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CodeGenerationModel(model_id: str, endpoint_name: typing.Optional[str] = None)
A language model that generates code.
.. rubric:: Examples
Getting answers:
generation_model = CodeGenerationModel.from_pretrained("code-bison@001") print(generation_model.predict( prefix="Write a function that checks if a year is a leap year.", ))
completion_model = CodeGenerationModel.from_pretrained("code-gecko@001") print(completion_model.predict( prefix="def reverse_string(s):", ))
Methods
CodeGenerationModel
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.
from_pretrained
from_pretrained(model_name: str) -> vertexai._model_garden._model_garden_models.T
Loads a _ModelGardenModel.
Exceptions | |
---|---|
Type | Description |
ValueError | If model_name is unknown. |
ValueError | If model does not support this class. |
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
) -> vertexai.language_models.TextGenerationResponse
Gets 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
) -> vertexai.language_models.TextGenerationResponse
Asynchronously 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.