I followed the instructions provided in the README file on the GitHub repository of meta-llama to download the required llama2-7b-chat model using CLI.

Now I have files that include params.json, consolidated.pth (I believe that's the weights file) and tokenizer.model (MODEL File).

My goal is to integrate it into the Streamlit application. However, I am not able to figure out how to do so. Can anyone help me understand how to get it running in Streamlit?

This is the first time I am integrating the LLM model into Streamlit. Please help me with any relevant links/codes.

NOTE: I have not used huggingface anywhere and I don't want huggingface solution.

import streamlit as st
from transformers import LlamaForCausalLM, LlamaTokenizer, LlamaConfig
import json
import torch

@st.cache_resource
def load_model_and_tokenizer():
    # Step 1: Load configuration locally
    config_path = r"path\\params.json"
    with open(config_path, "r") as f:
        config_data = json.load(f)
    
    config = LlamaConfig(**config_data)

    # Step 2: Initialize model architecture
    model = LlamaForCausalLM(config)

    # Step 3: Load weights
    weights_path = r"path\\consolidated.pth"
    state_dict = torch.load(weights_path, map_location="cpu")
    model.load_state_dict(state_dict)
    model.eval()

    # Step 4: Load tokenizer
    tokenizer_path = r"path\\tokenizer.model"
    tokenizer = LlamaTokenizer(tokenizer_path)

    return model, tokenizer

# Load model and tokenizer
model, tokenizer = load_model_and_tokenizer()

# Streamlit app
st.title("LLaMA Chatbot")
user_input = st.text_input("Enter your message:")

if user_input:
    with st.spinner("Generating response..."):
        inputs = tokenizer(user_input, return_tensors="pt")
        outputs = model.generate(inputs.input_ids, max_length=200)
        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
        st.write("### Response:")
        st.write(response)

This code was provided by ChatGPT and it shows an error for LlamaForCausalLM. I not quite getting my head around this code. Is this even the correct way?