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
gemma3
gpt-oss
llama
llama3
llm
llms
mistral
openai
qwen
reinforcement-learning
self-hosted
text-to-speech
tts
ui
unsloth
- Python 71.5%
- TypeScript 22.7%
- Shell 1.9%
- PowerShell 1.6%
- Rust 1.5%
- Other 0.7%
4699c7e291* studio: engage draft-mtp on vision MTP GGUFs The draft-mtp auto-promotion in LlamaCppBackend.load_model was gated on not effective_is_vision, and the spec-emit branch repeated the same guard. Every Unsloth -MTP GGUF repo ships an mmproj projector, so effective_is_vision was always True for those repos and the MTP speedup silently never engaged out of the box. llama.cpp #22673 explicitly states MTP is compatible with vision input. The bundled b9204 server happily loads both: a manual run with --mmproj ... --spec-type draft-mtp --spec-draft-n-max 6 logs "loaded multimodal model" followed by "adding speculative implementation 'draft-mtp'". Drop the vision gate from both sites and rewrite the matching short circuit in _already_in_target_state so reload checks reach the auto promotion path on vision MTP loads. Add three regression tests covering vision MTP match (auto and default), and non MTP vision repo unaffected. Verified on a B200 with unsloth/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL: base decode 179.7 t/s vs MTP decode 253.8 t/s, draft acceptance 0.57, 1.41x speedup on a 255 token completion. mmproj still loads and image input remains available. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio: prefer Qwen3.5 -MTP GGUF variants in default model lists With the vision gate dropped in the previous commit, draft-mtp now auto-engages on -MTP GGUF repos out of the box. Swap the four Qwen3.5 recommended entries in DEFAULT_MODELS_GGUF and DEFAULT_MODELS_STANDARD to their -MTP-GGUF counterparts so new users get the speedup by default: unsloth/Qwen3.5-4B-GGUF -> unsloth/Qwen3.5-4B-MTP-GGUF unsloth/Qwen3.5-9B-GGUF -> unsloth/Qwen3.5-9B-MTP-GGUF unsloth/Qwen3.5-35B-A3B-GGUF -> unsloth/Qwen3.5-35B-A3B-MTP-GGUF unsloth/Qwen3.5-0.8B-GGUF -> unsloth/Qwen3.5-0.8B-MTP-GGUF All four HF repos exist (HEAD 200) and ship the same UD-Q4_K_XL quant layout as the non-MTP variants. Non-Qwen3.5 entries are untouched. * bump version to 2026.5.4 Picks up the studio MTP vision-gate fix and the Qwen3.5 -MTP default swap in this PR. * studio: prefer Qwen3.6-35B-A3B-MTP-GGUF in default model lists Same rationale as the previous Qwen3.5 swap. The Qwen3.6 MTP variant exists at unsloth/Qwen3.6-35B-A3B-MTP-GGUF (HF HEAD 200) and now auto-engages draft-mtp out of the box with the gate fix. * studio: drop --spec-draft-n-max from 6 to 3 for draft-mtp n=6 is too greedy: on Qwen3.6 the draft has to guess 6 tokens ahead and acceptance crashes to ~0.45, leaving only ~14% throughput gain. PR ggml-org/llama.cpp#22673's author benched n=3 at ~0.72 acceptance and 2 to 3x speedup on the same Qwen3.6 family, and the README sample command uses n=2 or n=3. Match that. CPU/Mac branch already uses n=3, so this aligns both paths. * studio: set --spec-draft-n-max back to 6 for draft-mtp on GPU Reverts the n=3 tuning. n=6 is the original default; user-side comparisons hold the larger draft window steady so the toggle (next commit) is the primary on/off lever. * studio: add Speculative Decoding toggle under Max Tokens Adds a top-level kill switch (panel-switch under Max Tokens, mirroring Auto-Healing Tool Calls) that forces the /load request's speculative_type to "off" when disabled. The backend "off" branch in LlamaCppBackend.load_model skips both the draft-mtp auto-promotion and the spec-emit branch, so neither --spec-type draft-mtp nor --spec-default reaches llama-server. Wiring: - chat-runtime-store: new speculativeDecodingEnabled bool, default true, persisted to localStorage under unsloth_speculative_decoding, plus a setSpeculativeDecodingEnabled setter. - chat-settings-sheet: SpeculativeDecodingToggle rendered immediately beneath the Max Tokens slider for non-external