Transformers

AirLLM is an open-source inference library that aims to run very large language models on small GPUs by decomposing models layer-wise and streaming them during execution. The snapshot shows a detailed README with quickstart code, model examples, supported model notes, and recent update history.

A two-part self-study course for building a tiny LLM stack from scalar autograd to a working chat assistant, with modules covering transformers, pretraining, instruction tuning, retrieval, tool use, and agent loops.

A tiny GPT implementation in pure Go, trained on Jules Verne books, with runnable code and notes that explain the model’s evolution and design choices.

A hands-on workshop repository that walks developers through building a small GPT-style language model from scratch, including tokenization, transformer blocks, training, and text generation.

An educational GitHub repository about Non-Stochastic AI (NSAI) that frames deterministic AI architecture, masking mechanics, and runtime tooling with runnable playground demos.

An educational project that explains and demonstrates a homemade language processing unit inspired by Groq’s LPU, with a focus on running a small Transformer model and visualizing the idea behind AI hardware.

Open-source tool for removing refusal behavior from open-weight transformer language models by identifying and orthogonalizing refusal directions in activation space.