Error with `load_from_checkpoint` 
I am trying to fine-tune a language model and facing some issues with loading the model from the saved checkpoint.
I have defined my own model which takes in argparse.NameSpace as the input as is suggested in the documentation here.
class FineTuner(pl.LightningModule):
    def __init__(self, hparams):  # pass all the arguments
        super(FineTuner, self).__init__()
        self.hparams = hparams

After I am done with the fine-tuning, I try to load the model from saved checkpoint in a separate file as follows -
from model_file import FineTuner
model = FineTuner.load_from_checkpoint(ckpt_path)

but it gives me the following error -
AttributeError: 'dict' object has no attribute 'tokenizer_name'
I tried to look into the source code and found that the error is happening at this line.
I believe the error is occurring because the aforementioned line in saving.py file expects a **dict which is different from my model definition.
It would be great if you could solve this issue as I have seen many others are also having similar issues with model loading.
For others who might still be looking for the answer, here is the workaround that I am using right now
(but would still like to use the cleaner official version) -
ckpt_path = abs_path('./path/to/ckpt/file')
checkpoint = torch.load(ckpt_path, map_location=lambda storage, loc: storage)
model = my_model(checkpoint['hyper_parameters']) #(better solution than explicitly providing all the hyperparameters)
model.load_state_dict(checkpoint['state_dict'])

If one were to look at the source code, you would notice that they are kind of doing the same steps but with some extra stuff ;)
But hey, who does not like one-liners.
I am using version version 1.0.4.