raiyanyahya/how-to-train-your-gpt resource

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3152 stars · 384 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A 12-chapter educational repository that teaches how to build, train, and run a modern LLaMA-style GPT language model from scratch in Python/PyTorch, with every line of code commented. It includes 28 standalone topic explainers covering attention, RoPE, RMSNorm, SwiGLU, KV cache, and more.

Use cases

• learn how transformers and attention work from scratch
• build a GPT-style language model in PyTorch
• understand LLaMA architecture internals like RoPE and KV cache
• study a fully commented LLM training loop
• teach myself deep learning without prior ML experience
• run a from-scratch language model training in Colab

When to choose

• you want to understand LLM internals line by line rather than call APIs
• you have basic Python skills but no ML background
• you prefer analogies and step-by-step explanations over dense papers
• you want runnable notebooks for hands-on learning

When to avoid

• you need a production-ready LLM framework or inference server
• you want to fine-tune existing models efficiently at scale
• you need battle-tested, optimized training code for real workloads

Facets

learning-resource · maturity active

llm-training machine-learning deep-learning nlp transformers large-language-models deep-learning machine-learning tutorials education python cross-platform pytorch gpt llama from-scratch tutorial jupyter-notebook attention-mechanism tokenization educational natural-language-processing gpu

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