In 2025, AI is still evolving rapidly. While closed LLMs are continuously improving, open Small Language Models are emerging as powerful alternatives for specific use cases, consuming only a fraction of the resources.

Working in AI engineering, I often find it refreshing to step away from orchestration and get hands-on with fine-tuning, customizing, and optimizing Small Models. In this talk, I’ll share my journey working with Post-Training Small Language Models, full of joys, frustrations, and many lessons learned.

We’ll see together:

• How generative Language Models are trained and how we can further customize them
• Tips for collecting and generating data for fine-tuning
• Instruction Fine-Tuning and Preference Tuning (DPO)
• Key training libraries, with a focus on Hugging Face TRL.
• Low-resource fine-tuning methods (QLoRA, Spectrum).
• A look at quantization and model merging.

By the end, you’ll learn how to customize Small Language Models for your needs and potentially run them on your smartphone. I’ll also share practical examples from my experience improving open models for the Italian language.