Repository of LLMs and Language Resources

This repository serves as a curated list of available large language models (LLMs) and datasets for various languages, including resources for both high-resource and low-resource languages.

Language Models

Language	Link
Universal	models/llms
Slovak	models/slovak
Czech	models/czech

Language Resources

Language	Link
Slovak	datasets/slovak
Czech	datasets/czech

Contribution Guidelines

We encourage contributions to enhance this repository, whether by adding new languages, providing additional resources, or updating existing ones. Follow the guidelines below to contribute effectively:

Adding a New Language

1. Navigate to the models or datasets folder to locate the templates:

   • Language Model Template: models/template.md
   • Dataset Template: datasets/template.md

2. Create a new .md file for the language using the relevant template:

   • For language models: Save the file under models/<language>.md.
   • For datasets: Save the file under datasets/<language>.md.

3. Populate the template with relevant details, such as:

   • For datasets: Include resources like language corpora, datasets for tasks such as question answering, sentiment analysis, machine translation, etc.
   • For models: Add information about multilingual models (supporting the target language) or language-specific models and embeddings.

4. Update this README.md file:

   • Add the new language to the appropriate table under Language Models or Language Resources.

Updating Existing Resources

1. Locate the .md file for the language you want to modify:

   • Language models: models/<language>.md
   • Datasets: datasets/<language>.md

2. Edit the file to include the new resources, updates, or corrections.

3. Ensure all additions are formatted consistently and adhere to the template structure.

Submitting Your Contribution

1. Fork this repository and create a new branch for your changes.

2. Submit a pull request (PR) with a concise description of your contributions.

Additional Notes

• This repository focuses on providing comprehensive resources, particularly for low-resource languages. Contributions that help expand these resources are especially valuable.
• Ensure any added links are publicly accessible and reliable.