I'd love to understand this more. Are you saying the Qwen team spends their very impressive human and compute resources on publishing these amazing models and then botches the chat template with mundane bugs?
Like maybe I just misunderstand what's the hard part but wouldn't you assume that people who can put together an impressive model can also write a proper jinja chat template for it?
One really would think these companies (including Google) who spend many millions of dollars on compute could write a few hundred lines of Jinja correctly, so their investment works optimally or at all.
But they don't.
Then a couple of individuals on HuggingFace fix it, either a 2-person startup like Unsloth or a volunteer like froggeric.
I also don't understand how this repeatedly happens.
I did SFT / RL post-training on Qwen3 models a bit. This is an issue that dates back long ago.
My favorite theory is that they had many model variants internally, each using a slightly different chat template, so when it comes to the release they are not even sure what to use any more.
Yes for the first question. Google of all companies didn't even get it right with Gemma for a while until recently. For some reason it doesn't seem like people can actually get these templates right.
It’s unlikely they are benchmarking that downstream. They probably have private benchmark scripts with purpose-specific run telemetry and logging, etc.
I’m sure there is some basic testing but it might be agentic (LLM likes its own output) and maybe just some human smoke tests.
So what's needed to solve this is that someone publicly and popularly benchmarks using the chat template. So that they have an incentive to look good using it.
they likely use their internal infra to run benchmarks; aligning external releases with internal environments is always painful and somewhat underincentivized
The chat templates are usually the first thing that every major release bork on, and all new model architectures end up having a ~2 week initial window of small fixes before they’re not DoA
Temperature, top-up, top-k, min-p all control which token the model predicts next and how likely it is to select one token over the other.
You might understand this as "The capital of France is..." and the model isn't always going to select "Paris". Sometimes it will start a descriptive sentence or even get the answer wrong.
That selection of the next token is what these settings control, and lots of sub-optimal selections compound over time to produce a junk response.
Top-K: example setting 20. Select only from the 20 most likely tokens.
Top-P: example setting 0.9. Select tokens whose probably accumulates to this number. So say you have tokens with 0.7 then 0.2 then 0.1, the last will not be selected because the first two tokens already accumulated to >=0.9.
Min-P: example setting 0.05. Don't select tokens less probable than this value. So a token with 0.1 would be considered, a token with 0.01 would not.
The purpose of all of these is to exclude very unlikely next tokens.
Min-p is specifically "Don't select tokens less probable than a multiple of the top token's probability" with min_p of 0.1 multiplied by an example top probability of 0.3 being 0.03 as the truncation at that time step.
I've run the inference to get the answers I linked to. If someone else does the same thing, that involves extra energy. If I read your conversation instead of generating my own, then that's one less tree that has to be chopped down.
Until we go advanced geothermal or we crack fusion, energy is dirty. Read my inference or link me to yours so we don't boil the planet.
The Qwen team published the same sampler settings for 3.8 and presumably they used those while testing on benchmark. Do you believe they could have achieved higher result with top-n-sigma?
Diverging from the sampler used in RL training is not good for long multi-turn results-- it's a great way to knock models into reasoning loops that wouldn't otherwise.
Like maybe I just misunderstand what's the hard part but wouldn't you assume that people who can put together an impressive model can also write a proper jinja chat template for it?