sumink/llamaft

This model is a fine-tuned version of LLaMA 3.2-3B, trained on a carefully curated dataset of 500 samples selected using Facility Location (FL) optimization. The dataset was refined from a larger corpus through representative sample selection, ensuring that the most informative and diverse data points were retained while redundant and uninformative samples were removed.

Fine-tuning was conducted to improve task-specific performance while significantly reducing training cost and data inefficiencies. By leveraging FL-based data selection, we ensured that the final dataset maintained high coverage and diversity while requiring only 5% of the original dataset size.