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cff-version: 1.2.0
message: 'To cite package "ggmlR" in publications use:'
type: software
license: MIT
title: 'ggmlR: ''GGML'' Tensor Operations for Machine Learning'
version: 0.7.9
doi: 10.32614/CRAN.package.ggmlR
abstract: 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.
authors:
- family-names: Baramykov
  given-names: Yuri
  email: lbsbmsu@mail.ru
  orcid: https://orcid.org/0009-0000-7627-4217
repository: https://zabis13.r-universe.dev
repository-code: https://github.com/Zabis13/ggmlR
commit: b37a7ea0894687bc6824b1e81e3ee138680c32a4
url: https://github.com/Zabis13/ggmlR
date-released: '2026-06-10'
contact:
- family-names: Baramykov
  given-names: Yuri
  email: lbsbmsu@mail.ru
  orcid: https://orcid.org/0009-0000-7627-4217