Build Transformers from Scratch: Your 10-Day PyTorch Journey Begins
Ever typed something into Google Translate or asked ChatGPT a question? Chances are you were already touching a transformer model. Those architectures now sit at the core of most AI tools, yet they still feel like a mystery box.
Over the next ten days I’ll walk you through a hands-on PyTorch mini-course that builds a transformer from scratch. We’ll kick off with tokenization and embeddings, then piece by piece add the attention tricks that make the whole thing click. The point isn’t just to copy code - it’s to get why each part exists.
Take a tensor shaped [1, 10, 4, 128] for example: the 128 is clearly the embedding size, but the first three numbers (1, 10, 4) are a bit trickier. They correspond to batch, sequence length and number of heads, more or less, depending on the context. In the following lesson we dive into the attention block.
Lesson 04, titled Grouped Query Attention, is the signature component of the model and leans directly on what we’ve just covered.
While the last dimension (128) represents the embedding size, can you identify what the first three dimensions (1, 10, 4) represent in the context of transformer architecture? In the next lesson, you will learn about the attention block. Lesson 04: Grouped Query Attention The signature component of a transformer model is its attention mechanism.
When processing a sequence of tokens, the attention mechanism builds connections between tokens to understand their context. The attention mechanism predates transformer models, and several variants have evolved over time. In this lesson, you will learn to implement Grouped Query Attention (GQA).
A transformer model begins with a sequence of embedded tokens, which are essentially vectors. The modern attention mechanism computes an output sequence based on three input sequences: query, key, and value. These three sequences are derived from the input sequence through different projections: The projection is performed by a fully-connected neural network layer that operates on the input tensor’s last dimension.
When I walk through the steps, it starts to look like the real strength of transformers isn’t any single piece but the way the pieces fit together. The whole design is surprisingly modular - embeddings, the attention heads you’ll meet next, even the feed-forward bits each have a clear role. People often lose that picture when they start naming billion-parameter beasts; building everything from scratch tends to bring the simplicity back.
Going from a lone tensor to a working attention block kind of pulls the core ideas out of the fog that power today’s biggest models. In the upcoming section on grouped-query attention, you’ll probably notice the same patterns just stretched to larger scales. The basic rules don’t really change, even though the code gets more involved to cope with messy real-world data.
Knowing this baseline gives us a useful way to judge not only how the models behave, but also why the designers chose this particular layout.
Resources
- Transformer using PyTorch - GeeksforGeeks
- Build a transformer model from scratch using pytorch - GitHub
- Building Transformer Models from Scratch with PyTorch (10-day mini-course) - Machine Learning Mastery
- Transformers from Scratch in PyTorch - Frank Odom | Substack
- No Libraries, No Shortcuts: LLM from Scratch with PyTorch - Towards AI
More in LLMs & Generative AI
Google pushes Gemini rollout to 2026, delays Android Assistant swap
Why does this matter now? For years, Android users have expected a smooth handoff from the familiar...
OpenAI now adds tone controls and streamlined email editing to ChatGPT
OpenAI just rolled out a set of tweaks that feel oddly personal for a chatbot. While the core model...
Google releases FunctionGemma, a tiny model for natural‑language mobile control
Here's the thing: mobile phones are the most common computing platform, yet most AI assistants...
Chinese scammers used AI images for refund fraud; police held buyer eight days
When a shopper in Shanghai posted a video of a defective product, the footage looked...
Gemini pulls Google Calendar data to auto‑suggest meeting times in email replies
Why does this matter? In a flood of AI shortcuts that promise to shave minutes off daily tasks,...
Common Questions Answered
What are the first three dimensions (1, 10, 4) in the tensor example from the article?
The first three dimensions represent the batch size (1), the sequence length (10), and the number of attention heads (4) within the transformer architecture. Understanding these dimensions is crucial for grasping how the model processes input data in parallel.
What is the signature component of a transformer model explained in this course?
The signature component is the attention mechanism, which builds connections between tokens to understand their context within a sequence. This mechanism is fundamental to how transformers process information and will be explored in detail during the course.
What is the main goal of this 10-day PyTorch mini-course?
The main goal is to guide you through building a transformer model from scratch, starting with tokenization and embeddings and progressing to sophisticated attention mechanisms. This hands-on approach demystifies the inner workings of transformers, which are the backbone of modern AI.
According to the article, what is the true power of transformers?
The true power lies in the elegant composition and modularity of their individual components, such as embeddings and attention mechanisms. Each part serves a distinct and understandable purpose, which becomes clear when building the model from the ground up.