mlx.nn.RMSNorm# class mlx.nn.RMSNorm(dims: int, eps: float = 1e-05)# Applies Root Mean Square normalization [1] to the inputs. Computes \[y = \frac{x}{\sqrt{E[x^2] + \epsilon}} \gamma\] where \(\gamma\) is a learned per feature dimension parameter initialized at 1. [1]: https://arxiv.org/abs/1910.07467 Parameters: dims (int) – The feature dimension of the input to normalize over eps (float) – A small additive constant for numerical stability
