Comment by zambelli
5 hours ago
I was surprised as well. I did go with an extreme (but true) example in the post. In this case, native function-calling template likely is in play.
However, that doesn't explain the Lamaserver prompt vs llamafile at ~ +4pts, or vs Ollama (at ~ +30ish pts) that sits almost perfectly between llamaserver native and llamafile.
The backend affects almost all model families, and was just something I've never seen really talked about.
Do you have any suspicion about what is different between the backends?
That's an absolutely bonkers statistic: it would mean spurious differences in hosting container overwhelm the performance differences between models.
I genuinely don't, sadly. I'm a mathematician originally, evolved organically into ML then AI - but I never really was a SWE.
I feel like there's some backend decoding or chat template thing going on at a much lower level than what I'm best at. Maybe it's injecting headers or something that eventually compounds to model confusion? I really have no idea.
I really hope folks better than me at backend stuff take a look and dive into it though because it's definitely under-reported and super consistent across model families and backends ranging from ollama, lama.cpp native, prompt, llamafile, and even vLLM that I didn't formally benchmark in the repo.