Emergent Abilities in Large Language Models
Explore emergent abilities in large language models: sudden capabilities that appear at scale thresholds, phase transitions, and the mirage debate, with interactive visualizations.
Explore machine learning concepts related to llms. Clear explanations and practical insights.
Explore emergent abilities in large language models: sudden capabilities that appear at scale thresholds, phase transitions, and the mirage debate, with interactive visualizations.
Master prompt engineering for large language models: from basic composition to Chain-of-Thought, few-shot, and advanced techniques with interactive visualizations.
Deep dive into how different prompt components influence model behavior across transformer layers, from surface patterns to abstract reasoning.
Explore neural scaling laws in deep learning: power law relationships between model size, data, and compute that predict AI performance, with interactive visualizations.
Interactive visualization of LLM context windows - sliding windows, expanding contexts, and attention patterns that define model memory limits.
Interactive Flash Attention visualization - the IO-aware algorithm achieving memory-efficient exact attention through tiling and kernel fusion.
Interactive KV cache visualization - how key-value caching in LLM transformers enables fast text generation without quadratic recomputation.
Interactive exploration of tokenization methods in LLMs - BPE, SentencePiece, and WordPiece. Understand how text becomes tokens that models can process.