_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.
tune_model
tune_model(
training_data: typing.Union[str, pandas.core.frame.DataFrame],
*,
corpus_data: typing.Optional[str] = None,
queries_data: typing.Optional[str] = None,
test_data: typing.Optional[str] = None,
validation_data: typing.Optional[str] = None,
batch_size: typing.Optional[int] = None,
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[
vertexai.language_models.TuningEvaluationSpec
] = None,
default_context: typing.Optional[str] = None,
task_type: typing.Optional[str] = None,
machine_type: typing.Optional[str] = None,
accelerator: typing.Optional[str] = None,
accelerator_count: typing.Optional[int] = None,
accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None,
max_context_length: typing.Optional[str] = None,
output_dimensionality: typing.Optional[int] = None
) -> vertexai.language_models._language_models._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
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 |