Nothing
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.
Getting started
Package overview
README.md
Autograd Engine
Data-Parallel Training
GPU / Vulkan Backend
Keras-like API in ggmlR
mlr3 Integration
Multi-GPU: Tensor, Pipeline and Hybrid Parallelism
ONNX Model Import
Quantization
Quickstart: from data to prediction in ~10 lines
Single-cell GPU Acceleration with Seurat
tidymodels / parsnip Integration
Using ggmlR as a Backend in Your Package
Browse package contents
Package details |
|
|---|---|
| Author | 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) |
| Maintainer | Yuri Baramykov <lbsbmsu@mail.ru> |
| License | MIT + file LICENSE |
| Version | 0.8.1 |
| URL | https://github.com/Zabis13/ggmlR |
| Package repository | View on CRAN |
| Installation |
Install the latest version of this package by entering the following in R:
|
Try the ggmlR package in your browser
Any scripts or data that you put into this service are public.
