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import os
import huggingface_hub
import streamlit as st
from vllm import LLM, SamplingParams
@st.cache(show_spinner=False)
def get_system_message():
return """#Context:
You are an AI-based automated expert financial advisor named IRAI. You have a comprehensive understanding of finance and investing because you have trained on a extensive dataset based on of financial news, analyst reports, books, company filings, earnings call transcripts, and finance websites.
#Objective:
Answer questions accurately and truthfully given the data you have trained on. You do not have access to up-to-date current market data; this will be available in the future.
Style and tone:
Please answer in a friendly and engaging manner representing a top female investment professional working at a leading investment bank.
#Audience:
The questions will be asked by top technology executives and CFO of large fintech companies and successful startups.
#Response:
Answer, concise yet insightful."""
@st.cache_resource(show_spinner=False)
def init_llm():
huggingface_hub.login(token=os.getenv("HF_TOKEN"))
llm = LLM(model="InvestmentResearchAI/LLM-ADE-dev")
tok = llm.get_tokenizer()
tok.eos_token = '<|im_end|>' # Override to use turns
return llm
def get_response(prompt):
try:
sys_msg = get_system_message()
convo = [
{"role": "system", "content": sys_msg},
{"role": "user", "content": prompt},
]
llm = init_llm()
prompts = [llm.get_tokenizer().apply_chat_template(convo, tokenize=False)]
sampling_params = SamplingParams(temperature=0.3, top_p=0.95, max_tokens=500, stop_token_ids=[128009])
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
return output.outputs[0].text
except Exception as e:
return f"An error occurred: {str(e)}"
def get_response(prompt, custom_sys_msg):
try:
convo = [
{"role": "system", "content": custom_sys_msg},
{"role": "user", "content": prompt},
]
prompts = [llm.get_tokenizer().apply_chat_template(convo, tokenize=False)]
sampling_params = SamplingParams(temperature=0.3, top_p=0.95, max_tokens=2000, stop_token_ids=[128009])
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
return output.outputs[0].text
except Exception as e:
return f"An error occurred: {str(e)}"
def main():
st.title("LLM-ADE 9B Demo")
# Retrieve the default system message
sys_msg = get_system_message()
# UI for editable preprompt
user_modified_sys_msg = st.text_area("Preprompt: ", value=sys_msg, height=200)
input_text = st.text_area("Enter your text here:", value="", height=200)
if st.button("Generate"):
if input_text:
with st.spinner('Generating response...'):
response_text = get_response(input_text, user_modified_sys_msg)
st.write(response_text)
else:
st.warning("Please enter some text to generate a response.")
llm = init_llm()
if __name__ == "__main__":
main()
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