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| import torch | |
| import streamlit as st | |
| from transformers import pipeline | |
| from concurrent.futures import ThreadPoolExecutor | |
| # Function to load models only once using Streamlit's cache mechanism | |
| def load_models(): | |
| device = 0 if torch.cuda.is_available() else -1 | |
| base_pipe = pipeline( | |
| "text-generation", | |
| model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", | |
| device=device, | |
| ) | |
| irai_pipe = pipeline( | |
| "text-generation", | |
| model="InvestmentResearchAI/LLM-ADE_tiny-v0.001", | |
| device=device, | |
| ) | |
| return base_pipe, irai_pipe | |
| base_pipe, irai_pipe = load_models() | |
| alpaca_template = ( | |
| "<|system|>\n" | |
| "{sys}</s>\n" | |
| "<|user|>\n" | |
| "{input_text}</s>\n" | |
| "<|assistant|>\n" | |
| ) | |
| chatml_template = ( | |
| "<|im_start|>system\n" | |
| "{sys}<|im_end|>\n" | |
| "<|im_start|>user\n" | |
| "{input_text}<|im_end|>\n" | |
| "<|im_start|>assistant\n" | |
| ) | |
| system_prompt = "You are an AI assistant trained on an extensive dataset, including technology reports, investment reports, financial texts, and other relevant sources. Please answer the following question based on the knowledge you have acquired during your training. Do not make any assumptions or use information from external sources. If you don't have enough pre-existing knowledge to provide a complete answer, simply respond with \"I don't have enough pre-existing knowledge to comprehensively answer this question.\" If you can partially answer the question based on your training, please provide that partial answer and clarify that it may not be a complete response. Assume today is June 5, 2024, and respond as if you have no knowledge of events after your training data's cut-off date." | |
| executor = ThreadPoolExecutor(max_workers=2) | |
| def generate_base_response(input_text): | |
| formatted_input = alpaca_template.format(sys=system_prompt, input_text=input_text) | |
| result = base_pipe(formatted_input)[0]["generated_text"] | |
| return result.split("<|assistant|>")[1].strip() | |
| def generate_irai_response(input_text): | |
| formatted_input = chatml_template.format(sys=system_prompt, input_text=input_text) | |
| result = irai_pipe(formatted_input)[0]["generated_text"] | |
| return result.split("<|im_start|>assistant")[1].split("<|im_end|>")[0].strip() | |
| def generate_response(input_text): | |
| try: | |
| future_base = executor.submit(generate_base_response, input_text) | |
| future_irai = executor.submit(generate_irai_response, input_text) | |
| base_resp = future_base.result() | |
| irai_resp = future_irai.result() | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") | |
| return None, None | |
| return base_resp, irai_resp | |
| st.title("Base Model vs IRAI LLM-ADE") | |
| st.markdown("This is a demonstration of the [LLM-ADE paper](https://arxiv.org/abs/2404.13028) (knowledge cutoff is June 5, 2024)") | |
| user_input = st.text_area("Ask about finance related questions and mega-cap (top 15) stocks!", "") | |
| if st.button("Generate") or user_input: | |
| if user_input: | |
| base_response, irai_response = generate_response(user_input) | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.write("### Base Model (Tiny-Llama)") | |
| st.text_area(label="none", value=base_response, height=300, key="base_response", label_visibility="hidden") | |
| with col2: | |
| st.write("### LLM-ADE Enhanced") | |
| st.text_area(label="none", value=irai_response, height=300, key="irai_response", label_visibility="hidden") | |
| else: | |
| st.warning("Please enter some text") | |