ggmlR-package: ggmlR: 'GGML' Tensor Operations for Machine Learning

In ggmlR: 'GGML' Tensor Operations for Machine Learning

ggmlR: 'GGML' Tensor Operations for Machine Learning

Description

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.

Author(s)

Maintainer: Yuri Baramykov [e-mail] (ORCID)

Other contributors:

• Georgi Gerganov (Author of the GGML library) [contributor, copyright holder]

• Jeffrey Quesnelle (Contributor to ops.cpp) [contributor, copyright holder]

• Bowen Peng (Contributor to ops.cpp) [contributor, copyright holder]

• Mozilla Foundation (Author of llamafile/sgemm.cpp) [contributor, copyright holder]

See Also

Useful links:

• https://github.com/Zabis13/ggmlR

• Report bugs at https://github.com/Zabis13/ggmlR/issues

ggmlR documentation built on July 14, 2026, 1:08 a.m.