[otabdeveloper4]

> are materially ahead of every open source model out there at this time

They aren't. Any difference is in sampling parameters and post-training flavor choices. These aren't things that are "materially ahead", that's basically just LLM themes.

[achompas]

I’m sorry but you’re demonstrably incorrect.

Listen, I want more open weight models in the world. They create entrepreneurial opportunities and support use cases which the foundation labs don’t want to support.

But open weight models are consistently three to six months behind on performance compared to closed models, as confirmed by both benchmarks and personal use. They’re closer on coding and much further away on non-coding tasks.

There are theories as to why these models lag, which I won’t get into. But anyone claiming open-weight models are close to closed-weight models is ignoring significant evidence to the contrary.

[otabdeveloper4]

> three to six months behind on performance

Yeah, like I said - it's just a post-training difference. That's not a material difference, that's a difference of chrome and polish.

[ninjagoo]

> I’m sorry but you’re demonstrably incorrect.

Please so demonstrate?

[achompas]

The onus isn’t on me. It’s on anyone contradicting findings by most benchmarks, because most of them show a clear advantage for Opus and GPT over OSS models.

[ninjagoo]

So Big Claim No Demonstration? :-)

[orf]

I mean just use them and compare, the gap is obvious.

[otabdeveloper4]

I did, and I fixed Qwen's issues with trivial sampling and loop detection hacks.

If I can do this, then a company that wants to sell local models seriously could do it too.

[ninjagoo]

> I did, and I fixed Qwen's issues with trivial sampling and loop detection hacks.

Wow, that's amazing! Care to share the changes? Would love to try them out.

[otabdeveloper4]

It's not amazing at all.

What's amazing is that LLM technologies are so immature that even basic engineering diligence isn't being done. (Like detecting token loops, for example.)