Package: ggmlR
Type: Package
Title: 'GGML' Tensor Operations for Machine Learning
Version: 0.6.7
Authors@R: c(
    person("Yuri", "Baramykov",
        email = "lbsbmsu@mail.ru",
        role = c("aut", "cre")),
    person("Georgi", "Gerganov",
        role = c("ctb", "cph"),
        comment = "Author of the GGML library"),
    person("Jeffrey", "Quesnelle",
        role = c("ctb", "cph"),
        comment = "Contributor to ops.cpp"),
    person("Bowen", "Peng",
        role = c("ctb", "cph"),
        comment = "Contributor to ops.cpp"),
    person("Mozilla Foundation",
        role = c("ctb", "cph"),
        comment = "Author of llamafile/sgemm.cpp")
    )
Description: 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.
    Supports tensors up to 5D natively (GGML_MAX_DIMS=5).
    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. Supports 'ONNX' model import via
    built-in zero-dependency 'protobuf' parser: load 'pretrained' 'ONNX' models
    from 'PyTorch', 'TensorFlow', or other frameworks and run inference on
    'Vulkan' GPU or CPU. Covers 50+ 'ONNX' ops including convolutions,
    attention primitives, normalization, quantized ops, shape operations,
    'ScatterElements' (with 'Vulkan' 'atomicAdd' for GNN scatter-add),
    and fused custom ops (RelPosBias2D for 'BoTNet') — sufficient to run
    real-world models such as 'RoBERTa', 'BERT', 'GPT-NeoX', 'SqueezeNet',
    'Inception v3', 'BAT-ResNeXt', 'BoTNet', and 'MNIST' out of the box.
    Uses a dedicated weight buffer architecture for zero-overhead repeated
    inference — weights are loaded to GPU once and never re-transferred.
    Serves as backend for 'LLM' (Large Language Model)
    inference via 'llamaR' and Stable Diffusion image generation via 'sd2R'.
    See <https://github.com/ggml-org/ggml> for more information about the
    underlying library.
Depends: R (>= 4.1.0)
License: MIT + file LICENSE
URL: https://github.com/Zabis13/ggmlR
BugReports: https://github.com/Zabis13/ggmlR/issues
Encoding: UTF-8
SystemRequirements: C++17, GNU make, libvulkan-dev, glslc (optional,
        for GPU on Linux), 'Vulkan' 'SDK' (optional, for GPU on
        Windows)
Suggests: testthat (>= 3.0.0)
RoxygenNote: 7.3.3
Config/testthat/edition: 3
NeedsCompilation: yes
Packaged: 2026-03-29 17:30:03 UTC; yuri
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@mail.ru>
Repository: CRAN
Date/Publication: 2026-03-29 17:50:02 UTC
Built: R 4.4.3; x86_64-w64-mingw32; 2026-03-29 23:51:19 UTC; windows
Archs: x64
