Building a decoder-only transformer from first principles—no pretrained shortcuts
Modern NLP relies heavily on pretrained models, but this creates a knowledge gap: engineers can fine-tune transformers without understanding their internals. For low-resource languages, building custom architectures from scratch is essential:
The Challenge:
Can you build a functional GPT-style language model entirely from scratch—implementing multi-head attention, positional encoding, and custom tokenization—trained on a 268KB corpus without pretrained weights?
Self-attention is conceptually simple but implementation-heavy: you need to handle query/key/value projections, split into heads, compute scaled dot-products, apply masking, and concatenate—all while maintaining proper tensor shapes.
Implemented multi-head attention from scratch using PyTorch's einsum for efficient matrix operations. Added causal masking to prevent attending to future tokens. Validated against reference implementations to ensure correctness.
Successfully replicated GPT-2 attention mechanism—model learns contextual dependencies without pretrained weights.
Standard BPE tokenizers are optimized for English. Igala's tonal markers and agglutinative morphology break tokenization—you get inefficient subwords that destroy semantic meaning.
Built custom BPE from scratch that preserves tonal markers as atomic units. Preprocessed corpus to normalize dialectal variations. Tuned merge operations to respect morphological boundaries (verb stems, noun classes).
Reduced vocabulary size by 30% while improving token alignment with linguistic structure.
268KB of text is tiny for language modeling. GPT-3 trained on 45TB. Small datasets lead to severe overfitting—model memorizes training data instead of learning patterns.
Applied aggressive data augmentation (random masking, sentence shuffling). Used dropout (0.2) and weight decay for regularization. Trained with smaller model (6 layers, 512 dim) to match dataset size. Monitored validation perplexity religiously.
Achieved coherent text generation without catastrophic overfitting—model generalizes to unseen prompts.
Experiment with the first transformer language model built from scratch for Igala.