id
stringlengths 14
16
| text
stringlengths 45
2.73k
| source
stringlengths 49
114
|
|---|---|---|
6833f1fca458-70 | Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.Replicate[source]#
Wrapper around Replicate models.
To use, you should have the replicate python package installed,
and the environment variable REPLICATE_API_TOKEN set with your API token.
You can find your token here: https://replicate.com/account
The model param is required, but any other model parameters can also
be passed in with the format input={model_param: value, …}
Example
Validators
build_extra » all fields
set_callback_manager » callback_manager
set_verbose » verbose
validate_environment » all fields
__call__(prompt: str, stop: Optional[List[str]] = None) → str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-71 | Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) → int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-72 | Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) → None#
Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.SagemakerEndpoint[source]#
Wrapper around custom Sagemaker Inference Endpoints.
To use, you must supply the endpoint name from your deployed
Sagemaker model & the region where it is deployed.
To authenticate, the AWS client uses the following methods to
automatically load credentials:
https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
If a specific credential profile should be used, you must pass
the name of the profile from the ~/.aws/credentials file that is to be used.
Make sure the credentials / roles used have the required policies to
access the Sagemaker endpoint. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-73 | Make sure the credentials / roles used have the required policies to
access the Sagemaker endpoint.
See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
Validators
set_callback_manager » callback_manager
set_verbose » verbose
validate_environment » all fields
field content_handler: langchain.llms.sagemaker_endpoint.ContentHandlerBase [Required]#
The content handler class that provides an input and
output transform functions to handle formats between LLM
and the endpoint.
field credentials_profile_name: Optional[str] = None#
The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
has either access keys or role information specified.
If not specified, the default credential profile or, if on an EC2 instance,
credentials from IMDS will be used.
See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
field endpoint_kwargs: Optional[Dict] = None#
Optional attributes passed to the invoke_endpoint
function. See `boto3`_. docs for more info.
.. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>
field endpoint_name: str = ''#
The name of the endpoint from the deployed Sagemaker model.
Must be unique within an AWS Region.
field model_kwargs: Optional[Dict] = None#
Key word arguments to pass to the model.
field region_name: str = ''#
The aws region where the Sagemaker model is deployed, eg. us-west-2.
__call__(prompt: str, stop: Optional[List[str]] = None) → str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-74 | Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-75 | Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) → int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) → None#
Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.SelfHostedHuggingFaceLLM[source]#
Wrapper around HuggingFace Pipeline API to run on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another cloud
like Paperspace, Coreweave, etc.). | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-76 | like Paperspace, Coreweave, etc.).
To use, you should have the runhouse python package installed.
Only supports text-generation and text2text-generation for now.
Example using from_model_id:from langchain.llms import SelfHostedHuggingFaceLLM
import runhouse as rh
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
hf = SelfHostedHuggingFaceLLM(
model_id="google/flan-t5-large", task="text2text-generation",
hardware=gpu
)
Example passing fn that generates a pipeline (bc the pipeline is not serializable):from langchain.llms import SelfHostedHuggingFaceLLM
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse as rh
def get_pipeline():
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
"text-generation", model=model, tokenizer=tokenizer
)
return pipe
hf = SelfHostedHuggingFaceLLM(
model_load_fn=get_pipeline, model_id="gpt2", hardware=gpu)
Validators
set_callback_manager » callback_manager
set_verbose » verbose
field device: int = 0#
Device to use for inference. -1 for CPU, 0 for GPU, 1 for second GPU, etc.
field hardware: Any = None#
Remote hardware to send the inference function to.
field inference_fn: Callable = <function _generate_text>#
Inference function to send to the remote hardware.
field load_fn_kwargs: Optional[dict] = None#
Key word arguments to pass to the model load function.
field model_id: str = 'gpt2'# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-77 | field model_id: str = 'gpt2'#
Hugging Face model_id to load the model.
field model_kwargs: Optional[dict] = None#
Key word arguments to pass to the model.
field model_load_fn: Callable = <function _load_transformer>#
Function to load the model remotely on the server.
field model_reqs: List[str] = ['./', 'transformers', 'torch']#
Requirements to install on hardware to inference the model.
field task: str = 'text-generation'#
Hugging Face task (either “text-generation” or “text2text-generation”).
__call__(prompt: str, stop: Optional[List[str]] = None) → str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-78 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
classmethod from_pipeline(pipeline: Any, hardware: Any, model_reqs: Optional[List[str]] = None, device: int = 0, **kwargs: Any) → langchain.llms.base.LLM#
Init the SelfHostedPipeline from a pipeline object or string.
generate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) → int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-79 | Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) → None#
Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.SelfHostedPipeline[source]#
Run model inference on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another
cloud like Paperspace, Coreweave, etc.).
To use, you should have the runhouse python package installed.
Example for custom pipeline and inference functions:from langchain.llms import SelfHostedPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import runhouse as rh
def load_pipeline(): | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-80 | import runhouse as rh
def load_pipeline():
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")
return pipeline(
"text-generation", model=model, tokenizer=tokenizer,
max_new_tokens=10
)
def inference_fn(pipeline, prompt, stop = None):
return pipeline(prompt)[0]["generated_text"]
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
llm = SelfHostedPipeline(
model_load_fn=load_pipeline,
hardware=gpu,
model_reqs=model_reqs, inference_fn=inference_fn
)
Example for <2GB model (can be serialized and sent directly to the server):from langchain.llms import SelfHostedPipeline
import runhouse as rh
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
my_model = ...
