.ggmlr_batch_shards

Helper: partition <code>seq_len(M)</code> into <code>n</code> contiguous blocks, as even as
possible (earlier shards get the +1 when M is not divisible by n).

internal

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

.ggmlr_batch_shards function

<dl><dt>M</dt>
<dd>Number of rows (batch size).</dd>
<dt>n</dt>
<dd>Number of shards.</dd></dl>

Arguments

Split a batch index range into (near) equal contiguous shards — .ggmlr_batch_shards

<dl>

<dt>M</dt>
<dd>Number of rows (batch size).</dd>


<dt>n</dt>
<dd>Number of shards.</dd>

</dl>

.ggmlr_batch_shards: Split a batch index range into (near) equal contiguous shards

Description

Usage

Value

Arguments