A plain-English reference guide covering the jargon that shows up every time a new language model drops, from parameter counts to quantization methods. Contents 01 · Architecture & Model Design — Transformer · Dense Model · Mixture of Experts · Active Parameters · Feed-Forward Network · Layers · Hidden Dimension · Attention Heads 02 · Attention Mechanisms — Multi-Head Attention · Multi-Query Attention · Grouped-Query Attention · KV Cache · Sliding Window Attention · RoPE · RoPE Theta 03 · Sizing, Scale & Counting — Parameters · Embedding Parameters · Non-Embedding […]

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Large Language Models (LLMs) all predict text, but they differ a lot in how they follow instructions, use context, handle tools, and optimize for safety, speed, or cost. If you treat them as interchangeable, you’ll ship brittle prompts. If you treat them as different runtimes with different affordances, you’ll get reliable results. This post explains the major differences across […]

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