Unsloth is a local UI for training and running Gemma 4, Qwen3.6, DeepSeek, Kimi, GLM and other models.
https://unsloth.ai/docs
agent
deepseek
fine-tuning
gemma
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gpt-oss
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llama3
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openai
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reinforcement-learning
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unsloth
- Python 71.5%
- TypeScript 22.7%
- Shell 1.9%
- PowerShell 1.6%
- Rust 1.5%
- Other 0.7%
a09e70e8be* tests/studio: end-to-end Windows GPU detection mock test (#5106) Locks in the combined fix from #5322 + #5324 with a synthetic Windows scenario that CI runners without GPUs can execute. The test packs the real PyPI win_amd64 wheel layouts (cu12 modular and the new unsuffixed cu13 nvidia/cu13/bin/x86_64 layout) plus the exact filename set of the upstream b9103 cudart-llama-bin-win-cuda bundles, then mocks nvidia-smi output and asserts that: * Studio's nvidia-smi probe parses the CSV and reports the GPU. * After PR #5322 the install_dir/build/bin/Release/ tree contains all three cudart bundle DLLs alongside llama-server.exe. * After PR #5324 the PATH built by start_llama_server's win32 branch lists pip nvidia + torch/lib dirs in addition to the binary_dir. * cudart64_X.dll, cublas64_X.dll, and cublasLt64_X.dll are each reachable from at least one PATH entry, with cudart specifically reachable from BOTH the install dir and a pip nvidia dir (defence in depth). * Bare venvs without pip nvidia wheels still work via #5322's binary_dir drop; pre-#5322 installs still work via #5324's PATH augmentation. * A reconstructed pre-PR scenario (cudart absent from binary_dir and pip dirs not on PATH) leaves cudart unreachable, confirming the test would catch a future regression. Bonus housekeeping in studio/install_llama_prebuilt.py: drop the pointless f-prefix on the literal "llama-" in the windows_cuda_attempts pairing guard (no behaviour change; lint nit flagged in the post-merge review). The mocks model real artifact contents I verified empirically: * pip download nvidia-cuda-runtime --platform win_amd64 produces nvidia/cu13/bin/x86_64/cudart64_13.dll. * unzip on the b9103 cudart-llama-bin-win-cuda-13.1-x64.zip produces exactly cudart64_13.dll + cublas64_13.dll + cublasLt64_13.dll, no executables. * objdump -p on the b9103 ggml-cuda.dll shows a static PE import on cublas64_13.dll (the root cause of #5106 when cublas64_13.dll is unreachable). Refs #5106 #5322 #5324 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * test_5106_windows_gpu_detection_mock: don't shadow real httpx This file's name sorts before every other file in studio/backend/tests/ (starts with the digit '5'), so pytest collects it first. The previous ``sys.modules.setdefault("httpx", _httpx_stub)`` ran before any other test imported real httpx, which meant the stub permanently shadowed the real module for the rest of the collection. Tests that did ``from httpx import HTTPError, Response`` (test_anthropic_messages, test_browse_folders_route, test_training_*, etc) then failed at collection with ``ImportError: cannot import name 'HTTPError'`` because the stub did not define those names. The existing test_llama_cpp_windows_nvidia_path.py did not trigger the same issue because it sorts after test_a* / test_b* / etc, by which point the real httpx has already been imported and setdefault is a no-op. Switch the stub installation to ``importlib.util.find_spec(name) is None`` so we only fall back to the stub when the real module truly is not installed. Backend CI installs httpx, structlog, and the studio/backend/loggers package is reachable via the sys.path augmentation a few lines above, so on CI all three find_spec calls succeed and no stubs are installed at all. Also add HTTPError and Response to the stub module for the offline case, so anyone running this test outside CI with httpx absent still gets a stub that satisfies the broader test suite's imports. Refs #5106 * test_5106 + llama_cpp: extract win32 PATH helper and harden the regression test Follow-up to PR #5376's review feedback. Three real findings from the bot reviewers, plus one stale one. 1. (codex P2 line 201, gemini medium line 209) The regression test's _build_path_dirs_like_start_llama_server hand-copied the win32 branch of LlamaCppBackend.start_llama_server, so a future drop or reorder of _windows_pip_nvidia_dll_dirs(sys.prefix) in production would have passed the test silently. Extract a new staticmethod LlamaCppBackend._build_windows_path_dirs (binary_dir, prefix, cuda_path). Production start_llama_server now calls this helper. The test's wrapper is reduced to a one-line delegate that forwards to the staticmethod, so the regression asserts against the exact production logic instead of a parallel copy of it. 2. (codex P2 line 245) test_nvidia_smi_probe_reports_synthetic_gpu did not clear CUDA_VISIBLE_DEVICES. On a shared GPU runner with the variable set in the parent shell, _get_gpu_free_memory() filters the mocked CSV and returns [] or falls through to the torch fallback. Cleared CUDA_VISIBLE_DEVICES and NVIDIA_VISIBLE_DEVICES via monkeypatch.delenv(..., raising=False). 3. (codex P2 line 66) _maybe_stub gated on importlib.util.find_spec ("loggers"), which returns a spec because studio/backend/loggers/ is on sys.path. But the actual import chain loads loggers/handlers.py which does `from fastapi import Request, Response` at module load. In a lightweight env without fastapi installed, the stub never lands and `from core.inference.llama_cpp import LlamaCppBackend` raises during collection. Switched _maybe_stub to a real import attempt under try / except ImportError so the stub falls into place when the package is discoverable but not importable. CI has fastapi so this is purely a developer- machine ergonomics fix. The fourth comment (codex P1 line 85 "Keep the httpx stub from leaking across tests") was already addressed by |
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| .pre-commit-ci.yaml | ||
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| unsloth-cli.py | ||
Unsloth Studio lets you run and train models locally.
Features • Quickstart • Notebooks • Documentation
⚡ Get started
macOS, Linux, WSL:
curl -fsSL https://unsloth.ai/install.sh | sh
Windows:
irm https://unsloth.ai/install.ps1 | iex
Community:
⭐ Features
Unsloth Studio (Beta) lets you run and train text, audio, embedding, vision models on Windows, Linux and macOS.
Inference
- Search + download + run models including GGUF, LoRA adapters, safetensors
- Export models: Save or export models to GGUF, 16-bit safetensors and other formats.
- Tool calling: Support for self-healing tool calling and web search
- Code execution: lets LLMs test code in Claude artifacts and sandbox environments
- API inference endpoint: Deploy and run local LLMs in Claude Code, Codex tools with Unsloth
- Auto set inference settings and customize chat templates.
- We work directly with teams behind gpt-oss, Qwen3, Llama 4, Mistral, Gemma 1-3, and Phi-4, where we’ve fixed bugs that improve model accuracy.
- Upload images, audio, PDFs, code, DOCX and more file types to chat with.
Training
- Train and RL 500+ models up to 2x faster with up to 70% less VRAM, with no accuracy loss.
- Custom Triton and mathematical kernels. See some collabs we did with PyTorch and Hugging Face.
- Data Recipes: Auto-create datasets from PDF, CSV, DOCX etc. Edit data in a visual-node workflow.
- Reinforcement Learning (RL): The most efficient RL library, using 80% less VRAM for GRPO, FP8 etc.
- Supports full fine-tuning, RL, pretraining, 4-bit, 16-bit and, FP8 training.
- Observability: Monitor training live, track loss and GPU usage and customize graphs.
- Multi-GPU training is supported, with major improvements coming soon.
📥 Install
Unsloth can be used in two ways: through Unsloth Studio, the web UI, or through Unsloth Core, the code-based version. Each has different requirements.
Unsloth Studio (web UI)
Unsloth Studio (Beta) works on Windows, Linux, WSL and macOS.
