Unsloth (open-source library)
Unsloth (open-source library) is a framework maintained by Unsloth12. Its source code is public1 under the Apache License 2.0 (unsloth/, tests/, scripts/)2 and the GNU Affero General Public License v3.0 only (studio/, unsloth_cli/)234.
Unsloth is an open-source Python library for fine-tuning and reinforcement learning of language, diffusion, text-to-speech, and embedding models, supporting LoRA, QLoRA, full fine-tuning, pretraining, GRPO, and DPO, and exporting to formats such as GGUF. The same repository contains Unsloth Studio, a web interface, and the Unsloth Desktop app.1
- Repository: Repository (external site: github.com)
- Documentation: Documentation (installation) (external site: unsloth.ai)
- License: LICENSE (Apache 2.0 core) (external site: github.com)
- License: COPYING (AGPL-3.0 components) (external site: github.com)
- Release notes: Releases (external site: github.com)
Availability and license
Overall availability
Documented as available to the general public. Access conditions and license terms may still apply.12
Availability is separate from permission: read the license before using or redistributing.
Apache License 2.0 (unsloth/, tests/, scripts/) (external site: github.com)2
Plain-language guide to the Apache License 2.0GNU Affero General Public License v3.0 only (studio/, unsloth_cli/) (external site: github.com)234
Plain-language guide to the GNU Affero General Public License v3.0
The LICENSE file applies Apache 2.0 to the core package directories and the AGPL v3 to the optional Studio and command-line directories; source files in those directories carry the SPDX identifier AGPL-3.0-only. The PyPI metadata for the unsloth package declares Apache-2.0.246
Component reuse rights
- weights
- Unknown — no complete fact-level rights review
- code
- Unknown — no complete fact-level rights review
- data
- Unknown — no complete fact-level rights review
- documentation
- Unknown — no complete fact-level rights review
No complete system-rights review is recorded for this release.
Public materials checklist
| Item | Status | Notes and evidence |
|---|---|---|
| Source codeIs the source code publicly readable? | Public | 1 |
| DocumentationIs user documentation published? | Public | Installation and usage guides on unsloth.ai/docs.7 |
| InstallationAre installation instructions or packages publicly available? | Public | Documented installs by install script (macOS, Linux, WSL, and Windows PowerShell), pip or conda for Unsloth Core, Docker images (including a separate ROCm image), and desktop app downloads.17 |
| Supported platformsAre supported operating systems or hardware documented? | Public | The README lists Windows, Linux, WSL, and macOS, with NVIDIA, AMD, and Intel GPUs, CPUs, multi-GPU setups, and a Vulkan backend.1 |
| Release statusAre versioned releases published? | Public | Releases are published frequently on GitHub; the unsloth package version on PyPI at review was 2026.9.12, uploaded on September 28, 2026.56 |
What it is useful for
Fine-tuning and post-training open-weight models on a single machine or multi-GPU setup, and exporting the results for local inference.1
Organization context
People: Daniel Han, Michael Han
In the news
Unsloth release adds Browser use, Qwen-Image-2.1-Turbo, and text-diffusion LLM training
On October 12, 2026 Unsloth published release v0.1.906-beta on its GitHub repository. The release notes say models can now use a browser in Unsloth, opening, reading, and interacting with pages in their own tab, and that Unsloth asks the user before the model signs in, posts a form, or presses a button that buys, sends, or deletes. The notes also list running Qwen-Image-2.1-Turbo from the Images page, training text-diffusion LLMs with a new DiffusionTrainer, faster MoE GGUFs that are larger than available VRAM on NVIDIA and AMD GPUs, and speculative decoding for MLX on Mac. USASI has not tested this release.
Unsloth introduces Unsloth Desktop, an open-source app to run and train models locally
Unsloth announced Unsloth Desktop in a GitHub release published on August 11, 2026. The release notes describe it as an open-source app for Windows, macOS, and Linux that runs and trains AI models locally, and say models can also be exported to NVFP4, GGUF, and other formats. Listed features include training without code, sandboxed Python and Bash execution, web search, Deep Research, RAG, and MCP tools, and an OpenAI-compatible API for local models. The notes say it runs on CPU or multiple GPUs across NVIDIA, AMD, Intel, and Mac hardware. USASI has not tested this app.
U.S. eligibility
Eligible · basis: U.S.-governed project
The maintaining entity is Unsloth AI Inc.: the library is developed in the unslothai GitHub organization, and the repository's license and source-file copyright notices name the Unsloth AI Inc. team. The company's U.S. principal place of business is documented in an official registry: the USPTO registration of the UNSLOTH mark (No. 7973599), for downloadable software for training and fine-tuning large language models, names Unsloth AI Inc., a Delaware corporation, as owner with a San Francisco address; the Unsloth organization record explains why the trademark file supports reading this as the company's principal place of business. No foundation or multi-organization governance is documented.2489
Sources
This listing is not an endorsement, a safety assessment, or a federal approval.
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APA style
USASI. (2026, October 1). Unsloth (open-source library). United States of America Superintelligence. https://unitedstatesofamericasuperintelligence.com/open/unsloth-library/
BibTeX
@misc{usasi_unsloth_library,
author = {{USASI}},
title = {Unsloth (open-source library)},
year = {2026},
month = oct,
howpublished = {\url{https://unitedstatesofamericasuperintelligence.com/open/unsloth-library/}},
note = {United States of America Superintelligence. Last updated 2026-10-01}
}The date is when this page was last updated. Add the date you read it if your style needs one.
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