ggml_rms_norm: RMS Normalization (Graph)

In ggmlR: 'GGML' Tensor Operations for Machine Learning

RMS Normalization (Graph)

Description

Creates a graph node for RMS (Root Mean Square) normalization. Normalizes by x / sqrt(mean(x^2) + eps). CRITICAL for LLaMA models.

Usage

ggml_rms_norm(ctx, a, eps = 1e-05)

Arguments

ctx	GGML context
a	Input tensor
eps	Epsilon value for numerical stability (default: 1e-5)

Value

Tensor representing the RMS normalization operation

Examples

ctx <- ggml_init(16 * 1024 * 1024)
a <- ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4)
ggml_set_f32(a, c(1, 2, 3, 4))
result <- ggml_rms_norm(ctx, a, eps = 1e-5)
graph <- ggml_build_forward_expand(ctx, result)
ggml_graph_compute(ctx, graph)
output <- ggml_get_f32(result)
# sqrt(mean(output^2)) should be ~1
ggml_free(ctx)

ggmlR documentation built on July 14, 2026, 1:08 a.m.