@DanKornas: Building an LLM from scratch is easier when each layer has its own notebook. EveryonesLLM is a Google Colab-based tutor…
Summary
EveryonesLLM is an open-source Google Colab-based tutorial repository for building a nanoGPT-style LLM from scratch, with step-by-step chapters covering dataloading, embeddings, attention, training, and instruction tuning.
View Cached Full Text
Cached at: 06/18/26, 12:04 AM
Building an LLM from scratch is easier when each layer has its own notebook.
EveryonesLLM is a Google Colab-based tutorial repo for building a nanoGPT-style LLM from scratch.
It helps you learn the stack step by step by splitting the path into numbered chapters, from dataloading and embeddings to attention, training, scaling, instruction tuning, and a beta vision LLM track.
Key features:
• Colab-first lessons – each chapter links to an “Open in Colab” notebook with an estimated time • Layer-by-layer path – covers dataloaders, token/position embeddings, LayerNorm, attention, transformer blocks, logits, and nanoGPT • Training progression – includes CPU/GPU speed checks, larger models/datasets, LR schedule, checkpoints, pretraining, and instruction tuning • Exercise + answer notebooks – separate todo and answer notebooks make it easier to practice, then compare • Extra learning aids – README links a tensor map, demo/web app, and beta vision pretraining/instruction-tuning chapters
It’s open-source (MIT license).
Link in the reply
Similar Articles
@Modular: The MAX-LLM book just made it even easier to build an LLM from scratch. The new notebook format lets you run the GPT-2 …
The MAX-LLM book now provides interactive Jupyter notebooks that walk through building a complete GPT-2 implementation from scratch using the MAX framework, enabling users to explore tensor shapes, run components, and generate text.
@DanKornas: Stop learning LLMs from disconnected tutorials. LLM from Scratch is a hands-on PyTorch curriculum for builders who want…
A hands-on PyTorch curriculum that teaches LLM training from transformer basics through fine-tuning and alignment, including RLHF and GRPO.
@techNmak: Build LLMs from Scratch Found this gem from Vizuara, a 43-lecture series that actually delivers on its promise: buildin…
A 43-lecture series by Vizuara teaches how to build LLMs from scratch, covering transformer architecture, GPT internals, tokenization, and attention mechanisms with full Python implementations.
@GitHub_Daily: Want to understand the underlying principles of large language models? Most resources only cover theory or provide source code, leaving you still confused. Stumbled upon this open-source tutorial, EveryonesLLM, which guides us step by step to build a complete large language model from scratch on Google Colab, writing code throughout. The whole tutorial is divided into...
EveryonesLLM is an open-source tutorial that provides 29 chapters of Colab notebooks. It teaches users step by step to build a complete large language model from scratch on Google Colab, including pre-training and instruction fine-tuning, and supports Chinese.
@techNmak: This is the best way to learn how LLMs work. Interactive. 3D. Step-by-step. Covers: → Embedding → Layer Norm → Self-Att…
An interactive 3D step-by-step guide to learning how LLMs work, covering key transformer concepts like embedding, self-attention, and softmax. It recommends a visual approach over reading papers.