[Ecosystem] Unsloth

[danielhanchen]

Contact emails

daniel@unsloth.ai, michael@unsloth.ai

Project summary

Fine-tuning & Reinforcement Learning for LLMs. Train LLMs, VLMs 2x faster with 70% less VRAM

Project description

• We're one of the most popular fine-tuning, training & reinforcement learning libraries! (51K Github stars)
• Unsloth makes training LLMs, VLMs, Embeddings and audio TTS, OCR models 2x faster and use 70% less VRAM.
• We also contribute bug fixes in OSS models like Gemma, Qwen, Mistral, Llama, GPT-OSS and more.
• We have over 150 million total model downloads via https://huggingface.co/unsloth (4th largest overall), and over 30K-40K Pip downloads per day for our package!
• We collab directly with TorchAO, OpenEnv, ExecuTorch, and the entire PyTorch team on bringing the latest features of PyTorch to everyone!

Are there any other projects in the PyTorch Ecosystem similar to yours? If, yes, what are they?

• Transformers / Hugging Face: https://landscape.pytorch.org/?item=modeling--language--transformers
• Lightning AI / LitGPT: 20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale. https://landscape.pytorch.org/?item=modeling--language--litgpt

Project repo URL

https://github.com/unslothai/unsloth

Additional repos in scope of the application

No response

Project license

Apache-2.0 license

GitHub handles of the project maintainer(s)

danielhanchen, shimmyshimmer, Datta0, mmathew23

Is there a corporate or academic entity backing this project? If so, please provide the name and URL of the entity.

Unsloth AI Inc., https://unsloth.ai/

Website URL

https://unsloth.ai/

Documentation

• Yes! Docs: https://unsloth.ai/docs
• We also maintain lots of notebooks at https://unsloth.ai/docs/get-started/unsloth-notebooks
• We did a RL Mini Summit with OpenEnv, Hugging Face which is a step by step guide to RL: https://www.youtube.com/watch?v=jMSCJZAEYR8

How do you build and test the project today (continuous integration)? Please describe.

We test it manually for now - 30 notebooks on GCP every few days.
When new models come, we have to test immediately.
We also test once Gitthub, Discord, Reddit issues are resolved.
We're working with AMD, NVIDIA on streamlining CI CD

Version of PyTorch

We support 2.4.0 until 2.11 nightly.
We test currently on 2.9.1 and 2.10.0

Components of PyTorch

Everything, since we train LLMs! We use PyTorch directly:

1. torch.nn.*
2. torch.nn.functional.*
3. General torch functions and nearly all of PyTorch!

For example https://github.com/search?q=repo%3Aunslothai%2Funsloth%20%22torch%22&type=code shows 90 files using PyTorch, over 1,000 GitHub issues mentioning PyTorch, 130 PRs using PyTorch!

How long do you expect to maintain the project?

Forever :) This is our dream project!
Concretely > 20 years!

Additional information

We also collab with:

1. TorchAO https://pytorch.org/blog/torchao-quantized-models-and-quantization-recipes-now-available-on-huggingface-hub/
2. ExecuTorch: https://pytorch.org/blog/torchao-quantized-models-and-quantization-recipes-now-available-on-huggingface-hub/
3. Part of the PyTorch Conference 2025: https://pytorch.org/blog/torchao-quantized-models-and-quantization-recipes-now-available-on-huggingface-hub/
4. PyTorch at Neurips 2025: https://pytorch.org/blog/pytorch-foundation-at-neurips-2025/
5. OpenEnv: https://huggingface.co/blog/openenv
6. GPU Mode, OpenEnv Mini RL Conference: https://www.youtube.com/watch?v=jMSCJZAEYR8
7. PyTorch generally: https://www.youtube.com/watch?v=MQwryfkydc0

[jspisak]

Let's get this one in ASAP - great great project!!

[jspisak]

Looks great! There was only one minor thing i found which is that there is no CodeOwners.md stating who the maintainers are, etc. Beyond this we should move this one forward.

Functional Requirements:

✅ 1. PyTorch Support: Project must demonstrate benefit to the pytorch community and be validated with the latest versions of PyTorch.
✅ 2. Working CI: Projects must have a demonstrable working CI workflow for all declared architectures
✅ 3. Governance: Projects must have documented technical governance defined and implemented and it must be represented in their Github or Gitlab repository. Example: Review the LF’s Minimum Viable Governance framework.
✅ 4. User experience: The value proposition should be made clear for the project and documented in the README of the project repository.
✅ 5. Quick start: Project should have a working and demonstrable quick start.
✅ 6. Documentation: Projects must have clear and comprehensive documentation.
✅ 7. Permissive licensing: Users must be able to utilize ecosystem projects without licensing concerns. e.g. BSD-3, Apache-2 and MIT licenses
✅ 8. Functional Testing: Users should have developed tests submitted as a part of their project, and be confident that the project will work well with the latest releases of PyTorch.
✅ 9. Packages: Projects need to have support for binary installation options (pip/Conda).
✅ 10. Ongoing maintenance: Project maintainers must be committed to supporting and maintaining their projects for the next 12 months at a minimum. Projects must commit to updating their projects to the latest two releases of PyTorch (e.g. versions 2.6, 2.5.1).

Measurable Requirements:

✅ 1. Core Maintainers: The project must have a minimum of 2 core maintainers
✅ 2. Contributors: The project should have a minimum of 5 total contributors active in the last 90 days.
✅ 3. Commits: The project must have 30 commits in the last 90 days. This is off by like 15 days: it shouldn't be a deal breaker.
✅ 4. Stars: The project must have a minimum of 200 stars on github

Github Layout Requirements:

✅ 1. LICENSE.md at the root of the repository specifying the terms and conditions for using, distributing, and modifying the software. In addition, you should provide information on the license of any third-party code included in the project.
✅ 2. README.md welcomes new community members to the project and explains why the project is useful and how to get started. Should include information about release methodology, cadence, and criteria in the README.
✅ 3. CONTRIBUTING.md explains how to contribute to the project. The file explains the types of contributions needed and how the development process works.
🟡 4. CODEOWNERS that define individuals or teams responsible for code in a repository; document current project owners and Retired committers.
✅ 5. CODE_OF_CONDUCT.md sets the ground rules for participants’ behavior and helps facilitate a friendly, welcoming environment.

[danielhanchen]

Sorry on the delay! We added a CODEOWNERS - to be honest we didn't know what it was haha - https://github.com/unslothai/unsloth/blob/main/.github/CODEOWNERS. We had to take inspiration from vLLM's https://github.com/vllm-project/vllm/blob/main/.github/CODEOWNERS