Fine-tuned chemical language model for predicting molecular lipophilicity in drug design. Explores parameter-efficient fine-tuning strategies (LoRA, BitFit, IA3), layer freezing techniques, and influence-based data selection. Balances accuracy and computational efficiency for molecular property prediction tasks.
Build a production‑grade, modular pipeline for fine‑tuning large language models with LoRA on domain‑specific tasks (e.g., legal QA, medical summarization, financial reasoning).
Case Study of the Google Data Analytics Capstone course. Analyzing Fitbit's dataset to understand how can a wellness company play it smart?
[ELAMI-MICCAI 2025] Official implementation of "Pixels Under Pressure: Exploring Fine-Tuning Paradigms for Foundation Models in High-Resolution Medical Imaging"
A practical introduction to Transformer fine-tuning, designed for participants of the International Olympiad in Artificial Intelligence – covering LoRA, Adapters, and the limitations of parameter-efficient methods.
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