OpenArch – PyTorch implementations of modern LLM architectures

8.1 relevance

PyTorch implementations of modern LLM architectures, highly actionable and relevant to AI/ML.

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Why It Matters

For developers building or fine-tuning custom LLMs, this repo provides a clear, auditable reference for exactly how models like Llama 3 or DeepSeek R1 are structured, enabling more informed architectural decisions and easier experimentation without needing to untangle production code.

Key Takeaways

Clone OpenArch to study the architectural differences between modern LLMs in isolated, readable PyTorch files for faster understanding and prototyping.

PyTorch implementations of modern open-source LLM architectures (Llama, Qwen, DeepSeek, Gemma, GPT-OSS, Kimi, and more) — written from scratch for readability and learning, based on Sebastian Rasch...

Summary

OpenArch is a GitHub repository offering single-file, from-scratch PyTorch implementations of over a dozen modern LLM architectures from the LLM Architecture Gallery, including Llama 3, DeepSeek R1, and Gemma 3. Each implementation prioritizes readability over performance, making explicit architectural choices like attention type (MHA, GQA, MLA), normalization (RMSNorm, QK-Norm), and MoE routing. The project is actively seeking contributors to expand its coverage of 72 target architectures, with a focus on learning and side-by-side comparison rather than production use.

Author

anuj0456