InstructLab

Community Announcement (Sept 2, 2025)

Over the past year, we’ve been honored by your creativity, insights, and shared passion for advancing generative AI through InstructLab. Whether you added a new “knowledge” via pull request, offered feedback, joined a community call, or helped translate documentation, you’ve shaped our project in meaningful ways. Thank you.

To better align with evolving technical needs, we’re announcing an evolution for the InstructLab community. We will be refactoring the project by separating the components out to improve its maintainability and usability, primarily as a framework SDK for model tuning.

What's Changing

To enhance the long-term viability and efficiency of the InstructLab project, a strategic decision has been made to relocate its foundational building blocks into separate, dedicated project repositories. This carefully considered shift is anticipated to yield substantial benefits, primarily in the areas of maintainability and independent component maturation. This independent development will foster greater agility, allowing for more focused improvements and faster iteration cycles for individual parts of the project.

Looking Ahead

We’re excited about this next chapter and believe it will lead to more robust, flexible, and powerful tools for the generative AI community. We encourage you to follow the individual component projects in their new homes and continue contributing to their growth.

SDG: https://github.com/Red-Hat-AI-Innovation-Team/sdg_hub

Training: https://github.com/Red-Hat-AI-Innovation-Team/training_hub

Project Name: InstructLab

Description: InstructLab (based on the Large-scale Alignment for ChatBots technique) is an innovative open-source initiative led by Red Hat and IBM. The project aims to enhance the capabilities of Large Language Models (LLMs) through a community-driven approach that leverages a novel taxonomy-based curation process and synthetic data generation. InstructLab provides tools for users to engage with and improve LLMs, contributing skills and knowledge to the project’s taxonomy repository.

Key Features:

ilab Command-Line Interface (CLI): Allows users to interact with, train, and fine-tune LLMs using custom taxonomy data. The CLI supports various platforms including macOS, Fedora Linux, and Windows.
Synthetic Data Generation: Enhances LLM training through the creation of synthetic datasets.
Taxonomy Repository: A structured repository where users can submit and manage their contributions of skills and knowledge.

Community and Contribution:

InstructLab welcomes contributions from the open-source community. Users can submit pull requests to the taxonomy repository, participate in discussions, and contribute to ongoing development.
The project maintains a comprehensive guide for contributors, outlining best practices and governance.

Repository Links:

Contact and Support:

Join the InstructLab community on Slack for support and collaboration.
Refer to the documentation for detailed guides and troubleshooting tips.

Licenses:

InstructLab is released under the Apache-2.0 license.

For more details and to get involved, visit the InstructLab GitHub page.