GitHub - karpathy/llm.c: LLM training in simple, raw C/CUDA

The article discusses a method for training Language Learning Models (LLMs) in pure C/CUDA, eliminating the need for large dependencies like PyTorch or cPython. The author uses GPT-2 as an example, stating that it can be trained using around 1,000 lines of clean code in a single file. The author is currently working on a direct CUDA implementation to speed up the process, optimizing the CPU version with SIMD instructions, and exploring more modern architectures. The goal is to maintain simple reference implementations alongside optimized versions that can compete with PyTorch but with less code and dependencies.

The article also provides a quick start guide on how to download and tokenize a dataset, initialize with GPT-2 weights released by OpenAI, and train in raw C. The author also provides a sample output from a MacBook Pro and explains how to decode the token ids back to text. The author concludes by mentioning a simple unit test to ensure the C code agrees with the PyTorch code and states that the license for this project is MIT.

Key takeaways:

The author is developing a Language Learning Model (LLM) training in pure C/CUDA, eliminating the need for large dependencies like PyTorch or cPython.
The project aims to provide clean, simple reference implementations and optimized versions that can match the performance of PyTorch but with less code and dependencies.
The author is currently working on a direct CUDA implementation for faster performance, speeding up the CPU version with SIMD instructions, and implementing more modern architectures.
The author provides a detailed guide on how to download and tokenize a dataset, initialize with GPT-2 weights, compile and run the code, and decode the token ids back to text.

GitHub - karpathy/llm.c: LLM training in simple, raw C/CUDA

Key takeaways:

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