fastText.py

from gensim.models.fasttext import FastText

ft_model = FastText(
    sg=1, # use skip-gram: usually gives better results
    size=100, # embedding dimension (default)
    window=10, # window size: 10 tokens before and 10 tokens after to get wider context
    min_count=5, # only consider tokens with at least n occurrences in the corpus
    negative=15, # negative subsampling: bigger than default to sample negative examples more
    min_n=2, # min character n-gram
    max_n=5 # max character n-gram
)
ft_model.build_vocab(tok_text) # tok_text is our tokenized input text - a list of lists relating to docs and tokens respectivley

ft_model.train(
    tok_text,
    epochs=6,
    total_examples=ft_model.corpus_count, 
    total_words=ft_model.corpus_total_words)

ft_model.save('_fasttext.model') #save
ft_model = FastText.load('_fasttext.model') #load

It looks like in the newer version of FastText, this may need to be updated to read vector_size=100 instead of size=100.