Model Architecture Types

Transformers come in three shapes - encoder-only, decoder-only and encoder-decoder - distinguished by the attention mask and the training objective, and each shape can be made sparse with mixture-of-experts layers. This chapter explains what each shape can and cannot do, why decoder-only models took over general-purpose AI, and how to choose one for a task.

Encoder-Only Architecture

Concept

Encoder-only models use bidirectional self-attention - each token can attend to all other tokens simultaneously. There is no causal mask; the model sees the full context in both directions.

Training objective - Masked Language Modeling (MLM):

• Randomly mask 15% of tokens in the input
• Train the model to predict the masked tokens from context
• This forces the model to understand bidirectional context

Architecture specifics:

• Input sequence → bidirectional attention → contextual representations per token
• No autoregressive generation - the model produces a fixed-size representation for each token, not new tokens
• Classification and span-extraction heads are added on top of the final hidden states

When to use encoder-only:

• Text classification (sentiment analysis, intent detection): take the [CLS] token embedding → linear head
• Named Entity Recognition (NER): classify each token's hidden state → per-token labels
• Semantic similarity / embeddings: mean pool or [CLS] embedding → similarity search
• Extractive QA (SQuAD): predict start/end token positions within the context

Key models:

Model	Parameters	Context	Key innovation
BERT-base	110M	512	Bidirectional MLM + NSP
BERT-large	340M	512	Larger BERT
RoBERTa	125M/355M	512	Removed NSP, more data, better MLM
DeBERTa-v3	183M	512	Disentangled attention, ELECTRA pretraining
DistilBERT	66M	512	40% smaller, 60% faster, 97% of BERT quality
BGE-M3	~560M	8K	Multi-function embedding model

Tricky Q: Can a BERT-family model generate text?
No - BERT was never trained to autoregressively predict the next token. Its output is a contextual representation, not a probability distribution over the next token. You can build a seq2seq encoder-decoder on top of a BERT encoder, but the encoder alone cannot generate.

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Decoder-Only Architecture

Concept

Decoder-only models use causal (left-to-right) self-attention - each token can only attend to itself and all preceding tokens. The model is trained to predict the next token.

Training objective - Causal Language Modeling (CLM):

• Given tokens t₁, t₂, ..., tₙ₋₁, predict tₙ
• Loss is cross-entropy on the predicted next-token distribution vs the actual next token
• The simplicity of this objective is a key reason for the architecture's dominance

Why decoder-only won:

1. Unified objective: CLM pretraining + instruction fine-tuning (SFT) + RLHF all use the same token prediction framework - no architectural changes between stages
2. Natural generation: The architecture is inherently designed to generate; no adapter or cross-attention bridge needed
3. Emergent reasoning: At scale, decoder-only models develop chain-of-thought reasoning by learning to produce intermediate "thinking" tokens
4. In-context learning: Few-shot prompting works naturally by prepending examples before the query
5. Scalability: Simple objective → easy to scale to trillions of training tokens

Architecture flow:

flowchart LR
    A["📝 Input: 'The cat sat on the'"] --> B["✂️ Tokenize"] --> C["🔢 Embed"]
    C --> D["🔁 Transformer Block × N<br/>with causal mask"] --> E["🧮 Final LN"] --> F["📐 Linear"] --> G["🎯 Softmax"]
    G --> H["P(next token)"] --> I["🎲 Sample"] --> J["➕ Append"]
    J -.->|repeat| B

Key models:

Model	Params	Context	Key innovation
GPT-2	1.5B	1024	First large-scale CLM demo
GPT-3	175B	2048	In-context learning at scale
GPT-4 (2023)	Undisclosed	8K / 32K	Multimodal input, top-tier reasoning at release
LLaMA-2	7B–70B	4K	Open weights, commercial license
LLaMA-3.1	8B–405B	128K	GQA, strong open weights
Gemma 2	2B–27B	8K	Google open weights, interleaved local (sliding-window) and global attention
Mistral 7B	7B	8K (v0.1) / 32K (v0.2+)	GQA + sliding window attention (v0.1)
Phi-3-mini	3.8B	128K	High quality from small size
Falcon	7B–180B	2K–8K	Multi-Query Attention
DeepSeek-V3	671B MoE (37B active)	128K	MLA, fine-grained MoE, FP8 training

