Repository navigation
model-conversion
- Website
- Wikipedia
Core ML tools contain supporting tools for Core ML model conversion, editing, and validation.
Pytorch to Keras/Tensorflow/TFLite conversion made intuitive
Convert 3d model (STL/IGES/STEP/OBJ/FBX) to gltf and compression
Gradio based tool to run opensource LLM models directly from Huggingface
Demonstrates how to divide a DL model into multiple IR model files (division) and introduce a simplest way to implement a custom layer works with OpenVINO IR models.
A flexible utility for converting tensor precision in PyTorch models and safetensors files, enabling efficient deployment across various platforms.
Serving YOLOv8 detection model with tf-serving
This project demonstrates how to download a model from Hugging Face, convert it to GGUF format, and upload it back to Hugging Face using a Colab notebook.
Automated converter for ONNX models (particularly ESRGAN) to RKNN format for Rockchip NPUs. Features include Docker-based conversion and GitHub Actions automation.
This sample shows how to convert TensorFlow model to OpenVINO IR model and how to quantize OpenVINO model.
Objective of this repo is to help under understand ONNX and ONNX runtime and how it can plugged in your project for deploying your custom trained model on different platforms.
The Json.NET Interface Converter/Mapper is a JsonConverter attribute that allows interfaces to be mapped to concrete implementations of those interfaces for use when deserializing an object.
DiTTo is the Distribution Transformation Tool. It is an open-source tool to convert and modify electrical distribution system models.
Wrapture lets you go from a Python-trained model to deployable JavaScript with a single command. It generates TypeScript bindings and a Web/Node-compatible wrapper, using WebGPU/WASM-ready ONNX runtimes.
Web service to convert saved model format from Tensorflow to TensorflowJS JSON models. Not deployed anywhere yet ...
Wrapture lets you go from a Python-trained model to deployable JavaScript with a single command. It generates TypeScript bindings and a Web/Node-compatible wrapper, using WebGPU/WASM-ready ONNX runtimes.
