Building GPT from Scratch: What I Learned Implementing a Transformer in Raw PyTorch

2026-07-19 · Source: Machine Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, medium

Summary

An individual implemented a character-level GPT model from scratch using raw PyTorch, foregoing the "transformers" library, to deeply understand its internal mechanics. The project began with a simple bigram model to establish essential data plumbing, tokenizing Tiny Shakespeare's 1.1 million characters into a 65-character vocabulary. The core involved building self-attention with a causal mask, which restricts information flow to past tokens, and then assembling full Transformer blocks incorporating residual connections. The final model featured 6 layers, 6 attention heads, 384-dimensional embeddings, a 256-token context window, and approximately 10.8 million parameters. Trained for 5,000 iterations, it achieved a training loss of 1.08 and a validation loss of 1.48, demonstrating expected overfitting patterns and generating structurally plausible, though narratively incoherent, Shakespeare-like text.

Key takeaway

For AI students or machine learning engineers aiming to truly grasp large language model internals, implementing a Transformer from scratch in raw PyTorch is invaluable. This hands-on approach demystifies complex concepts like attention and causal masking, making theoretical knowledge concrete. You will observe fundamental behaviors like overfitting even with small models, solidifying your understanding beyond API calls. Consider replicating this exercise to build a foundational intuition for advanced LLM development.

Key insights

Building a Transformer from raw PyTorch demystifies attention, revealing causal masking as key to autoregressive generation.

Principles

• Attention is a differentiable, weighted lookup.
• Causal masking enables autoregressive generation.
• Small-scale models reproduce large-scale phenomena.

Method

Implement a bigram model first for plumbing, then build self-attention with QKV projections and causal masking, assemble Transformer blocks with residual connections, and stack layers.

In practice

• Start with a simple baseline model.
• Implement core components like attention manually.
• Observe overfitting on small datasets.

Topics

• GPT Implementation
• Transformer Architecture
• PyTorch
• Self-Attention
• Causal Masking
• Language Model Training

Code references

• NansKong/GPTmodel

Best for: AI Scientist, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.