easydel.trainers.odds_ratio_preference_optimization_trainer.orpo_config

class easydel.trainers.odds_ratio_preference_optimization_trainer.orpo_config.ORPOConfig(auto_shard_states: bool = True, aux_loss_enabled: bool = False, backend: tp.Optional[str] = None, clip_grad: tp.Optional[float] = None, custom_scheduler: tp.Optional[tp.Callable[[int], tp.Any]] = None, dataloader_num_workers: tp.Optional[int] = 0, dataloader_pin_memory: tp.Optional[bool] = False, do_eval: bool = False, do_last_save: bool = True, do_train: bool = True, eval_batch_size: tp.Optional[int] = None, evaluation_steps: tp.Optional[int] = None, extra_optimizer_kwargs: dict = <factory>, frozen_parameters: tp.Optional[str] = None, gradient_accumulation_steps: int = 1, ids_to_pop_from_dataset: tp.Optional[tp.List[str]] = <factory>, is_fine_tuning: bool = True, init_tx: bool = True, jax_distributed_config: tp.Optional[dict] = None, learning_rate: float = 1e-06, learning_rate_end: tp.Optional[float] = None, log_all_workers: bool = False, log_grad_norms: bool = True, report_metrics: bool = True, log_steps: int = 10, loss_config: tp.Optional[LossConfig] = None, low_mem_usage: bool = True, max_evaluation_steps: tp.Optional[int] = None, max_sequence_length: tp.Optional[int] = 4096, max_training_steps: tp.Optional[int] = None, model_name: str = 'ORPOTrainer', model_parameters: tp.Optional[dict] = None, metrics_to_show_in_rich_pbar: tp.Optional[tp.List[str]] = None, num_train_epochs: int = 10, offload_dataset: bool = False, offload_device_type: str = 'cpu', offload_device_index: int = 0, optimizer: AVAILABLE_OPTIMIZERS = EasyDeLOptimizers.ADAMW, performance_mode: bool = False, pruning_module: tp.Any = None, process_zero_is_admin: bool = True, progress_bar_type: tp.Literal['tqdm', 'rich', 'json'] = 'tqdm', remove_ckpt_after_load: bool = False, remove_unused_columns: bool = True, report_steps: int = 5, save_directory: str = 'EasyDeL-Checkpoints', save_optimizer_state: bool = True, save_steps: tp.Optional[int] = None, save_total_limit: tp.Optional[int] = None, scheduler: AVAILABLE_SCHEDULERS = EasyDeLSchedulers.NONE, sparsify_module: bool = False, sparse_module_type: AVAILABLE_SPARSE_MODULE_TYPES = 'bcoo', state_apply_fn_kwarguments_to_model: tp.Optional[dict] = None, step_partition_spec: PartitionSpec = PartitionSpec(('dp', 'fsdp'), 'sp'), step_start_point: tp.Optional[int] = None, shuffle_train_dataset: bool = True, total_batch_size: int = 32, training_time_limit: tp.Optional[str] = None, train_on_inputs: bool = True, truncation_mode: tp.Literal['keep_end', 'keep_start'] = 'keep_end', tx_mu_dtype: tp.Optional[jnp.dtype] = None, track_memory: bool = False, use_data_collactor: bool = True, use_wandb: bool = True, verbose: bool = True, wandb_entity: tp.Optional[str] = None, warmup_steps: int = 0, weight_decay: float = 0.01, weight_distribution_pattern: str = '.*?(layernorm|norm).*?', weight_distribution_log_steps: int = 0, max_length: ~typing.Optional[int] = 1024, max_prompt_length: ~typing.Optional[int] = 512, max_completion_length: ~typing.Optional[int] = None, beta: float = 0.1, disable_dropout: bool = True, label_pad_token_id: int = -100, padding_value: ~typing.Optional[int] = None, generate_during_eval: bool = False, is_encoder_decoder: ~typing.Optional[bool] = None, dataset_num_proc: ~typing.Optional[int] = None)

Bases: TrainingArguments

Configuration class for ORPO training settings.

This class inherits from TrainingArguments and holds configuration parameters specific to the ORPO model training. The dataclass automatically generates an initializer, and the __post_init__ method further processes some of the parameters after object initialization.

model_name

The name of the model. Default is “ORPOTrainer”.

Type

str

learning_rate

The learning rate used during training. Default is 1e-6.

Type

float

max_length

The maximum allowed sequence length for the input. Default is 1024.

Type

Optional[int]

max_prompt_length

The maximum allowed length of the prompt portion of the input. Default is 512.

Type

Optional[int]

max_completion_length

The maximum allowed length of the completion. If not provided, it is set to max_length - max_prompt_length.

Type

Optional[int]

beta

A hyperparameter beta, with a default value of 0.1.

Type

float

disable_dropout

Flag to disable dropout during training. Default is True.

Type

bool

label_pad_token_id

The token id used for padding labels. Default is -100.

Type

int

padding_value

The value used for padding sequences. Default is None.

Type

Optional[int]

generate_during_eval

Flag indicating whether to generate sequences during evaluation. Default is False.

Type

bool

is_encoder_decoder

Flag to indicate if the model is encoder-decoder. Default is None.

Type

Optional[bool]

model_init_kwargs

Additional keyword arguments for model initialization. Default is None.

Type

Optional[Dict[str, Any]]

dataset_num_proc

Number of processes to use for dataset processing. Default is None.

Type

Optional[int]

max_sequence_length

Computed attribute representing the maximum sequence length used for training. It is set in the __post_init__ method.

Type

int

beta: float = 0.1

dataset_num_proc: Optional[int] = None

disable_dropout: bool = True

extra_optimizer_kwargs: dict

classmethod from_dict(data: Dict[str, Any]) → T

Deserializes a dictionary into a PyTree object.

classmethod from_json(json_str: str) → T

Deserializes a JSON string into a PyTree object.

generate_during_eval: bool = False

ids_to_pop_from_dataset: tp.Optional[tp.List[str]]

is_encoder_decoder: Optional[bool] = None

label_pad_token_id: int = -100

learning_rate: float = 1e-06

max_completion_length: Optional[int] = None

max_length: Optional[int] = 1024

max_prompt_length: Optional[int] = 512

model_name: str = 'ORPOTrainer'

padding_value: Optional[int] = None

replace(**kwargs)

Creates a new instance with specified fields replaced.

to_dict() → Dict[str, Any]

Serializes the PyTree object to a dictionary.

to_json(**kwargs) → str

Serializes the PyTree object to a JSON string.