Build an LLM from Scratch 4: Implementing a GPT model from Scratch To Generate Text @SebastianRaschka
Build an LLM from Scratch 4: Implementing a GPT model from Scratch To Generate Text  @SebastianRaschka
Uploaded March 2025 | Updated September 2026, 2 weeks ago
Links to the book:
- amzn.to/4fqvn0D (Amazon)
- https://mng.bz/M96o (Manning)

Link to the GitHub repository: github.com/rasbt/LLMs-from-scratch

This is a supplementary video explaining how to code an LLM architecture from scratch.

00:00 4.1 Coding an LLM architecture
13:52 4.2 Normalizing activations withlayer normalization
36:02 4.3 Implementing a feed forward network with GELU activations
52:16 4.4 Adding shortcut connections
1:03:18 4.5 Connecting attention and linear layers in a transformer block
1:15:13 4.6 Coding the GPT model

You can find additional bonus materials on GitHub, for example converting the GPT-2 architecture into Llama 2 and Llama 3: github.com/rasbt/LLMs-from-scratch/tree/main/ch05/07_gpt_to_llama
Build an LLM from Scratch 4: Implementing a GPT model from Scratch To Generate TextL14.3.1.1 VGG16 OverviewL9.3.2 Multilayer Perceptron in PyTorch   Code Example Part 2/3 (Jupyter Notebook)L17.2 Sampling from a Variational AutoencoderBuild an LLM from Scratch 5: Pretraining on Unlabeled DataDeep Learning News #5, Feb 27 2021L19.5.2.3 BERT: Bidirectional Encoder Representations from Transformers13.3.1 L1-regularized Logistic Regression as Embedded Feature Selection (L13: Feature Selection)L18.5: Tips and Tricks to Make GANs WorkL8.8 Softmax Regression Derivatives for Gradient DescentL17.5 A Variational Autoencoder for Handwritten Digits in PyTorch   Code ExampleL5.1 Online, Batch, and Minibatch Mode
Sebastian Raschka |

Build an LLM from Scratch 4: Implementing a GPT model from Scratch To Generate Text

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