I implemented a transformer model from scratch using PyTorch, based on the paper Attention is All You Need. You can use my scripts to train your own billion or million parameter LLM using a single GPU.

This started as a pretraining tutorial. It now goes all the way from raw text to an aligned, reasoning style model, with every algorithm hand written in plain PyTorch (no trl, no peft, no transformers). The whole journey is one idea repeated: turn text into numbers, predict the next token, then keep changing the data and the loss until the model does what we want.

Here is the path we will walk, end to end:

raw text  ->  tokens  ->  a Transformer  ->  next-token loss  ->  a base model
base model  ->  SFT  ->  Reward Model  ->  {PPO, DPO}  ->  GRPO  ->  evaluation and chat

Below is the output of a trained 13 million parameter LLM, just so you can see where the small end of this starts:

In ***1978, The park was returned to the factory-plate that
the public share to the lower of the electronic fence that
follow from the Station's cities. The Canal of ancient Western
nations were confined to the city spot. The villages were directly
linked to cities in China that revolt that the US budget and in
Odambinais is uncertain and fortune established in rural areas.

Table of Contents

• Who this is for
• Prerequisites and Training Time
• Setup
• Code Structure
• Step 1: Preparing the Data
• Step 2: The Model, Built From Small Pieces
  • Multi Layer Perceptron (MLP)
  • Single Head Attention
  • Multi Head Attention
  • The Transformer Block
  • The Full Transformer
• Step 3: Pretraining the Base Model
• Step 4: Generating Text
• Step 5: Post-Training, Turning a Base Model Into an Assistant
  • SFT (Supervised Fine-Tuning)
  • The Reward Model
  • DPO, ORPO and KTO
  • PPO
  • GRPO / RLVR
• Step 6: Evaluation
• Step 7: Talking to the Model
• The Streamlit Control Panel
• The Documentation Site
• Run the Whole Thing
• What's Next

Who this is for

I tried to write this so one page works for very different readers:

• If you are a student, read top to bottom. Every block of code comes after a plain explanation of what it does and why, and most blocks are followed by the output you should expect.
• If you are a developer, the commands and file paths are all here. You can copy, run, and read the referenced source files directly.
• If you are a researcher, the post-training half is the interesting part: SFT, a Bradley-Terry reward model, PPO with GAE, DPO/ORPO/KTO, and GRPO, all from scratch on the same small Transformer, trained on real public datasets.

Ev