Description
llm-architecture-from-scratch is a public GitHub repository created by Yeonwoo Sung, dedicated to the implementation of large language model (LLM) architectures using PyTorch. This repository serves as a valuable resource for developers and researchers who are interested in constructing LLMs from the ground up. The repository includes a variety of models such as deepseekv3, gemma3, gpt2-xl, llama-3-2-3b, llama3-8b, llama4_maverick, mistral_3_1, olmo2, phi4, qwen3_235b_a22b, qwen3_32b, and xlstm. Each model is accompanied by detailed commit messages that provide insights into the development process, such as feature implementations, refactoring, and documentation updates.
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The repository is actively maintained, with the latest commit made in October 2026, ensuring that the content remains up-to-date with the latest advancements in LLM architectures. Users can explore the repository to understand the intricacies of LLM design and gain hands-on experience by experimenting with the provided code.
This project is particularly beneficial for AI researchers, machine learning engineers, and developers who aim to deepen their understanding of LLMs and contribute to the field of natural language processing. By offering a diverse set of models and comprehensive documentation, llm-architecture-from-scratch empowers users to build, test, and optimize their own LLMs, fostering innovation and collaboration within the AI community.
- CategoryAI Models & LLMs
- Pricing ModelOpen source
- DeploymentCloud, Self-hosted
- PlatformsWeb
LLM architecture from scratch's Core Features
Implementation of various LLM architectures
Models include deepseekv3, gemma3, gpt2-xl, and more
Developed using PyTorch
Detailed commit messages for each model
Active maintenance with recent updates
Comprehensive documentation
Resource for AI researchers and developers
Facilitates experimentation with LLMs
Getting Started with LLM architecture from scratch
Clone: Download the repository from GitHub
Install dependencies: Set up the required Python packages
Configure: Adjust model parameters as needed
Execute: Run the models to test their functionality
Optimize: Experiment with different configurations to improve performance
LLM architecture from scratch's Use Cases
- Model Development
- Research
- Education
- Collaboration
- Optimization








