What are your experiences with all the different variations of finetunes on small models (I have seen them perform better on public benchmarks in some cases, which shouldn't be ignored completely, but that doesn't seem to translate to better output on real work in my limited testing.
What are your observations? Any specific ones that you lean towards, or have had good experiences with?