quantize_row_q2_K_ref

quantize_row_q3_K_ref

quantize_row_q4_K_ref

quantize_row_q5_K_ref

quantize_row_q6_K_ref

quantize_row_q8_K_ref

Basic row-level K-quant quantization without importance matrix.

Provides 'R' bindings to the 'GGML' tensor library for machine
learning, designed primarily for 'Vulkan' GPU acceleration with full CPU
fallback. 'Vulkan' support is auto-detected at build time on Linux (when
'libvulkan-dev' and 'glslc' are installed) and on Windows (when 'Vulkan'
'SDK' is installed and 'VULKAN_SDK' environment variable is set); all
operations fall back to CPU transparently when no GPU is available.
Implements tensor operations, neural network layers, quantization, and a
'Keras'-like sequential model API for building and training networks.
Includes 'AdamW' (Adam with Weight decay) and 'SGD' (Stochastic Gradient
Descent) optimizers with 'MSE' (Mean Squared Error) and cross-entropy
losses. Also provides a dynamic 'autograd' engine ('PyTorch'-style) with
data-parallel training via 'dp_train()', broadcast arithmetic, 'f16'
(half-precision) support on 'Vulkan' GPU, and a multi-head attention layer
for building Transformer architectures. Serves as backend for 'LLM' (Large
Language Model) inference via 'llamaR' and Stable Diffusion image
generation via 'sdR'. See <https://github.com/ggml-org/ggml> for more
information about the underlying library.

Yuri Baramykov

ggmlR

'GGML' Tensor Operations for Machine Learning

Georgi Gerganov

Jeffrey Quesnelle

Bowen Peng

Mozilla Foundation 

quantize_row_q2_K_ref function

<dl><dt>src_data</dt>
<dd>Numeric vector of float values to quantize</dd>
<dt>n_elements</dt>
<dd>Number of elements to quantize</dd></dl>

Arguments

Quantize Row Reference (K-quants) — quantize_row_q2_K_ref

<dl>

<dt>src_data</dt>
<dd>Numeric vector of float values to quantize</dd>


<dt>n_elements</dt>
<dd>Number of elements to quantize</dd>

</dl>

Quantize Row Reference (K-quants)

quantize_row_q2_K_ref: Quantize Row Reference (K-quants)

Description

Usage

Value

Arguments

See Also