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Langchain使用 huggingface hub中的模型LLM_from langchain_community.llms import huggingfacehu

from langchain_community.llms import huggingfacehub

以下使用的模型是:google/flan-t5-xxl

from langchain.chains import LLMChain
from langchain_core.prompts import ChatPromptTemplate
from langchain import hub
from langchain_community.llms import HuggingFaceHub 
from langchain_community.llms import HuggingFaceTextGenInference
import os
os.environ["HUGGINGFACEHUB_API_TOKEN"] = "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"

prompt = ChatPromptTemplate.from_template("""
Question: {input}
Answer: Let's think step by step
"""
    )
    
repo_id = "google/flan-t5-xxl"  # See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options
   # repo_id = "databricks/dolly-v2-3b"
    
llm = HuggingFaceHub(
    repo_id= repo_id,
       # repo_id="LLM360/CrystalChat",
    
    model_kwargs={"temperature":0.2, "max_length":18000}
)
chain = LLMChain(llm=llm,prompt=prompt)
print(chain.invoke("In the first movie of Harry Potter, what is the name of the three-headed dog? "))

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确认可以使用的:
HuggingFaceH4/zephyr-7b-beta

repo_id=“databricks/dolly-v2-3b”,

#  repo_id="bigscience/bloomz-560m",
repo_id="google-t5/t5-small",
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openchat/openchat-3.5-0106

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