Fine-Tuning and Optimizing Small Language Models

Adapting open-source language models for domain-specific tasks often leads to challenges with GPU memory limits, high compute costs, and complex post-training workflows.
Adapting open-source language models for domain-specific tasks often leads to challenges with GPU memory limits, high compute costs, and complex post-training workflows. In this course, Fine-Tuning and Optimizing Small Language Models, you'll gain the ability to adapt, align, and compress small language models for efficient production deployment. First, you'll explore post-training fundamentals, tool ecosystems, and dataset preparation techniques - including synthetic data generation. Next, you'll discover how to execute memory-efficient fine-tuning using LoRA, QLoRA, and preference alignment methods like DPO and GRPO. Finally, you'll learn how to quantize your fine-tuned models and benchmark precision against deployment speed. When you're finished with this course, you'll have the skills and knowledge of small language model optimization needed to deliver tailored, resource-efficient AI models for your organization.
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