---

Encoder-Decoder Architecture

Concept

Encoder-decoder models have two components:

1. Encoder: reads the input with bidirectional attention → produces context representations
2. Decoder: generates output tokens autoregressively, attending to both its own output (causal self-attention) and the encoder output (cross-attention)

Cross-attention:

Q = decoder hidden states (what the decoder wants to know)
K = V = encoder output    (what the input provides)
cross_attention_output = softmax(Q·Kᵀ / √d_k) · V

This allows the decoder to directly "look up" relevant parts of the input at each generation step.

Training objectives:

• T5 (Span Corruption): Mask contiguous spans of input tokens (replace with single mask token), train decoder to reconstruct the spans
• BART (Denoising): Corrupt input via masking, shuffling, deletion; train decoder to reconstruct the clean sequence

When to use encoder-decoder:

• Machine translation: long input → long output with reordering
• Abstractive summarization: input document → shorter summary (not extractive)
• Document-to-structured output: parse a document into a structured format
• Question generation: answer + context → question

Key models:

Model	Parameters	Key innovation
T5-base/large	220M/770M	Text-to-text unified framework
FLAN-T5	80M–11B	Instruction fine-tuned T5
BART	140M/400M	Denoising pretraining
mT5	300M–13B	Multilingual T5
mBART	610M	Multilingual denoising

Tricky Q: Why did decoder-only models overtake encoder-decoder for summarization and translation?

Two reasons: (1) At sufficient scale, decoder-only models learn to produce appropriate-length summaries and translations via instruction fine-tuning, without the architectural inductive bias of cross-attention. (2) A single decoder-only model can handle many tasks via prompting, while encoder-decoder models require task-specific fine-tuning to perform well. Operational simplicity wins.

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Architecture Comparison

Concept

Dimension	Encoder-Only	Decoder-Only	Encoder-Decoder
Attention direction	Bidirectional	Causal (left-to-right)	Encoder: bi; Decoder: causal + cross
Training objective	MLM / ELECTRA	CLM (next token)	Denoising / span corruption
Can generate text?	No	Yes	Yes
Best for	Classification, NER, embeddings	Generation, reasoning, chat	Seq2seq tasks
In-context learning	Poor	Excellent	Moderate
Instruction fine-tuning	Awkward	Natural	Possible
Production dominance	Embedding models	General LLMs	Niche seq2seq

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Mixture of Experts (MoE)

Concept

MoE is an architectural modification to the FFN layer, not a new architecture class. Instead of one dense FFN per layer, the model has N "expert" FFNs and a learned router that selects top-K experts per token.

flowchart LR
    subgraph STD["Standard FFN"]
        T1["token"] --> F1["single FFN"] --> O1["output"]
    end
    subgraph MOE["MoE FFN"]
        T2["token"] --> R["router<br/>(softmax over N experts)"]
        R --> K["select top-K experts<br/>by router score"]
        K --> W["weighted sum of<br/>selected expert outputs"]
    end

    style STD fill:#e8f4fd,stroke:#4a9eca
    style MOE fill:#d4edda,stroke:#28a745

Key MoE concept: sparse activation

• Total parameters: N × (d_model × d_ffn) - much larger than a dense model
• Active parameters per token: only K experts are used - much smaller computation
• Example: Mixtral-8x7B has 8 expert FFNs per layer and routes each token to 2 of them. Only the FFN blocks are replicated - attention and embeddings are shared - so the total is ~47B parameters (not 8 × 7B = 56B), with ~13B active per token

