Create app.py

app.py

@@ -0,0 +1,49 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model_name = "Qwen/QwQ-32B-Preview"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Initialize persistent conversation with a system message
+system_message = {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."}
+messages = [system_message]
+
+# Chat loop to maintain persistence
+while True:
+    user_input = input("User: ")  # Get user input
+    if user_input.lower() in {"exit", "quit"}:
+        print("Chat session ended.")
+        break
+
+    # Append user message to the conversation history
+    messages.append({"role": "user", "content": user_input})
+
+    # Format the messages for the model
+    text = tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+    # Generate response
+    generated_ids = model.generate(
+        **model_inputs,
+        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]
+
+    # Append assistant's response to the conversation history
+    messages.append({"role": "assistant", "content": response})
+
+    # Display the assistant's response
+    print(f"Assistant: {response}")