Что за ошибка

{
	"name": "ValueError",
	"message": "You are trying to save a non contiguous tensor: `encoder.block.0.layer.0.SelfAttention.q.weight` which is not allowed. It either means you are trying to save tensors which are reference of each other in which case it's recommended to save only the full tensors, and reslice at load time, or simply call `.contiguous()` on your tensor to pack it before saving.",
	"stack": "---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[14], line 1
----> 1 trainer.train()

File ~/.local/lib/python3.10/site-packages/transformers/trainer.py:1938, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
   1936         hf_hub_utils.enable_progress_bars()
   1937 else:
-> 1938     return inner_training_loop(
   1939         args=args,
   1940         resume_from_checkpoint=resume_from_checkpoint,
   1941         trial=trial,
   1942         ignore_keys_for_eval=ignore_keys_for_eval,
   1943     )

File ~/.local/lib/python3.10/site-packages/transformers/trainer.py:2356, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
   2353     self.state.epoch = epoch + (step + 1 + steps_skipped) / steps_in_epoch
   2354     self.control = self.callback_handler.on_step_end(args, self.state, self.control)
-> 2356     self._maybe_log_save_evaluate(tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval)
   2357 else:
   2358     self.control = self.callback_handler.on_substep_end(args, self.state, self.control)

File ~/.local/lib/python3.10/site-packages/transformers/trainer.py:2807, in Trainer._maybe_log_save_evaluate(self, tr_loss, grad_norm, model, trial, epoch, ignore_keys_for_eval)
   2804     metrics = self._evaluate(trial, ignore_keys_for_eval)
   2806 if self.control.should_save:
-> 2807     self._save_checkpoint(model, trial, metrics=metrics)
   2808     self.control = self.callback_handler.on_save(self.args, self.state, self.control)

File ~/.local/lib/python3.10/site-packages/transformers/trainer.py:2886, in Trainer._save_checkpoint(self, model, trial, metrics)
   2884 run_dir = self._get_output_dir(trial=trial)
   2885 output_dir = os.path.join(run_dir, checkpoint_folder)
-> 2886 self.save_model(output_dir, _internal_call=True)
   2888 if not self.args.save_only_model:
   2889     # Save optimizer and scheduler
   2890     self._save_optimizer_and_scheduler(output_dir)

File ~/.local/lib/python3.10/site-packages/transformers/trainer.py:3454, in Trainer.save_model(self, output_dir, _internal_call)
   3451         self.model_wrapped.save_checkpoint(output_dir)
   3453 elif self.args.should_save:
-> 3454     self._save(output_dir)
   3456 # Push to the Hub when `save_model` is called by the user.
   3457 if self.args.push_to_hub and not _internal_call:

File ~/.local/lib/python3.10/site-packages/transformers/trainer.py:3525, in Trainer._save(self, output_dir, state_dict)
   3523             torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
   3524 else:
-> 3525     self.model.save_pretrained(
   3526         output_dir, state_dict=state_dict, safe_serialization=self.args.save_safetensors
   3527     )
   3529 if self.tokenizer is not None:
   3530     self.tokenizer.save_pretrained(output_dir)

File ~/.local/lib/python3.10/site-packages/transformers/modeling_utils.py:2793, in PreTrainedModel.save_pretrained(self, save_directory, is_main_process, state_dict, save_function, push_to_hub, max_shard_size, safe_serialization, variant, token, save_peft_format, **kwargs)
   2788     gc.collect()
   2790 if safe_serialization:
   2791     # At some point we will need to deal better with save_function (used for TPU and other distributed
   2792     # joyfulness), but for now this enough.
-> 2793     safe_save_file(shard, os.path.join(save_directory, shard_file), metadata={\"format\": \"pt\"})
   2794 else:
   2795     save_function(shard, os.path.join(save_directory, shard_file))

File ~/.local/lib/python3.10/site-packages/safetensors/torch.py:281, in save_file(tensors, filename, metadata)
    250 def save_file(
    251     tensors: Dict[str, torch.Tensor],
    252     filename: Union[str, os.PathLike],
    253     metadata: Optional[Dict[str, str]] = None,
    254 ):
    255     \"\"\"
    256     Saves a dictionary of tensors into raw bytes in safetensors format.
    257 
   (...)
    279     ```
    280     \"\"\"
--> 281     serialize_file(_flatten(tensors), filename, metadata=metadata)

File ~/.local/lib/python3.10/site-packages/safetensors/torch.py:485, in _flatten(tensors)
    476 if failing:
    477     raise RuntimeError(
    478         f\"\"\"
    479         Some tensors share memory, this will lead to duplicate memory on disk and potential differences when loading them again: {failing}.
   (...)
    482         \"\"\"
    483     )
--> 485 return {
    486     k: {
    487         \"dtype\": str(v.dtype).split(\".\")[-1],
    488         \"shape\": v.shape,
    489         \"data\": _tobytes(v, k),
    490     }
    491     for k, v in tensors.items()
    492 }

File ~/.local/lib/python3.10/site-packages/safetensors/torch.py:489, in <dictcomp>(.0)
    476 if failing:
    477     raise RuntimeError(
    478         f\"\"\"
    479         Some tensors share memory, this will lead to duplicate memory on disk and potential differences when loading them again: {failing}.
   (...)
    482         \"\"\"
    483     )
    485 return {
    486     k: {
    487         \"dtype\": str(v.dtype).split(\".\")[-1],
    488         \"shape\": v.shape,
--> 489         \"data\": _tobytes(v, k),
    490     }
    491     for k, v in tensors.items()
    492 }

File ~/.local/lib/python3.10/site-packages/safetensors/torch.py:403, in _tobytes(tensor, name)
    396     raise ValueError(
    397         f\"You are trying to save a sparse tensor: `{name}` which this library does not support.\"
    398         \" You can make it a dense tensor before saving with `.to_dense()` but be aware this might\"
    399         \" make a much larger file than needed.\"
    400     )
    402 if not tensor.is_contiguous():
--> 403     raise ValueError(
    404         f\"You are trying to save a non contiguous tensor: `{name}` which is not allowed. It either means you\"
    405         \" are trying to save tensors which are reference of each other in which case it's recommended to save\"
    406         \" only the full tensors, and reslice at load time, or simply call `.contiguous()` on your tensor to\"
    407         \" pack it before saving.\"
    408     )
    409 if tensor.device.type != \"cpu\":
    410     # Moving tensor to cpu before saving
    411     tensor = tensor.to(\"cpu\")

ValueError: You are trying to save a non contiguous tensor: `encoder.block.0.layer.0.SelfAttention.q.weight` which is not allowed. It either means you are trying to save tensors which are reference of each other in which case it's recommended to save only the full tensors, and reslice at load time, or simply call `.contiguous()` on your tensor to pack it before saving."
}