Error: RuntimeError: Could not infer dtype of DatasetInfo
from transformers import TextDataset, in general my question is how to pass list of strings (or txt file) to model and finetune it ?related with this question/issueGPT2 returns sequence of <|endoftext|> after finetuning🤗TransformersHere’s I tried to finetune gpt2 

but after few iterations I started to get same sequences, for ex. 
prompt: Prime number is 
model output: <|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|><|endoftext|> 
By the way, my text divided into sentences without [EOS] tokens, in other words every item is separate sentencetrain_dataset = TextDataset(
      tokenizer=tokenizer,
      file_path=math_example_path,  # < path to .txt file
      block_size=128)

from transformers import (
    TrainingArguments, 
    Trainer, 
    default_data_collator, 
)
import mlflow 
mlflow.end_run()

training_args = TrainingArguments(output_dir='test_trainer', 
                                  #evaluation_strategy='epoch',
                                  per_device_train_batch_size=1,
                                  per_device_eval_batch_size=1,
                                  gradient_accumulation_steps=20, #
                                  num_train_epochs = 2,
                                  fp16=False,)

trainer = Trainer(
    model=llm_model,
    args=training_args,
    train_dataset=dataset,
    #eval_dataset=dataset,
    tokenizer=tokenizer,
    # Data collator will default to DataCollatorWithPadding, so we change it.
    data_collator=default_data_collator,
    compute_metrics=None,
    preprocess_logits_for_metrics=None,
)

trainer.train()

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
Cell In[28], line 29
      9 training_args = TrainingArguments(output_dir='test_trainer', 
     10                                   #evaluation_strategy='epoch',
     11                                   per_device_train_batch_size=1,
   (...)
     14                                   num_train_epochs = 2,
     15                                   fp16=False,)
     17 trainer = Trainer(
     18     model=llm_model,
     19     args=training_args,
   (...)
     26     preprocess_logits_for_metrics=None,
     27 )
---> 29 trainer.train()

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\transformers\trainer.py:1539, in Trainer.train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)
   1537         REDACTED_TOKEN_utils.enable_progress_bars()
   1538 else:
-> 1539     return inner_training_loop(
   1540         args=args,
   1541         resume_from_checkpoint=resume_from_checkpoint,
   1542         trial=trial,
   1543         ignore_keys_for_eval=ignore_keys_for_eval,
   1544     )

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\transformers\trainer.py:1836, in Trainer._inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)
   1833     rng_to_sync = True
   1835 step = -1
-> 1836 for step, inputs in enumerate(epoch_iterator):
   1837     total_batched_samples += 1
   1839     if self.args.include_num_input_tokens_seen:

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\accelerate\data_loader.py:451, in DataLoaderShard.__iter__(self)
    449 # We iterate one batch ahead to check when we are at the end
    450 try:
--> 451     current_batch = next(dataloader_iter)
    452 except StopIteration:
    453     yield

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\torch\utils\data\dataloader.py:634, in _BaseDataLoaderIter.__next__(self)
    631 if self._sampler_iter is None:
    632     # TODO(https://github.com/pytorch/pytorch/issues/76750)
    633     self._reset()  # type: ignore[call-arg]
--> 634 data = self._next_data()
    635 self._num_yielded += 1
    636 if self._dataset_kind == _DatasetKind.Iterable and \
    637         self._IterableDataset_len_called is not None and \
    638         self._num_yielded > self._IterableDataset_len_called:

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\torch\utils\data\dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self)
    676 def _next_data(self):
    677     index = self._next_index()  # may raise StopIteration
--> 678     data = self._dataset_fetcher.fetch(index)  # may raise StopIteration
    679     if self._pin_memory:
    680         data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\torch\utils\data\_utils\fetch.py:54, in _MapDatasetFetcher.fetch(self, possibly_batched_index)
     52 else:
     53     data = self.dataset[possibly_batched_index]
---> 54 return self.collate_fn(data)

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\transformers\trainer_utils.py:772, in RemoveColumnsCollator.__call__(self, features)
    770 def __call__(self, features: List[dict]):
    771     features = [self._remove_columns(feature) for feature in features]
--> 772     return self.data_collator(features)

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\transformers\data\data_collator.py:92, in default_data_collator(features, return_tensors)
     86 # In this function we'll make the assumption that all `features` in the batch
     87 # have the same attributes.
     88 # So we will look at the first element as a proxy for what attributes exist
     89 # on the whole batch.
     91 if return_tensors == "pt":
---> 92     return torch_default_data_collator(features)
     93 elif return_tensors == "tf":
     94     return tf_default_data_collator(features)

File ~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\site-packages\transformers\data\data_collator.py:158, in torch_default_data_collator(features)
    156             batch[k] = torch.tensor(np.stack([f[k] for f in features]))
    157         else:
--> 158             batch[k] = torch.tensor([f[k] for f in features])
    160 return batch

RuntimeError: Could not infer dtype of DatasetInfo