[nzeid]

A few days ago I switched again from Qwen3.6 to Gemma 4 - for personal use I've experienced better average performance with the 26B version of the latter than the 27B of the former.

For someone who's been running local models for a long while, these are very very exciting times.

[girvo]

Oh that's fascinating. 3.6 27B is pretty damned good, but slow in wall-clock times on my DGX Spark-alike. It generates huge reams of thinking before it gets the (usually correct!) answer, so wall-clock time is rough for tasks even at ~20tk/s

I'm surprised the 26B-A4B is better? It should be faster too, interesting. I'm excited to try 31B with MTP, because MTP-2 is what makes 27B bearable on the GB10.

What are you using it for? Agent-based coding, or something else?

[nzeid]

General purpose, mostly internet research in the form of slow-crawling. (Emphasis on slow - I've ultimately landed on Scrapling's API for seamless content rendering, and I use image support so as not to exclude informative images or weirdly rendered text.)

For coding I don't need image support so I stuff the entire GPU with text-only mode. I don't have a workflow where I send LLMs off to generate thousands of lines of code but what little coding I did I did with Qwen3.6 and it was spectacular, as you likely suggest.

[glenngillen]

I've been thinking about doing more of this too. What spec machine are you running? And are you using long-running autonomous agents or more of the IDE/co-pilot style of collaboration?

[apexalpha]

I’ve been swapping between these too as well.

However I find qwen unbeatable for toolcallling. I think gemma wasnt trained on that at all.

[sigmoid10]

Gemma certainly was trained for tool calling, but the implementation in llama.cpp has been troubled because Gemma uses a different chat template format. The processor from the transformers library works fine though.

[apexalpha]

Oh I must've missed this.

The AI space moves so fast! I'll check it out again.

[intothemild]

Don't forget to update the gguf you have too. The templates in them were updated recently too

[nzeid]

I'm using llama.cpp with Gemma and tool calling is mission critical. It's perfectly fine on my end.

There are definitely differences in the eagerness to tool-call that you'll need to manage. And for all local models I've ever used, I've had to micromanage the tools provided by servers to eliminate any possibility that they reach for something wonky or confusing.

[magicalhippo]

> However I find qwen unbeatable for toolcallling. I think gemma wasnt trained on that at all.

Gemma4 chat template seems to had multiple issues, at least with llama.cpp, not sure they're all fixed yet. It assumed simple types for parameters for example.