Class ChatModel (1.37.0)

  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. Use LanguageModel.from_pretrained(model_name=...) instead.

Parameters

Name

Description

model_id

str

Identifier of a Vertex LLM. Example: "text-bison@001"

endpoint_name

typing.Optional[str]

Vertex Endpoint resource name for the model

from_pretrained

  from_pretrained 
 ( 
 model_name 
 : 
 str 
 ) 
 - 
> vertexai 
 . 
 _model_garden 
 . 
 _model_garden_models 
 . 
 T

Loads a _ModelGardenModel.

Parameter

Name

Description

model_name

str

Name of the model.

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 
 [ 
 TuningEvaluationSpec 
 ] 
 = 
 None 
 ) 
 - 
> _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

Parameter

Name

Description

training_data

typing.Union[str, pandas.core.frame.DataFrame]

A Pandas DataFrame or a URI pointing to data in JSON lines format. The dataset schema is model-specific. See https://cloud.google.com/vertex-ai/docs/generative-ai/models/tune-models#dataset_format

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

Class ChatModel (1.37.0) Stay organized with collections Save and categorize content based on your preferences.

Methods

ChatModel

from_pretrained

get_tuned_model

list_tuned_model_names

start_chat

tune_model

Class ChatModel (1.37.0)