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.
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.
Google 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.
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
Some weeks ago a new official Gemma 4 release was posted that corrected some of the chat template problems. So the official release files on hugging face should be the way to go.
The updated version will handle tool calling better by default, but the reasoning quality is no longer preserved and is mutilated quite badly.
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.
If you are on Mac, have a look at the Llama-macOS app. They claim sensible settings for the linked model downloads. I'd expect the authors of Llama.cpp and the Huggingface folks to know this stuff.
https://news.ycombinator.com/item?id=49328008
If your package manager / configurator isn’t claude code or codex, you’re wasting time.
Unless, of course, your goal is to actually understand what is going on, regardless of whether that is difficult.
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