This package provides R bindings to the GGML tensor library for machine learning, optimized for Vulkan GPU acceleration with a transparent CPU fallback. The package features a Keras'-like sequential API and a PyTorch'-style autograd engine for building, training, and deploying neural networks. Key capabilities include high-performance 5D tensor operations, f16 precision, and efficient quantization. It supports native ONNX model import (50+ operators) and GGUF weight loading from the llama.cpp and Hugging Face ecosystems. Designed for zero-overhead inference via dedicated weight buffering, it integrates seamlessly as a parsnip engine for tidymodels and provides first-class learners for the mlr3 framework. See <https://github.com/ggml-org/ggml> for more information about the underlying library.