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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.

Interesting. I see papers where researchers will finetune models in the 7 to 12b range and even beat or be competitive with frontier models. I wish I knew how this was possible, or had more intuition on such things. If anyone has paper recommendations, I’d appreciate it.

They're using a revolutionary new method called "training on the test set".

So, curve fitting the training data? So, we should expect out of sample accuracy to be crap?

Yeah, that's usually what tends to happen with those tiny models that are amazing in benchmarks.

More parameters improves general knowledge a lot, but you have to quantize more in order to fit in a given amount of memory, which if taken to extremes leads to erratic behavior. For casual chat use even Q2 models can be compelling, agentic use requires more regularization thus less quantized parameters and lowering the total amount to compensate.

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