- CPU: Supported for Chat and Data Recipes currently
- NVIDIA: Training works on RTX 30/40/50, Blackwell, DGX Spark, Station and more
- macOS: Currently supports chat and Data Recipes. MLX training is coming very soon
- AMD: Chat + Data works. Train with Unsloth Core. Studio support is out soon.
- Coming soon: Training support for Apple MLX, AMD, and Intel.
- Multi-GPU: Available now, with a major upgrade on the way
macOS, Linux, WSL:
curl -fsSL https://unsloth.ai/install.sh | sh
Windows:
irm https://unsloth.ai/install.ps1 | iex
Launch
unsloth studio -p 8888
For cloud VMs or LAN access, add
-H 0.0.0.0to bind on all interfaces.
Update
To update, use the same install commands as above. Or run (does not work on Windows):
unsloth studio update
Docker
Use our Docker image unsloth/unsloth container. Run:
docker run -d -e JUPYTER_PASSWORD="mypassword" \
-p 8888:8888 -p 8000:8000 -p 2222:22 \
-v $(pwd)/work:/workspace/work \
--gpus all \
unsloth/unsloth
Developer, Nightly, Uninstall
To see developer, nightly and uninstallation etc. instructions, see advanced installation.
Unsloth Core (code-based)
Linux, WSL:
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv unsloth_env --python 3.13
source unsloth_env/bin/activate
uv pip install unsloth --torch-backend=auto
Windows:
winget install -e --id Python.Python.3.13
winget install --id=astral-sh.uv -e
uv venv unsloth_env --python 3.13
.\unsloth_env\Scripts\activate
uv pip install unsloth --torch-backend=auto
For Windows, pip install unsloth works only if you have PyTorch installed. Read our Windows Guide.
You can use the same Docker image as Unsloth Studio.
AMD, Intel:
For RTX 50x, B200, 6000 GPUs: uv pip install unsloth --torch-backend=auto. Read our guides for: Blackwell and DGX Spark.
To install Unsloth on AMD and Intel GPUs, follow our AMD Guide and Intel Guide.
📒 Free Notebooks
Train for free with our notebooks. You can use our new free Unsloth Studio notebook to run and train models for free in a web UI. Read our guide. Add dataset, run, then deploy your trained model.
| Model | Free Notebooks | Performance | Memory use |
|---|---|---|---|
| Gemma 4 (E2B) | ▶️ Start for free | 1.5x faster | 50% less |
| Qwen3.5 (4B) | ▶️ Start for free | 1.5x faster | 60% less |
| gpt-oss (20B) | ▶️ Start for free | 2x faster | 70% less |
| Qwen3.5 GSPO | ▶️ Start for free | 2x faster | 70% less |
| gpt-oss (20B): GRPO | ▶️ Start for free | 2x faster | 80% less |
| Qwen3: Advanced GRPO | ▶️ Start for free | 2x faster | 70% less |
| embeddinggemma (300M) | ▶️ Start for free | 2x faster | 20% less |
| Mistral Ministral 3 (3B) | ▶️ Start for free | 1.5x faster | 60% less |
| Llama 3.1 (8B) Alpaca | ▶️ Start for free | 2x faster | 70% less |
| Llama 3.2 Conversational | ▶️ Start for free | 2x faster | 70% less |
| Orpheus-TTS (3B) | ▶️ Start for free | 1.5x faster | 50% less |
- See all our notebooks for: Kaggle, GRPO, TTS, embedding & Vision
- See all our models and all our notebooks
- See detailed documentation for Unsloth here
🦥 Unsloth News
- API inference endpoint: Deploy and run local LLMs in Claude Code, Codex tools. Guide
- Qwen3.6: Qwen3.6-35B-A3B can now be trained and run in Unsloth Studio. Blog
- Gemma 4: Run and train Google’s new models directly in Unsloth. Blog
- Introducing Unsloth Studio: our new web UI for running and training LLMs. Blog
- Qwen3.5 - 0.8B, 2B, 4B, 9B, 27B, 35-A3B, 112B-A10B are now supported. Guide + notebooks
- Train MoE LLMs 12x faster with 35% less VRAM - DeepSeek, GLM, Qwen and gpt-oss. Blog
- Embedding models: Unsloth now supports ~1.8-3.3x faster embedding fine-tuning. Blog • Notebooks
- New 7x longer context RL vs. all other setups, via our new batching algorithms. Blog
- New RoPE & MLP Triton Kernels & Padding Free + Packing: 3x faster training & 30% less VRAM. Blog
- 500K Context: Training a 20B model with >500K context is now possible on an 80GB GPU. Blog
- FP8 & Vision RL: You can now do FP8 & VLM GRPO on consumer GPUs. FP8 Blog • Vision RL
📥 Advanced Installation
The below advanced instructions are for Unsloth Studio. For Unsloth Core advanced installation, view our docs.
