0%|                                                                                                                              | 0/2 [00:00<?, ?it/s]`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...
/opt/conda/lib/python3.10/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
  warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
Traceback (most recent call last):
  File "/home/sagemaker-user/output-7b-26k-lora/../lora_finetuning_push_to_hub_save_local.py", line 236, in <module>
    train()
  File "/home/sagemaker-user/output-7b-26k-lora/../lora_finetuning_push_to_hub_save_local.py", line 223, in train
    trainer.train()
  File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 1539, in train
    return inner_training_loop(
  File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 1809, in _inner_training_loop
    tr_loss_step = self.training_step(model, inputs)
  File "/opt/conda/lib/python3.10/site-packages/transformers/trainer.py", line 2665, in training_step
    self.accelerator.backward(loss)
  File "/opt/conda/lib/python3.10/site-packages/accelerate/accelerator.py", line 1853, in backward
    loss.backward(**kwargs)
  File "/opt/conda/lib/python3.10/site-packages/torch/_tensor.py", line 487, in backward
    torch.autograd.backward(
  File "/opt/conda/lib/python3.10/site-packages/torch/autograd/__init__.py", line 200, in backward
    Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn