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The model.alpha property only holds the initially-configured starting-alpha – it's not updated to the effective learning-rate through training.

So, even if the value is being decreased properly (and I expect that it is), you wouldn't see it in the logging you've added.

Separate observations about your code:

• in gensim versions at least through 3.5.0, maximum training throughput is most often reached with some value for workers between 3 and the number of cores – but usually not the full number of cores (if it's higher than 12) or larger. So workers=multiprocessing.cpu_count()*4 is likely going to much slower than what you could achieve with a lower number.

• if your corpus is large enough to support 600-dimensional vectors, and discarding words with fewer than min_count=10 examples, negative sampling may work faster and get better results than the hs mode. (The pattern in published work seems to be to prefer negative-sampling with larger corpuses.)