Developer installs: macOS, Linux, WSL:
git clone https://github.com/unslothai/unsloth
cd unsloth
./install.sh --local
unsloth studio -p 8888
Then to update :
unsloth studio update
Developer installs: Windows PowerShell:
git clone https://github.com/unslothai/unsloth.git
cd unsloth
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
.\install.ps1 --local
unsloth studio -p 8888
Then to update :
unsloth studio update
Nightly: MacOS, Linux, WSL:
git clone https://github.com/unslothai/unsloth
cd unsloth
git checkout nightly
./install.sh --local
unsloth studio -p 8888
Then to launch every time:
unsloth studio -p 8888
Nightly: Windows:
Run in Windows Powershell:
git clone https://github.com/unslothai/unsloth.git
cd unsloth
git checkout nightly
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
.\install.ps1 --local
unsloth studio -p 8888
Then to launch every time:
unsloth studio -p 8888
Uninstall
You can uninstall Unsloth Studio by deleting its install folder usually located under $HOME/.unsloth/studio on Mac/Linux/WSL and %USERPROFILE%\.unsloth\studio on Windows. Using the rm -rf commands will delete everything, including your history, cache:
- MacOS, WSL, Linux:
rm -rf ~/.unsloth/studio - Windows (PowerShell):
Remove-Item -Recurse -Force "$HOME\.unsloth\studio"
For more info, see our docs.
Deleting model files
You can delete old model files either from the bin icon in model search or by removing the relevant cached model folder from the default Hugging Face cache directory. By default, HF uses:
- MacOS, Linux, WSL:
~/.cache/huggingface/hub/ - Windows:
%USERPROFILE%\.cache\huggingface\hub\
💚 Community and Links
| Type | Links |
|---|---|
 Discord |
Join Discord server |
 r/unsloth Reddit |
Join Reddit community |
| 📚 Documentation & Wiki | Read Our Docs |
_logo_late_2025.svg){kind=logo} Twitter (aka X) |
Follow us on X |
| 🔮 Our Models | Unsloth Catalog |
| ✍️ Blog | Read our Blogs |
Citation
You can cite the Unsloth repo as follows:
@software{unsloth,
author = {Daniel Han, Michael Han and Unsloth team},
title = {Unsloth},
url = {https://github.com/unslothai/unsloth},
year = {2023}
}
If you trained a model with 🦥Unsloth, you can use this cool sticker!
License
Unsloth uses a dual-licensing model of Apache 2.0 and AGPL-3.0. The core Unsloth package remains licensed under Apache 2.0, while certain optional components, such as the Unsloth Studio UI are licensed under the open-source license AGPL-3.0.
This structure helps support ongoing Unsloth development while keeping the project open source and enabling the broader ecosystem to continue growing.
Thank You to
- The llama.cpp library that lets users run and save models with Unsloth
- The Hugging Face team and their libraries: transformers and TRL
- The Pytorch and Torch AO team for their contributions
- NVIDIA for their NeMo DataDesigner library and their contributions
- And of course for every single person who has contributed or has used Unsloth!