Model Architecture Comparison Across Paradigms Encoder Only Encoder Decoder Decoder Only

via “encoder-decoder models for sequence-to-sequence tasks with beam search”

Hugging Face's model library — thousands of pretrained transformers for NLP, vision, audio.

Unique: Provides encoder-decoder models with unified API for multiple tasks (translation, summarization, QA), supporting beam search and other decoding strategies. Cross-attention between encoder and decoder enables context-aware generation.

vs others: More flexible than task-specific models because the same architecture works for multiple tasks. More efficient than decoder-only models for tasks with long inputs because encoder processes input once.

via “model architecture comparison across paradigms (encoder-only, encoder-decoder, decoder-only)”

📚 从零开始构建大模型

Unique: Organizes three major transformer paradigms into parallel chapters (chapter 3) with identical implementation patterns, making architectural differences explicit through code rather than conceptual descriptions, enabling direct comparison of attention masking, loss computation, and training objectives

vs others: More systematic than scattered tutorials because it treats encoder-only, encoder-decoder, and decoder-only as equal-weight design choices with comparable implementations, rather than positioning decoder-only as the default and others as variants