Currently HashingTF works like CountVectorizer (the equivalent in scikit-learn is HashingVectorizer). That is, it works on a sequence of strings and computes term frequencies.
The use cases for feature hashing extend to arbitrary feature values (binary, count or real-valued). For example, scikit-learn's FeatureHasher can accept a sequence of (feature_name, value) pairs (e.g. a map, list). In this way, feature hashing can operate as both "one-hot encoder" and "vector assembler" at the same time.
Investigate adding a more generic feature hasher (that in turn can be used by HashingTF).