MoE advantages:

• Scale model capacity without proportional compute increase
• Different experts can specialize in different domains/languages/task types
• Efficient at inference for large models (only K/N of FFN is computed per token)

MoE challenges:

• Load balancing: without constraints, router collapses all tokens to the same 1–2 experts
• Communication overhead in distributed training (all-to-all for expert routing)
• MoE saves compute, not memory: all experts must be resident in GPU memory (47B for Mixtral), and the KV cache is unchanged because attention is not sparse

Models:

• Mixtral-8x7B: 8 experts, top-2 routing, 47B total / 13B active
• DeepSeek-V3, Qwen3-235B-A22B, Llama 4, gpt-oss, Kimi K2: fine-grained MoE with many small experts - see Modern Architectures
• Switch Transformer (Google): pioneered MoE at scale with top-1 routing

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Model Family Comparison Table

Concept

A reference table of influential model families and the architectural idea each is known for. It is deliberately historical - for the current lineup see the Model Landscape. Closed-model internals are listed as undisclosed unless the vendor published them.

Model Family	Org	Type	Context	Architecture innovations
GPT-2	OpenAI	Decoder	1K	First demo of large CLM
GPT-3	OpenAI	Decoder	2K	175B in-context learning
GPT-4 / 4o	OpenAI	Undisclosed	128K	Multimodal, top-tier at release
GPT-4.1	OpenAI	Undisclosed	1M	Extended context, code + general
LLaMA-2	Meta	Decoder	4K	Open weights, GQA (70B)
LLaMA-3 / 3.1	Meta	Decoder	8K / 128K	GQA all sizes, 405B
LLaMA-4 Scout	Meta	MoE Transformer	10M	Ultra-long context, open weights
LLaMA-4 Maverick	Meta	MoE Transformer	1M	Balanced capability/cost
Gemma	Google	Decoder	8K	Multi-query attention (2B), open weights
Gemma 2	Google	Decoder	8K	GQA + local+global attn
Gemini 2.5 Pro	Google DeepMind	Undisclosed	1M	Complex reasoning, multimodal
Gemma 3	Google	Decoder	128K	5:1 local:global attention, QK-norm
Mistral 7B	Mistral	Decoder	8K / 32K	GQA + sliding window (v0.1)
Mixtral 8x7B	Mistral	Decoder (MoE)	32K	Top-2 MoE, 47B/13B active
Phi-3-mini	Microsoft	Decoder	128K	3.8B, textbook-quality data
Qwen3	Alibaba Cloud	Dense + MoE	32K native (128K with YaRN)	Hybrid thinking mode, Apache-2.0
DeepSeek-V3 / R1	DeepSeek	MoE (671B / 37B active)	128K	MLA, aux-loss-free balancing, open reasoning (R1)
gpt-oss	OpenAI	MoE (117B / 5.1B active)	128K	Open weights, MXFP4 experts, banded attention
Command A	Cohere	Decoder	256K	Tuned for RAG, tool use and enterprise search
BERT-base	Google	Encoder	512	MLM + NSP, bidirectional
RoBERTa	Meta	Encoder	512	Better BERT training
DeBERTa-v3	Microsoft	Encoder	512	Disentangled attention
T5 / FLAN-T5	Google	Enc-Dec	512–2K	Text-to-text, instruction
BART	Meta	Enc-Dec	1K	Denoising pretraining

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LLM Types by Modality

Concept

Beyond architecture type, LLMs are also categorized by the modalities they handle. This affects model selection, embedding strategy, and system design.

