Projects / Unsloth

Unsloth

Desktop & Productivity

A desktop/local UI and Python toolkit for running and training language and diffusion models, with component-specific licenses.

Use with conditions
Last updated2026-10-02
Open source licenseApache-2.0 Core; AGPL-3.0 optional Studio UI
PlatformsWindows · macOS · Linux x64 · Linux ARM64: documented Ubuntu package

About this project

Unsloth combines a model-running and training toolkit with desktop and Studio interfaces. The documented workflows include chat or inference, preparation of datasets, fine-tuning, and export or deployment of supported models. The Python Core path and the graphical interfaces are different ways to use the project, with different prerequisites.

The README links desktop packages for Windows, macOS and Linux, including x64 and an Ubuntu ARM64 option. It also documents uv and Python setup for the code-based path. Downloading a graphical app does not establish that a chosen training workload fits the device: model size, accelerator backend, memory, storage and dataset requirements still need to be checked.

The main Core package is Apache-2.0, while optional components such as the Studio UI use AGPL-3.0. Model weights and datasets retain separate licenses and usage conditions. The project's training-speed and memory-saving comparisons are author claims about selected workloads, not independently reproduced performance for every backend or model.

Decision guide
01

Why it is worth a look

  • Local model chat and inference interfaces
  • Fine-tuning workflows for supported models
02

Before you use it

  • Core is Apache-2.0; optional Studio UI is AGPL-3.0
  • Model-weight and dataset rights are separate
  • Hardware and memory requirements depend on the model and backend
  • Maintainer performance comparisons do not guarantee your result
Manual setupHigh
With an AI agentModerate
DeploymentNot verified
Additional requirementsNot verified

Core is Apache-2.0; optional Studio UI is AGPL-3.0; Model-weight and dataset rights are separate; Hardware and memory requirements depend on the model and backend; Maintainer performance comparisons do not guarantee your result; Some requirements have not been verified. Check the official setup instructions.

03

What it can do

  • Local model chat and inference interfaces
  • Fine-tuning workflows for supported models
  • Dataset preparation and model export tools

Who it fits

Good for

Exploring local model interfaces and supported training workflows; Preparing and exporting models with explicit resource planning

Not a fit

Training arbitrary large models without a hardware check; Treating the repository's first SPDX tag as the license of every component

Getting started

  1. Choose the documented Desktop, Studio or Python Core installation path
  2. Check model, backend, memory, storage and dataset requirements for the selected workload
  3. Start with a small authorized model and dataset, then inspect outputs before training or exporting