Fine-Tuning LLMs Using LoRA and QLoRA

This self-paced online course delves into fine-tuning Large Language Models (LLMs) using Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA). It focuses on parameter-efficient methods and LLM quantization, offering hands-on exercises to efficiently adapt AI models with minimal resources. Learners gain practical insights into fine-tuning workflows, Llama 3 model customization, and model optimization techniques.

• Before You Learn / Who This Course Is For

  • Suitable for beginners in AI and machine learning

  • Ideal for those interested in LLM fine-tuning

  • No prior experience required; self-paced learning

• What to Expect

  • Self-paced online format with hands-on exercises

  • Covers LLM quantization and LoRA/QLoRA techniques

  • Focus on optimizing models for performance and efficiency

• What You'll Achieve

  • Earn a Certificate of Completion

  • Master foundational skills in LLM fine-tuning

  • Enhance career prospects in AI and model adaptation