--- title: "train-llm-from-scratch" type: "tool" slug: "fareedkhan-dev-train-llm-from-scratch" canonical_url: "https://www.graphcanon.com/tools/fareedkhan-dev-train-llm-from-scratch" github_url: "https://github.com/FareedKhan-dev/train-llm-from-scratch" homepage_url: "https://fareedkhan-dev.github.io/train-llm-from-scratch/" stars: 8138 forks: 1119 primary_language: "Python" license: "MIT" categories: ["model-training", "llm-frameworks"] tags: ["training", "llm", "gemini", "large-language-models", "openai", "transformers"] updated_at: "2026-07-07T18:36:29.559119+00:00" --- # train-llm-from-scratch > A Python repository for training an LLM from scratch using PyTorch. Provides scripts to train a transformer model without relying on external libraries like `trl`, `peft` or `transformers`. Includes end-to-end instructions from raw text data preprocessing to model evaluation. ## Facts - Repository: https://github.com/FareedKhan-dev/train-llm-from-scratch - Homepage: https://fareedkhan-dev.github.io/train-llm-from-scratch/ - Stars: 8,138 · Forks: 1,119 · Open issues: 2 · Watchers: 59 - Primary language: Python - License: MIT - Last pushed: 2026-06-24T18:58:50+00:00 ## Categories - [Model Training](/categories/model-training.md) - [LLM Frameworks](/categories/llm-frameworks.md) ## Tags training, llm, gemini, large language models, openai, transformers ## Related tools - [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. (★ 175,659) - [prompts.chat](/tools/f-prompts-chat.md) - The world's largest open-source prompt library for AI (★ 165,019) - [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models (★ 162,347) - [open-webui](/tools/open-webui-open-webui.md) - User-friendly AI Interface (Supports Ollama, OpenAI API, ...) (★ 144,575) - [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) - 100+ AI Agent & RAG apps you can actually run — clone, customize, ship. (★ 116,702) - [LLMs-from-scratch](/tools/rasbt-llms-from-scratch.md) - Implement a ChatGPT-like LLM in PyTorch from scratch (★ 98,711) - [TradingAgents](/tools/tauricresearch-tradingagents.md) - TradingAgents: Multi-Agents LLM Financial Trading Framework (★ 91,610) - [caveman](/tools/juliusbrussee-caveman.md) - Cuts 65% of tokens in AI coding agent responses. (★ 86,150) ## README (excerpt) ```text
# Train LLM From Scratch **I am Looking for a PhD position in AI**. [GitHub](https://github.com/FareedKhan-dev)
I implemented a transformer model from scratch using PyTorch, based on the paper [Attention is All You Need](https://arxiv.org/abs/1706.03762). You can use my scripts to train your own **billion** or **million** parameter LLM using a single GPU. This started as a pretraining tutorial. It now goes all the way from raw text to an aligned, reasoning style model, with every algorithm hand written in plain PyTorch (no `trl`, no `peft`, no `transformers`). The whole journey is one idea repeated: turn text into numbers, predict the next token, then keep changing the data and the loss until the model does what we want. Here is the path we will walk, end to end: ``` raw text -> tokens -> a Transformer -> next-token loss -> a base model base model -> SFT -> Reward Model -> {PPO, DPO} -> GRPO -> evaluation and chat ``` Below is the output of a trained 13 million parameter LLM, just so you can see where the small end of this starts: ``` In ***1978, The park was returned to the factory-plate that the public share to the lower of the electronic fence that follow from the Station's cities. The Canal of ancient Western nations were confined to the city spot. The villages were directly linked to cities in China that revolt that the US budget and in Odambinais is uncertain and fortune established in rural areas. ``` ## Table of Contents - [Who this is for](#who-this-is-for) - [Prerequisites and Training Time](#prerequisites-and-training-time) - [Setup](#setup) - [Code Structure](#code-structure) - [Step 1: Preparing the Data](#step-1-preparing-the-data) - [Step 2: The Model, Built From Small Pieces](#step-2-the-model-built-from-small-pieces) - [Multi Layer Perceptron (MLP)](#multi-layer-perceptron-mlp) - [Single Head Attention](#single-head-attention) - [Multi Head Attention](#multi-head-attention) - [The Transformer Block](#the-transformer-block) - [The Full Transformer](#the-full-transformer) - [Step 3: Pretraining the Base Model](#step-3-pretraining-the-base-model) - [Step 4: Generating Text](#step-4-generating-text) - [Step 5: Post-Training, Turning a Base Model Into an Assistant](#step-5-post-training-turning-a-base-model-into-an-assistant) - [SFT (Supervised Fine-Tuning)](#sft-supervised-fine-tuning) - [The Reward Model](#the-reward-model) - [DPO, ORPO and KTO](#dpo-orpo-and-kto) - [PPO](#ppo) - [GRPO / RLVR](#grpo--rlvr) - [Step 6: Evaluation](#step-6-evaluation) - [Step 7: Talking to the Model](#step-7-talking-to-the-model) - [The Streamlit Control Panel](#the-streamlit-control-panel) - [The Documentation Site](#the-documentation-site) - [Run the Whole Thing](#run-the-whole-thing) - [What's Next](#whats-next) ## Who this is for I tried to write this so one page works for very different readers: - If you are a **student**, read top to bottom. Every block of code comes after a plain explanation of what it does and why, and most blocks are followed by the output you should expect. - If you are a **developer**, the commands and file paths are all here. You can copy, run, and read the referenced source files directly. - If you are a **researcher**, the post-training half is the interesting part: SFT, a Bradley-Terry reward model, PPO with GAE, DPO/ORPO/KTO, and GRPO, all from scratch on the same small Transformer, trained on real public datasets. Every diagram in this README is colored the same way, so the colors mean something: - green is raw data - teal is stored, tokenized data on disk - blue is a plain processing step - yellow is the model or a training step - orange is the reinforcement learning and reward parts - red is a loss - grey is a saved checkpoint - purple is the final output or evaluation ## Prerequisites and Training Time You need a basic understanding of object oriented programming, neural networks, and PyTorch. Below are so ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/fareedkhan-dev-train-llm-from-scratch`](/api/graphcanon/tools/fareedkhan-dev-train-llm-from-scratch) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_