Struct TrainerConfig 

pub struct TrainerConfig {

    pub teacher_model: String,
    pub student_model: String,
    pub output_dir: PathBuf,
    pub distillation_loss: DistillationLoss,
    pub progressive: Option<ProgressiveDistillation>,
    pub attention_transfer: Option<AttentionTransfer>,
    pub fine_tune: FineTuneConfig,
    pub epochs: usize,
    pub steps_per_epoch: usize,
    pub log_every_n_steps: usize,
    pub save_every_n_steps: usize,
    pub eval_every_n_steps: usize,
    pub max_grad_norm: f32,
    pub seed: u64,
}

Distillation training configuration

Fields

teacher_model: String

Teacher model ID or path

student_model: String

Student model ID or path

output_dir: PathBuf

Output directory for checkpoints and logs

distillation_loss: DistillationLoss

Distillation loss configuration

progressive: Option<ProgressiveDistillation>

Progressive distillation (hidden state matching)

attention_transfer: Option<AttentionTransfer>

Attention transfer

fine_tune: FineTuneConfig

Fine-tuning configuration for student

epochs: usize

Number of training epochs

steps_per_epoch: usize

Steps per epoch (0 = auto-detect from dataset)

log_every_n_steps: usize

Logging frequency (steps)

save_every_n_steps: usize

Checkpoint frequency (steps)

eval_every_n_steps: usize

Evaluation frequency (steps)

max_grad_norm: f32

Maximum gradient norm for clipping

seed: u64

Random seed

Implementations

impl TrainerConfig

pub fn new(teacher: impl Into<String>, student: impl Into<String>) -> Self

Create new trainer config with teacher and student models

pub fn temperature(self, temp: f32) -> Self

Set temperature for distillation

pub fn alpha(self, alpha: f32) -> Self

Set alpha for soft vs hard loss weight

pub fn with_progressive(self, layer_mapping: Vec<(usize, usize)>) -> Self

Enable progressive distillation with layer mapping

pub fn with_attention_transfer(self, weight: f32) -> Self

Enable attention transfer

pub fn output_dir(self, path: impl Into<PathBuf>) -> Self

Set output directory

pub fn epochs(self, n: usize) -> Self

Set number of epochs

Trait Implementations

impl Clone for TrainerConfig

fn clone(&self) -> TrainerConfig

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for TrainerConfig

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

Formats the value using the given formatter.

impl Default for TrainerConfig

fn default() -> Self

Returns the “default value” for a type.