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Train a 26M-parameter GPT from scratch in just 2 hours using optimized techniques. This project provides the code and guidance for quickly training small language models, ideal for educational purposes or resource-constrained environments.
This project, 'minimind', enables users to train a small, 26-million parameter Generative Pre-trained Transformer (GPT) model from the ground up in approximately 2 hours. It focuses on providing an efficient and understandable training pipeline.
Training large language models is computationally expensive and time-consuming. This project addresses the need for a fast, accessible way to train a functional language model from scratch, making it feasible for learning and experimentation.
Highly optimized training script designed for speed on readily available hardware.
Implementation of a compact 26M-parameter GPT model architecture.
Clear and well-documented code suitable for understanding the training process.
The project's fast training capability and small model size make it suitable for various applications, including:
Use the codebase to understand the training loop, gradient descent, and other core concepts of transformer training without requiring extensive compute time.
Demystifies the transformer training process and makes hands-on learning accessible.
Quickly train a small language model on domain-specific text data for prototyping or tasks where a large model is overkill or impractical.
Enables rapid iteration on model architectures or training methodologies for smaller datasets.
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