ggml_arange

Creates a 1D F32 tensor with values from <code>start</code> (inclusive) to
<code>stop</code> (exclusive) in steps of <code>step</code>.

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_arange function

<dl><dt>ctx</dt>
<dd>GGML context</dd>
<dt>start</dt>
<dd>Start value (inclusive)</dd>
<dt>stop</dt>
<dd>Stop value (exclusive)</dd>
<dt>step</dt>
<dd>Step between values (default 1)</dd></dl>

Arguments

Arange (Graph) — ggml_arange

<dl>

<dt>ctx</dt>
<dd>GGML context</dd>


<dt>start</dt>
<dd>Start value (inclusive)</dd>


<dt>stop</dt>
<dd>Stop value (exclusive)</dd>


<dt>step</dt>
<dd>Step between values (default 1)</dd>

</dl>

ggml_arange: Arange (Graph)

Description

Usage

Value

Arguments