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Comment by disiplus | Hacker News Reader

i dont know what are you talking about, i replaced an older gpt4o with a finetuned qwen. there is a huge amount of "AI, that can be done with those models, or partly by those models." Huge amount of people would not notice the difference. And if you prepare the context correctly, even bigger slice of people would not notice.

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If it helps, I mean it in a really literal sense. qwen3.6 27b is currently $3.20 per million tokens on openrouter right now which is way overpriced. As good as the 27b is, kimi k2.5 $3.00 and it's just in another league in terms of capability. There's no reason to spend money on it.

And even alibaba's own qwen3.6-plus is $1.95, so it's kinda easy to come to a conclusion that alibaba (nor anyone else) is really interested in hosting that model.

And don't get me wrong, I fully agree with you, qwen3.6 27b is an amazing model. I run it on my own hardware and every day I'm constantly surprised with what it can zero shot.

Genuinely curious, what are you "fine tuning" these smaller models to do reliably? I hear this talked about a lot but very few people actually cough up examples, and I'd love to actually hear of one.

depends, a super small one finetuned to do function calling instead sending it to big model and waiting, instead, you ask for a revenue in last month, i do a small llm function call -> show results. some bigger ones, analysis, summary, classification. what is great with smaller ones, and im looking at 2b, 4b is you can get a huge throughput with just vllm and a couple of consumer gpus. what i usually do is basically distillation of a big one onto smaller one.

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