txtinstruct: Building Instruction-Tuned Models with Custom Data
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.
Repository Info
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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.
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.
pip install txtinstruct
Alternatively, to install directly from the GitHub repository:
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.
Examples
To help you get started and understand how to build models with txtinstruct, the project provides illustrative example notebooks.
- Introducing txtinstruct - Learn how to build instruction-tuned datasets and models.
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
- Medium Article: Instruction-tune models using your own data with txtinstruct
- txtai Project: https://github.com/neuml/txtai