Unsloth is a local UI for training and running Gemma 4, Qwen3.6, DeepSeek, Kimi, GLM and other models.
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unsloth
- Python 71.5%
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9369dd47e6* Add FP8/FP4 compressed export to save_pretrained_merged
Adds compressed-tensors export (for vLLM) to save_pretrained_merged /
push_to_hub_merged via llm-compressor, alongside the existing lora /
merged_16bit / merged_4bit / gguf / torchao paths:
model.save_pretrained_merged("model", tokenizer, save_method="fp8")
Supported save_method values: fp8 (FP8_DYNAMIC), mxfp4, nvfp4 (W4A4) and
mxfp8. The LoRA is merged to 16bit at save_directory, then a quantized
checkpoint is written to save_directory + "-<fmt>". nvfp4 needs a small
calibration set (defaults to ultrachat, overridable via calibration_dataset).
Notes:
- llm-compressor is installed lazily on first use, pinning the current torch
and transformers via a constraints file so they are not upgraded (a plain
install pulls transformers>=5 and breaks Unsloth).
- Quantization runs in a separate process (unsloth/_compressed_quantize.py,
launched by file path) so Unsloth's transformers attention patches do not
interfere with the forward llm-compressor runs during calibration, mirroring
how GGUF export shells out to llama.cpp.
- mxfp8 needs a newer llm-compressor (transformers>=5); it is recognised and
raises a clear error until that stack is available.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Address review: main-process guard, calibration subsampling, tokenizer + dtype handling
- Route the 16bit merge through unsloth_generic_save for both LoRA and full
finetuned models, so non-PEFT models are written in 16bit consistently
instead of saving the original (possibly quantized) weights directly.
- Honor is_main_process: only the main process quantizes and writes the
compressed output, so distributed ranks do not race on the same dirs.
- Subsample an in-memory calibration Dataset before save_to_disk so large
training sets are not fully copied to a temp dir.
- Tolerate a missing tokenizer in the converter (data-free exports); still
require one for calibration based schemes.
- Open config.json via a context manager in both files.
- Drop the redundant nvfp4 entry from the unsupported-name check (fp4 covers it).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Add direct LoRA to GGUF export and harden FP8/FP4 compressed export
- Run llm-compressor install and scheme check before the 16bit merge so
unsupported schemes (e.g. mxfp8) fail fast without writing a checkpoint
- Only the main process installs, merges, quantizes and uploads; isolate
hub pushes to a temp dir and clean all temp dirs in a finally
- Forward standard save kwargs (state_dict, max_shard_size, ...) to the merge
- Fall back to the first dataset split for Hub calibration ids
- Export LoRA adapters to GGUF via convert_lora_to_gguf.py: modernize
save_pretrained_ggml/push_to_hub_ggml and add save_method="lora" to
save_pretrained_gguf/push_to_hub_gguf; resolve base from the adapter config
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fix LoRA GGUF shell-injection test and compressed export trailing-slash path
- Update tests/saving/test_save_shell_injection.py for the new delegation: the
LoRA to GGUF conversion now lives in _unsloth_save_lora_gguf, so assert it
passes argv as a list with no shell=True and that the legacy ggml wrappers
delegate to it instead of calling subprocess.Popen directly
- Normalize the local save_directory before building the "<dir>-<fmt>" sibling
so a trailing slash no longer nests the compressed output inside the 16bit dir
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Polish FP8/FP4 and LoRA GGUF export after review
- Warn (not silently downgrade) when an explicit quantization_method is not a
valid LoRA GGUF outtype; default stays f16
- Correct the inference hardware note: MXFP8 is 8-bit (cc >= 8.9), only FP4
needs Blackwell for full activation quantization
- Document that a local fp8/fp4 save keeps the 16bit merge at save_directory
and writes the quantized checkpoint to save_directory + "-<fmt>"
* Use sequential calibration pipeline and validate Hub access early
- nvfp4 calibration no longer forces the memory-hungry "basic" pipeline. The
quantization runs in a clean subprocess, so llm-compressor's default
sequential pipeline (layer-by-layer onloading) works and lets large models
that do not fit at once still calibrate; fall back to "basic" only if tracing
fails
- For push_to_hub compressed exports, create/validate the repo up front so a bad
token or denied repo fails before the merge and quantization instead of after
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Harden compressed export: explicit sequential pipeline, base-tokenizer calibration, GPU memory
- nvfp4 calibration now passes pipeline="sequential" explicitly (layer-by-layer
onloading) instead of relying on the inferred default, with a "basic" fallback
- Calibration datasets with a messages column no longer require a chat template:
base / non-chat tokenizers fall back to concatenating message contents
- Free the in-memory model's CUDA memory before the quantize subprocess loads its
own copy from disk (best-effort, single-device non-quantized only; restored
afterward), so a single GPU need not hold two copies at once
- Create the calibration temp dir in the system temp location instead of next to
the save directory, avoiding stray dirs in the workspace
* Free the failed calibration model before the basic-pipeline retry
In the sequential -> basic NVFP4 fallback, release the partially-processed model
and clear the CUDA cache before loading a fresh copy, so the retry does not
transiently hold two model copies on the GPU.
