Given that it apparently defaults to 'xhigh', this is probably the answer.
Granted, it's still much lower tokens/s than you'll get out of many MoE models.
Edit: Even set to medium or low there's still a lot of second guessing, less consistency, lower 'acceptable response' rate, and slower/more token churn vs gemma4:26b-a3b. I think gemma4 is just a better 'general purpose' model.
I haven't tried lowering thinking, however, I actually asked a solid question earlier regarding a real world scenario I encountered and all that excessive thinking made it give me an amazing answer. The thinking actually all made sense, and honestly I found it thought of similar stuff to what I thought when I drew my own conclusion.
I don't usually rely on AI for much (I'm actually kind of anti-AI, although I follow stuff like this enthusiastically because of the rapid advancements, "average joe" access, and openness), however for what I asked? It was spot on.
The subject was a bit personal, so I won't share. I was just curious what AI would say about the situation and it definitely surprised me, especially since Qwen, while usually great on development/coding stuff, has shown weaknesses in other areas.
Definitely a solid release, and this one runs on my 4090 with minimal loss of quality!
In my experience with Qwen3.6 35B-A3B, disabling thinking made the model generate inaccurate replies. Ask it for the recipe of egg salad and it gives you the recipe of an omelette.
Did I miss something, is it possible to have that model be reliable without thinking?
Turning off thinking mode you need a different tokenizer and a whole host of settings. It's more like switching your vehicle from gas to diesel, than turning your headlights from lo to hi.
I used the settings recommended on huggingface/unsloth's model page + whatever suggestion from various LLMs - didn't research too much myself which setting did what.
Frontier model labs don't know anything about good sampling settings, and why should they? Their belief is that "sampling doesn't matter, we can simply scale our model and improve logprobs quality that way"
Ignore whatever nonsense qwen says about what settings to use. Turn off top-p and top-k. Turn on top-n-sigma and keep it at its defaults.
See repetitions that you don't like? Ignore the "repetition, frequency, presence" penalties and turn on DRY (don't repeat yourself).
Want more creativity? Temperature is okay but XTC (eXclude Top Choices) is better.
Llama.cpp quietly has had far advanced samplers compared to the shit stuff Qwen et al recommends for years.
Given that it apparently defaults to 'xhigh', this is probably the answer.
Granted, it's still much lower tokens/s than you'll get out of many MoE models.
Edit: Even set to medium or low there's still a lot of second guessing, less consistency, lower 'acceptable response' rate, and slower/more token churn vs gemma4:26b-a3b. I think gemma4 is just a better 'general purpose' model.
I haven't tried lowering thinking, however, I actually asked a solid question earlier regarding a real world scenario I encountered and all that excessive thinking made it give me an amazing answer. The thinking actually all made sense, and honestly I found it thought of similar stuff to what I thought when I drew my own conclusion.
I don't usually rely on AI for much (I'm actually kind of anti-AI, although I follow stuff like this enthusiastically because of the rapid advancements, "average joe" access, and openness), however for what I asked? It was spot on.
The subject was a bit personal, so I won't share. I was just curious what AI would say about the situation and it definitely surprised me, especially since Qwen, while usually great on development/coding stuff, has shown weaknesses in other areas.
Definitely a solid release, and this one runs on my 4090 with minimal loss of quality!
In my experience with Qwen3.6 35B-A3B, disabling thinking made the model generate inaccurate replies. Ask it for the recipe of egg salad and it gives you the recipe of an omelette.
Did I miss something, is it possible to have that model be reliable without thinking?
Turning off thinking mode you need a different tokenizer and a whole host of settings. It's more like switching your vehicle from gas to diesel, than turning your headlights from lo to hi.
The official docs recommend different sampler options for thinking off. Have you tried adjusting those?
Thank you and sibling poster for the suggestions.
I used the settings recommended on huggingface/unsloth's model page + whatever suggestion from various LLMs - didn't research too much myself which setting did what.
Frontier model labs don't know anything about good sampling settings, and why should they? Their belief is that "sampling doesn't matter, we can simply scale our model and improve logprobs quality that way"
Ignore whatever nonsense qwen says about what settings to use. Turn off top-p and top-k. Turn on top-n-sigma and keep it at its defaults.
See repetitions that you don't like? Ignore the "repetition, frequency, presence" penalties and turn on DRY (don't repeat yourself).
Want more creativity? Temperature is okay but XTC (eXclude Top Choices) is better.
Llama.cpp quietly has had far advanced samplers compared to the shit stuff Qwen et al recommends for years.
Thank you, this is exactly what I needed.