GoKawiilOpenArch
Python implementations of modern open-source LLM architectures — written from scratch, one model at a time.
This repository contains hand-written PyTorch implementations of the model architectures cataloged in Sebastian Raschka's LLM Architecture Gallery. Each model is implemented to the best of my knowledge from the original papers, technical reports, reference config.json files, and the excellent writeups by Sebastian Raschka and Machine Learning Mastery.
The goal is not to compete with transformers or other production libraries. The goal is clarity and learning: a single readable file per architecture, with the structural choices (attention type, normalization, layer mix, MoE routing, positional encoding) made explicit and easy to compare side-by-side.
Why this repo?
Modern LLM architectures share a common skeleton but differ in dozens of small, important choices:
Attention: MHA, GQA, MQA, MLA, sliding-window, linear/DeltaNet hybrids
Normalization: pre-norm, post-norm, QK-Norm, sandwich norm, RMSNorm
Positional encodings: RoPE, NoPE, partial RoPE, YaRN
Decoder type: dense vs sparse MoE (with or without shared experts), hybrid Mamba/attention
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