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app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import spaces 
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2-0.5B-Instruct",
+    torch_dtype="auto",
+    device_map="auto"
+).to(device)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
+
+@spaces.GPU
+
+def chatbot(user_input, history):
+    system_message = {"role": "system", "content": "You are a helpful assistant."}
+    messages = history + [{"role": "user", "content": user_input}]
+    
+    if len(history) == 0:
+        messages.insert(0, system_message)
+    
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    
+    model_inputs = tokenizer([text], return_tensors="pt").to(device)
+    attention_mask = torch.ones(model_inputs.input_ids.shape, device=device)
+
+    generated_ids = model.generate(
+        model_inputs.input_ids,
+        attention_mask=attention_mask,
+        max_new_tokens=512
+    )
+    
+    generated_ids = [
+        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+    
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    
+    history.append({"role": "user", "content": user_input})
+    history.append({"role": "assistant", "content": response})
+    
+    gradio_history = [[msg["role"], msg["content"]] for msg in history]
+
+    return gradio_history, history
+
+with gr.Blocks() as demo:
+    chatbot_interface = gr.Chatbot()
+    state = gr.State([])
+
+    with gr.Row():
+        txt = gr.Textbox(show_label=False, placeholder="Ask anything")
+        txt.submit(chatbot, [txt, state], [chatbot_interface, state])
+
+demo.launch()