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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unsloth
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ab56e4a9ed* Studio: serialise GGUF reload and inherit unsloth-run extra args Closes #5401. Three related GGUF reload bugs reproduced against `unsloth studio run -m unsloth/Qwen3-0.6B-GGUF --gguf-variant Q4_K_M --top-k 20 --seed 42`: 1. The `POST /api/inference/load` already-loaded short-circuit only compared `model_identifier` and `hf_variant`. A same-(model, variant) Apply that flipped `cache_type_kv` / `speculative_type` / `chat_template_override` / `max_seq_length` / `llama_extra_args` returned `status="already_loaded"` and the new setting silently never reached llama-server. 2. The frontend chat-settings Apply path POSTs `/unload` then `/load` without round-tripping `llama_extra_args`. Every reload after `unsloth run --some-flag X` quietly dropped `--some-flag X` from the spawned `llama-server` command line. 3. `LlamaCppBackend.load_model` released `_lock` between Phase 1 (kill) and Phase 3 (spawn) so two concurrent loads each passed Phase 1 with `self._process is None`. Both ran Phase 2 (download), both reached Phase 3, and the Phase 3 defensive `_kill_process()` from #5171 collapsed them to one survivor only after both `subprocess.Popen` calls had landed. For the 86 GB MoE in #5161 / the model in #5401 the overlap window was tens of seconds, long enough to OOM the host. With a 0.6B model the pgrep timeline showed two simultaneous PIDs for 3.3 s on `main`. Fix: `studio/backend/core/inference/llama_cpp.py` * Add `self._serial_load_lock = threading.Lock()`. The whole body of `load_model` runs under this lock so two concurrent `/api/inference/load` requests are strictly sequential. The fine-grained `_lock` and the Phase 3 defensive `_kill_process()` from #5171 are kept as a second layer. `/unload`, `/status`, and `/load-progress` are unaffected because they only touch the fine-grained lock or read properties. * Add `self._extra_args` plus an `extra_args` property, written inside `load_model` whenever the caller supplies a non-`None` value. `unload_model()` deliberately does not reset it so the route layer can inherit the args across the frontend's `/unload` + `/load` gap. `studio/backend/routes/inference.py` * Add `_request_matches_loaded_settings(request, llama_backend)` that compares `max_seq_length`, `cache_type_kv`, `speculative_type`, `chat_template_override`, and `llama_extra_args` between the incoming request and the live backend. Same-(model, variant) requests whose runtime settings differ now fall through to a real reload instead of returning `already_loaded`. A missing `llama_extra_args` field on the request is treated as "inherit current", so the short-circuit still fires when the only difference is the frontend not echoing the CLI flags back. * GGUF load branch inherits `llama_extra_args` from `llama_backend.extra_args` when the request omits the field, re-validates through `validate_extra_args`, and forwards the result to `load_model(...)`. An explicit `[]` from the caller is still honoured as "clear". Verified end to end against a live `unsloth studio run` instance: | Scenario | Before | After | | --------------------------------------------------------------- | --------- | ------------------------------------------------------------------------ | | `/load` same (model, variant, settings) | 1 PID, `already_loaded` | unchanged | | `/load` same model, variant, new `cache_type_kv=q8_0` ctx=8192 | `already_loaded`, settings dropped | `loaded`, `/status` reports the new settings, new server has `-c 8192 --cache-type-k q8_0 --top-k 20 --seed 42` | | Frontend Apply `/unload` + `/load`, new settings, no `llama_extra_args` field | Drops `--top-k 20 --seed 42` | Preserves `--top-k 20 --seed 42` | | `/unload` + two parallel `/load` | Two PIDs for 3.3 s | Max simultaneous count = 1 across the full pgrep timeline | | `/load` with `llama_extra_args=[]` (explicit clear) | n/a | `loaded`, new server has no `--top-k` / `--seed` | | `/load` with `llama_extra_args=["--top-k","30","--seed","7"]` (override) | n/a | `loaded`, new server has the supplied flags | `pytest studio/backend/tests` is green except for one pre-existing terminal-width-sensitive assertion (`test_studio_api.py::test_help_output`) and the pre-existing `test_studio_api.py` fixture errors that fail on unmodified main too. No new regressions. * Studio: track requested n_ctx so Auto-slider flips trigger a reload Review feedback on PR #5427 from gemini-code-assist. The original short-circuit compared ``request.max_seq_length`` against ``llama_backend.context_length`` (the effective context). VRAM-fit logic can cap the running server below what the caller asked for, so this comparison incorrectly returns ``already_loaded`` when the user flips the slider from an explicit length (e.g. 8192) back to "Auto" (0): the explicit request was capped to, say, 4096, and the new "Auto" request reads ``backend.context_length == 4096`` and decides nothing changed. Track the originally requested ``n_ctx`` on the backend instead and compare against that. ``requested_n_ctx == 0`` means the last load asked for the model's native length; ``request.max_seq_length == 0`` matches it. Verified in the sandbox suite (now 90 tests): - ``test_explicit_to_auto_triggers_reload`` -- loaded with explicit 8192, then Apply with ``max_seq_length=0`` falls through to a real reload and the new server runs at the native 40960. - ``test_auto_to_explicit_triggers_reload`` -- inverse direction. - ``test_explicit_to_same_explicit_short_circuits`` -- re-Apply with the same explicit value still short-circuits (no needless reload). - Existing scenarios (kv change, spec change, template change, extra args inherit, parallel-load stress, frontend Apply flow) unchanged. ``pytest studio/backend/tests`` still green on the same set of tests; the pre-existing ``test_help_output`` failure and ``test_studio_api`` fixture errors are unaffected. * Studio: tighten comments in the 5401 fix Trim the verbose explanatory comments and docstrings introduced in |
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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
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!