run_localGPT.py

import click
import torch
import logging
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import HuggingFacePipeline
# from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.vectorstores import Chroma
from transformers import LlamaForCausalLM, LlamaTokenizer, pipeline
from transformers import AutoTokenizer, AutoModelForCausalLM
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
from constants import CHROMA_SETTINGS, EMBEDDING_MODEL_NAME, PERSIST_DIRECTORY
from transformers import GenerationConfig


def load_model(device_type, model_id, model_basename=None):
    """
    Select a model for text generation using the HuggingFace library.
    If you are running this for the first time, it will download a model for you.
    subsequent runs will use the model from the disk.

    Args:
        device_type (str): Type of device to use, e.g., "cuda" for GPU or "cpu" for CPU.
        model_id (str): Identifier of the model to load from HuggingFace's model hub.
        model_basename (str, optional): Basename of the model if using quantized models. 
            Defaults to None.

    Returns:
        HuggingFacePipeline: A pipeline object for text generation using the loaded model.

    Raises:
        ValueError: If an unsupported model or device type is provided.
    """
    
    logging.info(f'Loading Model: {model_id}, on: {device_type}')
    logging.info('This action can take a few minutes!')

    if model_basename is not None:
        # The code supports all huggingface models that ends with GPTQ and have some variation of .no-act.order or .safetensors in their HF repo.
        print('Using AutoGPTQForCausalLM for quantized models')

        if '.safetensors' in model_basename:
            # Remove the ".safetensors" ending if present
            model_basename = model_basename.replace('.safetensors', "")

        tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
        logging.info('Tokenizer loaded')

        model = AutoGPTQForCausalLM.from_quantized(
            model_id,
            model_basename=model_basename,
            use_safetensors=True,
            trust_remote_code=True,
            device="cuda:0",
            use_triton=False,
            quantize_config=None
        )
    elif device_type.lower() == 'cuda': # The code supports all huggingface models that ends with -HF or which have a .bin file in their HF repo.
        print('Using AutoModelForCausalLM for full models')
        tokenizer = AutoTokenizer.from_pretrained(model_id)
        logging.info('Tokenizer loaded')

        model = AutoModelForCausalLM.from_pretrained(
            model_id,
            device_map='auto',
            torch_dtype=torch.float16,
            low_cpu_mem_usage=True,
            trust_remote_code=True,
            # max_memory={0: "15GB"} # Uncomment this line with you encounter CUDA out of memory errors
        )
        model.tie_weights()
    else:
        print('Using LlamaTokenizer')
        tokenizer = LlamaTokenizer.from_pretrained(model_id)
        model = LlamaForCausalLM.from_pretrained(model_id)

    # Load configuration from the model to avoid warnings
    generation_config = GenerationConfig.from_pretrained(model_id)
    # see here for details: https://huggingface.co/docs/transformers/main_classes/text_generation#transformers.GenerationConfig.from_pretrained.returns

    # Create a pipeline for text generation
    pipe = pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        max_length=2048,
        temperature=0,
        top_p=0.95,
        repetition_penalty=1.15,
        generation_config=generation_config
    )

    local_llm = HuggingFacePipeline(pipeline=pipe)
    logging.info('Local LLM Loaded')

    return local_llm


# chose device typ to run on as well as to show source documents.
@click.command()
@click.option(
    "--device_type",
    default="cuda",
    type=click.Choice(
        [
            "cpu", "cuda", "ipu", "xpu", "mkldnn", "opengl", "opencl", "ideep", "hip", "ve", "fpga", "ort",
            "xla", "lazy", "vulkan", "mps", "meta", "hpu", "mtia",
        ],
    ),
    help="Device to run on. (Default is cuda)",
)
@click.option(
    "--show_sources",
    default=True,
    type=click.Choice(
        [
            False,
            True,
        ]
    ),
    help="Show sources along with answers (Default is Fals)",
)
def main(device_type, show_sources):
    '''
    This function implements the information retreival task.


    1. Loads an embedding model, can be HuggingFaceInstructEmbeddings or HuggingFaceEmbeddings
    2. Loads the existing vectorestore that was created by inget.py
    3. Loads the local LLM using load_model function - You can now set different LLMs.
    4. Setup the Question Answer retreival chain.
    5. Question answers.
    '''

    logging.info(f'Running on: {device_type}')
    logging.info(f'Display Source Documents set to: {show_sources}')

    embeddings = HuggingFaceInstructEmbeddings(
        model_name=EMBEDDING_MODEL_NAME, model_kwargs={"device": device_type}
    )

    # uncomment the following line if you used HuggingFaceEmbeddings in the ingest.py
    # embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME)

    # load the vectorstore
    db = Chroma(
        persist_directory=PERSIST_DIRECTORY,
        embedding_function=embeddings,
        client_settings=CHROMA_SETTINGS,
    )
    retriever = db.as_retriever()

    # load the LLM for generating Natural Language responses

    # for HF models
    # model_id = "TheBloke/vicuna-7B-1.1-HF"
    # model_id = "TheBloke/Wizard-Vicuna-7B-Uncensored-HF"
    # model_id = "TheBloke/guanaco-7B-HF"
    # model_id = 'NousResearch/Nous-Hermes-13b' # Requires ~ 23GB VRAM. Using STransformers alongside will 100% create OOM on 24GB cards. 
    # llm = load_model(device_type, model_id=model_id)

    # for GPTQ (quantized) models
    # model_id = "TheBloke/Nous-Hermes-13B-GPTQ"
    # model_basename = "nous-hermes-13b-GPTQ-4bit-128g.no-act.order"
    # model_id = "TheBloke/WizardLM-30B-Uncensored-GPTQ"
    # model_basename = "WizardLM-30B-Uncensored-GPTQ-4bit.act-order.safetensors" # Requires ~21GB VRAM. Using STransformers alongside can potentially create OOM on 24GB cards.
    # model_id = "TheBloke/wizardLM-7B-GPTQ"
    # model_basename = "wizardLM-7B-GPTQ-4bit.compat.no-act-order.safetensors"
    model_id = "TheBloke/WizardLM-7B-uncensored-GPTQ"
    model_basename = "WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors"
    llm = load_model(device_type, model_id=model_id, model_basename = model_basename)

    qa = RetrievalQA.from_chain_type(
        llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True
    )
    # Interactive questions and answers
    while True:
        query = input("\nEnter a query: ")
        if query == "exit":
            break
        # Get the answer from the chain
        res = qa(query)
        answer, docs = res["result"], res["source_documents"]

        # Print the result
        print("\n\n> Question:")
        print(query)
        print("\n> Answer:")
        print(answer)

        if show_sources:  # this is a flag that you can set to disable showing answers.
            # # Print the relevant sources used for the answer
            print(
                "----------------------------------SOURCE DOCUMENTS---------------------------"
            )
            for document in docs:
                print("\n> " + document.metadata["source"] + ":")
                print(document.page_content)
            print(
                "----------------------------------SOURCE DOCUMENTS---------------------------"
            )


if __name__ == "__main__":
    logging.basicConfig(format='%(asctime)s - %(levelname)s - %(filename)s:%(lineno)s - %(message)s',
                        level=logging.INFO)
    main()