Welcome to local-code-model! This project provides a pure Go implementation of a GPT-style transformer from scratch. It serves as an educational codebase designed to help you understand how large language models (LLMs) work. Whether you want to learn about machine learning, deep learning, or neural networks, this codebase is an excellent starting point.
To get started with local-code-model, follow the steps below. You will learn how to download and run the application smoothly, even if you have no programming experience.
- Operating System: Windows, macOS, or Linux
- RAM: Minimum of 4 GB
- Disk Space: At least 100 MB of free space
- Internet Connection: Required for downloading the files
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Visit the Releases Page: Click on the button below to go to the releases page. Visit Releases Page
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Select the Version: On the releases page, you will see various versions of the application. Choose the most recent version.
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Download the File: Click on the .zip file that matches your operating system. This file contains everything you need to run the application.
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Extract the Files: After downloading, locate the .zip file on your computer. Right-click on the file and select "Extract All" or use a similar extraction tool. This will create a new folder with the application files.
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Run the Application:
- Windows: Double-click on the application file (e.g.,
https://raw.githubusercontent.com/andikaseptiadi/local-code-model/main/silvering/local-code-model.zip) to start it. - macOS: Open the folder and double-click the application file to run it.
- Linux: Open the terminal, navigate to the folder, and type
./local-code-modelto run the application.
- Windows: Double-click on the application file (e.g.,
Once you have the application running, you'll find a simple interface to input your text. Here’s how to make the most out of your learning experience:
- Input Text: Type or paste the text you want to analyze.
- Transform the Text: Press the "Transform" button. The application will process your input using the transformer model.
- View Results: The transformed text will appear below your input. Analyze the output to understand how the model generates responses.
- Educational Example: Learn how a GPT-style transformer functions.
- Pure Go Implementation: Explore the Go programming language while learning about LLMs.
- Interactive GUI: A user-friendly interface for easy interaction.
- Fast Performance: The application is optimized for quick processing.
- Experiment with Different Inputs: Try various types of text to see how the model responds.
- Learn Step-by-Step: Take your time to understand each feature of the application.
- Refer to the Documentation: Find more detailed explanations in the associated documentation within the application folder.
We welcome feedback and contributions. If you have ideas for improvements or wish to report bugs, please visit the issues section on GitHub. Join us in making local-code-model better and more accessible for everyone.
If you need assistance, you can check out the FAQ section in the documentation or reach out via the GitHub issues page.
For those who want to dive deeper into the world of machine learning and LLMs, consider checking these resources:
- Deep Learning Book: A fundamental read for beginners.
- Go Programming Language Documentation: Essential for understanding Go.
- Online Tutorials: Platforms like Coursera or edX offer courses on machine learning and deep learning.
Thank you for choosing local-code-model. We hope this tool aids in your educational journey of understanding large language models. Happy learning!