Beyond-NanoGPT: Concise and annotated implementations of key deep learning ideas.If you want to not just run pre-built models, but understand how they work "under the hood," the Beyond-NanoGPT repository is what you need. This project, created by a CS graduate student at Stanford University, serves as a bridge between simple examples like nanoGPT and complex implementations, offering dozens of implementations of modern deep learning methods.
Everything is written from scratch in PyTorch, with detailed comments – perfect for those who are tired of abstract papers and ruthless production code. Each line of code is written in a way that makes it clear how to use it in practice.
Stuck at the level of reading endless tutorials and want to move forward? This repository is a great step. It won't make you an expert in a week, but it will give you the tools to understand modern papers and start your own experiments. And yes, there's no fancy web interface or ready-made SaaS solutions here – just code, comments, and your curiosity. As it should be in research.
Getting started is very simple: clone the repository, install the dependencies, and you can start diving into the code. Architectures? There's a Vision Transformer for image classification, a Diffusion Transformer for generation, ResNet, and even an MLP-Mixer. Each script is a separate experiment.
For example, to train DiT on the CIFAR-10 dataset, you just need to run
train_dit.py
. Everything is designed for a single GPU, so you can practice even without access to powerful clusters. And if you want to understand the mechanisms of attention, separate notebooks will show you how Grouped-Query, linear, sparse, or cross-attention work – with visualizations and explanations.
The project isn't just about architectures; there are also practical techniques. Want to speed up the inference of a language model? Take a look at the implementation of KV-caching or speculative decoding – methods that are actively used in LLM infrastructure.
Interested in RL? The reinforcement learning section includes classics like DQN and PPO for Cartpole, and plans include a neural network for chess with MCTS. Moreover, the code not only works but also explains the nuances: why a baseline is important in REINFORCE, how to avoid gradient explosion in transformers, or what makes RoPE embeddings better than standard ones.
Some sections (Flash Attention, RLHF) are still under development. But the plans are ambitious: the author promises everything from weight quantization to distributed RL.
Licensing: MIT License.
GitHub
