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: 0.7.2
Depends: R (≥ 4.1.0)
Imports: generics
Suggests: Rcpp, testthat (≥ 3.0.0), mlr3 (≥ 0.21.0), paradox, R6, checkmate, digest, mlr3pipelines, parsnip, tibble, rlang, dials, lgr, knitr, rmarkdown, rpart, future
Published: 2026-04-15
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 [issues need fixing before 2026-05-07]

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_0.7.2.tar.gz
Windows binaries: r-devel: ggmlR_0.7.2.zip, r-release: ggmlR_0.7.2.zip, r-oldrel: ggmlR_0.7.0.zip
macOS binaries: r-release (arm64): ggmlR_0.7.0.tgz, r-oldrel (arm64): ggmlR_0.6.3.tgz, r-release (x86_64): ggmlR_0.7.2.tgz, r-oldrel (x86_64): ggmlR_0.7.2.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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