| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import torch | |
| device = "cuda" # the device to load the model onto | |
| model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B-Chat", torch_dtype=torch.bfloat16, device_map="auto") | |
| tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-MoE-A2.7B-Chat") | |
| 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(device) | |
| generated_ids = model.generate(model_inputs.input_ids, 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] | |
| print(response) | |