llm = SelfHostedPipeline.from_pipeline(
pipeline=my_model,
hardware=gpu,
model_reqs=["./", "torch", "transformers"],
)
Example passing model path for larger models:from langchain.llms import SelfHostedPipeline
import runhouse as rh
import pickle
from transformers import pipeline
generator = pipeline(model="gpt2")
rh.blob(pickle.dumps(generator), path="models/pipeline.pkl"
).save().to(gpu, path="models")
llm = SelfHostedPipeline.from_pipeline(
pipeline="models/pipeline.pkl",
hardware=gpu,
model_reqs=["./", "torch", "transformers"],
)
Validators
set_callback_manager » callback_manager
set_verbose » verbose
field hardware: Any = None#
Remote hardware to send the inference function to. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-81 | field hardware: Any = None#
Remote hardware to send the inference function to.
field inference_fn: Callable = <function _generate_text>#
Inference function to send to the remote hardware.
field load_fn_kwargs: Optional[dict] = None#
Key word arguments to pass to the model load function.
field model_load_fn: Callable [Required]#
Function to load the model remotely on the server.
field model_reqs: List[str] = ['./', 'torch']#
Requirements to install on hardware to inference the model.
__call__(prompt: str, stop: Optional[List[str]] = None) → str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-82 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
classmethod from_pipeline(pipeline: Any, hardware: Any, model_reqs: Optional[List[str]] = None, device: int = 0, **kwargs: Any) → langchain.llms.base.LLM[source]#
Init the SelfHostedPipeline from a pipeline object or string.
generate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) → int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-83 | Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) → None#
Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.StochasticAI[source]#
Wrapper around StochasticAI large language models.
To use, you should have the environment variable STOCHASTICAI_API_KEY
set with your API key.
Example
from langchain.llms import StochasticAI
stochasticai = StochasticAI(api_url="")
Validators
build_extra » all fields
set_callback_manager » callback_manager
set_verbose » verbose
validate_environment » all fields
field api_url: str = ''#
Model name to use.
field model_kwargs: Dict[str, Any] [Optional]#
Holds any model parameters valid for create call not
explicitly specified. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-84 | Holds any model parameters valid for create call not
explicitly specified.
__call__(prompt: str, stop: Optional[List[str]] = None) → str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-85 | dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) → int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) → None#
Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns. | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-86 | Try to update ForwardRefs on fields based on this Model, globalns and localns.
pydantic model langchain.llms.Writer[source]#
Wrapper around Writer large language models.
To use, you should have the environment variable WRITER_API_KEY
set with your API key.
Example
from langchain import Writer
writer = Writer(model_id="palmyra-base")
Validators
set_callback_manager » callback_manager
set_verbose » verbose
validate_environment » all fields
field base_url: Optional[str] = None#
Base url to use, if None decides based on model name.
field beam_search_diversity_rate: float = 1.0#
Only applies to beam search, i.e. when the beam width is >1.
A higher value encourages beam search to return a more diverse
set of candidates
field beam_width: Optional[int] = None#
The number of concurrent candidates to keep track of during
beam search
field length: int = 256#
The maximum number of tokens to generate in the completion.
field length_pentaly: float = 1.0#
Only applies to beam search, i.e. when the beam width is >1.
Larger values penalize long candidates more heavily, thus preferring
shorter candidates
field logprobs: bool = False#
Whether to return log probabilities.
field model_id: str = 'palmyra-base'#
Model name to use.
field random_seed: int = 0#
The model generates random results.
Changing the random seed alone will produce a different response
with similar characteristics. It is possible to reproduce results
by fixing the random seed (assuming all other hyperparameters
are also fixed)
field repetition_penalty: float = 1.0#
Penalizes repeated tokens according to frequency.
field stop: Optional[List[str]] = None# | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-87 | Penalizes repeated tokens according to frequency.
field stop: Optional[List[str]] = None#
Sequences when completion generation will stop
field temperature: float = 1.0#
What sampling temperature to use.
field tokens_to_generate: int = 24#
Max number of tokens to generate.
field top_k: int = 1#
The number of highest probability vocabulary tokens to
keep for top-k-filtering.
field top_p: float = 1.0#
Total probability mass of tokens to consider at each step.
__call__(prompt: str, stop: Optional[List[str]] = None) → str#
Check Cache and run the LLM on the given prompt and input.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
async agenerate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#
Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model#
Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-88 | Duplicate a model, optionally choose which fields to include, exclude and change.
Parameters
include – fields to include in new model
exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kwargs: Any) → Dict#
Return a dictionary of the LLM.
generate(prompts: List[str], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Run the LLM on the given prompt and input.
generate_prompt(prompts: List[langchain.schema.PromptValue], stop: Optional[List[str]] = None) → langchain.schema.LLMResult#
Take in a list of prompt values and return an LLMResult.
get_num_tokens(text: str) → int#
Get the number of tokens present in the text.
get_num_tokens_from_messages(messages: List[langchain.schema.BaseMessage]) → int#
Get the number of tokens in the message.
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode#
Generate a JSON representation of the model, include and exclude arguments as per dict().
encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps(). | https://python.langchain.com/en/latest/reference/modules/llms.html |
6833f1fca458-89 | encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().
save(file_path: Union[pathlib.Path, str]) → None#
Save the LLM.
Parameters
file_path – Path to file to save the LLM to.
Example:
.. code-block:: python
llm.save(file_path=”path/llm.yaml”)
classmethod update_forward_refs(**localns: Any) → None#
Try to update ForwardRefs on fields based on this Model, globalns and localns.
previous
Writer
next
Chat Models
By Harrison Chase
© Copyright 2023, Harrison Chase.
Last updated on Apr 21, 2023. | https://python.langchain.com/en/latest/reference/modules/llms.html |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.