the code should be like this
mport torch
from transformers import AutoTokenizer, AutoModelForCausalLM

load base LLM model and tokenizer

model = AutoModelForCausalLM.from_pretrained(
"philschmid/llama-2-7b-instruction-generator",
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
load_in_4bit=True,
)
tokenizer = AutoTokenizer.from_pretrained("philschmid/llama-2-7b-instruction-generator")

prompt = f"""### Instruction:
Use the Input below to create an instruction, which could have been used to generate the input using an LLM.

Input:

Dear [boss name],

I'm writing to request next week, August 1st through August 4th,
off as paid time off.

I have some personal matters to attend to that week that require
me to be out of the office. I wanted to give you as much advance
notice as possible so you can plan accordingly while I am away.

Please let me know if you need any additional information from me
or have any concerns with me taking next week off. I appreciate you
considering this request.

Thank you, [Your name]

Response:

"""

input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda()
outputs = model.generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=0.9)

print(f"Generated instruction:\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]}")