Sebastian Raschka
LLM research engineer and author
Explains language models through small implementations, connecting tokens and attention to the code for building, training and adapting a GPT-style model.
Why read Sebastian Raschka?
Read Raschka when you want to connect a model diagram to calculations you can inspect. His attention tutorial starts with a six-word sentence, turns the words into vectors, then constructs queries, keys, values and a context vector. You can follow how information from other words changes a word's representation.
Continue with the LLMs from Scratch repository to see that mechanism inside a GPT-style model, followed by pretraining and fine-tuning. The chapter map gives you a sequence to follow rather than a collection of unrelated examples. Python knowledge is useful; the repository points readers new to PyTorch to its introductory appendix.
Selected work
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Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch
How does attention give a word context? Raschka builds a small PyTorch example from word vectors to attention weights and a context vector, then explains multiple heads and cross-attention.
Cite this selection
XFollowList, “Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch” reading notes in the Sebastian Raschka profile. https://xfollowlist.com/people/sebastian-raschka#work-4589679c427c
The linked work is the original source; this permalink identifies XFollowList’s reason to read it. Original work published .
Profile-source checking date and references · Dated reading map
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Build a Large Language Model (From Scratch): companion code
How do the pieces become a model? Follow the chapter map from text data to a GPT implementation, pretraining and fine-tuning. Start with the introductory PyTorch appendix if tensor operations are unfamiliar.
Cite this selection
XFollowList, “Build a Large Language Model (From Scratch): companion code” reading notes in the Sebastian Raschka profile. https://xfollowlist.com/people/sebastian-raschka#work-e0cbbf211a55
The linked work is the original source; this permalink identifies XFollowList’s reason to read it. Original publication date is unknown.
Profile-source checking date and references · Dated reading map
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Using Local Coding Agents
How do you pair a local model with a coding agent? Raschka separates the runtime, model and agent client, then measures speed, memory and performance on his own tool-use tasks. Start with the setup and test sections.
Cite this selection
XFollowList, “Using Local Coding Agents” reading notes in the Sebastian Raschka profile. https://xfollowlist.com/people/sebastian-raschka#work-9b152c447df8
The linked work is the original source; this permalink identifies XFollowList’s reason to read it. Original work published .
Profile-source checking date and references · Dated reading map
Projects & roles
LLMs from Scratch
The official companion code for Raschka's book, including a small GPT-style model and training examples.
Ahead of AI
His publication for longer explanations of model architectures, training methods and evaluation.
Sources & further reading
Profile sources checked . This describes checking these references, separately from the publication dates of selected works.
- His current biography describes his LLM research engineering and writing, distinguishes earlier university and Lightning AI roles, and links @rasbt.Link to this source note
- His book companion hub documents the chapter sequence and links the official implementation.Link to this source note
- The implementation connects token inputs, causal attention, transformer blocks and next-token generation.Link to this source note
- Original material read for the local-model selection.Link to this source note
Cite this profile’s source check
XFollowList, Sebastian Raschka profile sources. Sources checked 2026-10-09. https://xfollowlist.com/people/sebastian-raschka#sources-checked