My last post, about the maths you need to start understanding LLMs, took off on Hacker News over the weekend. It's always nice to see lots of people reading and -- I hope! -- enjoying something that you've written. But there's another benefit. If enough people read something, some of them will spot errors or confusing bits -- "given enough eyeballs, all bugs are shallow". Commenter bad_ash made the excellent point that in the phrasing I originally had, a naive reader might think that activati...| Giles' blog
Archive of Giles Thomas’s blog posts from September 2025. Insights on AI, startups, and software development, plus occasional personal reflections.| www.gilesthomas.com
A quick refresher on the maths behind LLMs: vectors, matrices, projections, embeddings, logits and softmax.| Giles' Blog
I'm getting towards the end of chapter 4 of Sebastian Raschka's book "Build a Large Language Model (from Scratch)". When I first read this chapter, it seemed to be about tricks to use to make LLMs trainable, but having gone through it more closely, only the first part -- on layer normalisation -- seems to fit into that category. The second, about the feed-forward network is definitely not -- that's the part of the LLM that does a huge chunk of the thinking needed for next-token prediction. An...| Giles' blog
I've now finished chapter 4 in Sebastian Raschka's book "Build a Large Language Model (from Scratch)", having worked through shortcut connections in my last post. The remainder of the chapter doesn't introduce any new concepts -- instead, it shows how to put all of the code we've worked through so far into a full GPT-type LLM. You can see my code here, in the file gpt.py -- though I strongly recommend that if you're also working through the book, you type it in yourself -- I found that even t...| Giles' blog
How AI chatbots like ChatGPT work under the hood -- the post I wish I’d found before starting 'Build a Large Language Model (from Scratch)'.| Giles' Blog
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I'm still working through chapter 4 of Sebastian Raschka's book "Build a Large Language Model (from Scratch)". This chapter not only puts together the pieces that the previous ones covered, but adds on a few extra steps. I'd previously been thinking of these steps as just useful engineering techniques ("folding, spindling and mutilating" the context vectors) to take a model that would work in theory, but not in practice, and make it something trainable and usable -- but in this post I'll expl...| Giles' blog
Working through layer normalisation -- why do we do it, how does it work, and why doesn't it break everything?| Giles' Blog
The way we get from context vectors to next-word prediction turns out to be simpler than I imagined -- but understanding why it works took a bit of thought.| Giles' Blog
On sabbatical / created @PythonAnywhere.com, which found a home at @anacondainc.bsky.social / XP / Python / PSF Fellow / opinions my own / blog at https://www.gilesthomas.com| Bluesky Social
A pause to take stock: realising that attention heads are simpler than I thought explained why we do the calculations we do.| Giles' Blog
Finally getting to the end of chapter 3 of Raschka’s LLM book! This time it’s multi-head attention: what it is, how it works, and why the code does what it does.| Giles' Blog
Posts in the 'LLM from scratch' category on Giles Thomas’s blog. Insights on AI, startups, software development, and technical projects, drawn from 30 years of experience.| www.gilesthomas.com
Batching speeds up training and inference, but for LLMs we can't just use matrices for it -- we need higher-order tensors.| Giles' Blog
Posts in the 'AI' category on Giles Thomas’s blog. Insights on AI, startups, software development, and technical projects, drawn from 30 years of experience.| www.gilesthomas.com
Adding dropout to the LLM's training is pretty simple, though it does raise one interesting question| Giles' Blog
Causal, or masked self-attention: when we're considering a token, we don't pay attention to later ones. Following Sebastian Raschka's book 'Build a Large Language Model (from Scratch)'. Part 9/??| Giles' Blog
Moving on from a toy self-attention mechanism, it's time to find out how to build a real trainable one. Following Sebastian Raschka's book 'Build a Large Language Model (from Scratch)'. Part 8/??| Giles' Blog
Boost your learning: Test Yourself PDF Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up! In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You’ll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language ...| Manning Publications
When we turn up the strength of the “Golden Gate Bridge” feature, Claude’s responses begin to focus on the Golden Gate Bridge. For a short time, we’re making this model available for everyone to interact with.| www.anthropic.com
