nip.language_model_server.types.LmTrainingConfig

class nip.language_model_server.types.LmTrainingConfig(*, model_name: str, method: ~typing.Literal['dpo'], dpo_config: ~nip.language_model_server.types.LmDpoTrainingConfig = <factory>, training_lora_config: ~nip.language_model_server.types.LmLoraAdapterConfig | None = None, seed: int = 6198, per_device_train_batch_size: int = 2, model_already_lora_strategy: ~typing.Literal['reuse', 'stack'] = 'reuse', mixed_precision: ~typing.Literal['fp16', 'bf16', 'no'] = 'fp16', gradient_checkpointing: bool = True, use_liger_kernel: bool = True, logging_steps: int = 1)

Configuration for training a language model with the language model server.

Attributes

__fields_set__
model_computed_fields
model_config	Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].
model_extra	Get extra fields set during validation.
model_fields
model_fields_set	Returns the set of fields that have been explicitly set on this model instance.
model_name	The name of the model to be trained, typically a Hugging Face identifier.
method	The method to be used for training.
dpo_config	Configuration specific to DPO training.
training_lora_config	Configuration for the LoRA adapter to use when training.
seed	The random seed to use for training, for reproducibility.
per_device_train_batch_size	The batch size per device (GPU) for training.
model_already_lora_strategy	Strategy for handling models that are already LoRA-adapted.
mixed_precision	The mixed precision to use during training.
gradient_checkpointing	Whether to use gradient checkpointing to save memory during training.
use_liger_kernel	Whether to use the Liger loss function during training.
logging_steps	The period (in steps) at which to log training metrics.

Methods