🤖 Fine-tuning an Ultra-Small LLM for Question Classification
Using the ultra-small local model Qwen 0.6B with the QLoRA method via the Unsloth framework allowed for increasing question classification accuracy in a RAG system from 10% to 92%. The key success was achieved by replacing textual category names with "opaque" two-letter IDs (e.g., AA, BB), which minimizes semantic confusion.
🌍 This demonstrates the possibility of using extremely small models (SLM) for specialized preprocessing tasks, which significantly reduces computational resource costs in RAG systems without sacrificing quality.
👤 For developers, this is an effective hack: replacing complex text responses with simple token labels significantly increases the stability of even the smallest models.
Source 1: https://www.teachmecoolstuff.com/viewarticle/fine-tuning-a-local-llm-to-categorize-questions