pip -q install langchain huggingface_hub transformers sentence_transformers accelerate bitsandbytes

import os
os.environ['HUGGINGFACEHUB_API_TOKEN'] = prompttoken

from langchain import PromptTemplate, HuggingFaceHub, LLMChain

template = """Question: {question}
Answer: Let's think step by step."""

prompt = PromptTemplate(template=template, input_variables=["question"])

llm_chain = LLMChain(prompt=prompt, 
                     llm=HuggingFaceHub(repo_id="google/flan-t5-xl", 
                                        model_kwargs={"temperature":0, 
                                                      "max_length":64}))


from langchain.llms import HuggingFacePipeline
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, AutoModelForSeq2SeqLM, AutoTokenizer, AutoModelForSeq2SeqLM

model_id = 'Kaludi/chatgpt-gpt4-prompts-bart-large-cnn-samsum'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSeq2SeqLM.from_pretrained(model_id, from_tf=True)

pipeline = pipeline(
    "text2text-generation",
    model=model, 
    tokenizer=tokenizer, 
    max_length=128
)

local_llm = HuggingFacePipeline(pipeline=pipeline)


llm_chain = LLMChain(prompt=prompt, 
                     llm=local_llm
                     )

question = "Excel Sheet"

print(llm_chain.run(question))