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Train Your Own LLM Model from Scratch

train-llm-from-scratch offers a simple, step-by-step method of training a large language model from scratch, from data set download to text generation. It is a practical resource for your learning process.

Updates

  • October 19, 2026: Stars 11,240 → 12,282.
  • September 27, 2026: Stars 8,864 → 11,240.
  • August 2, 2026: Stars 2,086 → 8,864.

What do you learn?

  • End-to-end flow covering data download, training and text production.
  • Application method based on the inner workings of the LLM.
  • Practical use with directly operable notebooks.

How to install, how to use?

Paste this into your AI agent (Claude Code · Codex · Antigravity)

This repository (FareedKhan-dev/train-llm-from-scratch) is a tutorial on training a transformer/LLM from scratch with PyTorch. Review the README and give me a step-by-step implementation plan in Turkish: explaining the required hardware (GPU), data download and pre-processing, model configuration, training and text generation stages, in order.

Repository: https://github.com/FareedKhan-dev/train-llm-from-scratch. Follow the official instructions in the README of this repository for setup and execution.

Before starting, check which OS we are on and if the necessary tools (e.g., Python, Node.js, Git, Docker) are installed. Install any missing ones step-by-step according to this system.

Create a new, empty folder to work in. Do not touch any files outside this folder and do not delete anything. Tell me what you are going to do in one sentence before running each command.

Tell me what I should see on the screen at the end of each step. If an error occurs, I will paste the error message to you exactly as it is, and we will fix it together.

Clone repository
git clone https://github.com/FareedKhan-dev/train-llm-from-scratch.git
cd train-llm-from-scratch
Install dependencies
pip install -r requirements.txt

Related dictionary terms

Who it is forThose who want to learn LLM education manually
DifficultyIntermediate–advanced Python/ML foundation
FormatJupyter notebooks step by step
FeeFree · open source (MIT)
LicenseMIT · details below
License: MIT · you can freely use, adapt and share content and code.

Links

TreScout did not build this tool · we found it in GitHub trends and wrote it up. This page describes the repository as of 2026-05-30: The star count and our text belong to that day, the repository may have changed since. Check the repository link for the current state. The introductory text was prepared with AI from the repository's own documentation, and this page was machine-translated from the Turkish original · the Turkish version prevails.