llm anatomy

7 posts under this topic.

• Jun 21, 2026

  Anatomy of an LLM: LoRA and fine-tuning

  How to specialize a finished model without touching its billions of weights: the low-rank hypothesis and the LoRA trick, the whole family of parameter-efficient fine-tuning (PEFT) methods, quantization and QLoRA, and finally - why fine-tune at all, that is, how a raw model becomes an assistant through instruction tuning and RLHF.

• 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 11, 2026

  Anatomy of an LLM: tokens and embeddings

  A network does not see letters - it sees vectors. How text is split into tokens, why not into words nor characters, how subword algorithms (BPE, WordPiece, Unigram) build a vocabulary, and how the famous geometry of meaning is born, where "king - man + woman ≈ queen" - together with an honest critique of that analogy, and with the positional encoding that primers leave out.

• Jun 10, 2026

  Anatomy of an LLM: networks, parameters and training

  We take a handful of neurons and arrange them into a network: why the whole model boils down to matrix multiplication, how backpropagation turns random noise into an understanding of language, where the famous billions of parameters come from, and what the Kaplan and Chinchilla scaling laws really say - with formulas, history and concrete details.

• Jun 09, 2026

  Anatomy of an LLM: the neuron and activation functions

  The smallest building block of a language model under the microscope: where the artificial neuron came from, why without an activation function the whole network would be useless, and how ReLU, GELU and SwiGLU really differ - with formulas, history and concrete details from GPT, LLaMA and Gemma.

• 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.