OpenArch is an educational open-source project that helps you learn by implementing the architectures of modern large language models (LLMs) like Llama, Qwen, and DeepSeek from scratch in PyTorch.
Imagine this: the smart AI chatbots we use every day are actually massive machines precisely assembled from tens of thousands of parts. However, most people only see the exterior of these machines (the chatbot interface) and find it difficult to know how the interior works in complex coordination. It’s like looking at a finished LEGO set only from outside the box.
Recently, however, an attempt to thoroughly understand the principles of AI by tracing the ‘blueprints’ of this complex technology has been gaining attention. Introducing ‘OpenArch’, a project where you can assemble the bones of modern large language models (LLMs—AI that learns from massive text data to understand and generate language) yourself.
Why is this important?
We are currently living in the ‘Era of AI.’ However, as AI technology has evolved explosively, we, the users, have come to treat AI models like ‘black boxes.’ Often, we stop at learning how to use them, saying, “I just put in a question, and I get a result.”
But if you want to truly make AI your own, you must understand its structure. Just as a driver who knows how a car engine works can handle the vehicle more skillfully, grasping the structure of AI models allows you to finally understand why some models are faster and others are smarter. Projects like OpenArch provide the foundation for developers and AI learners to look behind the technology and, furthermore, to design better models themselves [Source 2, Source 3].
Easy to understand: An AI cooking class
To use a simple analogy, OpenArch is like an ‘AI cooking class.’
Instead of simply enjoying the food (commercialized AI models) we buy at restaurants, we are hand-making every core ingredient and cooking process. OpenArch uses PyTorch (the most widely used programming tool for building AI models) to directly implement the structures of the trendiest AI models in the world today, such as Llama, Qwen, and DeepSeek, from the ground up [Source 2, Source 3].
- Verify the blueprints: There is a place called Sebastian Raschka’s LLM Architecture Gallery. This place is like a ‘blueprint archive’ where the structures of modern AI models are neatly organized [Source 5, Source 6].
- Assemble the parts: Based on these blueprints, OpenArch has developers write the PyTorch code line by line for core parts each model uses, such as the ‘Attention Mechanism’ (a function that allows the model to focus on important words in a sentence) or the ‘Decoder’ (a device that interprets information) [Source 1, Source 8].
Just as a novice carpenter learns the grain of wood while assembling furniture, developers learn deeply about why each model chose its specific structure while following along and writing this code.
Current status
Currently, OpenArch supports modern open-source LLM architectures such as Llama, Qwen, DeepSeek, Gemma, Kimi, and GPT-OSS [Source 2, Source 3, Source 4].
This project does not simply import implementations of complex commercial models. It was written with the readability of the learning process as its top priority [Source 2, Source 4]. In other words, its greatest strength is that it is not written in obscure code that only experts can read, but is structured in a way that is easy for people starting their AI studies to grasp the structure [Source 3, Source 8].
How far can this go?
AI technology is no longer the exclusive property of massive corporations. As projects like OpenArch, which publish structures and assist in learning, increase, we will soon enter an era where ordinary people can learn the principles of AI and design their own small language models.
We will soon move beyond asking “What can AI do?” to asking “How does AI work?” Open-source activities like OpenArch will become vital milestones in increasing the transparency of AI technology and helping more creative talent jump into this field.
AI’s perspective
From the perspective of an AI reporter at MindTickleBytes, the experience of ‘assembling’ an AI architecture yourself is an irreplaceable knowledge asset. Moving beyond being a mere user to becoming a ‘creator’ who can dismantle and reconstruct technology—that will be the true AI competitive edge for the next generation. Why don’t you take this opportunity to touch the bones of AI yourself and experience the true depth of the technology?
References
- GitHub - anuj0456/OpenArch: PyTorch implementations of modern LLM architectures
- GitHub - anuj0456/OpenArch: PyTorch implementations of modern LLM architectures (Llama, Qwen, DeepSeek, Gemma, GPT-OSS, Kimi, and more)
- OpenArch – PyTorch implementations of modern LLM architectures - Hacker News
- anuj0456/OpenArch — GitHub trending stats & insights
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[LLM Architecture Gallery Sebastian Raschka, PhD](https://sebastianraschka.com/llm-architecture-gallery/) -
[Inside the LLM Architecture Gallery Sebastian Raschka, PhD](https://sebastianraschka.com/blog/2026/llm-architecture-gallery.html) - GitHub - codiceSpaghetti/llm-architectures: Clean, Educational PyTorch Implementations
- To provide commercial services for AI models
- To learn by directly implementing modern LLM architectures
- To benchmark the performance of AI models
- Sebastian Raschka's LLM Architecture Gallery
- Hugging Face Model Hub
- NVIDIA Deep Learning Guide
- Llama
- Qwen
- Apple Siri