HeyJaredSrirupa Datta
- Indexed articles, last 90 days
- 2
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- Aug 17, 2026
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- Jul 14, 2026
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Bigger Wasn’t Better: Benchmarking Small Models for digiKam’s Natural Language Search (opens the original)
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GSoC 2026 • digiKam • Post 3: The Benchmark, and the Fine-Tuning Decision At the end of my last post I promised a comparison: Qwen2.5 against TinyLlama on real digiKam queries. This is that post. It grew a third model along the way, and the result surprised me enough that I want to walk through it honestly, because the tidy expectation I started with turned out to be wrong. If you’re just joining: in the first post I introduced the goal - bringing natural-language search to digiKam, so you can f
The Model Was Never the Hard Part: Integrating Qwen2.5 into digiKam for Natural Language Search (opens the original)
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GSoC 2026 • digiKam • Post 2: Inference, Bugs, and the Build In my first post, I introduced the goal: type a plain-English search into digiKam and have a local LLM translate it into structured search criteria. That post built the whole pipeline - prompt builder, JSON parser, intent resolver, against a mock backend that returned canned responses, so everything could be tested before a real model was wired in. This post is about swapping that mock for real llama.cpp inference, and everything that
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