Note that FastText (& similar dense word-vector models) don’t perform meaningfully when using toy-sized data or parameters. (All their useful/predictable/testable benefits depend on large, varied datasets & the subtle arrangements of many final vectors.)
But, if you just need a relatively meaningless object/file of the right type, your approach should work. The main parameter that would make a FastText model larger without regard to the tiny training-set is the bucket parameter, with a default value of 2000000. It will allocate that many character-ngram (word-fragment) slots, even if all your actual words don’t have that many ngrams.
Setting bucket to some far-smaller value, in initial model creation, should make your plug/stand-in file far smaller as well.
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