models. - use-chat-model-runtime: when speculativeDecodingEnabled is false, override speculative_type to "off" in the loadModel call so the switch wins over any pre-existing speculativeType state (including the existing per-model toggle in Model Settings). Verified end to end on unsloth/Qwen3.6-35B-A3B-MTP-GGUF:UD-Q4_K_XL: toggle ON emits --spec-type draft-mtp --spec-draft-n-max 6; toggle OFF emits zero --spec-* flags on the same MTP GGUF. * studio: relocate Speculative Decoding toggle into Model Settings Move the toggle out from under Max Tokens and back into the Model Settings section, directly beneath KV Cache Dtype, where the existing Apply/Reset workflow already drives a reload on dirty. This way flipping the switch in the UI actually picks up: the section becomes dirty, Apply re-runs /load with the new speculative_type. Drop the !currentModelIsMultimodal gate so vision MTP GGUFs can also disable speculative decoding from the UI. Switch the toggle's off-value from null to "off" so the backend's "off" short-circuit fires for MTP models too (null normalises to None which re-triggers the draft-mtp auto-promotion). Tooltip now reads "Faster generation with 0% accuracy hit". Remove the now-redundant speculativeDecodingEnabled bool + setter from the runtime store and the load-time override in use-chat-model-runtime; the toggle binds directly to speculativeType. * studio: restore OOM/TIGHT badge on recommended GGUF rows The recommended-list row passed vramStatus=null for any GGUF repo because the existing useRecommendedModelVram hook reads safetensors totals from HF model info, which GGUF-only repos do not expose. As a result, an OOM Q-quant repo would render with only a "GGUF" badge and no visual signal that nothing in it fits. Add useGgufRecommendedFit: per repo, fetch the variant list via the existing /api/models/gguf-variants endpoint, take the smallest variant's size_bytes, and classify with the same 0.7*GPU + 0.7*RAM thresholds as GgufVariantExpander. Session-scoped cache + in-flight dedup so a repo is requested at most once. Wire the result into the three GGUF row sites in pickers.tsx so OOM and TIGHT badges show on the collapsed cards. * Revert "studio: restore OOM/TIGHT badge on recommended GGUF rows" This reverts commit 07793b1240df72b13e51d6dc15f63c4ee8c6cba9. The new useGgufRecommendedFit hook was treating the symptom. PR #5561 identified the real root cause: useGpuInfo was calling /api/system with plain fetch instead of authFetch, so the session-auth check failed silently and gpu.available stayed false everywhere. With no GPU info, every fit check (variant expander, recommended carousel) fell back to "no signal" and dropped the OOM/TIGHT badges. Reverting the over-engineered hook and applying the authFetch fix in the next commit, which restores the existing badges with one line. * chore: replace qwen suggested with MTP variant * fix: restore GPU info auth for GGUF fit badges --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: imagineer99 <samleejackson0@gmail.com> |
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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
The recommended way to fully remove Unsloth Studio is the matching uninstall script for your OS. It stops any running servers, removes the install dir, the launcher data dir, the desktop shortcut, and any platform-specific entries (macOS .app bundle + Launch Services on Mac; Start Menu, HKCU\Software\Unsloth registry key and user PATH entries on Windows):
- MacOS, WSL, Linux:
curl -fsSL https://unsloth.ai/uninstall.sh | sh - Windows (PowerShell):
irm https://unsloth.ai/uninstall.ps1 | iex
If you only want to drop the install dir and keep the launcher/shortcut for a later reinstall, you can instead run rm -rf ~/.unsloth/studio (Mac/Linux/WSL) or Remove-Item -Recurse -Force "$HOME\.unsloth\studio" (Windows). The model cache at ~/.cache/huggingface is not touched by any of these.
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!