Class CodeGenerationModel (1.75.0)
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CodeGenerationModel(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.
Methods
batch_predict
batch_predict(
*,
dataset: typing.Union[str, typing.List[str]],
destination_uri_prefix: str,
model_parameters: typing.Optional[typing.Dict] = None
) -> google.cloud.aiplatform.jobs.BatchPredictionJobStarts a batch prediction job with the model.
| Exceptions | |
|---|---|
| Type | Description |
ValueError |
When source or destination URI is not supported. |
from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.TLoads a _ModelGardenModel.
| Exceptions | |
|---|---|
| Type | Description |
ValueError |
If model_name is unknown. |
ValueError |
If model does not support this class. |
get_tuned_model
get_tuned_model(
tuned_model_name: str,
) -> vertexai.language_models._language_models._LanguageModelLoads the specified tuned language model.
list_tuned_model_names
list_tuned_model_names() -> typing.Sequence[str]Lists the names of tuned models.
predict
predict(
prefix: str,
suffix: typing.Optional[str] = None,
*,
max_output_tokens: typing.Optional[int] = None,
temperature: typing.Optional[float] = None,
stop_sequences: typing.Optional[typing.List[str]] = None,
candidate_count: typing.Optional[int] = None
) -> vertexai.language_models.TextGenerationResponseGets model response for a single prompt.
predict_async
predict_async(
prefix: str,
suffix: typing.Optional[str] = None,
*,
max_output_tokens: typing.Optional[int] = None,
temperature: typing.Optional[float] = None,
stop_sequences: typing.Optional[typing.List[str]] = None,
candidate_count: typing.Optional[int] = None
) -> vertexai.language_models.TextGenerationResponseAsynchronously gets model response for a single prompt.
predict_streaming
predict_streaming(
prefix: str,
suffix: typing.Optional[str] = None,
*,
max_output_tokens: typing.Optional[int] = None,
temperature: typing.Optional[float] = None,
stop_sequences: typing.Optional[typing.List[str]] = None
) -> typing.Iterator[vertexai.language_models.TextGenerationResponse]Predicts the code based on previous code.
The result is a stream (generator) of partial responses.
predict_streaming_async
predict_streaming_async(
prefix: str,
suffix: typing.Optional[str] = None,
*,
max_output_tokens: typing.Optional[int] = None,
temperature: typing.Optional[float] = None,
stop_sequences: typing.Optional[typing.List[str]] = None
) -> typing.AsyncIterator[vertexai.language_models.TextGenerationResponse]Asynchronously predicts the code based on previous code.
The result is a stream (generator) of partial responses.
tune_model
tune_model(
training_data: typing.Union[str, pandas.core.frame.DataFrame],
*,
train_steps: typing.Optional[int] = 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,
accelerator_type: typing.Optional[typing.Literal["TPU", "GPU"]] = None,
max_context_length: typing.Optional[str] = None
) -> vertexai.language_models._language_models._LanguageModelTuningJobTunes 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 |