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title: README
emoji: 🌖
colorFrom: gray
colorTo: yellow
sdk: static
pinned: false
---
Welcome to the **nanochat students** organization\! This is a community organization for students following Andrej Karpathy's [nanochat course](https://github.com/karpathy/nanochat). We are learning to build a full-stack LLM implementation from tokenization to web serving, all for under $100.
## What is nanochat?
nanochat is a complete implementation of an LLM like ChatGPT in a minimal, hackable codebase. It's designed as the capstone project for the LLM101n course by Eureka Labs, teaching you to build and train your own ChatGPT clone end-to-end.
## What You'll Find Here
This organization hosts community-contributed resources to help you learn and succeed with nanochat. You'll find:
- notebooks that break down the implementation.
- spaces that demo or illustrate the concepts we’re learning.
- trained models and checkpoints from the community
- relevant curated datasets.
## Getting Help and Sharing Ideas
The [Discussions](https://huggingface.co/spaces/nanochat-students/README/discussions) section is where you can ask questions, share your training results and report cards, discuss optimization techniques, and collaborate on experiments.
## Contributing
We welcome contributions from all students or experts. Here's how you can help: notebooks, demos, models, and articles:
- Join the org, we'll give you write access.
- If you make anything, share it in this discussion [thread](https://huggingface.co/spaces/nanochat-students/README/discussions/1)
- If you can, help answer questions in [discussions](https://huggingface.co/spaces/nanochat-students/README/discussions)
Let's make this a fun, supportive, and efficient community of learners.
## **Resources**
- nanochat repo - [karpathy/nanochat](https://github.com/karpathy/nanochat)
- Introduction post: ["Introducing nanochat: The best ChatGPT that $100 can buy"](https://github.com/karpathy/nanochat/discussions/1)
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## Journal!
Check out these steps to join in or get help:
### Day 1
Support on your Python environment using uv, create a virtual environment, and install all necessary dependencies for the nanochat project.
Train a custom BPE tokenizer using Rust bindings.
Base training across 8 GPUs using torchrun, with metrics tracked in a shared trackio space below.
