Struct Lora 

pub struct Lora {
    pub rank: usize,
    pub alpha: f64,
    pub init_std: Option<f64>,
    pub param_group: ParamGroup,
}

A Reparameterizer that attaches LoRA adapters to 2-D weight parameters.

It is applied via Module::apply_lora.

All existing floating-point parameters are frozen. Matching rank-2 parameters receive trainable LoRA adapters; other parameters remain frozen without adapters. No model or layer code needs to change—the same Linear (and any other module) keeps working, now producing base + scale * (a @ b) for adapted weights.

Fields

rank: usize

Rank of the low-rank decomposition.

alpha: f64

Scaling numerator; the adapter contribution is scaled by alpha / rank.

init_std: Option<f64>

Standard deviation used to initialize the A factor. Defaults to 1 / rank.

param_group: ParamGroup

The parameter group on which to apply the LoRA.

Implementations

impl Lora

pub fn new(rank: usize, alpha: f64) -> Self

Create a new LoRA reparameterizer with the given rank and alpha.

pub fn set_param_group(self, group: ParamGroup) -> Self

Set the parameter group on which to apply LoRA adapters.

Trait Implementations

impl Clone for Lora

fn clone(&self) -> Lora

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for Lora

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Reparameterizer for Lora

type Reparam = LoraAdapter

Reparameterization produced for a parameter.

fn reparameterize<const D: usize>( &mut self, path: &str, param: Param<Tensor<D>>, ) -> (Param<Tensor<D>>, Option<Self::Reparam>)

Prepare a parameter and optionally create a reparameterization for it.