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import gradio as gr
import random
import time
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load Vicuna 7B model and tokenizer
model_name = "lmsys/vicuna-7b-v1.3"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
with gr.Blocks() as demo:
with gr.Row():
vicuna_chatbot = gr.Chatbot(label="Vicuna", live=True)
msg = gr.Textbox()
clear = gr.ClearButton([msg, vicuna_chatbot])
def respond(message, chat_history, chatbot_idx):
input_ids = tokenizer.encode(message, return_tensors="pt")
output = model.generate(input_ids, max_length=50, num_beams=5, no_repeat_ngram_size=2)
bot_message = tokenizer.decode(output[0], skip_special_tokens=True)
chat_history.append((message, bot_message))
time.sleep(2)
return "", chat_history
msg.submit(respond, [msg, vicuna_chatbot, 0])
demo.launch()
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