🤖 AI / Machine Learning22 min
Building a Transformer-Based LLM from Scratch in Python
A
Muhammad Asim Chattha
Software Developer & Cybersecurity Researcher
#Transformers#LLMs#PyTorch#Attention#Deep Learning
Building a language model from scratch is the best way to truly understand how modern AI works. This comprehensive guide walks through every component of a transformer-based LLM.
What You'll Build
- **Tokenization**: Byte-Pair Encoding (BPE) from scratch in pure Python
- **Multi-Head Self-Attention** mechanism with causal masking for autoregressive generation
- **Positional encodings**: both sinusoidal and learned embeddings
- **Feed-forward networks** with GELU activations and residual connections
- **Training loop** with gradient accumulation, mixed precision, and learning rate scheduling
- **Inference optimization**: KV-caching and speculative decoding techniques
Architecture Overview
The transformer architecture follows the "Attention Is All You Need" paper with modern improvements including pre-layer normalization, RoPE (Rotary Position Embeddings), and FlashAttention-compatible patterns.
Training Pipeline
We'll train on a curated dataset combining WikiText-103, a subset of The Pile, and custom technical documentation, using a single GPU with gradient accumulation to simulate larger batch sizes.
Full implementation available in PyTorch with a companion Colab notebook.