xTuring

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xTuring

Create and Customize AI Models Easily with xTuring

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About xTuring

https://xturing.stochastic.ai/overview/ The xTuring overview page provides a comprehensive introduction to xTuring, an open-source AI personalization library. Designed for simplicity and efficiency, xTuring helps users deploy personalized AI models effortlessly, making advanced AI tools accessible to a broader audience. The page is structured to guide users from installation to practical use, ensuring they can start customizing AI models quickly.

Key Features

  • 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

Tags

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

FAQs

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.