Type	Description	Examples	Use Cases
Text-Only	Trained on text corpora only	GPT-3, LLaMA, BERT, Falcon	General NLP, summarization, generation
Multilingual	Trained on multilingual corpora	XLM-R, mT5, BLOOM, GPT-4	Translation, cross-lingual search
Multimodal	Input: image/video/audio + text	GPT-4o, Gemini, Qwen-VL, LLaVA	Image captioning, audio Q&A, OCR, document understanding
Code	Specialized in programming languages	Codex, CodeLLaMA, CodeGemma, StarCoder	Code generation, completion, refactoring
Speech-Text	Integrate speech recognition and synthesis	Whisper, SeamlessM4T	Transcription, speech translation
Image/Vision	Image understanding and classification	ViT, ConvNeXT, DETR, Mask2Former	Object detection, segmentation, depth estimation

Modality in system design: When building a multi-modal pipeline (e.g., processing scanned PDFs + structured data + free text), you must choose whether to use a single multi-modal foundation model (simplicity, one context) or multiple specialized models (higher per-task accuracy, more complex orchestration). For RAG over images, multimodal embeddings (CLIP, Gemini Embeddings) are required - text-only embeddings cannot capture visual content.

Models by specific task (quick reference):

Model	Task
Wav2Vec2	Audio classification, automatic speech recognition (ASR)
Vision Transformer (ViT), ConvNeXT	Image classification
DETR	Object detection
Mask2Former	Image segmentation
GLPN	Depth estimation
BERT	Text classification, token classification, question answering
GPT-2, LLaMA	Text generation
BART, T5	Summarization and translation
Codex, CodeLLaMA	Code generation
Whisper	Speech-to-text transcription

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When to Choose Which Architecture

Concept

Use encoder-only (BERT/DeBERTa/BGE) when:

• Task is purely classification, NER, or span extraction
• You need high-quality fixed-size embeddings for search/RAG
• Inference must be very fast (smaller models, no generation overhead)
• Labels are sentence-level or token-level - not generative

Use decoder-only (LLaMA/Gemma/GPT) when:

• Task requires free-form text generation
• You want a single model for multiple tasks via prompting
• You need in-context learning (few-shot examples in prompt)
• You plan to instruction fine-tune for custom tasks

Use encoder-decoder (T5/FLAN-T5/BART) when:

• Task is explicitly seq2seq with fixed input and output schemas
• You have limited compute - smaller fine-tuned encoder-decoder can beat large decoder-only for specific structured tasks
• Translation or summarization at scale where encoder-decoder efficiency matters

Practical rule of thumb: Default to decoder-only for new projects. Switch to encoder-only if you need embeddings or fast token classification. Encoder-decoder is a niche choice for legacy systems or very specific seq2seq workloads.

Code

from transformers import (
    AutoTokenizer, AutoModelForSequenceClassification,  # Encoder-only
    AutoModelForCausalLM,                               # Decoder-only
    AutoModelForSeq2SeqLM,                             # Encoder-Decoder
    pipeline
)

# --- Encoder-only: text classification ---
classifier = pipeline(
    "text-classification",
    model="distilbert-base-uncased-finetuned-sst-2-english"
)
result = classifier("This movie was absolutely fantastic!")
print(f"Encoder-only classification: {result}")

# --- Decoder-only: text generation ---
generator = pipeline(
    "text-generation",
    model="gpt2",  # small, runs locally
    max_new_tokens=50,
    temperature=0.7
)
result = generator("The transformer architecture revolutionized AI because")
print(f"\nDecoder-only generation: {result[0]['generated_text']}")

# --- Encoder-Decoder: summarization ---
summarizer = pipeline(
    "summarization",
    model="facebook/bart-large-cnn",
    max_length=60,
    min_length=20
)
article = """
The transformer architecture, introduced in the paper "Attention Is All You Need" by Vaswani 
et al. in 2017, revolutionized natural language processing. It replaced recurrent neural 
networks with a self-attention mechanism that could process all tokens simultaneously, 
enabling much faster training and better handling of long-range dependencies.
"""
result = summarizer(article)
print(f"\nEncoder-Decoder summary: {result[0]['summary_text']}")

# --- Checking architecture type programmatically ---
from transformers import AutoConfig

for model_name in ["bert-base-uncased", "gpt2", "facebook/bart-large"]:
    config = AutoConfig.from_pretrained(model_name)
    print(f"{model_name}: {config.model_type} - {config.architectures}")

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Functional Model Categories

Concept

Beyond the three architecture shapes, people describe models by what they are trained to do. These are overlapping labels, not separate architectures - one model can be an MoE, a reasoning model and a vision-language model at once.

