Dive deep into the intricacies of fine-tuning Large Language Models (LLMs) with a comprehensive 47-minute video tutorial. Explore key concepts such as GPTs as statistical models, the reversal curse, and synthetic dataset generation. Learn practical skills including selecting optimal batch sizes, determining appropriate learning rates, and choosing the right number of training epochs. Follow along with step-by-step instructions for dataset generation and fine-tuning script implementation. Analyze performance through hyperparameter ablation studies and base model comparisons. Conclude with valuable recommendations for fine-tuning LLMs specifically for memorization tasks, equipping you with the knowledge to enhance model performance in your own projects.
Fine-tuning LLMs - Every Step Explained for Memorization Tasks
Overview
Syllabus
Fine-tuning on a custom dataset
Video Overview
GPTs as statistical models
What is the reversal curse?
Synthetic dataset generation
Choosing the best batch size
What learning rate to use for fine-tuning?
How many epochs to train for?
Choosing the right base model
Step by step dataset generation
Fine-tuning script, step-by-step
Performance Ablation: Hyperparameters
Performance Ablation: Base Models
Final Recommendations for Fine-tuning for Memorization
Taught by
Trelis Research
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