Unsloth is a local UI for training and running Gemma 4, Qwen3.6, DeepSeek, Kimi, GLM and other models.
https://unsloth.ai/docs
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63c6750532* fix(studio/mmproj): block cross-family projectors in flat local GGUF dirs (#5347) When a flat local GGUF directory holds several unrelated models with their own mmproj siblings, detect_mmproj_file() returned the first projector it walked into. For the layout reported in #5347 (Qwen weights + a Gemma mmproj in the same dir) that meant llama-server was launched with --mmproj pointing at the Gemma projector, which fails to load and surfaces as a confusing crash. Disambiguation rules: - Drop candidates whose family token (qwen/gemma/llama/mistral/phi/...) disagrees with the model's family. Candidates with no recognised family token (e.g. the HF-convention 'mmproj-F16.gguf') are kept. - Among same-family candidates, prefer the one whose stem shares the longest prefix with the model (Qwen3.5-9B mmproj beats Qwen3.5-35B mmproj for a Qwen3.5-9B model). - If every candidate is dropped, return None — better than attaching a wrong projector and getting a server-launch failure. Tests cover the cross-family block, multi-candidate prefix tie-break, HF-convention 'mmproj-F16.gguf', unrecognised families, and the existing search_root walk. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio/mmproj: word-bounded family match, expanded token list, launcher guard Tighten the family-token detector to match only on word boundaries so substring collisions stop tagging false families: phi no longer matches sapphire, yi no longer matches yip, mimo no longer matches mimosa, and mistral does not bleed into ministral/magistral/devstral. Pick the token whose first occurrence is leftmost in the filename rather than the first hit in tuple order, so merge models disambiguate predictably (llama-phi tags llama; phi-llama tags phi). Expand _MODEL_FAMILY_TOKENS with the families an audit of the unsloth HF org turned up that the previous list missed: devstral, ministral, magistral (Mistral-derivative naming), nemotron, kimi, nanonets, cosmos, mimo, apriel, lfm. Without these, a flat local GGUF directory containing one of these weights plus an unrelated renamed projector still hit the original #5347 failure. Add mmproj_matches_model_family() and call it at the llama-server launch site in core/inference/llama_cpp.py. detect_mmproj_file already drops cross-family candidates at discovery time, but mmproj_path can also reach the launcher via config injection or future overrides; this guard keeps those paths from silently loading a known-wrong projector. Tests: 12 new cases covering substring rejection, leftmost-position selection, new family tokens, a new flat-dir Nemotron + Gemma rejection case, and the launcher-level guard. All 21 detect_mmproj_file tests and the existing 106 llama_cpp tests pass. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio/mmproj: pair via GGUF general.* metadata, not just filenames Real Unsloth vision GGUFs carry rich identity metadata that has been ignored by the discovery path. Every projector under the unsloth org has general.type='mmproj' plus general.base_model.0.repo_url pointing at the same upstream HF repo as its weight, and the equivalent basename, base_model.0.name, and base_model.0.organization fields. A flat-dir mismatch is therefore decidable from the headers alone, no matter how the user has renamed the files. Add utils/models/gguf_metadata.py with read_gguf_general_metadata(): a fast (~30 ms) header walk that pulls only the general.* string fields and skips everything else, cached by (resolved path, mtime_ns, size). Mirrors the parser shape already used by LlamaCppBackend._read_gguf_metadata so the format handling is consistent. is_mmproj_by_metadata() returns True/False/None from general.type, and pairing_score() returns 100 for an exact base_model URL match, 80 for basename plus organization match, 60 for basename only, -1 for definitive metadata disagreement, and 0 when neither side has enough metadata to decide. Rewire detect_mmproj_file() to a two-stage selector: 1. Detect projectors via metadata (general.type) when present, else fall back to the filename substring heuristic. This recovers headerless projectors AND projectors whose name does not contain 'mmproj' but whose header advertises one. 2. Score each candidate against the weight via pairing_score. Drop candidates with score -1 (definitive metadata disagreement). For candidates with score 0 (no usable metadata) fall back to the existing filename family-token check, dropping recognised-family mismatches. Pick the survivor with the highest (score, longest_prefix, -len(stem)) tuple, so a metadata URL match always wins over a filename-prefix match. Tests: 16 new cases. tests/test_gguf_metadata.py covers the parser (missing file, non-GGUF, string extraction, walking past arrays and uint32s, cache invalidation by mtime/size) and the score helpers. tests/test_detect_mmproj_file.py adds end-to-end cases that synthesise real on-disk GGUF headers: URL match wins over a longer-prefix sibling, URL mismatch returns None even when filenames match, a projector named 'vision-projector.gguf' is still discovered via general.type, and a 100-score header match outranks a near-perfect filename prefix on a headerless candidate. All 75 tests across detect_mmproj_file, gguf_metadata, llama_cpp load progress, cached gguf routes, trained model scan, and vision cache pass. * studio/mmproj: shorten comments and docstrings across the #5347 changes Trim verbose explanations to one-line statements of intent. The behaviour is unchanged: 161 tests across detect_mmproj_file, gguf_metadata, llama_cpp_load_progress (+ matrix), llama_server_args, llama_cpp_cache_aware_disk_check, trained_model_scan, and vision_cache all pass. * studio/mmproj: shorten remaining detect_mmproj_file body comments Trim the docstring and the dir-walking block comments inside detect_mmproj_file to one-liners. Behaviour unchanged; 44 mmproj + gguf_metadata + llama_cpp_load_progress tests pass. * studio/mmproj: cap gguf_metadata cache below ceiling on every insert The eviction branch popped exactly one entry when len >= max, so the cache size could only converge to the cap when entries were added slowly enough for natural growth. After a sandbox sim that reduced the cap mid-run, len stayed above the cap because each insert popped one and added one. Switch to a while loop so we evict until len is strictly below the cap before inserting. Steady-state behaviour at the default 4096 ceiling is unchanged. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Daniel Han <danielhanchen@gmail.com> |
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| .pre-commit-ci.yaml | ||
| .pre-commit-config.yaml | ||
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| cli.py | ||
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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!