nanochat - Karpathy's free and open source low-cost model training program

What is nanochat?

nanochat is an open source project released by Andrej Karpathy, a legend in AI and former Tesla AI director, that enables individuals to quickly train a small ChatGPT-like language model at a very low cost and simplicity. The entire project uses only about 8,000 lines of code and implements the entire process from data preparation, pre-training, mid-training (dialogs, multiple-choice questions, tool use), supervised fine-tuning (SFT), reinforcement learning fine-tuning (RL) to inference deployment. Users can train a small ChatGPT model capable of basic dialog, creating story poems, and answering simple questions in as little as 4 hours by simply booting up a GPU machine and running a script, with the entire process costing as little as ~$100.

Features of nanochat

• Low cost and high efficiency: For a cost of only about $100, a small ChatGPT-like language model can be trained in 4 hours on a GPU server.
• Minimalist Code Architecture: The whole project is only about 8000 lines of code, with a clear structure, very few dependencies, easy to understand and modify, suitable for learning and research.
• Full process coverage: Covering the entire process from data preparation, pre-training, mid-training, supervised fine-tuning, reinforcement learning fine-tuning, to inference deployment, with complete functionality.
• efficient lexer: A lexer implemented using the Rust language is fast and efficient to train, and can better adapt to the model training needs.
• Flexible training process: Supporting multiple training phases and datasets, users can adjust the training process to optimize model performance according to their needs.
• WebUI Interactive Interface: Provide ChatGPT-like web interface, users can interact with the model through the WebUI, which is easy to use and test.
• Highly scalable: The code structure is well-designed, easy to extend and improve, and users can further develop and optimize based on it.
• Community Friendly: The project is open source and has an active community where users can access a wealth of resources and support to promote the project together.

Core benefits of nanochat

• Low cost and high efficiency: For a cost of about $100 and 4 hours of training time, a small ChatGPT-like language model can be quickly built on a single GPU server, significantly lowering the threshold for training large language models.
• Minimalist Code Architecture: The project is only about 8000 lines of code, clear structure, minimal dependencies, easy to understand and modify, suitable for learning and research, but also convenient for developers to carry out secondary development and optimization.
• Full process coverage: Complete realization of the whole process from data preparation, pre-training, mid-term training, supervised fine-tuning, reinforcement learning fine-tuning to inference deployment, providing users with a one-stop model development experience.
• efficient lexer: The classifier implemented in Rust language is fast and efficient in training, which can better adapt to the model training needs and improve the overall training efficiency.
• Flexible training process: Supporting a variety of training phases and datasets, users can adjust the training process according to their needs, optimize the model performance, and adapt to different application scenarios.
• WebUI Interactive Interface: Provide ChatGPT-like web interface, users can interact with the model through WebUI, which is easy to use and test, and lowers the threshold of use.
• Highly scalable: The code structure is well-designed, easy to extend and improve, and users can further develop and optimize on this basis to explore more possibilities.

What is nanochat's official website

• Github repository:: https://github.com/karpathy/nanochat

Who nanochat is for

• Individual learners: Individuals looking for a low-cost, quick introduction to large language model training and development can use nanochat to build and optimize their own small language models in a short period of time.
• technology enthusiast: Tech enthusiasts who are interested in AI and big language modeling and want to dive deeper into how it works and the training process with hands-on practice, nanochat provides clear code and a complete process.
• developers: Developers who want to integrate or develop ChatGPT-like features in existing projects, nanochat's minimalist architecture and flexible code facilitate secondary development and extensions.
• educator: Educators who need teaching tools to help their students understand large language models, nanochat's low barrier to entry and clear structure make it an ideal educational program.
• researcher: Researchers working on natural language processing or machine learning research, nanochat can be used as a research baseline or experimental platform to help them explore new model architectures and training methods.
• Corporate Team: Enterprise teams looking to quickly build an internal language model to meet specific business needs, nanochat's efficiency and flexibility allows for a quick response to the organization's needs.