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https://github.com/bbycroft/llm-viz
3D Visualization of an GPT-style LLM
https://github.com/bbycroft/llm-viz
Last synced: 10 days ago
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3D Visualization of an GPT-style LLM
- Host: GitHub
- URL: https://github.com/bbycroft/llm-viz
- Owner: bbycroft
- Created: 2023-03-01T08:24:43.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-24T23:41:32.000Z (3 months ago)
- Last Synced: 2024-08-25T00:34:32.960Z (3 months ago)
- Language: TypeScript
- Homepage: https://bbycroft.net
- Size: 5.74 MB
- Stars: 3,638
- Watchers: 31
- Forks: 402
- Open Issues: 11
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Metadata Files:
- Readme: README.md
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README
# 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`