T5ForConditionalGeneration.from_pretrained load pytorch *.pt checkpoint fails
I am using T5ForConditionalGeneration and want to load a custom *.pt checkpoint:

       model = T5ForConditionalGeneration.from_pretrained(my_custom_checkpoint) 
       model = model.to(device)

However, I am getting the error below:

        File "/home/usr/lib/python3.7/site-packages/transformers/modeling_utils.py", line 962, in from_pretrained
    **kwargs,
        File "/home/usr/lib/python3.7/site-packages/transformers/configuration_utils.py", line 372, in from_pretrained
        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
        File "/home/usr/anaconda2/envs/maml_adaptation/lib/python3.7/site-packages/transformers/configuration_utils.py", line 423, in get_config_dict
        config_dict = cls._dict_from_json_file(resolved_config_file)
        File "/home/usr/anaconda2/envs/maml_adaptation/lib/python3.7/site-packages/transformers/configuration_utils.py", line 506, in _dict_from_json_file
        text = reader.read()
        File "/home/usr/anaconda2/envs/maml_adaptation/lib/python3.7/codecs.py", line 322, in decode
        (result, consumed) = self._buffer_decode(data, self.errors, final)
        UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte

What is causing this error? I can load the *.pt checkpoint fine using torch.load, but how to do it using T5ForConditionalGeneration? Thank you!
The `from_pretrained` method expects `pytorch_mode.bin` file in the checkpoint folder, if you have saved your checkpoint with a different name, then you could initialize the model with the config and load the weights from `state_dict`

```python
model = T5ForConditionalGeneration.from_config("....")
model.load_state_dict(torch_state_dict)

# save the model with save_pretrained, so next time it can be loaded using  from_pretrained
model.save_pretrained("....")
```

Also looking at the stack trace, there seems to be some issue with your config file, could you maybe check if it contains valid JSON ?
<|||||>Thank you for your response. How do I convert from a *.pt checkpoint to a *.bin checkpoint in Pytorch? Does the transformers  library provide any such utility function? Thank you!<|||||>@Crista23, Simply rename your `.pt` checkpoint into `pytorch_model.bin` - it's a different name for the same file.<|||||>@LysandreJik Thank you for the answer! I have renamed the checkpoint I am trying to load from *.pt to *.bin, but I get the same UnicodeDecodeError.<|||||>`UnicodeDecodeError` error seems related to `config.json`, could you verify that it contains valid JSON, try to just load the json or maybe post the file, so we could take a look.<|||||>Hi @patil-suraj , thanks for your response. Where is this config.json supposed to be found? I keep on looking and cannot find it among my files.<|||||>It should be in the directory where you saved your checkpoint. the `save_pretrained` saves that file in the given directory.<|||||>@patil-suraj thank you for the clarification! Unfortunately there is no config.json file in the saved checkpoints directory. What I am doing is take t5-small, adapt it for my own purposes using the fairseq library, and then try to load the latest fairseq checkpoint into T5ForConditionalGeneration (which gives me the error) - the format in which fairseq saves checkpoints is a directory containing *.pt checkpoints only. <|||||>I see in that case, see if you can save the model using `.save_pretrained` as well, which will let you use `from_pretrained`.

If it's a t5-small model then you could load the config and pass it to `from_pretrained`, for example

```python
config = T5Config.from_pretrained("t5-small")
model = T5ForConditionalGeneration.from_pretrained("path_to_model_dir", config=config)
```

this assumes you have at least `pytorch_model.bin` in the model dir. If not then rename the `.pt` file to `.bin` file as Lysandre suggested.<|||||>Thank you so much @patil-suraj, that fixed it! <|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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