Explore three methods for compressing Large Language Models (LLMs) - Quantization, Pruning, and Knowledge Distillation/Model Distillation - with accompanying Python code examples. Learn about the challenges of model size and the benefits of compression techniques. Follow along with a practical demonstration of combining Knowledge Distillation and Quantization to compress a BERT-based phishing classifier model. Access additional resources including a blog post, GitHub repository, pre-trained models, and dataset for further exploration of LLM compression techniques.