Class _TunableModelMixin (1.45.0)

Version 1.45.0

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_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