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<img src="https://huggingface.co/datasets/nanochat-students/images/resolve/main/students.png" alt="nanochat students banner" style="width: 100%; height: 500px; object-fit: cover; object-position: center;">
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#
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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.
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##
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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.
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##
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This organization hosts community-contributed resources to help you learn and succeed with nanochat. You'll find:
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## Getting Help and Sharing Ideas
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4. **Join discussions**: Share your results and learn from others
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5. **Contribute back**: Share your own notebooks, models, or datasets\! -->
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##
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We welcome contributions from all students or experts. Here's how you can help:
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<img src="https://huggingface.co/datasets/nanochat-students/images/resolve/main/students.png" alt="nanochat students banner" style="width: 100%; height: 500px; object-fit: cover; object-position: center;">
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# nanochat students
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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.
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## What is nanochat?
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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.
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## What You'll Find Here
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This organization hosts community-contributed resources to help you learn and succeed with nanochat. You'll find:
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- notebooks that break down the implementation.
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- spaces that demo or illustrate the concepts we’re learning.
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- trained models and checkpoints from the community
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- relevant curated datasets.
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## Getting Help and Sharing Ideas
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4. **Join discussions**: Share your results and learn from others
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5. **Contribute back**: Share your own notebooks, models, or datasets\! -->
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## Contributing
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We welcome contributions from all students or experts. Here's how you can help:
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