As usual, the Jinja templates are messed up so use this [0] to reduce or turn off thinking, fix tool calling, keep a 100% KV cache hit rate, etc.
[0] https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates
[0] https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates
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?
If you're using llamacpp, turn on top-n-sigma with sigma of 1, turn off top-p/top-k. You'll thank me later.
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.
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.
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.
Source: One of the min_p authors
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.
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.
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.