04 / Blog
Blog
Practical guides and notes from my work in machine learning research.
Fine-tuning a small language model with LoRA
A practical starting point for supervised fine-tuning: define the task, prepare data, train an adapter, and evaluate the result.
Read article ↗A leakage-safe evaluation checklist for machine learning
How to keep information from the test set out of preprocessing, model selection, and reported results.
Read article ↗What changes when a model meets a new dataset?
A practical way to think about external validation, distribution shift, and the limits of internal scores.
Read article ↗What trustworthy mental-health AI requires
Reliability, causal sensitivity, and interpretation matter alongside predictive performance.
Read article ↗