ggml_result

A <code>ggml_result</code> is the contract object returned by the dispatch layer
and consumed by the injection layer. The embedding is stored cell-by-component
(cells in rows), ready to drop into <code>reducedDim()</code> / a Seurat reduction.

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_result function

<dl><dt>embedding</dt>
<dd>A numeric matrix, cells in rows, components in columns.</dd>
<dt>metadata</dt>
<dd>Named list (e.g. <code>stdev</code>, <code>loadings</code>, backend used).</dd>
<dt>timings</dt>
<dd>Named numeric vector of elapsed seconds per stage.</dd></dl>

Arguments

Construct a single-cell result — ggml_result

<dl>

<dt>embedding</dt>
<dd>A numeric matrix, cells in rows, components in columns.</dd>


<dt>metadata</dt>
<dd>Named list (e.g. <code>stdev</code>, <code>loadings</code>, backend used).</dd>


<dt>timings</dt>
<dd>Named numeric vector of elapsed seconds per stage.</dd>

</dl>

ggml_result: Construct a single-cell result

Description

Usage

Value

Arguments