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
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
[
TuningEvaluationSpec
]
=
None
)
-
> _LanguageModelTuningJob
Tunes a model using reinforcement learning from human feedback.
This method launches and returns an asynchronous model tuning job. Usage:
tuning_job = model.tune_model_rlhf(...)
... 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
RuntimeError
If the model does not support tuning
