alt Hacker NewsCan someone explain how a 27B model (quantized no less) ever be comparable to a model like Sonnet 4.0 which is likely in the mid to high hundreds of billions of parameters?
Is it really just more training data? I doubt it’s architecture improvements, or at the very least, I imagine any architecture improvements are marginal.
Replies
There's diminishing returns bigly when you increase parameter count.
The sweet spot isn't in the "hundreds of billions" range, it's much lower than that.
Anyways your perception of a model's "quality" is determined by careful post-training.
The short answer is that there are more things that matter than parameter count, and we are probably nowhere near the most efficient way to make these models. Also: the big AI labs have shown a few times that internally they have way more capable models
Considering the full fat Qwen3.5-plus is good, but barely Sonnet 4 good in my testing (but incredibly cheap!) I doubt the quantised versions are somehow as good if not better in practice.
It doesn’t. I’m not sure it outperforms chatgpt 3
AFAIK post-training and distillation techniques advanced a lot in the past couple of years. SOTA big models get new frontier and within 6 months it trickles down to open models with 10x less parameters.
And mind the source pre-training data was not made/written for training LLMs, it's just random stuff from Internet, books, etc. So there's a LOT of completely useless an contradictory information. Better training texts are way better and you can just generate & curate from those huge frontier LLMs. This was shown in the TinyStories paper where GPT-4 generated children's stories could make models 3 orders of magnitude smaller achieve quite a lot.
This is why the big US labs complain China is "stealing" their work by distilling their models. Chinese labs save many billions in training with just a bunch of accounts. (I'm just stating what they say, not giving my opinion).