Emergent Abilities in Large Language Models
Explore emergent abilities in large language models: sudden capabilities at scale thresholds, phase transitions, and the mirage debate.
Explore machine learning concepts related to LLMs. Clear explanations and practical insights.
Explore emergent abilities in large language models: sudden capabilities at scale thresholds, phase transitions, and the mirage debate.
Interactive visualization of LLM context windows - sliding windows, expanding contexts, and attention patterns that define model memory limits.
Master prompt engineering for large language models: from basic composition to Chain-of-Thought, few-shot, and advanced techniques.
Interactive Flash Attention visualization - the IO-aware algorithm achieving memory-efficient exact attention through tiling and kernel fusion.
Deep dive into how different prompt components influence model behavior across transformer layers, from surface patterns to abstract reasoning.
Interactive KV cache visualization - how key-value caching in LLM transformers enables fast text generation without quadratic recomputation.
Explore neural scaling laws in deep learning: power law relationships between model size, data, and compute that predict AI performance.
Interactive exploration of tokenization methods in LLMs - BPE, SentencePiece, and WordPiece. Understand how text becomes tokens that models can process.
A small draft model proposes tokens, the target model checks them all in one pass, and a rejection rule keeps the output distribution exactly the target's.
CLIP, BLIP-2, LLaVA and Flamingo align images with text by different routes. Compare their objectives, bridges, data, and which parts train or stay frozen.