Skip to content

goncalopimentaa/train-400-context-3

Folders and files

NameName
Last commit message
Last commit date

Latest commit

Β 

History

15 Commits
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 
Β 

Repository files navigation

🌟 train-400-context-3 - Learn How Language Models Work

πŸš€ Getting Started

Welcome to the train-400-context-3 project! This application demonstrates, at a very small scale, how a language model is trained. Specifically, it focuses on a three-context model with an explicit transform stage. Whether you're curious about machine learning or looking for a way to understand language models better, you're in the right place.

πŸ“₯ Download Now

Download Latest Release

πŸ“‹ Features

  • Educational Insight: Learn the basics of how language models work.
  • Interactive Experience: Engage with a simple, easy-to-follow model.
  • Proven Concepts: Understand core ideas like next-token prediction and softmax regression.
  • Reproducibility: Follow along with steps to replicate the training process.

πŸ’» System Requirements

Before you start, make sure your system meets the following requirements:

  • Operating System: Windows, macOS, or Linux.
  • Python Version: Python 3.6 or higher.
  • Memory: At least 4 GB of RAM.
  • Storage: Minimum of 200 MB available space.

πŸ’‘ How to Use

  1. Visit the Release Page: Go to the Releases page.
  2. Select the Latest Release: Look for the most recent version. Currently, that is version 1.0.
  3. Download: Click on the download link for your operating system.
  4. Install: Follow the standard installation process for your system.
  5. Run the App: Once installed, launch the application to start exploring language models.

πŸ” Understanding the Model

This project uses a three-context model, meaning it takes three previous words to predict the next one. This setup allows the model to provide more relevant predictions based on context. Here’s a quick overview of how it works:

  • Input: The model takes in three words.
  • Processing: Using the softmax regression method, it calculates the probability of possible next words.
  • Output: The model predicts the most likely next word based on the provided input.

πŸ“– Learning Resources

To help you get the most from this project, consider these learning materials:

  • Documentation: Read our online documentation for in-depth explanations of concepts and features.
  • Videos: Check out video tutorials that walk you through setup and usage.
  • Community Forums: Join discussions and ask questions in community forums related to language models and machine learning.

🌐 Community & Support

Feel free to reach out if you have questions or need support:

  • GitHub Issues: Log any issues or feedback directly on the GitHub repository.
  • Community: Participate in discussions with other users and contributors.

πŸ“¦ Download & Install

To get started, you can download the software directly from our Releases page.

  1. Click the link to navigate to the page.
  2. Choose the version that matches your operating system.
  3. Download the file and follow the installation instructions provided.

πŸ”§ Troubleshooting

In case you experience issues while downloading or running the application, here are some common solutions:

  • Check System Requirements: Ensure your system meets the necessary specifications.
  • Reinstall the Software: Sometimes, a fresh installation resolves many problems.
  • Consult the Community: If you’re still having trouble, ask for help in the community forums.

πŸ“ˆ Next Steps

After you have successfully installed and run the application, consider diving deeper into the following:

  • Try modifying the input to see how the predictions change.
  • Experiment with the model settings for advanced insights.
  • Share your results and experiences with others in the community.

Thank you for your interest in the train-400-context-3 project. Enjoy exploring the world of language models!

Releases

No releases published

Packages

 
 
 

Contributors

Languages