langchain.llms.self_hosted_hugging_face.SelfHostedHuggingFaceLLM¶
class langchain.llms.self_hosted_hugging_face.SelfHostedHuggingFaceLLM[source]¶
Bases: SelfHostedPipeline
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.).
To use, you should have the runhouse python package installed.
Only supports text-generation, text2text-generation and summarization 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)
Construct the pipeline remotely using an auxiliary function.