Class ChatModel (1.122.0)

ChatModel(model_id:str,endpoint_name:typing.Optional[str]=None)

ChatModel represents a language model that is capable of chat.

Examples::

chat_model = ChatModel.from_pretrained("chat-bison@001")chat = chat_model.start_chat(    context="My name is Ned. You are my personal assistant. My favorite movies are Lord of the Rings and Hobbit.",    examples=[        InputOutputTextPair(            input_text="Who do you work for?",            output_text="I work for Ned.",        ),        InputOutputTextPair(            input_text="What do I like?",            output_text="Ned likes watching movies.",        ),    ],    temperature=0.3,)chat.send_message("Do you know any cool events this weekend?")

Methods

ChatModel

ChatModel(model_id:str,endpoint_name:typing.Optional[str]=None)

Creates a LanguageModel.

This constructor should not be called directly.UseLanguageModel.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.

get_tuned_model

get_tuned_model(tuned_model_name:str,)->vertexai.language_models._language_models._LanguageModel

Loads the specified tuned language model.

list_tuned_model_names

list_tuned_model_names()->typing.Sequence[str]

Lists the names of tuned models.

start_chat

start_chat(*,context:typing.Optional[str]=None,examples:typing.Optional[typing.List[vertexai.language_models.InputOutputTextPair]]=None,max_output_tokens:typing.Optional[int]=None,temperature:typing.Optional[float]=None,top_k:typing.Optional[int]=None,top_p:typing.Optional[float]=None,message_history:typing.Optional[typing.List[vertexai.language_models.ChatMessage]]=None,stop_sequences:typing.Optional[typing.List[str]]=None)->vertexai.language_models.ChatSession

Starts a chat session with the model.

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,default_context:typing.Optional[str]=None,accelerator_type:typing.Optional[typing.Literal["TPU","GPU"]]=None,tuning_evaluation_spec:typing.Optional[vertexai.language_models.TuningEvaluationSpec]=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 worktuned_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
`AttributeError`	If any attribute in the "tuning_evaluation_spec" is not supported

tune_model_rlhf

tune_model_rlhf(*,prompt_data:typing.Union[str,pandas.core.frame.DataFrame],preference_data:typing.Union[str,pandas.core.frame.DataFrame],model_display_name:typing.Optional[str]=None,prompt_sequence_length:typing.Optional[int]=None,target_sequence_length:typing.Optional[int]=None,reward_model_learning_rate_multiplier:typing.Optional[float]=None,reinforcement_learning_rate_multiplier:typing.Optional[float]=None,reward_model_train_steps:typing.Optional[int]=None,reinforcement_learning_train_steps:typing.Optional[int]=None,kl_coeff:typing.Optional[float]=None,default_context:typing.Optional[str]=None,tuning_job_location:typing.Optional[str]=None,accelerator_type:typing.Optional[typing.Literal["TPU","GPU"]]=None,tuning_evaluation_spec:typing.Optional[vertexai.language_models.TuningEvaluationSpec]=None)->vertexai.language_models._language_models._LanguageModelTuningJob

Tunes a model using reinforcement learning from human feedback.