llm-tuning-lab is my personal lab for experimenting with the fine-tuning of small language models. It is meant for practitioners and researchers who want a place to collect working recipes, sanity-check ideas, and iterate quickly without committing to a heavyweight framework. Think of it as a notebook of experiments rather than a finished product: an experimental and evolving repository.

The goal is practical: gather reusable recipes and scripts for adapting compact models to specific tasks, so that lessons learned in one experiment carry over to the next. Because it is a living workspace, expect the contents to shift as I test new approaches and prune the ones that do not hold up.

This project sits close to my current interest in LLMs and their practical adaptation. Working with small models keeps the iteration loop short and the compute footprint modest, which makes them a good vehicle for understanding what actually moves the needle during fine-tuning before scaling any of it up.

Explore the code on GitHub.