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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
# Load model and tokenizer (CPU only)
tokenizer = AutoTokenizer.from_pretrained("unsloth/qwen2.5-math-1.5b")
model = AutoModelForCausalLM.from_pretrained(
"unsloth/qwen2.5-math-1.5b",
device_map="cpu" # forces CPU
)
generator = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
device=-1 # CPU
)
# Gradio response function
def respond(message, history, system_message, max_tokens, temperature, top_p):
full_prompt = f"{system_message}\n"
for h in history:
full_prompt += f"User: {h['user']}\nBot: {h['bot']}\n"
full_prompt += f"User: {message}\nBot:"
output = generator(
full_prompt,
max_new_tokens=max_tokens,
do_sample=True,
temperature=temperature,
top_p=top_p
)[0]["generated_text"]
# Extract the new bot response only
bot_response = output[len(full_prompt):].strip()
history.append({"user": message, "bot": bot_response})
return history, history
# Chat interface
chatbot = gr.ChatInterface(
respond,
type="messages",
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=512, value=256, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.05, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.05, label="Top-p (nucleus sampling)"),
]
)
with gr.Blocks() as demo:
with gr.Sidebar():
gr.LoginButton()
chatbot.render()
if __name__ == "__main__":
demo.launch()
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