base on 3D Visualization of an GPT-style LLM # Brendan Bycroft's Home Page & Projects This repository contains my (Brendan's) homepage, as well as a number of non-trivial projects. They are kept in a single repository for ease of deployment, as well as sharing a bunch of js utils which are otherwise a pain to share around. ## Projects The main projects are: * LLM Visualization: 3D interactive model of a GPT-style LLM network running inference. * [WIP] CPU Simulation: A 2D digital schematic editor with full a execution model, showcasing a simple RISC-V based CPU ### LLM Visualization This project displays a 3D model of a working implementation of a GPT-style network. That is, the network topology that's used in OpenAI's GPT-2, GPT-3, (and maybe GPT-4). The first network displayed with working weights is a tiny such network, which sorts a small list of the letters A, B, and C. This is the demo example model from Andrej Karpathy's [minGPT](https://github.com/karpathy/minGPT) implementation. The renderer also supports visualizing arbitrary sized networks, and works with the smaller gpt2 size, although the weights aren't downloaded (it's 100's of MBs). ### CPU Simulation (WIP; not exposed yet!) This project runs 2D schematic digital circuits, with a fully fledged editor. The intent is to add a number of walkthroughs, showing things such as: * how a simple RISC-V CPU is constructed * the constituent parts down to gate level: instruction decode, ALU, add, etc * higher level CPU ideas, like various levels of pipelining, caching, etc ## Running Locally 1. Install dependencies: `yarn` 1. Start the dev server: `yarn dev` ", Assign "at most 3 tags" to the expected json: {"id":"5650","tags":[]} "only from the tags list I provide: []" returns me the "expected json"