ggmlR: 'GGML' Tensor Operations for Machine Learning

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.

Version:	0.7.6
Depends:	R (≥ 4.1.0)
Imports:	generics, R6
Suggests:	testthat (≥ 3.0.0), mlr3 (≥ 0.21.0), paradox, digest, parsnip, tibble, rlang, dials, lgr, knitr, rmarkdown
Published:	2026-04-22
DOI:	10.32614/CRAN.package.ggmlR
Author:	Yuri Baramykov [aut, cre], Georgi Gerganov [ctb, cph] (Author of the GGML library), Jeffrey Quesnelle [ctb, cph] (Contributor to ops.cpp), Bowen Peng [ctb, cph] (Contributor to ops.cpp), Mozilla Foundation [ctb, cph] (Author of llamafile/sgemm.cpp)
Maintainer:	Yuri Baramykov <lbsbmsu at mail.ru>
BugReports:	https://github.com/Zabis13/ggmlR/issues
License:	MIT + file LICENSE
URL:	https://github.com/Zabis13/ggmlR
NeedsCompilation:	yes
SystemRequirements:	C++17, GNU make, libvulkan-dev, glslc (optional, for GPU on Linux), 'Vulkan' 'SDK' (optional, for GPU on Windows)
Materials:	README, NEWS
CRAN checks:	ggmlR results

Documentation:

Reference manual:	ggmlR.html , ggmlR.pdf
Vignettes:	Autograd Engine (source, R code) Data-Parallel Training (source, R code) Using ggmlR as a Backend in Your Package (source, R code) GPU / Vulkan Backend (source, R code) Keras-like API in ggmlR (source, R code) mlr3 Integration (source, R code) ONNX Model Import (source, R code) Quantization (source, R code) tidymodels / parsnip Integration (source, R code)

Downloads:

Package source:	ggmlR_0.7.6.tar.gz
Windows binaries:	r-devel: ggmlR_0.7.6.zip, r-release: ggmlR_0.7.6.zip, r-oldrel: ggmlR_0.7.6.zip
macOS binaries:	r-release (arm64): ggmlR_0.7.6.tgz, r-oldrel (arm64): ggmlR_0.7.6.tgz, r-release (x86_64): ggmlR_0.7.6.tgz, r-oldrel (x86_64): ggmlR_0.7.6.tgz
Old sources:	ggmlR archive

Reverse dependencies:

Reverse depends:	llamaR
Reverse imports:	sd2R
Reverse linking to:	llamaR, sd2R
Reverse suggests:	cayleyR

Linking:

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