Learn how to extend the context length of Large Language Models (LLMs) during inference through a technical deep dive video that introduces grouped self-attention as an alternative to classical transformer self-attention mechanisms. Explore the challenges of out-of-distribution issues related to positional encoding when LLMs process text sequences beyond their pre-training context window. Examine implementation details, smooth transition techniques, and benchmark data while following along with code demonstrations based on the research paper "LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning." Master practical solutions for handling longer sequences in neural networks without requiring model retraining or fine-tuning.