import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
import bitsandbytes as bnb

model_name = "peterxyz/detect-llama-34b"
tokenizer = AutoTokenizer.from_pretrained(model_name)

nf4_config = BitsAndBytesConfig(
   load_in_4bit=True,
   bnb_4bit_quant_type="nf4",
   bnb_4bit_use_double_quant=True,
   bnb_4bit_compute_dtype=torch.bfloat16
)

import gc
torch.cuda.empty_cache()
gc.collect()

model_nf4 = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=nf4_config)
model = PeftModel.from_pretrained(model_nf4, model_name)

from datasets import load_dataset

dataset = load_dataset("peterxyz/smart-contract-vuln-detection")

input_text = """
pragma solidity ^0.5.0;

contract ModifierEntrancy {

  mapping (address => uint) public tokenBalance;
  string constant name = "Nu Token";
  Bank bank;

  constructor() public{
      bank = new Bank();
  }

  //If a contract has a zero balance and supports the token give them some token
  function airDrop() hasNoBalance supportsToken  public{
    tokenBalance[msg.sender] += 20;
  }

  //Checks that the contract responds the way we want
  modifier supportsToken() {
    require(keccak256(abi.encodePacked("Nu Token")) == bank.supportsToken());
    _;
  }

  //Checks that the caller has a zero balance
  modifier hasNoBalance {
      require(tokenBalance[msg.sender] == 0);
      _;
  }
}

contract Bank{

    function supportsToken() external returns(bytes32) {
        return keccak256(abi.encodePacked("Nu Token"));
    }

}

identify vulnerability of this code given above
"""
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))