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Datasets and models for instruction-tuning


txtinstruct is a framework for training instruction-tuned models.

architecture

The objective of this project is to support open data, open models and integration with your own data. One of the biggest problems today is the lack of licensing clarity with instruction-following datasets and large language models. txtinstruct makes it easy to build your own instruction-following datasets and use those datasets to train instructed-tuned models.

txtinstruct is built with Python 3.7+ and txtai.

Installation

The easiest way to install is via pip and PyPI

pip install txtinstruct

You can also install txtinstruct directly from GitHub. Using a Python Virtual Environment is recommended.

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

Python 3.7+ is supported

See this link to help resolve environment-specific install issues.

Examples

The following example notebooks show how to build models with txtinstruct.

Notebook Description
Introducing txtinstruct Build instruction-tuned datasets and models Open In Colab

Metadata

Release files for txtinstruct 0.1.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

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Source distribution for txtinstruct 0.1.0
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Table of built distributions (wheels) for txtinstruct 0.1.0
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txtinstruct-0.1.0-py3-none-any.whl Python 3 none any Details

Total release size: 20.8 kB

Release files / txtinstruct-0.1.0.tar.gz

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