* Harden calibration data handling and compressed-export edge cases
- Calibration messages without a chat template now handle multimodal (list)
content, None content, and null message rows instead of crashing on join
- Raise a clear error when the calibration dataset is empty after subsampling
- Reset llm-compressor's global session before freeing the model in the
sequential -> basic NVFP4 fallback, so the old model is actually released
- LoRA GGUF export accepts a single-element list quantization_method
- Attach datasets metadata to the pushed repo on compressed hub exports
- Warn (instead of silently) if the model cannot be restored to its device
- Raise a clear error if the LoRA base model id cannot be determined
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Handle DatasetDict calibration, MoE routers, and MTP models in compressed export
- Reduce an in-memory DatasetDict calibration set to a single split before row
subsampling, so save_to_disk does not copy every split to the temp dir
- For MoE models, keep the router/gate unquantized and pass
moe_calibrate_all_experts so every expert is calibrated
- Warn when a model carries MTP / speculative-decoding tensors that the
compressed export does not include
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Support many more compressed-tensors schemes and address review
- Expand save_method to cover the full set of compressed-tensors preset schemes:
FP8 (dynamic/static/block), INT8, W8A8, W8A16, W4A16(+asym), W4A8, W4AFP8,
MXFP4(+A16), NVFP4(+A16), plus the gated MXFP8; calibration is used only for
the static-activation schemes (FP8 static, NVFP4)
- Broaden the near-miss save_method error to cover int/w-prefixed names
- MoE: also keep the Qwen shared-expert gate unquantized
- Strip non-model-input columns from already-tokenized calibration data so the
collator does not choke on a leftover messages column
- Forward the Hub token to the LoRA converter and the quantize subprocess so
gated/private base models and calibration datasets work without a global login
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Collapse compressed-tensors export help line so ruff-format converges
The print line in print_quantization_methods needed two ruff-format passes to
reach a fixpoint (merge implicit string concat, then collapse the single-arg
print). pre-commit.ci applies one pass per run, so it kept reformatting. Land
the converged single-line form directly.
* Add CPU-only regression tests for the export API
Cover all export paths without a GPU, for slow CPU-only CI:
- pure-function checks of the compressed-tensors scheme registry and save_method
normalization (aliases, calibration flags, near-miss errors)
- AST checks that every merged saver dispatches compressed export, the GGUF savers
expose the lora branch, torchao routes PTQ/QAT, the public methods stay attached,
and the export subprocesses remain shell-safe (argv list, sys.executable, no shell)
- monkeypatched dispatch checks that fp8/nvfp4/merged_16bit, the LoRA-GGUF outtype
resolution, and torchao PTQ/QAT reach the right helper with the right arguments
* Run the CPU-only export tests in consolidated CI
tests/saving is --ignored by the Repo tests (CPU) job, so the new GPU-free export
tests are added by path to consolidated-tests-ci.yml (collection sanity + Bucket-A run),
alongside the existing CPU saving tests, so they actually execute on CPU CI.
* Add GPU GGUF export + llama-cli inference smoke test
tests/saving/test_gguf_export_and_inference.py: skipif no CUDA. Trains a tiny
phrase-imprinting LoRA, exports a full-model q8_0 GGUF (merge -> convert_hf_to_gguf
-> llama-quantize), asserts a valid GGUF (magic + size), and - when a llama-cli
binary is available - runs one bounded generation (byte cap + watchdog kill) and
asserts the trained phrase round-trips through HF -> GGUF -> quantize -> inference.
The llama-cli step skips gracefully since the export only builds llama-quantize.
* Fix variant mismatch in compressed (FP8/FP4) export
save_pretrained_merged(..., save_method=fp8/nvfp4, variant=...) forwarded
the variant into the intermediate 16bit merge, so Transformers wrote
variant-named shards (model.<variant>.safetensors). The converter
subprocess then reloaded that directory with the default weight filenames,
so the compressed export failed after doing the merge.
