| import streamlit as st |
| from vllm import LLM, SamplingParams |
|
|
| llm = LLM(model="InvestmentResearchAI/LLM-ADE-small-v0.1.0") |
| tok = llm.get_tokenizer() |
| tok.eos_token = '<|eot_id|>' |
|
|
|
|
| template = """<|begin_of_text|><|start_header_id|>system<|end_header_id|> |
| |
| You are a helpful financial assistant that answers the user as accurately, truthfully, and concisely as possible.<|eot_id|><|start_header_id|>user<|end_header_id|> |
| |
| {user_message}<|eot_id|><|start_header_id|>assistant<|end_header_id|> |
| |
| """ |
|
|
|
|
| def get_response(prompt): |
| try: |
| prompts = [template.format(user_message=prompt)] |
| sampling_params = SamplingParams(temperature=0.3, top_p=0.95) |
| 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") |
| |
| 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) |
| st.write(response_text) |
| else: |
| st.warning("Please enter some text to generate a response.") |
|
|
| if __name__ == "__main__": |
| main() |
|
|
|
|
