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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Model name
MODEL_NAME = "Qwen/Qwen2.5-0.5B-Instruct-GGUF"
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.float16, device_map="auto")
def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
messages = [{"role": "system", "content": system_message}]
# Add chat history to messages
for user_msg, assistant_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if assistant_msg:
messages.append({"role": "assistant", "content": assistant_msg})
messages.append({"role": "user", "content": message})
# Tokenize input
inputs = tokenizer(message, return_tensors="pt").to("cpu")
# Generate response
with torch.no_grad():
outputs = model.generate(
**inputs, max_length=max_tokens, temperature=temperature, top_p=top_p
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
# Define Gradio interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=512, value=64, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=1.5, value=0.3, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=0.8, value=0.75, step=0.05, label="Top-p (nucleus sampling)"),
],
)
# Launch Gradio app
if __name__ == "__main__":
demo.launch()
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