Context Windows: The Memory Limits of LLMs
Interactive visualization of context window mechanisms in LLMs - sliding windows, expanding contexts, and attention patterns that define what models can "remember".
Explore machine learning concepts related to llms. Clear explanations and practical insights.
Interactive visualization of context window mechanisms in LLMs - sliding windows, expanding contexts, and attention patterns that define what models can "remember".
Interactive visualization of Flash Attention - the breakthrough algorithm that makes attention memory-efficient through tiling, recomputation, and kernel fusion.
Interactive visualization of key-value caching in LLMs - how caching transformer attention states enables efficient text generation without quadratic recomputation.
Understanding emergent abilities in large language models - sudden capabilities that appear at scale thresholds, from arithmetic to reasoning and self-reflection.
Master the art of prompt engineering - from basic composition to advanced techniques like Chain-of-Thought and Tree-of-Thoughts.
Deep dive into how different prompt components influence model behavior across transformer layers, from surface patterns to abstract reasoning.
Understanding neural scaling laws - the power law relationships between model size, data, compute, and performance that govern AI capabilities and guide development decisions.
Interactive exploration of tokenization methods in LLMs - BPE, SentencePiece, and WordPiece. Understand how text becomes tokens that models can process.