Model Architectures

How AI models are built — transformers, attention mechanisms, mixture-of-experts, and the design decisions that shape capability.

MONA mapping transformer pipeline stages onto a service architecture diagram for backend developers
MONA Bridge 11 min

Transformer Internals for Developers: What Maps, What Breaks

Transformer internals mapped for backend developers. Learn which service-architecture instincts still apply, where …

Geometric diagram showing a transformer splitting in half with the decoder side scaling upward through layered attention patterns
MONA explainer 10 min

Why Decoder-Only Beat Encoder-Decoder: Scaling Laws, Data Efficiency, and the Simplicity Advantage

Decoder-only models won the scaling race by doing less. Learn how a simpler training objective, scaling laws, and MoE …

Geometric diagram showing input tokens compressed through an encoder into a fixed-length vector then expanded by a decoder into a new output sequence
MONA explainer 11 min

What Is Encoder-Decoder Architecture and How Sequence-to-Sequence Models Process Language

Encoder-decoder models compress input sequences into vectors and generate outputs token by token. Learn how seq2seq …

Geometric illustration of a decoder-only transformer generating tokens sequentially through causal masked attention layers
MONA explainer 10 min

What Is Decoder-Only Architecture and How Autoregressive LLMs Generate Text Token by Token

Decoder-only architecture powers every major LLM today. Learn how causal masking, KV cache, and autoregressive …

Diagram showing encoder hidden states branching into attention-weighted paths reaching a decoder network
MONA explainer 10 min

From Context Vectors to Cross-Attention: How Encoder-Decoder Design Overcame the Bottleneck Problem

The encoder-decoder bottleneck crushed long sequences into one vector. Learn how attention replaced compression with …