transformer

3 posts under this topic.

• Jun 13, 2026

  Anatomy of an LLM: the Transformer block

  How one attention layer becomes a whole model: the three architecture families and why today's LLMs are almost always "decoders", the residual connections and normalization that let us stack dozens of layers, the FFN as a store of knowledge, and finally - how a concrete word emerges from a vector of numbers, from the softmax to temperature and nucleus sampling.

• Jun 12, 2026

  Anatomy of an LLM: the attention mechanism

  The heart of the Transformer: how every token "looks around" at the others and takes from them what it needs to sharpen its own meaning. From the bottleneck in old translation networks, through the famous scaled-attention formula and multi-head attention, to the KV-cache, FlashAttention, and the careful question - what do attention heads actually do, when "attention is not explanation".

• Jun 08, 2026

  Anatomy of an LLM: from a single neuron to the attention mechanism

  How is a language model really built? A guide without magic: neuron, weights, tokens, embeddings, the attention mechanism, the Transformer block and LoRA - step by step, with interactive diagrams to play with yourself.