Try qwen 3.6. it will knock your socks off

Yea, No doubt Qwen 3.6 open weights are far more strong

Have you tried the Gemma 4 series, out of curiosity? I haven’t run a local model in a while, but the benchmarks look good. I’d take a free local tool-use model if it was relatively consistent.

Makes sense, dense for small models, dense or MoE for larger ones, end up fitting various hardware setups pretty neatly, no need for MoE at smaller scale and dense too heavy at large scale.

I never want LLM to span me with emojis. What is the use case for that? I find it highly annoying.

So are we saying it's fine that the article is written by an LLM as long as it doesn't have the tell-tale signs of LLMs?

Third line in to the article: "But there’s one result in the benchmarks I keep coming back to."

I hear this sort of thing all the time now on YouTube from media/news personalities:

“And that’s the part nobody seems to be talking about.”

"And here's what keeps me up at night."

“This is where the story gets complicated.”

“Here’s the piece that doesn’t quite fit.”

“And this is where the usual explanation starts to break down.”

“Here’s what I can’t stop thinking about.”

“The part that should worry us is not the obvious one.”

“And that’s where the real problem begins.”

“But the more interesting question is the one no one is asking.”

“And this is where things stop being simple.”

It doesn't really worry me but I think its interesting that LLM speak sounds so distinctive, and how willing these media personalities are to be so obvious in reading out on TV what the LLM spat out.

I've never studied what LLMs say in depth is it is interesting that my brain recognises the speech pattern so easily.

Are you referring to the literal use of the expression "full stop"? I don't see it anymore in the article, maybe they edited it out?

MoE models will have far more world knowledge than dense models with the same amount of active parameters. MoE is a no-brainer if your inference setup is ultimately limited by compute or memory throughput - not total memory footprint - or alternately if it has fast, high-bandwidth access to lower-tier storage to fetch cold model weights from on demand.

I think you mean inference compute? I believe all expert weights are updated in each backward pass during MoE training. The first benefit was getting a sort of structured pruning of weights through the mechanism of expert selection so that the model didn’t need to go through ‘unnecessary’ parts of the model for a given token. This then let inference use memory more efficiently in memory constrained environments, where non-hot or less common experts could be put into slow RAM, or sometimes even streamed off storage.

But I don’t think it necessarily saved training cost; if it did, I’d be interested to learn how!