Category	What it is	Examples	Typical use
Reasoning model	A decoder-only LLM post-trained, largely with RL on verifiable tasks, to produce a long chain of thought before answering (see Post-Training & Reasoning)	DeepSeek-R1, OpenAI o-series, Qwen3 in thinking mode	Maths, code, multi-step planning
Mixture of experts	Sparse FFN layers: a router sends each token to a few experts	Mixtral, DeepSeek-V3, Qwen3-235B-A22B, gpt-oss	Large capacity at lower compute per token
Vision-language model	A vision encoder (often a ViT) whose features are mapped into the LM's embedding space by a projector, trained on image-text data, or a natively multimodal model	LLaVA, Qwen-VL, Gemma 3, GPT-4o	Documents, screenshots, image Q&A, agent perception
Small language model	Models of roughly 1-8B parameters, trained (often with distillation from a larger model) to run on a single GPU or device	Phi, Gemma, Qwen and Llama small sizes	On-device use, latency-critical sub-tasks, fine-tuning
Computer-use / action model	An LLM trained or tuned to output actions - tool calls, clicks, keystrokes - from screenshots or UI trees	OpenAI's Computer-Using Agent, Anthropic's computer-use capability, Gemini computer-use models	Browser and desktop agents (see Computer-Use and Browser Agents)
Embedding model	Usually an encoder (or a decoder adapted) trained with contrastive objectives to output a fixed vector	BGE, E5, Gemini and OpenAI embedding models	Search, RAG, clustering
Large concept model (research)	Models that predict the next sentence-level embedding ("concept") instead of the next token	Meta's LCM (2024)	Research into language-agnostic reasoning

Distinctions worth knowing:

• Reasoning model vs chat model: same architecture; the difference is post-training that rewards correct final answers and lets the model spend tokens thinking first.
• Vision-language training typically goes: pretrained vision encoder and LM → train the projector on image-caption pairs → instruction-tune on visual tasks (LLaVA's recipe); newer models are trained multimodally from the start.
• Small model vs quantized large model: a small model has fewer parameters; quantization keeps the parameter count and lowers the precision. They trade off differently (see Quantized Inference).
• Action models vs tool use: any tool-calling LLM can act through tools; computer-use models are additionally trained on screen-and-action data so they can operate interfaces that have no API.

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Study Notes

Must-know for interviews:

• Three architecture families: encoder-only (bidirectional, MLM, classification), decoder-only (causal, CLM, generation), encoder-decoder (cross-attention, seq2seq)
• Decoder-only dominates production for general LLMs - simple CLM objective + instruction fine-tuning = powerful general purpose model
• BERT uses bidirectional attention - cannot generate; best for embeddings and classification
• MoE: sparse routing selects top-K experts per token → large total params, small active params
• LLaMA-3, Gemma, Mistral all use GQA for efficient KV caching
• When in doubt: reach for a decoder-only model (e.g. Llama, Qwen, Gemma)

Check Yourself

Exercises

References

• Devlin et al., BERT (2019)
• Raffel et al., Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (T5) (2020)
• Lewis et al., BART (2020)
• Brown et al., Language Models are Few-Shot Learners (GPT-3) (2020)
• Fedus et al., Switch Transformers (2022); Jiang et al., Mixtral of Experts (2024)
• Liu et al., Visual Instruction Tuning (LLaVA) (2023)
• LCM team (Meta), Large Concept Models (2024)

Last reviewed: 2026-09