lmflow.pipeline.finetuner#

The Finetuner class simplifies the process of running finetuning process on a language model for a TunableModel instance with given dataset.

Attributes#

Classes#

Initializes the Finetuner class with given arguments.

Module Contents#

lmflow.pipeline.finetuner.logger[source]#

class lmflow.pipeline.finetuner.Finetuner(model_args, data_args, finetuner_args, *args, **kwargs)[source]#

Bases: lmflow.pipeline.base_tuner.BaseTuner

Initializes the Finetuner class with given arguments.

Parameters:

model_argsModelArguments object.: Contains the arguments required to load the model.
data_argsDatasetArguments object.: Contains the arguments required to load the dataset.
finetuner_argsFinetunerArguments object.: Contains the arguments required to perform finetuning.
argsOptional.: Positional arguments.
kwargsOptional.: Keyword arguments.

model_args[source]#

data_args[source]#

finetuner_args[source]#

last_checkpoint = None[source]#

group_text(tokenized_datasets, model_max_length)[source]#: Groups texts together to form blocks of maximum length model_max_length and returns the processed data as a dictionary.

create_customized_optimizer(base_trainer_class, model_args)[source]#

tune(model, dataset, transform_dataset_in_place=True, data_collator=None)[source]#

Perform tuning for a model

Parameters:

modelTunableModel object.: TunableModel to perform tuning.
dataset:: dataset to train model.