please add option to load fine-tuned file to CPU if trained on GPU
I fine-tuned the pytorch_model.bin on a GPU machine (google cloud) but need to use it on my home computer (no GPU). When I tried to open it using `model = BertForMaskedLM.from_pretrained(bert_version)` I got the following error:

     RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() 
     is False. If you are running on a CPU-only machine, please use torch.load with map_location='cpu'
     to map your storages to the CPU.

Perhaps you can add an option into `from_pretrained()`  such as `cpu=True` which will then call

`torch.load(weights_path, map_location=lambda storage, location: 'cpu')`
Indeed, this will be in the next release, thanks!<|||||>is there is a change according the CPU issue ?<|||||>Should be fixed now. Do you still have an error?<|||||>i am loading the model and i dont know how to load on CPU , gives me "model.to" to is not defined. Can you tell me how to send the model to run on CPU if trained on GPU.<|||||>Would love some explanation on how to do this as well!<|||||>I have the same question. how to load a model trained on GPU to CPU?<|||||>I am watching this as well.<|||||>I have the same question. Is there any option for using CPU-only?<|||||>watching this as well<|||||>same