lab_settings package

Submodules

Settings manager.

class core_utils.project.lab_settings.InferenceParams(num_samples: int, max_length: int, batch_size: int, predictions_path: Path, device: str)

Bases: object

Inference parameters.

__init__(*args: Any, **kwargs: Any) → None

batch_size: int

device: str

max_length: int

num_samples: int

predictions_path: Path

class core_utils.project.lab_settings.LabSettings(config_path: Path)

Bases: object

Main model for working with settings.

__init__(config_path: Path) → None

Initialize LabSettings.

Parameters:

config_path (pathlib.Path) – Path to configuration

_dto: LabSettingsModel

property parameters: ParametersModel | None

Property for additional parameters.

Returns:

Parameters DTO.

Return type:

ParametersModel | None

property target_score: int

Property for target score.

Returns:

A target score.

Return type:

int

class core_utils.project.lab_settings.LabSettingsModel(parameters: ParametersModel | None, target_score: int)

Bases: object

DTO for storing labs settings.

__init__(*args: Any, **kwargs: Any) → None

parameters: ParametersModel | None

target_score: int

class core_utils.project.lab_settings.ParametersModel(model: str, dataset: str, metrics: list[Metrics])

Bases: object

Additional parameters of a lab.

__init__(*args: Any, **kwargs: Any) → None

dataset: str

metrics: list[Metrics]

model: str

class core_utils.project.lab_settings.SFTParams(max_length: int, batch_size: int, max_fine_tuning_steps: int, device: str, finetuned_model_path: Path, learning_rate: float, rank: int, alpha: int, target_modules: list[str] | None = None)

Bases: object

Fine-tuning parameters.

__init__(*args: Any, **kwargs: Any) → None

alpha: int

batch_size: int

device: str

finetuned_model_path: Path

learning_rate: float

max_fine_tuning_steps: int

max_length: int

rank: int

target_modules: list[str] | None = None