alt Hacker NewsGoogle provided incorrect settings and an imperfect template.
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.
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I am very much a beginner to local LLM stuff and I find it incredibly hard to figure out how to run models optimally with the correct settings for my hardware. The number of different variations of the same model and how each quant work is super confusing as well.
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.
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And what's the right way to use Gemma? Where can I find the correct template and settings if those aren't the ones provided by Google, Unsloth, and aren't built into llama.cpp? I discarded using Gemma 4 because it got into weird loops when tool calling