#model conversion

Access LLaMa and RWKV models in ONNX format to enhance inference efficiency on devices with limited memory. This project bypasses the need for torch or transformers, supports memory pooling, and is compatible with FPGA/NPU/GPGPU hardware, enabling streamlined conversion to fp16 or TVM.

Paddle2ONNX facilitates the transformation of PaddlePaddle models into the ONNX format, supporting integration with multiple inference engines such as TensorRT and OpenVINO. Compatible with PaddlePaddle version 2.6.0 and ONNXRuntime 1.10.0 and above, it offers command-line tools for model parameter adjustments and ONNX model optimization, enhancing deployment flexibility across platforms. Developers can also engage in its development by following contribution guidelines, making it a valuable tool for cross-platform machine learning deployment.

Coremltools is a Python package facilitating the conversion of machine learning models from libraries such as TensorFlow and PyTorch to Apple's Core ML format. It supports both neural and non-neural frameworks like scikit-learn and XGBoost, allowing model optimization and operation directly on macOS. Coremltools enhances app integration using Xcode by enabling model prediction and verification on-device, maintaining data privacy and app responsiveness without needing a network connection. Discover installation options and explore guides for effective use.

tensorflow-onnx efficiently converts models from TensorFlow, Keras, Tensorflow.js, and Tflite to ONNX, supporting multiple TensorFlow versions, Python environments, and ONNX opsets. It provides developers with a reliable solution for seamless model migration, enhancing cross-platform compatibility and performance. Features include detailed CLI usage, troubleshooting documentation, and installation guides. Regular updates ensure support for emerging frameworks and experimental features, aligning with current AI workflows.

X2Paddle facilitates the conversion of deep learning models from frameworks like Caffe, TensorFlow, ONNX, and PyTorch to PaddlePaddle. It provides detailed API comparison documents and supports automated conversion for both prediction and training models. The tool is designed for ease of use, enabling model conversion through a single command or API call. It can be installed via pip or source and is known for its flexibility, supporting deployment on varied hardware such as CPU, GPU, and ARM, while integrating with PaddleLite for enhanced support. X2Paddle offers up-to-date tools for efficient model conversion.

ONNXMLTools enables the conversion of machine learning models from toolkits like TensorFlow, scikit-learn, and Core ML into the ONNX format, supporting frameworks such as LightGBM, XGBoost, and CatBoost. This tool improves model interoperability and deployment across platforms. Installation via PyPi or source builds are available, with Python 3.7+ compatibility and specific framework dependencies. Detailed examples and extensive testing bolster reliability in model conversions.

This project allows scikit-learn models to be transformed into ONNX format for optimized performance with ONNX Runtime. Compatibility with opset 21 and support for a variety of external converters extend its functionality across numerous models. Easily installable via PyPi or source, the project offers extensive documentation and community support for smooth integration. Contributions are warmly accepted under the Apache License v2.0.

Exporters facilitates the conversion of Hugging Face Transformers models to Core ML, ensuring deployment across Apple platforms like macOS and iOS. It offers ready-made configurations for models like BERT and GPT2, supports the ML Program format, and provides options for model optimization and quantization. The package underscores the importance of validation on macOS and suggests pre-optimization with Hugging Face's 'Optimum' for mobile use.

RKLLM facilitates AI model deployment on Rockchip platforms like RK3588 and RK3576, supporting various models including LLAMA and ChatGLM3-6B. With tools like RKLLM-Toolkit and Runtime, it maximizes NPU performance. Version 1.1 offers new model support and optimized processing, noted for its quantization accuracy, though not backwards compatible. Compatible with Python 3.8 and 3.10, it suits diverse environments.