# txtinstruct: Building Instruction-Tuned Models with Custom Data

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txtinstruct is a Python framework designed for training instruction-tuned models. It focuses on supporting open data and models, enabling users to build their own instruction-following datasets and train models without licensing ambiguity. This project simplifies the process of creating custom instruction-tuned solutions.

GitHub: https://github.com/neuml/txtinstruct
OSRepos URL: https://osrepos.com/repo/neuml-txtinstruct

## Summary

txtinstruct is a Python framework designed for training instruction-tuned models. It focuses on supporting open data and models, enabling users to build their own instruction-following datasets and train models without licensing ambiguity. This project simplifies the process of creating custom instruction-tuned solutions.

## Topics

- Python
- Instruction Tuning
- LLM
- Datasets
- Machine Learning
- NLP
- AI
- Open Source

## Repository Information

Last analyzed by OSRepos: Sun Nov 23 2025 08:00:59 GMT+0000 (Western European Standard Time)
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## Content

## Introduction

txtinstruct is a powerful Python framework designed for training instruction-tuned models. Its core mission is to champion open data, open models, and seamless integration with your own proprietary data. A significant challenge in today's AI landscape is the lack of clear licensing for many instruction-following datasets and large language models. txtinstruct directly addresses this by providing an intuitive way to build your own instruction-following datasets and subsequently use them to train custom instruction-tuned models, thereby avoiding licensing ambiguities. The project is built with Python 3.8+ and leverages the capabilities of [txtai](https://github.com/neuml/txtai).

## Installation

Installing txtinstruct is straightforward, with options via pip and PyPI or directly from GitHub. Using a Python Virtual Environment is highly recommended for managing dependencies.

To install via pip:

bash
pip install txtinstruct


Alternatively, to install directly from the GitHub repository:

bash
pip install git+https://github.com/neuml/txtinstruct


txtinstruct supports Python 3.8 and newer versions. For assistance with environment-specific installation issues, refer to the [txtai installation guide](https://github.com/neuml/txtai#installation).

## Examples

To help you get started and understand how to build models with txtinstruct, the project provides illustrative example notebooks.

*   [Introducing txtinstruct](https://github.com/neuml/txtinstruct/blob/master/examples/01_Introducing_txtinstruct.ipynb) - Learn how to build instruction-tuned datasets and models.
    [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/neuml/txtinstruct/blob/master/examples/01_Introducing_txtinstruct.ipynb)

## Why Use txtinstruct

txtinstruct offers a compelling solution for developers and researchers working with instruction-tuned models. It empowers you to:

*   **Create Custom Datasets**: Easily build your own instruction-following datasets tailored to specific needs and domains.
*   **Train Specialized Models**: Utilize these custom datasets to train instruction-tuned models that are unique to your applications.
*   **Ensure Licensing Clarity**: Overcome the common problem of ambiguous licensing by owning the data and models you create.
*   **Leverage Open Source**: Benefit from an open-source framework built on Python and integrated with txtai, fostering transparency and community contributions.

## Links

Explore txtinstruct further through these official resources:

*   **GitHub Repository**: [https://github.com/neuml/txtinstruct](https://github.com/neuml/txtinstruct)
*   **Medium Article**: [Instruction-tune models using your own data with txtinstruct](https://medium.com/neuml/instruction-tune-models-using-your-own-data-with-txtinstruct-3008d8c8d025)
*   **txtai Project**: [https://github.com/neuml/txtai](https://github.com/neuml/txtai)