ggmlR: 'GGML' Tensor Operations for Machine Learning

Provides 'R' bindings to the 'GGML' tensor library for efficient machine learning computation. Implements core tensor operations including element-wise arithmetic, reshaping, and matrix multiplication. Supports neural network layers (attention, convolutions, normalization), activation functions, and quantization. Features optimization/training API with 'AdamW' (Adam with Weight decay) and 'SGD' (Stochastic Gradient Descent) optimizers, 'MSE' (Mean Squared Error) and cross-entropy losses. Multi-backend support with CPU and optional 'Vulkan' GPU (Graphics Processing Unit) acceleration. See <https://github.com/ggml-org/ggml> for more information about the underlying library.

Version:	0.5.1
Suggests:	testthat (≥ 3.0.0), knitr, rmarkdown
Published:	2026-02-09
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
Materials:	README, NEWS
CRAN checks:	ggmlR results [issues need fixing before 2026-02-24]

Documentation:

Reference manual:	ggmlR.html , ggmlR.pdf
Vignettes:	Multi-GPU Inference (source, R code) Working with Quantized Models (source, R code) Vulkan GPU Backend (source, R code)

Downloads:

Package source:	ggmlR_0.5.1.tar.gz
Windows binaries:	r-devel: ggmlR_0.5.1.zip, r-release: ggmlR_0.5.1.zip, r-oldrel: ggmlR_0.5.1.zip
macOS binaries:	r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): ggmlR_0.5.1.tgz, r-oldrel (x86_64): ggmlR_0.5.1.tgz

Linking:

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