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Data sourced from GitHub API

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  2. Organizations
  3. Unsloth AI
Unsloth AIOrganization

Unsloth AI

@unslothai • Run and train AI models locally. 🦥. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.

Portfolio concentration

99%

Top three share

Shows whether the organization is driven by one breakout repo or several visible projects.

Breadth

13 repos

Visible snapshot

11 repositories updated in the last 90 days.

Leading language

Python

Portfolio mix

Python (6), C++ (4), Jupyter Notebook (2)

Average size

6.7K

Stars per repository

Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.

Back to organizationsCompare repositories
Updated: 2026-10-23(4d ago)GitHub API fallback13 repositories

Portfolio Shape

99%

of the visible star count comes from this organization's top three repositories.

Average Repository Size

6.7K

stars per repository in this same snapshot.

Current Mix

Python

is the most common language here, with 11 repositories updated in the last 90 days.

Why this rank

This organization stands out because one flagship repo drives 90% of its visible star count.

Flagship share 90%Breakout repo: unsloth

Organization pages work best when you separate portfolio breadth from flagship concentration. In Unsloth AI's case, the visible top three repositories account for about 99% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.

The dominant language mix here is Python (6), C++ (4), Jupyter Notebook (2). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.

Source: GitHub API fallback. This is the same cache-first snapshot used by the organization ranking list, so the summary view and the detail view should stay aligned.

Top Repositories

#RepositoryLanguageStars🍴 ForksUpdated
1unslothai/unsloth

Local UI to run and train LLMs and diffusion models. Supports GGUF, MLX, Qwen3.8, DeepSeek-V4.1, MiniMax-H3, Gemma 4, FLUX and more.

Python78.7K7.3KToday
2unslothai/notebooks

250+ Fine-tuning & RL Notebooks for text, vision, audio, embedding, TTS models.

Jupyter Notebook5.8K958Yesterday
3unslothai/hyperlearn

2-2000x faster ML algos, 50% less memory usage, works on all hardware - new and old.

Jupyter Notebook2.5K1701 years ago
4unslothai/unsloth-zoo

Utils for Unsloth https://github.com/unslothai/unsloth

Python345334Today
5unslothai/llama.cpp

LLM inference in C/C++

C++24856Today
6unslothai/cut-cross-entropy

Apple's Cut Cross Entropy

Python3491 years ago
7unslothai/stable-diffusion.cpp

Diffusion model(SD,Flux,Wan,Qwen Image,Z-Image,...) inference in pure C/C++

C++1444 days ago
8unslothai/transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Python13101 months ago
9unslothai/gpt-oss

gpt-oss-120b and gpt-oss-20b are two open-weight language models by OpenAI

Python1121 months ago
10unslothai/whisper.cpp

Port of OpenAI's Whisper model in C/C++

C++952 days ago
11unslothai/unsloth-staging-1

Staging PRs for Unsloth

Python402 months ago
12unslothai/audio.cpp

An all-in-one, pure C++ inference engine for audio models, powered by ggml. Supports TTS, STT, VAD, voice conversion, music generation, and more, with highly optimized performance. No Python dependency.

C++23Today
13unslothai/ruff

An extremely fast Python linter and code formatter, written in Rust.

004 days ago

Next step after the organization read

Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.
Open flagship repoCompare repositoriesBack to organizations

Learn and methodology

Keep trust-building context reachable, but behind the first data read instead of ahead of it.
GuideMethodologyArticlesWeekly Digest

How to read this organization snapshot

Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.

For broader background on GitStar's ranking logic and editorial guidance, see Methodology & Editorial Standards.