{"slug": "how-transformers-work-from-self-attention-to-modern-llm-architecture", "title": "How Transformers Work — From Self-Attention to Modern LLM Architecture", "summary": "A developer explains how the Transformer architecture works, from self-attention to modern LLMs. The key innovation is that Transformers compare tokens directly via attention rather than processing sequences step by step like RNNs. This design shift, combined with multi-head attention and positional encoding, enables efficient large-scale language generation.", "body_md": "Transformers changed AI because they stopped reading sequences one token at a time.\n\nInstead of moving step by step like an RNN, a Transformer compares tokens directly.\n\nThat one design shift made modern LLMs possible.\n\nA Transformer is a neural network architecture built around attention.\n\nIt looks at a sequence of tokens and learns how those tokens relate to each other.\n\nThis matters because language is contextual.\n\nA word is not understood alone.\n\nIt is understood through its relationship with surrounding words.\n\nThat is why Self-Attention became the core mechanism.\n\nA simplified Transformer flow looks like this:\n\nTokens → Embeddings → Positional Information → Self-Attention → Feed-Forward Network → Output\n\nMore compactly:\n\nTransformer = token representations + attention + position + stacked blocks\n\nThe model first converts text into token vectors.\n\nThen it injects position information.\n\nThen each Transformer block updates the token representations using attention and feed-forward layers.\n\nAt a high level, a Transformer processes text like this:\n\n```\nsplit text into tokens\n\nconvert tokens into embeddings\n\nadd positional information\n\nfor each Transformer block:\n    compute Self-Attention\n\n    mix token information\n\n    apply feed-forward transformation\n\n    keep stable flow with residual connections and normalization\n\nproduce contextual token representations\n```\n\nFor decoder-based LLMs, generation continues like this:\n\n```\npredict next token\n\nappend generated token\n\nreuse cached keys and values\n\nrepeat until stopping condition\n```\n\nThis is why Transformers are practical for large-scale generation.\n\nThey can learn relationships across many tokens.\n\nAnd with caching, they can generate efficiently.\n\nTake this sentence:\n\nThe animal did not cross the street because it was tired.\n\nWhat does “it” refer to?\n\nA simple left-to-right model may struggle if long context matters.\n\nSelf-Attention lets the token “it” compare itself with other tokens like “animal” and “street.”\n\nThe model can assign stronger attention to the token that best explains the meaning.\n\nThat is the intuition.\n\nAttention lets tokens ask:\n\nWhich other tokens matter for understanding me?\n\nThis comparison explains why Transformers became so important.\n\nRNN:\n\nTransformer:\n\nSo the Transformer was not just faster.\n\nIt changed how sequence relationships are represented.\n\nRNNs remember through recurrence.\n\nTransformers relate through attention.\n\nSelf-Attention computes relationships between tokens in the same sequence.\n\nEach token creates three vectors:\n\nThe intuition is simple:\n\nQuery = what this token is looking for\n\nKey = what each token offers for matching\n\nValue = information to retrieve if the match is strong\n\nThe core formula is:\n\nAttention(Q, K, V) = softmax((QK^T) / sqrt(d_k))V\n\nThis means:\n\nThat is how each token becomes context-aware.\n\nOne attention calculation is useful.\n\nBut one view is not enough.\n\nMulti-Head Attention runs several attention heads in parallel.\n\nEach head can focus on a different type of relationship.\n\nOne head may track syntax.\n\nAnother may track semantic similarity.\n\nAnother may track long-distance references.\n\nThen the outputs are combined into one representation.\n\nThis makes attention richer than a single similarity calculation.\n\nSelf-Attention does not automatically know token order.\n\nIf you only give it a bag of token embeddings, the model needs another signal to know which token came first.\n\nThat is why positional information is added.\n\nCommon positional methods include:\n\nAPE gives each position its own vector.\n\nRPE focuses on relative distance between tokens.\n\nRoPE rotates query and key vectors based on position, making relative position work naturally inside attention.\n\nThis is why RoPE became common in modern LLMs.\n\nThe original Transformer used an Encoder-Decoder structure.\n\nEncoder:\n\nDecoder:\n\nEncoder-Decoder:\n\nModern GPT-style LLMs are mostly decoder-based.