Thanks for that suggestion, I am not sure how I do it in this case though.
I load word vectors like this:
vectors = vocab.FastText()
self.text.build_vocab(train_data, vectors=vectors, max_size=vocab_size, unk_init=torch.Tensor.normal_)

The model uses embedding layer:
nn.Embedding(vocab_size, embedding_dim, padding_idx=padding_idx)

In worst case, I can use the train_data again to build vocabulary as well which fine, but if I will lose the modified word vectors. If I save them(vectors) at start using torch.save, the embedding layers I suspect would change the field (self.text) only.