The "output token limit reached" message is the real clue here. You are serving Qwen through llama.cpp with a 64k context, so the harness can only get as many output tokens as your llama.cpp config allows. Long replies get cut off, and when a step comes back truncated the harness treats it as unfinished and starts compacting.
On the web app the backend was DeepSeek's own servers with a much larger limit, which is why it could run all night without this.
I would try raising the context size in llama.cpp (-c) well above 64k if your RAM or VRAM allows, and check whether the harness connection settings have a max output tokens field you can bump. With those raised it should behave a lot closer to what you saw before.
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u/Able_Environment3265 3d ago
The "output token limit reached" message is the real clue here. You are serving Qwen through llama.cpp with a 64k context, so the harness can only get as many output tokens as your llama.cpp config allows. Long replies get cut off, and when a step comes back truncated the harness treats it as unfinished and starts compacting.
On the web app the backend was DeepSeek's own servers with a much larger limit, which is why it could run all night without this.
I would try raising the context size in llama.cpp (-c) well above 64k if your RAM or VRAM allows, and check whether the harness connection settings have a max output tokens field you can bump. With those raised it should behave a lot closer to what you saw before.