Struct AdapterTrainingState 

pub struct AdapterTrainingState {
    pub global_step: usize,
    pub epoch: usize,
    pub steps_in_epoch: usize,
    pub accumulated_steps: usize,
    pub best_val_loss: Option<f64>,
    /* private fields */
}

Training state for adapter fine-tuning.

Fields

global_step: usize

Current global step

epoch: usize

Current epoch

steps_in_epoch: usize

Steps within current epoch

accumulated_steps: usize

Accumulated gradient steps (for gradient accumulation)

best_val_loss: Option<f64>

Best validation loss seen

Implementations

impl AdapterTrainingState

pub fn new(config: AdapterTrainingConfig) -> AdapterTrainingState

Create new training state with the given configuration.

pub fn current_lr(&self) -> f64

Get the current learning rate based on schedule.

pub fn should_update(&self) -> bool

Check if gradient accumulation is complete.

pub fn step(&mut self) -> bool

Step after processing a batch.

Returns true if an optimizer step should be taken.

pub fn new_epoch(&mut self)

Start a new epoch.

pub fn update_best_val_loss(&mut self, val_loss: f64) -> bool

Update best validation loss.

Returns true if this is the new best loss.

pub fn gradient_accumulation_steps(&self) -> usize

Get gradient accumulation steps.

pub fn max_grad_norm(&self) -> Option<f64>

Get maximum gradient norm for clipping.

pub fn weight_decay(&self) -> f64

Get weight decay.

Trait Implementations

impl Clone for AdapterTrainingState

fn clone(&self) -> AdapterTrainingState

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for AdapterTrainingState

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

Formats the value using the given formatter.