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# import os | |
# from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline | |
# from langchain.llms import HuggingFacePipeline | |
# from langchain import PromptTemplate, LLMChain | |
# from dotenv import load_dotenv | |
# Define the model directory and name | |
# MODEL_DIR = "/home/user/model" | |
# MODEL_NAME = "Giang07/Llama-2-7b-chat-QLoRa" | |
# MODEL_NAME = "meta-llama/Meta-Llama-3-8B" | |
# MODEL_NAME = "microsoft/Phi-3-mini-4k-instruct" | |
# Load environment variables from .env file | |
# load_dotenv() | |
# Now you can use the token | |
# api_token = os.getenv('HF_TOKEN') | |
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer | |
from langchain.llms import HuggingFacePipeline | |
MODEL_NAME = "microsoft/Phi-3-mini-4k-instruct" | |
def load_model(): | |
""" | |
Load or download the model and tokenizer. | |
""" | |
# config_path = os.path.join(MODEL_DIR, "config.json") | |
# if not os.path.exists(config_path): | |
# os.makedirs(MODEL_DIR, exist_ok=True) | |
# # model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, use_auth_token=api_token) | |
# model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, trust_remote_code=True) | |
# model.save_pretrained(MODEL_DIR) | |
# # tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_auth_token=api_token) | |
# tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True) | |
# tokenizer.save_pretrained(MODEL_DIR) | |
# else: | |
# model = AutoModelForCausalLM.from_pretrained(MODEL_DIR) | |
# tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR) | |
model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_NAME) | |
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) | |
return model, tokenizer | |
def create_pipeline(model, tokenizer): | |
""" | |
Create a text-generation pipeline. | |
""" | |
# hf_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer) | |
# Initialize the Hugging Face pipeline | |
hf_pipeline = HuggingFacePipeline( | |
model=model, | |
tokenizer=tokenizer, | |
hf_pipeline_kwargs={"return_full_text": False} # Adjust based on the function you need | |
) | |
return hf_pipeline | |
def generate_text(hf_pipeline, input_text): | |
""" | |
Generate text using the Hugging Face pipeline. | |
# """ | |
# prompt_template = PromptTemplate( | |
# input_variables=["input_text"], | |
# template="Translate the following English text to French: {input_text}" | |
# ) | |
# llm = HuggingFacePipeline(pipeline=hf_pipeline) | |
# llm_chain = LLMChain(prompt_template=prompt_template, llm=llm) | |
# return llm_chain.run({"input_text": input_text}) | |
# Define a prompt template if needed (this is an example, adjust accordingly) | |
prompt_template = PromptTemplate( | |
input_variables=["input_text"], | |
template="Please summarize the following text: {input_text}" | |
) | |
# Example input text for the task | |
# input_text = "LangChain is a library that facilitates the development of applications using language models." | |
# Run the LangChain pipeline | |
output = hf_pipeline.generate(input_text, prompt_template=prompt_template) | |
return output | |