from transformers import AutoModelForCausalLM, AutoTokenizer def get_response(prompt: str): model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen2.5-32B-Instruct", torch_dtype="auto", device_map="auto", ) tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-32B-Instruct") prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}, ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) 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] return response