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GitHub - tanishqkumar/beyond-nanogpt: Minimal and annotated implementations of key ideas from modern deep learning research.

Jun 01, 2025 - github.com
**Beyond-NanoGPT** is an educational repository designed to bridge the gap between nanoGPT and research-level deep learning. It offers annotated, from-scratch implementations of nearly 100 modern deep learning techniques, enabling newcomers to conduct their own experiments. The repository covers a wide range of topics, including KV caching, speculative decoding for LLMs, vision transformers, MLP-mixers, various attention mechanisms, generative models like denoising diffusion models, and reinforcement learning algorithms such as PPO, A3C, and AlphaZero. The code is meticulously documented to explain subtle details often overlooked in papers and production codebases.

The repository provides a quickstart guide for cloning the repo, installing minimal dependencies, and running Python scripts on a single GPU. It includes implementations across various domains such as architectures, attention variants, language models, reinforcement learning, generative models, and machine learning systems. The codebase is designed to be educational, encouraging users to read, modify, and re-implement the code. Users are invited to contribute, provide feedback, and request new features, with the author committing to implementing new techniques based on community interest.

Key takeaways:

  • Beyond-NanoGPT is a comprehensive educational repository designed to bridge the gap between beginner-level understanding and research-level expertise in deep learning.
  • The repository includes annotated, from-scratch implementations of nearly 100 modern techniques in deep learning, covering areas such as architectures, attention variants, language models, reinforcement learning, and generative models.
  • It provides a hands-on learning experience with code that is self-documenting, allowing users to read, modify, and re-implement the techniques to deepen their understanding.
  • The codebase is designed to run on a single GPU, with detailed comments explaining subtle details often overlooked in papers and production codebases.
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