Fine-tuning llama3.2 3B for personalized health coaching using Apple Watch data and MLX

A developer created a personalized health coach LLM by fine-tuning llama3.2 3B on a Mac using Apple Health and Whoop data. The entire fine-tuning process took approximately 15 minutes using MLX.

Technical pipeline

The implementation follows this workflow:

• Apple Health and Whoop data stored in local SQLite database
• SQL RAG layer converts natural language queries to SQL
• Claude API used once to generate ~270 gold-standard training examples (anonymized question/SQL/result pairs, no personal health data sent)
• LoRA fine-tuning on llama3.2 3B via MLX
• Fused model served locally at 127.0.0.1:8080

Before vs. after fine-tuning

The source provides concrete examples of the improvement:

Before fine-tuning: "Your HRV is an important measure of autonomic nervous system function..." [500 words of generic advice]

After fine-tuning: "Your HRV averaged 68ms this week, down 12% from last week's 77ms. Coincides with 3 nights under 7 hours sleep. Consider reducing training intensity for 48 hours."

Memory footprint and hardware

• Model (4-bit): ~2 GB
• LoRA adapter: ~50 MB
• Training memory: ~4-5 GB total
• Runs on M-series Mac, no GPU needed

The developer mentions including technical details on SQL hallucination guardrails, cross-metric context enrichment, and the training pipeline in their full writeup. They also offer to answer questions about the MLX setup or RAG layer implementation.