𝗛𝗼𝘄 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 𝗪𝗼𝗿𝗸
Transformers changed AI. They stopped reading text one word at a time.
Old models like RNNs moved step by step. Transformers compare all words in a sequence at once. This design makes modern LLMs possible.
A Transformer is a neural network built on attention. It looks at a sequence of tokens and learns how they relate. This is vital because language depends on context. A word only has meaning through its relationship with other words.
The Core Process:
- Tokens convert into embeddings
- Positional information adds order
- Self-attention computes relationships
- Feed-forward networks process the data
- Output produces contextual representations
Self-Attention allows a token to ask: Which other tokens matter for my meaning?
In the sentence "The animal did not cross the street because it was tired," the word "it" refers to the animal. Self-attention lets the model link "it" to "animal" instead of "street."
How Attention Works: Each token creates three vectors:
- Query: What this token seeks
- Key: What each token offers
- Value: The information to retrieve
Multi-Head Attention runs several of these processes at once. One head might track grammar. Another might track meaning. This makes the model smarter.
Evolution of the Architecture: The original Transformer used an Encoder-Decoder structure. Modern LLMs like GPT are mostly decoder-only. They predict the next token, add it to the sequence, and repeat.
Modern LLMs use several upgrades to stay fast and efficient:
- RoPE: Improves how the model understands word order
- RMSNorm: Simplifies normalization
- GQA: Reduces memory cost during generation
- SwiGLU: Strengthens the neural layers
- MoE: Uses sparse experts to scale larger
Transformers work by turning a sequence into a set of relationships. They refine these relationships through stacked blocks.
If you want to learn this, follow this order:
- Attention Mechanism
- Self-Attention and QKV
- Multi-Head Attention
- Positional Encoding
- Decoder Architecture
- KV Cache and Efficient Attention
Source: https://dev.to/zeromathai/how-transformers-work-from-self-attention-to-modern-llm-architecture-4j1o
Optional learning community: https://t.me/GyaanSetuAi