I don’t mind the model taking time to respond, but seeing the whole thinking/reasoning process on screen gets distracting really fast.
Is there a clean way to hide it while still letting the model think normally in the background?
I’ve been testing local models for retrieval augmented generation, document querying, and structured outputs, but I keep running into tradeoffs between reasoning, context handling, schema reliability, and hardware efficiency.
So far I’ve tried Gemma, Minimax, and a bit of Command-R, and I’m now looking into Qwen and LFM2. Gemma felt solid overall, but schema outputs became inconsistent under heavier workloads. Minimax felt weaker than I expected, though that might’ve been my setup.
Curious what models people are actually sticking with for serious local workflows.
I’m trying to run a local setup for retrieval augmented generation and some machine learning work.
Curious what models people are actually using right now and how they’re performing.
Tried a longer run with Ollama and got:
System:
Feels solid, but not sure if this is expected or if I’m hitting the ceiling.
Anyone getting better numbers on similar hardware? Any Ollama tweaks worth trying?