dequantize_row_tq1_0

dequantize_row_tq2_0

Converts ternary quantized data back to float values.
TQ1_0 and TQ2_0 are extreme compression formats.

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 

dequantize_row_tq1_0 function

<dl><dt>raw_data</dt>
<dd>Raw vector containing quantized data</dd>
<dt>n_elements</dt>
<dd>Number of elements to dequantize</dd></dl>

Arguments

Dequantize Row (Ternary) — dequantize_row_tq1_0

<dl>

<dt>raw_data</dt>
<dd>Raw vector containing quantized data</dd>


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

</dl>

dequantize_row_tq1_0: Dequantize Row (Ternary)

Description

Usage

Value

Arguments

See Also