July 29 2026 Note

This note provides a technical overview of Generative Pre-trained Transformers (GPTs), detailing their foundational architecture, the core mechanics of self-attention via Query, Key, and Value vectors, and key processing stages including encoding, decoding, inference layers, and quantization techniques.

First Layer: Position 128
Second Layer: 32 Token Internal meaning (4 batch, 8 token each)
Third Layer: 3 Tables

In AI transformer models, Q (Query), K (Key), and V (Value) are three distinct vector representations created for every token to power the self-attention mechanism. They function like a retrieval system where a token asks a question, searches an index, and retrieves content. [1, 2, 3, 4, 5]

• Query (Q): What a specific token is looking for right now (like typing a search term).
• Key (K): What kind of information a token offers (like an index label or tag on a database entry).
• Value (V): The actual core data or meaning the token shares when its key matches a query.

• Matching: The model multiplies Q and K vectors together to score how relevant each word is to the current word.
• Blending: It applies those scores as weights to the V vectors to build a new, context-aware meaning for the text.

Self-attention mechanisms are the signature feature of transformers, empowering them to process an entire input sequence at once. Transformers can self-direct their “attention” to the most important tokens in the input sequence, no matter where they are.

By contrast, older recurrent neural networks (RNNs) and convolutional neural networks (CNNs) assess input data sequentially or hierarchically. Self-attention allows GPTs to process context and reply at length with language that feels natural, rather than merely guessing the next word in a sentence.

Encoding is the process of mapping tokens onto a virtual three-dimensional vector space. Tokens encoded nearby in the 3D space are assumed to be more similar in meaning. This mathematical vectorization of an input sequence is known as an embedding.

The encoder blocks in the transformer network assign each embedding a weight, which determines its relative importance. Meanwhile, position encoders capture semantics, enabling GPT models to differentiate between groupings of the same words but in different orders—for example, “The egg came before the chicken” as compared to “The chicken came before the egg.”

Decoders predict the most statistically probable response to the embeddings prepared by the encoders. Self-attention mechanisms permit the decoder to identify the most important portions of the input sequence, while advanced algorithms determine the output most likely to be correct.

Video Reference: It's about time we learn Transformers.., by Caleb Writes Code.
https://www.youtube.com/watch?v=7gkaDEpHg

Quantization is the process of reducing data precision, most commonly by converting 32-bit floating-point numbers into lower-precision formats like 8-bit integers to shrink file sizes and speed up computing. May loss Precision.

• Transformers rely on Q (Query), K (Key), and V (Value) vectors to power a retrieval-like self-attention system that scores relevance and blends contextual meaning.
• Unlike the sequential processing of RNNs and CNNs, transformers process entire sequences in parallel, enabling natural, context-aware language generation through dedicated encoder and decoder blocks.
• Inference layers handle the final computational steps required to transform encoded representations into actionable outputs.
• Quantization optimizes model performance by reducing precision (e.g., from 32-bit float to 8-bit int), significantly shrinking file sizes and accelerating computation at the potential cost of some accuracy.