Fine– Tuning and Optimizing Small Language Models
Fine– Tuning and Optimizing Small Language Models
Download this premium online course featuring high-quality video training, step-by-step lessons, practical demonstrations, and expert instruction. With Fine– Tuning and Optimizing Small Language Models, you'll gain practical knowledge through structured learning, hands-on examples, and real-world applications. This comprehensive eLearning resource is ideal for students, professionals, freelancers, and lifelong learners looking to develop valuable skills and stay current with modern industry practices at their own pace.
Released 9/2026
By Ned Bellavance
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Advanced | Genre: eLearning | Language: English + subtitle | Duration: 3h 28m 53s | Size: 854.9 MB
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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