ggml_model_backend

Reports the backend the model was actually compiled onto, making a
silent <code>backend = "auto"</code> fallback to CPU (when no GPU is available)
inspectable. Works on a raw sequential/functional model or a fitted parsnip
engine object.

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.

Yuri Baramykov

ggmlR

'GGML' Tensor Operations for Machine Learning

ggml_model_backend function

<dl><dt>object</dt>
<dd>A compiled/fitted <code>ggml_sequential_model</code>,
<code>ggml_functional_model</code> or <code>ggmlr_parsnip_model</code>.</dd>
<dt>verbose</dt>
<dd>If <code>FALSE</code> (default) returns a single string
(<code>"vulkan"</code> or <code>"cpu"</code>). If <code>TRUE</code> returns a list with
details (see Value).</dd></dl>

Arguments

Backend a fitted ggml model actually ran on — ggml_model_backend

<dl>

<dt>object</dt>
<dd>A compiled/fitted <code>ggml_sequential_model</code>,
<code>ggml_functional_model</code> or <code>ggmlr_parsnip_model</code>.</dd>


<dt>verbose</dt>
<dd>If <code>FALSE</code> (default) returns a single string
(<code>"vulkan"</code> or <code>"cpu"</code>). If <code>TRUE</code> returns a list with
details (see Value).</dd>

</dl>

ggml_model_backend: Backend a fitted ggml model actually ran on

Description

Usage

Value

Arguments

See Also