ggmlR: 'GGML' Tensor Operations for Machine Learning
Provides 'R' bindings to the 'GGML' tensor library for machine
learning, designed primarily for 'Vulkan' GPU acceleration with full CPU
fallback. 'Vulkan' support is auto-detected at build time on Linux (when
'libvulkan-dev' and 'glslc' are installed) and on Windows (when 'Vulkan'
'SDK' is installed and 'VULKAN_SDK' environment variable is set); all
operations fall back to CPU transparently when no GPU is available.
Implements tensor operations, neural network layers, quantization, and a
'Keras'-like sequential model API for building and training networks.
Includes 'AdamW' (Adam with Weight decay) and 'SGD' (Stochastic Gradient
Descent) optimizers with 'MSE' (Mean Squared Error) and cross-entropy
losses. Also provides a dynamic 'autograd' engine ('PyTorch'-style) with
data-parallel training via 'dp_train()', broadcast arithmetic, 'f16'
(half-precision) support on 'Vulkan' GPU, and a multi-head attention layer
for building Transformer architectures. Serves as backend for 'LLM' (Large
Language Model) inference via 'llamaR' and Stable Diffusion image
generation via 'sdR'. See <https://github.com/ggml-org/ggml> for more
information about the underlying library.
| Version: |
0.6.1 |
| Depends: |
R (≥ 4.1.0) |
| Suggests: |
testthat (≥ 3.0.0) |
| Published: |
2026-02-22 |
| 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:
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