- 1.71.1 (latest)
- 1.71.0
- 1.70.0
- 1.69.0
- 1.68.0
- 1.67.1
- 1.66.0
- 1.65.0
- 1.63.0
- 1.62.0
- 1.60.0
- 1.59.0
- 1.58.0
- 1.57.0
- 1.56.0
- 1.55.0
- 1.54.1
- 1.53.0
- 1.52.0
- 1.51.0
- 1.50.0
- 1.49.0
- 1.48.0
- 1.47.0
- 1.46.0
- 1.45.0
- 1.44.0
- 1.43.0
- 1.39.0
- 1.38.1
- 1.37.0
- 1.36.4
- 1.35.0
- 1.34.0
- 1.33.1
- 1.32.0
- 1.31.1
- 1.30.1
- 1.29.0
- 1.28.1
- 1.27.1
- 1.26.1
- 1.25.0
- 1.24.1
- 1.23.0
- 1.22.1
- 1.21.0
- 1.20.0
- 1.19.1
- 1.18.3
- 1.17.1
- 1.16.1
- 1.15.1
- 1.14.0
- 1.13.1
- 1.12.1
- 1.11.0
- 1.10.0
- 1.9.0
- 1.8.1
- 1.7.1
- 1.6.2
- 1.5.0
- 1.4.3
- 1.3.0
- 1.2.0
- 1.1.1
- 1.0.1
- 0.9.0
- 0.8.0
- 0.7.1
- 0.6.0
- 0.5.1
- 0.4.0
- 0.3.1
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_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.