Foundational Model Fine-Tuning for Generative AI Application Engineers
Master the art of adapting and fine-tuning foundational models to build high-performing, production-ready generative AI applications.
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Foundational Models and Fine-Tuning Essentials
Unit 1: Foundational Models: The Big Picture
What are FMs?
Pre-training Power
Why Fine-Tune?
Unit 2: Strategies for Adaptation
Full Fine-Tuning
Parameter-Efficient FT
LoRA and QLoRA
Other PEFT Methods
Unit 3: Data for Fine-Tuning
Data Prep for Fine-Tuning
Instruction & Preference
Practical Fine-Tuning, Evaluation, and Optimization
Unit 1: Hands-On Fine-Tuning with Frameworks
Your First Fine-Tune
PEFT in Action
Beyond LoRA: QLoRA & More
Unit 2: Evaluating Fine-Tuned Models
Metrics That Matter
Human in the Loop
Building Eval Pipelines
Unit 3: Optimizing Fine-Tuning Workflows
Hyperparameter Harmony
Scaling Up Fine-Tuning