This package provides R bindings to the GGML tensor library for machine learning, designed primarily for Vulkan GPU acceleration with full CPU fallback. Vulkan support is auto-detected at build time on Linux (when libvulkan-dev and glslc are installed) and on Windows (when Vulkan SDK is installed and VULKAN_SDK environment variable is set); all operations fall back to CPU transparently when no GPU is available. Implements tensor operations, neural network layers, quantization, and a Keras'-like sequential model API for building and training networks. Includes AdamW (Adam with Weight decay) and SGD (Stochastic Gradient Descent) optimizers with MSE (Mean Squared Error) and cross-entropy losses. Also provides a dynamic autograd engine ('PyTorch'-style) with data-parallel training via dp_train()', broadcast arithmetic, f16 (half-precision) support on Vulkan GPU, and a multi-head attention layer for building Transformer architectures. Serves as backend for LLM (Large Language Model) inference via llamaR and Stable Diffusion image generation via sdR'. See <https://github.com/ggml-org/ggml> for more information about the underlying library.