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544 points tosh | 6 comments | | HN request time: 1.125s | source | bottom
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simonw ◴[] No.43464243[source]
32B is one of my favourite model sizes at this point - large enough to be extremely capable (generally equivalent to GPT-4 March 2023 level performance, which is when LLMs first got really useful) but small enough you can run them on a single GPU or a reasonably well specced Mac laptop (32GB or more).
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1. YetAnotherNick ◴[] No.43464443[source]
I don't think these models are GPT-4 level. Yes they seem to be on benchmarks, but it has been known that models increasingly use A/B testing in dataset curation and synthesis(using GPT 4 level models) to optimize not just the benchmarks but things which could be benchmarked like academics.
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2. simonw ◴[] No.43464533[source]
I'm not talking about GPT-4o here - every benchmark I've seen has had the new models from the past ~12 months out-perform the March 2023 GPT-4 model.

To pick just the most popular one, https://lmarena.ai/?leaderboard= has GPT-4-0314 ranked 83rd now.

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3. th0ma5 ◴[] No.43465368[source]
How have you been able to tie benchmark results to better results?
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4. simonw ◴[] No.43465877{3}[source]
Vibes and intuition. Not much more than that.
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5. tosh ◴[] No.43468989[source]
Also "GPT-4 level" is a bit loaded. One way to think about it that I found helpful is to split how good a model is into "capability" and "knowledge/hallucination".

Many benchmarks test "capability" more than "knowledge". There are many use cases where the model gets all the necessary context in the prompt. There a model with good capability for the use case will do fine (e.g. as good as GPT-4).

That same model might hallucinate when you ask about the plot of a movie while a larger model like GPT-4 might be able to recall better what the movie is about.

6. th0ma5 ◴[] No.43474204{4}[source]
Don't you think that presenting this as learning or knowledge is unethical?