code: def test_llama(self):
      dtypes.default_float = dtypes.float16
  
      args_tiny = {
          "dim": 1024,
          "hidden_dim": 2048,
          "n_heads": 8,
          "n_layers": 8,
          "norm_eps": 1e-05,
          "vocab_size": 1000,
      }
      model = LLaMaTransformer(
          **(args_tiny if CI else LLAMA_MODEL_PARAMS["1"]["7B"]["args"])
      )
      derandomize_model(model)
  
      @TinyJit
      def test(t):
          return model(t, 0).realize()
  
      # TODO: test first token vs rest properly
      helper_test(
          "test_llama",
          lambda: (Tensor([[1, 2, 3, 4]]),),
          test,
          0.27 if CI else 14.9,
          191 if CI else 719,
          all_jitted=True,
      )
  
imports: import unittest, time, gc
  import numpy
  from tinygrad.tensor import Tensor
  from tinygrad.nn import optim
  from tinygrad.nn.state import get_parameters
  from tinygrad.jit import TinyJit
  from tinygrad import Device, GlobalCounters, dtypes
  from tinygrad.helpers import CI
  from tinygrad.shape.symbolic import Variable
  from extra.lr_scheduler import OneCycleLR
  from test.helpers import derandomize_model
  from examples.gpt2 import Transformer, MODEL_PARAMS
  from examples.hlb_cifar10 import SpeedyResNet, hyp
  from examples.llama import Transformer, MODEL_PARAMS
  from examples.stable_diffusion import UNetModel, ResBlock
label_desc: Use image node_id 5 for calling the TestRealWorld obj's underlying member method code with example usage: obj.test_llama() and returns: model