lmflow.pipeline.finetuner#

Attributes#

send_example_telemetry

logger

Classes#

Finetuner

Initializes the Finetuner class with given arguments.

Module Contents#

lmflow.pipeline.finetuner.send_example_telemetry = None[source]#
lmflow.pipeline.finetuner.logger[source]#
class lmflow.pipeline.finetuner.Finetuner(model_args: lmflow.args.ModelArguments, data_args: lmflow.args.DatasetArguments, finetuner_args: lmflow.args.FinetunerArguments, *args, **kwargs)[source]#

Bases: lmflow.pipeline.base_tuner.BaseTuner

Initializes the Finetuner class with given arguments.

Parameters:

  • model_args (ModelArguments object.) – Contains the arguments required to load the model.

  • data_args (DatasetArguments object.) – Contains the arguments required to load the dataset.

  • finetuner_args (FinetunerArguments object.) – Contains the arguments required to perform finetuning.

  • args (Optional.) – Positional arguments.

  • kwargs (Optional.) – 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.

tune(model: lmflow.models.hf_decoder_model.HFDecoderModel | lmflow.models.hf_text_regression_model.HFTextRegressionModel, dataset: lmflow.datasets.dataset.Dataset, transform_dataset_in_place=True, data_collator=None)[source]#

Perform tuning for a model

Parameters:

  • model (TunableModel object.) – TunableModel to perform tuning.

  • dataset – dataset to train model.