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Minimind: Train a 26M-Parameter GPT from Scratch in 2 Hours
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.
Python
Added on 2025年6月27日22,409
Stars2,650
ForksPython
LanguageProject Introduction
Summary
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.
Problem Solved
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.
Core Features
Fast Training (2 hours)
Highly optimized training script designed for speed on readily available hardware.
Small Model Size (26M)
Implementation of a compact 26M-parameter GPT model architecture.
Educational Focus
Clear and well-documented code suitable for understanding the training process.
Tech Stack
Python
PyTorch
Transformers (potentially)
CUDA (for GPU acceleration)
使用场景
The project's fast training capability and small model size make it suitable for various applications, including:
Scenario 1: Educational Tool for ML Training
Details
Use the codebase to understand the training loop, gradient descent, and other core concepts of transformer training without requiring extensive compute time.
User Value
Demystifies the transformer training process and makes hands-on learning accessible.
Scenario 2: Prototyping and Experimentation
Details
Quickly train a small language model on domain-specific text data for prototyping or tasks where a large model is overkill or impractical.
User Value
Enables rapid iteration on model architectures or training methodologies for smaller datasets.
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