Out of the loop here. What did Google and unsloth and llama do to mutilate Gemma? I can understand Google shenanigans but llama and gunsmith is kind of surprising.
Unsloth modified the template and then finetuned their own version of the model to optimize for some benchmarks as a means of validating quants.
Google and Llama.cpp then adopt template changes by default, so anyone downloading the new model or even using the original model will now automatically be using it incorrectly.
Llama.cpp also uses the same inference setting defaults regardless which version of the model you use and some settings are simply defaults it uses for all models.
Then even if you account for all of these, you have to be using Gemma 4 itself correctly, which many people do not.
All of these little changes and inconsistencies hurt some of the model's original capabilities. Even if you go directly to Google's repo and download the full float 16 weights with the template they have there now, you cannot simply assume you're getting the best results.
When I tried to run llama.cpp directly I was getting max 9tk/s on qwen3.5-9B, then I tried LM Studio with the same model and got 77tk/s. I haven't figured out yet how to get MTP working properly in either.
Then create your own reasoning tests to verify that it is working correctly. You can set a specific seed value to make sure the generation is the same every time, that way you can identify any tokens that are different.
Afterwards, try making small incremental changes to the template and validate your tests each time in order to try to adopt the improvements from the newer templates. If the reasoning quality degrades, undo your changes and try again or test alternative solutions.
Would appreciate any kind of pointer to the latest!