LLM From Scratch is a hands-on educational workshop project that teaches developers how to build and train a GPT-style language model entirely from scratch using PyTorch. Instead of relying on high-level abstractions or prebuilt frameworks, the project walks users through implementing every core component manually, including tokenization, transformer architecture, training loops, and autoregressive text generation. The repository is intentionally simplified to focus on conceptual clarity, using a compact model of roughly 10 million parameters that can train on consumer hardware such as laptops within a relatively short time. Inspired by Andrej Karpathy’s nanoGPT, the project emphasizes learning through direct implementation and experimentation rather than black-box usage. The workshop documentation explains concepts such as self-attention, embeddings, gradient clipping, optimizer scheduling, and decoding strategies in a practical and approachable way.

Features

  • Step-by-step GPT model implementation in PyTorch
  • Manual construction of tokenization and transformer layers
  • Training pipeline with backpropagation and optimization
  • Autoregressive text generation with sampling controls
  • Lightweight 10M-parameter model suitable for laptops
  • Educational documentation explaining core LLM concepts

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Additional Project Details

Operating Systems

Linux, Mac, Windows

Registered

2026-05-07