Pop the variant out of the intermediate merge (internal staging that the
subprocess reloads with default names) and forward it via --variant so it
is applied to the final compressed checkpoint instead. Add a CPU AST guard
for the contract.
* Harden export paths from review
- install_llm_compressor: fall back to uv pip when this interpreter has no
pip seeded (uv-created/relocatable venvs), instead of failing with
No module named pip.
- LoRA GGUF export: if convert_lora_to_gguf.py is missing (a prebuilt or
reused CWD llama.cpp install carries binaries but not the converter
script), force a dedicated source checkout that ships it.
- push_to_hub_gguf(save_method=lora): return on non-main ranks, matching the
local save_pretrained_gguf lora branch, so only rank 0 converts/uploads.
- compressed export VLM detection: require a vision_config or a
ForVisionText2Text architecture; a bare *ForConditionalGeneration also
matches text seq2seq models (T5/BART/Whisper) and is no longer treated as
a VLM on its own.
- GGUF GPU smoke test: drop SFTConfig(max_length=1024), which raises under
newer TRL padding-free training; length enforcement is not needed here.
* Add imatrix option to GGUF export, enabling IQ low-bit quants
save_pretrained_gguf / push_to_hub_gguf gain imatrix_file:
None -> no imatrix (unchanged)
'/path' -> pass to llama-quantize --imatrix (a *.gguf_file is renamed to *.gguf)
True -> download the upstream unsloth/<base>-GGUF imatrix (imatrix_unsloth.dat or
.gguf_file), raising a clear error if none exists
An importance matrix unlocks the IQ low-bit quants (iq2_xxs, iq4_xs, ...), which were hard
disabled before. They are gated: requesting one without an imatrix raises a clear error.
- _resolve_imatrix_file resolves path/True (PEFT base first, normalized via get_model_name,
derives unsloth/<base>-GGUF, copies out of the HF cache before renaming *.gguf_file).
- IMATRIX_QUANTS registry replaces the old commented-out IQ entries; save_to_gguf accepts a
resolved imatrix and threads it into the quantize calls.
- The --imatrix flag is emitted by unsloth_zoo's quantize_gguf (companion change). save.py
fails fast with an upgrade hint if the installed unsloth_zoo lacks the imatrix kwarg.
Tests: tests/saving/test_imatrix_export.py (CPU: resolution, repo derivation, IQ gate,
--imatrix wiring) wired into CI; tests/saving/test_gguf_export_and_inference.py extended with
GPU iq2_xxs/iq4_xs export + inference. Verified end to end on Llama-3.2-1B: imatrix
auto-downloaded, iq2_xxs/iq4_xs exported and run via llama.cpp.
Note: requires the companion unsloth_zoo quantize_gguf imatrix change.
* Address imatrix/compressed review feedback: unsloth org GGUF repo, fail-fast, calibration split
- imatrix auto-resolve (imatrix_file=True): derive the upstream repo as unsloth/<base>-GGUF
instead of <org>/<base>-GGUF, so official bases (e.g. meta-llama/Llama-3.1-8B-Instruct) find
the matching Unsloth GGUF imatrix repo rather than failing on a nonexistent meta-llama/...-GGUF.
- Resolve/validate the imatrix before the 16-bit merge in save_pretrained_gguf, so a bad path or
an unavailable upstream imatrix fails fast instead of after a long, multi-GB merge.
- Compressed calibration: when a Hub dataset has no "train" split, resolve the first split name
and slice it, instead of materializing the whole dataset just to take num_samples rows. Keeps
the original materialize-then-subselect path as a last resort.
Tests: add unsloth/<base>-GGUF mapping for an official base id, and create the imatrix file in the
quantize_gguf flag test (quantize_gguf now validates the imatrix exists).
---------
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
|
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| .github | ||
| images | ||
| scripts | ||
| studio | ||
| tests | ||
| unsloth | ||
| unsloth_cli | ||
| .gitattributes | ||
| .gitignore | ||
| .pre-commit-ci.yaml | ||
| .pre-commit-config.yaml | ||
| build.sh | ||
| cli.py | ||
| CODE_OF_CONDUCT.md | ||
| CONTRIBUTING.md | ||
| COPYING | ||
| install.ps1 | ||
| install.sh | ||
| LICENSE | ||
| pyproject.toml | ||
| README.md | ||
| 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.
- Chat with images, audio, PDFs, code, DOCX and more. Connect API providers (OpenAI, Anthropic) or servers (vLLM, Ollama).
