'GGML' Tensor Operations for Machine Learning R package ggmlR version 0.8.2 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. Software Depends: R (>= 4.1.0) Imports: generics, R6, methods, stats Suggests: testthat (>= 3.0.0), mlr3 (>= 0.21.0), paradox, digest, parsnip, tibble, rlang, dials, lgr, knitr, rmarkdown, Matrix, Seurat, SeuratObject, RSpectra, irlba, uwot, FNN, SingleCellExperiment, SummarizedExperiment, S4Vectors, withr, hardhat, rsample, tune, workflows Yuri Baramykov <lbsbmsu@mail.ru> Comprehensive R Archive Network (CRAN) Yuri Baramykov [aut, cre] (ORCID: <https://orcid.org/0009-0000-7627-4217>), 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) MIT + file LICENSE (https://CRAN.R-project.org/package=ggmlR/LICENSE) 2026-07-22 application/tgz https://CRAN.R-project.org/package=ggmlR doi:10.32614/CRAN.package.ggmlR