Student-generated mathematics metaphors reveal students’ attitudes and beliefs but are costly to code manually. We evaluate LoRA-based fine-tuning of compact open-weight LLMs for valence-intensity and thematic coding. Fine-tuning substantially improves coding performance and reliability, making these models competitive with proprietary prompt-only LLMs while supporting local, privacy-conscious deployment.