File size: 989 Bytes
85ea4bd 4644a82 85ea4bd 26a5f67 85ea4bd 26a5f67 6b88819 85ea4bd 4644a82 85ea4bd 4644a82 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 |
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
base_model = "meta‑llama/Llama‑2‑7b‑chat‑hf" # base tokenizer
adapter = "FinGPT/fingpt‑mt_llama2‑7b_lora"
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(adapter, device_map="auto")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
chat_history = []
def chat(user_input, history):
prompt = (history + "\nUser: " + user_input) if history else ("User: " + user_input)
output = pipe(prompt, max_new_tokens=300, do_sample=True)[0]["generated_text"]
history = prompt + "\nAssistant: " + output
return output, history
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
txt = gr.Textbox(placeholder="Ask a finance question...", show_label=False)
state = gr.State("")
txt.submit(lambda msg, hist: (chatbot + [(msg, chat(msg, hist)[0])], chat(msg, hist)[1]), [txt, state], [chatbot, state])
demo.launch()
|