platforms.","Reduce model binary size for distribution on devices with limited storage."],"what_it_does":"litert-converter is a model conversion tool that transforms machine learning models into LiteRT format, the official runtime for running inference on mobile and embedded devices. It bridges the gap between model development (typically in TensorFlow or similar frameworks) and deployment on resource-constrained hardware like Android and iOS, handling the translation to a format optimized for low latency and small binary footprint.\n\nThe package depends on a substantial ML stack\u2014numpy, protobuf, flatbuffers, lark, xdsl, and ml_dtypes\u2014to parse, transform, and serialize models. It is actively maintained and recently released, with wheels pre-built for Python 3.10 through 3.14 on macOS arm64 and Linux x86_64, though this platform specificity means Windows users or those on other architectures will need to build from source or find alternative solutions.","worth_installing":"Yes, if you are targeting mobile or embedded device deployment and your model is compatible with LiteRT. The package is actively maintained, permissively licensed, and directly supported by the TensorFlow ecosystem. Install friction is moderate due to platform-specific wheels and a large dependency tree, but this is typical for ML tooling. Verify that your target platform (macOS arm64 or Linux x86_64) and Python version (3.10\u20133.14) are supported before committing."},"id":"litert-converter","links":{"html":"https://skillfed.io/packages/litert-converter","md":"https://skillfed.io/packages/litert-converter.md","pypi":"https://pypi.org/project/litert-converter/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-10","license_spdx":null,"license_treatment":"permissive","name":"litert-converter","python_support":"unspecified","summary":"LiteRT is for mobile and embedded devices."},"popularity":{"monthly_downloads":244790,"position":8745,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.3.1"}