Transformer
from Scratch
A complete PyTorch implementation of the original "Attention Is All You Need" Transformer architecture — built from raw matrix operations to real translation results, with zero shortcuts.
Tech Stack
Libraries & concepts powering this implementation
PyTorch
Core DL Framework
Python
Implementation Language
Multi-Head Attention
Core Mechanism
Positional Encoding
Sequence Awareness
Encoder-Decoder
Architecture
Layer Normalization
Training Stability
Architecture Components
Every layer built and documented with its mathematical derivation
Input Embeddings
Token-to-dense-vector mapping
Positional Encoding
Sinusoidal position information injection
Multi-Head Attention
Parallel self-attention across multiple heads
Feed-Forward Network
Position-wise non-linear transformation
Layer Normalization
Network stability with residual connections
Linear Projection
Decoder output to vocabulary probabilities
Key Features
Pure Self-Attention
Full self-attention implementation with no recurrent layers — captures global dependencies in O(1) sequential operations
Encoder-Decoder Stack
6-layer encoder + 6-layer decoder stack, faithful to the original "Attention Is All You Need" architecture
Built From First Principles
No transformer libraries used — every operation built from matrix algebra with full mathematical grounding
Real Translation Task
Trained and evaluated on real language translation tasks with full BLEU score computation
What You'll Learn
How self-attention works mathematically (Q, K, V formulation)
Why multi-head attention captures richer representations
Why positional encoding is critical for sequence order
The encoder-decoder cross-attention mechanism in detail
LayerNorm placement and residual connection math
BLEU score computation and translation quality evaluation
Training tricks for transformers (learning rate warmup, label smoothing)
Why transformers dominate modern NLP and LLM architectures
Explore the Code
Every line documented, every equation explained. Built for deep understanding, not just results.
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