Good results fine tuning a local LLM like Qwen 3:0.6B to categorize questions
7.3 relevance
Score Breakdown
technical depth 8
novelty 5
actionability 9
community 7
strategic 4
personal 10
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Practical guide to fine-tuning small local LLM for classification, highly actionable for developers.
Summary
A developer fine-tuned Qwen 3:0.6B (600M parameters) using Unsloth on ~850 household questions to classify queries into metadata categories like 'pool' or 'hvac', improving vector search precision in a RAG chatbot. The baseline untuned model achieved only 10% accuracy on 131 test cases, while fine-tuning aims to make the tiny LLM a reliable classifier for narrowing vector database search space.