Tag: transformer architecture
Key, Query, and Value Projections in LLM Attention: What the Matrices Learn
Explore how Key, Query, and Value matrices power LLM attention. We break down the math, the database analogy, and what these projections actually learn during training.
Multi-Head Attention in LLMs: How Parallel Processing Powers AI Language
Discover how multi-head attention powers large language models by processing language from multiple perspectives simultaneously. Learn its mechanics, benefits over RNNs, and real-world impact.
Rotary Position Embeddings (RoPE) vs ALiBi: Which LLM Positioning Method Wins?
Compare RoPE and ALiBi positional embeddings in LLMs. Learn how rotation matrices and linear biases solve the context window problem for models like Llama.
