easydel.trainers.supervised_fine_tuning_trainer.sft_config

class easydel.trainers.supervised_fine_tuning_trainer.sft_config.SFTConfig(auto_shard_states: bool = True, aux_loss_enabled: bool = False, backend: str | None = None, clip_grad: float | None = None, custom_scheduler: tp.Callable[[int], tp.Any] | None = None, dataloader_num_workers: int | None = 0, dataloader_pin_memory: bool | None = False, do_eval: bool = False, do_last_save: bool = True, do_train: bool = True, eval_batch_size: int | None = None, evaluation_steps: int | None = None, extra_optimizer_kwargs: dict = <factory>, frozen_parameters: str | None = None, grain_shard_index: int | None = None, grain_shard_count: int | None = None, gradient_accumulation_steps: int = 1, ids_to_pop_from_dataset: list[str] | None = <factory>, is_fine_tuning: bool = True, init_tx: bool = True, jax_distributed_config: dict | None = None, learning_rate: float = 2e-05, learning_rate_end: float | None = None, log_all_workers: bool = False, log_grad_norms: bool = True, report_metrics: bool = True, log_steps: int = 10, loss_config: LossConfig | None = None, low_mem_usage: bool = True, max_evaluation_steps: int | None = None, max_sequence_length: int | None = 4096, max_training_steps: int | None = None, per_epoch_training_steps: int | None = None, per_epoch_evaluation_steps: int | None = None, model_name: str | None = None, model_parameters: dict | None = None, metrics_to_show_in_rich_pbar: list[str] | None = None, generation_top_p: float | None = None, generation_top_k: int | None = None, generation_temperature: float | None = None, generation_do_sample: bool | None = None, generation_num_return_sequences: int | None = None, generation_max_new_tokens: int | None = None, generation_shard_inputs: bool = True, generation_interval: int | None = None, generation_prompts: list[str | dict[str, tp.Any]] = <factory>, generation_use_train_prompts: bool = False, generation_num_prompts: int = 1, generation_dataset_prompt_field: str | None = 'prompt', generation_extra_kwargs: dict[str, tp.Any] | None = None, generation_config_overrides: dict[str, tp.Any] | None = None, generation_seed: int | None = None, generation_preview_print: bool = False, generation_log_to_wandb: bool = True, use_esurge_generation: bool = True, esurge_use_tqdm: bool = True, esurge_hbm_utilization: float | None = 0.45, esurge_max_num_seqs: int | None = None, esurge_min_input_pad: int | None = None, esurge_page_size: int | None = 32, esurge_silent_mode: bool = True, 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_interval_minutes: float | None = None, save_directory: str = 'EasyDeL-Checkpoints', save_optimizer_state: bool = True, save_steps: int | None = None, save_total_limit: int | None = None, scheduler: AVAILABLE_SCHEDULERS = EasyDeLSchedulers.NONE, shuffle_seed_train: int = 64871, sparsify_module: bool = False, sparse_module_type: AVAILABLE_SPARSE_MODULE_TYPES = 'bcoo', state_apply_fn_kwarguments_to_model: dict | None = None, step_partition_spec: PartitionSpec = PartitionSpec(('dp', 'fsdp'), 'sp'), step_start_point: int | None = None, resume_if_possible: bool = True, shuffle_train_dataset: bool = True, total_batch_size: int = 32, training_time_limit: str | None = None, train_on_inputs: bool = True, trainer_prefix: str | None = 'sfttrainer', truncation_mode: tp.Literal['keep_end', 'keep_start'] = 'keep_end', tx_mu_dtype: jnp.dtype | None = None, track_memory: bool | float = False, use_data_collactor: bool = True, use_grain: bool = True, use_wandb: bool = True, verbose: bool = True, wandb_entity: str | None = None, wandb_name: str | None = None, warmup_steps: int = 0, weight_decay: float = 0.01, weight_distribution_pattern: str = '.*', weight_distribution_log_steps: int = 50, _can_log_metrics: bool | None = None, _im_a_hidden_checkpoint_manager: Checkpointer | None = None, dataset_text_field: str | None = 'text', add_special_tokens: bool = False, packing: bool = False, packing_strategy: str = 'bfd', assistant_only_loss: bool = False, dataset_num_proc: int | None = None, dataset_batch_size: int = 1000, dataset_kwargs: dict[str, typing.Any] | None = None, eval_packing: bool | None = None, num_of_sequences: int = 1024)

Bases: TrainingArguments

Configuration class for the [SFTTrainer].

Parameters

• model_name (str) – The name of the model. Defaults to “SFTTrainer”.

• dataset_text_field (str, optional) – Name of the text field of the dataset. If provided, the trainer will automatically create a [ConstantLengthDataset] based on dataset_text_field. Defaults to None.

• packing (bool, optional) – Controls whether the [ConstantLengthDataset] packs the sequences of the dataset. Defaults to False.

• learning_rate (float, optional) – Initial learning rate for [AdamW] optimizer. The default value replaces that of [~transformers.TrainingArguments]. Defaults to 2e-5.

• dataset_num_proc (int, optional) – Number of processes to use for processing the dataset. Only used when packing=False. Defaults to None.

• dataset_batch_size (int, optional) – Number of examples to tokenize per batch. If dataset_batch_size <= 0 or dataset_batch_size is None, tokenizes the full dataset as a single batch. Defaults to 1000.

• dataset_kwargs (dict[str, Any], optional) – Dictionary of optional keyword arguments to pass when creating packed or non-packed datasets. Defaults to None.

• eval_packing (bool, optional) – Whether to pack the eval dataset. If None, uses the same value as packing. Defaults to None.

• num_of_sequences (int, optional) – Number of sequences to use for the [ConstantLengthDataset]. Defaults to 1024.

add_special_tokens: bool = False

assistant_only_loss: bool = False

dataset_batch_size: int = 1000

dataset_kwargs: dict[str, Any] | None = None

dataset_num_proc: int | None = None

dataset_text_field: str | None = 'text'

eval_packing: bool | None = None

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.

learning_rate: float = 2e-05

num_of_sequences: int = 1024

packing: bool = False

packing_strategy: str = 'bfd'

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.

trainer_prefix: str | None = 'sfttrainer'