\n\nThey generate text one token at a time.\n\nThe decoder predicts the next token, appends it, and repeats.\n\nOnce the model produces logits, it needs to choose the next token.\n\nDifferent decoding strategies create different behavior.\n\nGreedy decoding:\n\nBeam search:\n\nTop-k sampling:\n\nTop-p sampling:\n\nSo generation quality is not only about the model.\n\nIt also depends on decoding.\n\nFull Attention is powerful but expensive.\n\nIf the sequence length is n, attention has roughly O(n^2) cost.\n\nThat means longer context becomes expensive quickly.\n\nThis is why efficient attention matters.\n\nLocal Attention reduces the view to nearby tokens.\n\nSparse Attention computes only selected attention links.\n\nFlashAttention keeps the formula but improves GPU memory access.\n\nThe key idea:\n\nDo less unnecessary work, or move data more efficiently.\n\nBoth make longer context more practical.\n\nAutoregressive generation has another problem.\n\nWhen generating one token at a time, the model repeatedly needs past key and value tensors.\n\nKV Cache stores those tensors.\n\nSo the model does not recompute them from scratch at every step.\n\nThe flow looks like this:\n\nGenerated tokens → cached keys and values → new query attends to cache → next token\n\nThis makes inference faster.\n\nBut it creates a memory problem.\n\nLonger context means a larger KV Cache.\n\nThat is why modern LLMs use techniques like:\n\nThese methods reduce the memory cost of storing key-value information.\n\nModern LLMs still use the Transformer idea.\n\nBut the block has evolved.\n\nA typical modern block looks like this:\n\nInput\n\n→ RMSNorm or Pre-Layer Normalization\n\n→ Self-Attention with GQA and RoPE\n\n→ Residual Connection\n\n→ RMSNorm or Pre-Layer Normalization\n\n→ Feed-Forward Network with SwiGLU or Mixture of Experts\n\n→ Residual Connection\n\nImportant upgrades include:\n\nSo today’s Transformer is not exactly the 2017 Transformer copied directly.\n\nIt is an evolved architecture family.\n\nOriginal Transformer:\n\nModern LLM architecture:\n\nThe core idea stayed the same.\n\nThe engineering changed dramatically.\n\nIf Transformer architecture feels too large, learn it in this order:\n\nThis order works because you first understand the relationship mechanism.\n\nThen you understand generation.\n\nThen you understand why modern LLMs needed efficiency upgrades.\n\nThe Transformer is the architecture language of modern LLMs.\n\nThe shortest version is:\n\nTransformer = attention + position + stacked blocks + efficient generation\n\nSelf-Attention computes token relationships.\n\nPositional encoding injects order.\n\nThe decoder generates tokens.\n\nKV Cache makes autoregressive inference practical.\n\nModern upgrades like RoPE, RMSNorm, GQA, SwiGLU, and MoE make the architecture scalable.\n\nIf you remember one idea, remember this:\n\nTransformers work by turning a sequence into a set of contextual relationships, then refining those relationships through stacked attention-based blocks.\n\nWhen learning Transformers, do you find it easier to start from the attention formula, the decoder generation loop, or the modern LLM block structure?\n\nOriginally published at zeromathai.com.\n\nOriginal article: [https://zeromathai.com/en/transformer-architecture-overview-en/](https://zeromathai.com/en/transformer-architecture-overview-en/)\n\nGitHub Resources\n\nAI diagrams, study notes, and visual guides:\n\n[https://github.com/zeromathai/zeromathai-ai](https://github.com/zeromathai/zeromathai-ai)", "url": "https://wpnews.pro/news/how-transformers-work-from-self-attention-to-modern-llm-architecture", "canonical_source": "https://dev.to/zeromathai/how-transformers-work-from-self-attention-to-modern-llm-architecture-4j1o", "published_at": "2026-06-15 15:12:47+00:00", "updated_at": "2026-06-15 15:36:49.333155+00:00", "lang": "en", "topics": ["large-language-models", "neural-networks", "natural-language-processing", "machine-learning", "artificial-intelligence"], "entities": ["Transformer", "RNN", "GPT", "RoPE", "Self-Attention", "Multi-Head Attention", "Encoder-Decoder", "LLM"], "alternates": {"html": "https://wpnews.pro/news/how-transformers-work-from-self-attention-to-modern-llm-architecture", "markdown": "https://wpnews.pro/news/how-transformers-work-from-self-attention-to-modern-llm-architecture.md", "text": "https://wpnews.pro/news/how-transformers-work-from-self-attention-to-modern-llm-architecture.txt", "jsonld": "https://wpnews.pro/news/how-transformers-work-from-self-attention-to-modern-llm-architecture.jsonld"}}