{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/14"},{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/8"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/6"},{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/17"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/3"}],"enrichment":{"capability":"Converts PyTorch models to .tflite format for on-device deployment on Android, iOS, and IoT devices via LiteRT, with CPU coverage and initial GPU and NPU support.","skillfed_tags":["model-conversion","edge-deployment","quantization"],"use_cases":["Convert a trained vision model to .tflite for inference on Android or iOS devices","Quantize and optimize a transformer-based LLM for on-device deployment using the Generative API","Export a PyTorch model for IoT edge devices that cannot reach cloud inference services","Package a converted model and tokenizer into a .litertlm container for production mobile deployment","Benchmark model performance on target hardware using LiteRT's compiled model API after conversion"],"what_it_does":"LiteRT Torch bridges PyTorch and on-device deployment by converting trained PyTorch models into LiteRT's .tflite format. The library integrates with torch.export() to capture models, then serializes them for execution on mobile and IoT hardware. It provides two main workflows: a PyTorch Converter (Beta) for general model conversion, and a Generative API (Alpha) specialized for transformer-based models and large language models with quantization support.\n\nThe package targets developers building edge ML applications where models must run entirely on-device without cloud connectivity. It handles the conversion pipeline and offers initial support for CPU, GPU, and NPU execution. With 19 runtime dependencies including torch, transformers, jax, and tf-nightly, it brings a substantial environment; deployment of the converted .tflite files themselves requires the separate LiteRT runtime.","worth_installing":"Yes, if you are converting PyTorch models for on-device deployment and targeting Android, iOS, or IoT. The library is actively maintained, has low install friction, and offers both general and LLM-specific conversion paths. Conditions: requires Python 3.10 through 3.13 (3.11 recommended), Linux, and a large dependency footprint; the Generative API is Alpha, so production LLM workflows should expect iteration."},"id":"litert-torch","links":{"html":"https://skillfed.io/packages/litert-torch","md":"https://skillfed.io/packages/litert-torch.md","pypi":"https://pypi.org/project/litert-torch/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-04","license_spdx":null,"license_treatment":"permissive","name":"litert-torch","python_support":"supports_current","summary":"Support PyTorch model conversion with LiteRT."},"popularity":{"monthly_downloads":166622,"position":10487,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.9.3"}