ggml_unmarshal_model

Reconstructs a ggmlR model previously produced by
<code>ggml_marshal_model</code>. Validates the container's format tag,
schema version, and (if digest is installed) the SHA-256 checksum of
the payload before deserializing.

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_unmarshal_model function

<dl><dt>x</dt>
<dd>A <code>"ggmlR_marshaled"</code> container.</dd>
<dt>backend</dt>
<dd>Backend selection passed through to
<code>ggml_load_model</code>. Default <code>"auto"</code>.</dd></dl>

Arguments

Unmarshal a ggmlR model from an in-memory container — ggml_unmarshal_model

<dl>

<dt>x</dt>
<dd>A <code>"ggmlR_marshaled"</code> container.</dd>


<dt>backend</dt>
<dd>Backend selection passed through to
<code>ggml_load_model</code>. Default <code>"auto"</code>.</dd>

</dl>

ggml_unmarshal_model: Unmarshal a ggmlR model from an in-memory container

Description

Usage

Value

Arguments

See Also