1 points•0 comments•1h ago•by LorenDB•srirupa19.github.io ↗

The Model Was Never the Hard Part

The post describes replacing a mock backend with a real Qwen2.5 model in digiKam by wiring llama.cpp inference through a dedicated worker thread to keep the GUI responsive. A bug where the model’s KV cache was not cleared between queries caused searches to fail after multiple runs; the fix is to clear the model memory at the start of each inference. The author argues that deploying an LLM in an application is largely about integration—build systems, packaging, and infrastructure—rather than the model itself, and outlines next steps including automated real-inference tests and cache/ambiguity improvements.

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