Only supports text-generation, text2text-generation and summarization for now.
Example using from_model_id:from langchain.llms import HuggingFacePipeline
hf = HuggingFacePipeline.from_model_id(
    model_id="gpt2",
    task="text-generation",
    pipeline_kwargs={"max_new_tokens": 10},
)
Example passing pipeline in directly:from langchain.llms import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline(
    "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
)
hf = HuggingFacePipeline(pipeline=pipe)
Validators
raise_deprecation » all fields
set_verbose » verbose
field model_id: str = 'gpt2'#
Model name to use.
field model_kwargs: Optional[dict] = None#
Key word arguments passed to the model.
field pipeline_kwargs: Optional[dict] = None#
Key word arguments passed to the pipeline.
field tags: Optional[List[str]] = None#
Tags to add to the run trace.
field verbose: bool [Optional]#
Whether to print out response text.
__call__(prompt: str, stop: Optional[List[str]] = None, callbacks: Optional[Union[List[langchain.callbacks.base.BaseCallbackHandler], langchain.callbacks.base.BaseCallbackManager]] = None, **kwargs: Any) → str#
Check Cache and run the LLM on the given prompt and input.
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/llms.html