xTuring
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About
xTuring is an open-source software that empowers users to personalize Large Language Models (LLMs) to work exactly as desired. It provides a simple, user-friendly interface for customizing LLMs for specific data or applications, offering complete control over the AI personalization process. The platform is guided by three core principles: Simplicity and Productivity, ensuring AI tasks are easy for all users; Efficiency of Compute and Memory, optimizing resource usage for smooth project execution; and Agility and Customizability, allowing models to adapt to evolving AI needs. xTuring functions as a personal AI workshop, enabling users to improve existing AI models or create specialized AI tools. It supports various fine-tuning techniques, including LoRA, 8-bit, and 4-bit precision, optimized for low-resource environments. The team behind xTuring aims to make AI simple and accessible, fostering innovation and collaboration.
Platform
Task
Features
• simple and user-friendly interface
• agility and customizability for ai models
• efficient compute and memory utilization
• extensive model support (bloom, falcon, llama, gpt-j, etc.)
• supports 4-bit precision fine-tuning
• supports 8-bit precision fine-tuning
• supports lora fine-tuning
• open-source software for llm personalization
FAQs
How to fine-tune a LLM?
You can refer to our quickstart guide on fine-tuning for a tutorial on how to fine-tune any model of your choice. A list of all supported models is also available.
How to load a model not there in the supported models list?
If your desired model isn't in the supported list, a guide is available on how to easily load and use any other model of your choice.
How to use an existing dataset for instruction fine-tuning?
A tutorial demonstrates how to load the Alpaca Dataset and prepare it in the instruction fine-tuning format for your models.
How to setup xTuring to start contributing?
To contribute to xTuring, you need to perform an editable install of the source on your machine. The necessary steps are outlined in our contributing guide.
Which all fine-tuning memory-efficient techniques are supported by xTuring and how to use them with models not on the supported models' list?
xTuring supports Low-Rank Adaption (LoRA), 8-bit precision, LoRA with 8-bit precision, and LoRA with 4-bit precision. A guide explains how to apply these with any model.
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