Class _TunableModelMixin (1.45.0) 

_TunableModelMixin(model_id: str, endpoint_name: typing.Optional[str] = None)

Model that can be tuned with supervised fine tuning (SFT).

Methods

_TunableModelMixin

_TunableModelMixin(model_id: str, endpoint_name: typing.Optional[str] = None)

Creates a LanguageModel.

This constructor should not be called directly. Use LanguageModel.from_pretrained(model_name=...) instead.

Parameters
Name Description
model_id str

Identifier of a Vertex LLM. Example: "text-bison@001"
endpoint_name typing.Optional[str]

Vertex Endpoint resource name for the model

tune_model

tune_model(
    training_data: typing.Union[str, pandas.core.frame.DataFrame],
    *,
    train_steps: typing.Optional[int] = None,
    learning_rate: typing.Optional[float] = None,
    learning_rate_multiplier: typing.Optional[float] = None,
    tuning_job_location: typing.Optional[str] = None,
    tuned_model_location: typing.Optional[str] = None,
    model_display_name: typing.Optional[str] = None,
    tuning_evaluation_spec: typing.Optional[TuningEvaluationSpec] = None,
    default_context: typing.Optional[str] = None,
    accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None,
    max_context_length: typing.Optional[str] = None
) -> _LanguageModelTuningJob

Tunes a model based on training data.

This method launches and returns an asynchronous model tuning job. Usage:

tuning_job = model.tune_model(...)
... do some other work
tuned_model = tuning_job.get_tuned_model()  # Blocks until tuning is complete
Parameter
Name Description
training_data typing.Union[str, pandas.core.frame.DataFrame]

A Pandas DataFrame or a URI pointing to data in JSON lines format. The dataset schema is model-specific. See https://cloud.google.com/vertex-ai/docs/generative-ai/models/tune-models#dataset_format
Exceptions
Type Description
ValueError If the "tuning_job_location" value is not supported
ValueError If the "tuned_model_location" value is not supported
RuntimeError If the model does not support tuning