I needed to build a customer service chatbot for my company, so I tried fine-tuning with LoRA.
1. Base model: Llama 3.1 8B (good Korean support)
2. Dataset: Refined 50,000 in-house CS logs and converted them into instruction format
3. Training: Ran QLoRA on 16GB VRAM (4-bit quantization)
4. Result: Accuracy increased from 68% to 89%, and hallucinations decreased
The most effective addition was including data on 'responses to incorrect questions.'
If anyone has FT experience, please share tips on data preprocessing.