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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the model and tokenizer locally
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Ensure the model runs on GPU if available
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Define the function to handle chat responses
def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
# Prepare the prompt by combining history and system messages
prompt = system_message + "\n"
for user_input, assistant_response in history:
prompt += f"User: {user_input}\nAssistant: {assistant_response}\n"
prompt += f"User: {message}\nAssistant:"
# Tokenize the input prompt
inputs = tokenizer(prompt, return_tensors="pt").to(device)
# Generate a response
outputs = model.generate(
inputs.input_ids,
max_length=max_tokens,
temperature=temperature,
top_p=top_p,
pad_token_id=tokenizer.eos_token_id,
)
# Decode the generated tokens and yield the response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
yield response.split("Assistant:")[-1].strip()
# Create the Gradio interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
],
)
# Launch the Gradio app
if __name__ == "__main__":
demo.launch()
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