How deeply does Domain-Specific Small Language Models cover transformer architecture?

Explains motivation, self-attention, embeddings, and encoder-decoder roles, but stops short of mathematical attention internals.

Transformer architecture

As explained in Domain-Specific Small Language Models

A neural network architecture for sequence modeling built around self-attention and parallel processing, underlying both large and small language models.

Transformer architecture is a sequence-modeling design that replaces recurrent processing with self-attention, allowing an entire text sequence to be processed in parallel. This improves training speed and scalability compared with RNN, LSTM, and GRU approaches. section 1.2

Self-attention relates positions across a sequence to build contextual representations, while word embeddings represent words as vectors whose relationships encode semantic and syntactic information. Transformer variants divide responsibilities differently: encoder-only BERT models support classification and prediction, while decoder-only GPT models support text generation. section 1.2 section 1.7

Operational implications include:

Parallel processing reduces the sequential bottleneck that limits recurrent architectures. section 1.2
Pretrained Transformer models provide lower-cost starting points than developing and training a model from scratch. section 1.4
Inference still requires attention to execution speed, accuracy, compute cost, scalability, and deployment constraints. section 1.4 section 4.1

GPT-Neo as an inference reference

GPT-Neo large, with 2.7 billion trainable parameters, illustrates a decoder-style Transformer used for content generation. The example connects the architecture to Hugging Face Transformers and Accelerate libraries and to quantization-related inference decisions. section 4.1

How Domain-Specific Small Language Models covers it

Cover of Domain-Specific Small Language Models

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Also explained in

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Natural Language Processing in Action, Second EditionA transformer is a neural network architecture that uses attention mechanisms to process sequences and model relationships among their elements.
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Sutskever's ListA Transformer is a neural network architecture based primarily on attention mechanisms for modeling relationships among sequence elements.
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Deep Learning with Python, Third EditionA neural-network architecture based on attention mechanisms, used here as the core architecture for large language models.
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Build a Text-to-Image Generator (from Scratch)A transformer architecture is a neural-network architecture that uses attention mechanisms to process relationships among elements in a sequence or other structured input.
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Deep Learning with R, Third EditionA Transformer is a neural-network architecture designed to process sets or sequences using attention-based contextual representations.
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Transformers in ActionA transformer is a neural network architecture that processes sequential and other structured data using attention mechanisms.
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Deep Learning with PyTorch, Second EditionA neural network architecture for sequence modeling and content understanding based on attention rather than recurrence or convolution.
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LLMs in ProductionA transformer architecture is a neural-network architecture that models sequence relationships primarily through attention rather than recurrence or convolution.
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Grokking AI ApplicationsA transformer is a neural-network architecture for sequential data that uses attention to relate elements of an input sequence.
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The AI Pocket BookA neural network architecture that contextualizes each token using surrounding tokens rather than compressing the whole prompt into one summary vector.
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Time Series Forecasting Using Foundation ModelsA neural-network architecture built from stacked attention and feed-forward processing components, typically organized into encoder and decoder parts.
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Machine Learning Algorithms in DepthA transformer model is a neural network architecture that uses attention-based mechanisms to model contextual relationships in structured sequences or other data.
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Inside Deep Learning: Math, Algorithms, ModelsA sequence model built from positional encoding and multiheaded attention, used here as the standard original design for NLP and other tasks.
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Grokking AI Algorithms, Second EditionThe Transformer is a neural-network architecture based primarily on attention mechanisms for processing sequences and modeling context.
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Hugging Face in ActionThe Transformer architecture is a neural-network architecture based on attention mechanisms for processing sequences and other structured inputs.
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Introduction to Generative AI, Second EditionA transformer is a neural-network architecture for sequence modeling that uses attention, especially self-attention, rather than recurrence.
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Build AI-Enhanced Web AppsA transformer model is a neural-network architecture that uses attention mechanisms to model contextual relationships in sequence data.
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Build a Large Language Model (From Scratch)a neural network architecture based on self-attention mechanisms that enables processing of sequential data and underpins many large language models.
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Build a Website with ChatGPTA transformer is a neural-network architecture that processes sequences using attention mechanisms to model relationships among their components.
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Data Analysis with LLMsThe Transformer is a neural-network architecture designed to model relationships in sequences, especially sequences with variable-length inputs or outputs.
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Generative AI in ActionThe Transformer architecture is a neural-network architecture based primarily on attention mechanisms for processing sequences.
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Essential GraphRAGA transformer architecture is a neural-network architecture that processes sequences using attention-based representations and supports efficient language modeling.

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Related concepts

attentionuses · Attention is a neural-network mechanism that computes input-dependent weights to combine information according to its relevance to a computation.
encoderhas part · An encoder is a component that transforms input data into an encoded representation for subsequent processing, reconstruction, comparison, or transmission.
decodehas part · Decoding is the process of transforming an encoded or internal representation into an interpretable or target-form output.
Vision Transformerincludes · A Vision Transformer is a Transformer-based neural-network architecture that processes images as sequences of tokenized regions using attention.
model architecturekind of · The structural organization of a computational system’s components, relationships, and operations that determines how it processes information or behaves.
GPT-2includes · GPT-2 is a family of autoregressive, decoder-only Transformer language models developed by OpenAI that generate text by predicting successive tokens from preceding context.

This explainer was generated by Manning's knowledge graph from the text of Domain-Specific Small Language Models. The section links point to the parts of the book it draws on.