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684 points prettyblocks | 1 comments | | HN request time: 0.217s | source

I mean anything in the 0.5B-3B range that's available on Ollama (for example). Have you built any cool tooling that uses these models as part of your work flow?
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linsomniac ◴[] No.42788414[source]
I have this idea that a tiny LM would be good at canonicalizing entered real estate addresses. We currently buy a data set and software from Experian, but it feels like something an LM might be very good at. There are lots of weirdnesses in address entry that regexes have a hard time with. We know the bulk of addresses a user might be entering, unless it's a totally new property, so we should be able to train it on that.
replies(1): >>42798829 #
1. thesz ◴[] No.42798829[source]
From my experience (2018), run LLM output through beam search over different choices of canonicalization of certain part of text. Even 3-gram models (yeah, 2018) fare better this way.