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xTuring

Natural Language ProcessingFree

Create and Customize AI Models Easily with xTuring

Last updated Apr 28, 2026

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What is xTuring?

xTuring is an open-source AI personalization library designed to help users create and deploy customized AI models, known as Large Language Models (LLMs). It offers an easy-to-use interface, making it accessible for both beginners and experienced developers. The library supports various memory-efficient fine-tuning techniques, including Low-Rank Adaption (LoRA), INT8, and INT4 precisions. With xTuring, users can tailor AI models to fit their specific data and application needs, ensuring high efficiency and adaptability.

xTuring's Top Features

Key capabilities that make xTuring stand out.

Open-source

Easy-to-use interface

Supports LoRA, INT8, INT4 precisions

Efficient compute and memory usage

Customizable AI models

Supports a wide range of LLMs

Community support through Discord and Twitter

Detailed documentation and quick start guides

Editable installation for contributions

Licensed under Apache 2.0

Use Cases

Who benefits most from this tool.

AI Researchers

Fine-tune Large Language Models for specific research purposes.

Data Scientists

Use existing datasets for AI model training and fine-tuning to derive insights.

Software Developers

Integrate customized AI models into applications for enhanced functionality.

AI Enthusiasts

Experiment with personal AI projects by customizing various models.

Educators

Teach students about AI model customization and fine-tuning techniques.

Businesses

Develop AI solutions tailored to business-specific needs and data.

Startup Founders

Prototype AI-driven features quickly and efficiently.

Contributors

Enhance the xTuring library by adding new models and features.

Tech Communities

Build collaborative AI projects and share knowledge within the community.

AI Service Providers

Offer personalized AI solutions to clients using customized models.

Explore Top AI Use Cases

Tags

open-sourceAIpersonalizationlibraryLarge Language ModelsLLMsbeginner-friendlydeveloper-friendlymemory-efficientfine-tuningLoRAINT8INT4model customizationhigh efficiencyadaptability

xTuring's Pricing

Free plan available

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Frequently Asked Questions

How do I fine-tune a Large Language Model (LLM) with xTuring?
Refer to our tutorial on fine-tuning any model of your choice. The list of supported models can be found on our website.
What if the model I want to use isn't in the supported models list?
You can follow our guide on how to load any other model of your choice that is not listed in the supported models.
Can I use an existing dataset for instruction fine-tuning?
Yes, you can. We have a tutorial that demonstrates how to load the Alpaca Dataset and prepare it for instruction fine-tuning.
How do I set up xTuring to start contributing?
To contribute to xTuring, you need to do an editable install of the source code on your machine. The setup steps are available on our website.
What fine-tuning techniques does xTuring support?
xTuring supports LoRA, 8-bit precision, LoRA with 8-bit precision, and LoRA with 4-bit precision techniques for memory-efficient fine-tuning.
How do I install xTuring via pip?
Ensure you have Python 3.0+, create a virtual environment, activate it, and run 'pip install xTuring' to install the library.
Can I install xTuring using Conda?
Yes, create a Conda environment with 'conda create -n venv', activate it, and run 'pip install xTuring' to install the library.
How can I test if xTuring is installed properly?
Run the following commands in the terminal: 'python', 'from xturing.models import BaseModel', 'model = BaseModel.create("opt")', 'outputs = model.generate(texts=["Hi How are you?"])', and then print the outputs to see the generated text.
What should I do if I encounter issues during installation?
Refer to the installation guide on our website, ensuring you meet all prerequisites such as Python 3.0+ and virtual environment setup.
How can I get support or join the xTuring community?
Join our Discord community and follow us on Twitter @stochasticai for support and updates.