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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}")
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