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: Training, MLX and GGUF inference are ALL supported.
- AMD: Chat + Data works. Train with Unsloth Core. Studio support is out soon.
- Multi-GPU: Available now, with a major upgrade on the way
macOS, Linux, WSL:
curl -fsSL https://unsloth.ai/install.sh | sh
Use the same command to update.
Windows:
irm https://unsloth.ai/install.ps1 | iex
Use the same command to update.
Launch
unsloth studio -p 8888
For cloud or global access, add -H 0.0.0.0. By default, Unsloth is accessible only locally.
To reach Studio over HTTPS, use unsloth studio --secure. Studio stays bound to localhost and is reached only through a free Cloudflare tunnel, which publishes it at a public https://*.trycloudflare.com URL (it fails closed if the tunnel can't start, so the raw port is never exposed). This makes Studio reachable from the internet, so anyone with the link and API key can use it and run code: keep your API key private (see Remote access below).
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
- Connections: Connect any API provider (OpenAI, Anthropic) or server (vLLM, Ollama). Guide
- MTP: Run Qwen3.6 MTP in Unsloth. MTP settings are autoset specific to your hardware. Guide
- 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 / Nightly / Experimental installs: macOS, Linux, WSL:
The developer install builds from the main branch, which is the latest (nightly) source.
git clone https://github.com/unslothai/unsloth
cd unsloth
./install.sh --local
unsloth studio -p 8888
To install into an isolated location (its own virtual env, auth/, studio.db, cache and llama.cpp build), set UNSLOTH_STUDIO_HOME and pass it again at launch:
UNSLOTH_STUDIO_HOME="$PWD/.studio" ./install.sh --local
UNSLOTH_STUDIO_HOME="$PWD/.studio" unsloth studio -p 8888
Then to update :
cd unsloth && git pull
./install.sh --local
unsloth studio -p 8888
Developer / Nightly / Experimental installs: Windows PowerShell:
The developer install builds from the main branch, which is the latest (nightly) source.
git clone https://github.com/unslothai/unsloth.git
cd unsloth
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass
.\install.ps1 --local
unsloth studio -p 8888
To install into an isolated location (its own virtual env, auth/, studio.db, cache and llama.cpp build), set UNSLOTH_STUDIO_HOME and pass it again at launch:
$env:UNSLOTH_STUDIO_HOME="$PWD\.studio"; .\install.ps1 --local
$env:UNSLOTH_STUDIO_HOME="$PWD\.studio"; unsloth studio -p 8888
Then to update :
cd unsloth; git pull
.\install.ps1 --local
unsloth studio -p 8888
Remote access: --secure (HTTPS tunnel) vs raw port
By default unsloth studio binds to 127.0.0.1 (this machine only). To reach it from another device, pick one of:
--secure(recommended): serve only through a free Cloudflare HTTPS link. Studio stays bound to localhost and the tunnel provides the public URL; it fails closed (does not start) if the tunnel can't come up, so the raw port is never exposed.
unsloth studio --secure -p 8888
-H 0.0.0.0: bind the raw port on all network interfaces, reachable from anywhere on the network. Only use this on a trusted network.
unsloth studio -H 0.0.0.0 -p 8888
Server-side tools (web search, Python and terminal code execution) run as your user and are on by default. Anyone who can reach the server with the API key can run code on this machine, so keep your API key private and pass --disable-tools when exposing Studio.
Advanced launch options
Installer options can be passed as environment variables. On macOS, Linux and WSL place the variable after the pipe so the shell passes it to sh; on Windows set it with $env: before piping to iex.
Skip PyTorch (GGUF-only mode):
curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_NO_TORCH=1 sh
$env:UNSLOTH_NO_TORCH=1; irm https://unsloth.ai/install.ps1 | iex
Pin the Python version:
curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_PYTHON=3.12 sh
$env:UNSLOTH_PYTHON='3.12'; irm https://unsloth.ai/install.ps1 | iex
Install to a custom location with UNSLOTH_STUDIO_HOME:
curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_STUDIO_HOME=/abs/path sh
$env:UNSLOTH_STUDIO_HOME='C:\path'; irm https://unsloth.ai/install.ps1 | iex
On macOS, the installer defaults to the system certificate store (UV_SYSTEM_CERTS=1) so uv trusts the CAs in your Keychain, needed behind TLS-inspecting proxies (Cisco Umbrella, Zscaler, etc.). Opt out with:
curl -fsSL https://unsloth.ai/install.sh | UV_SYSTEM_CERTS=0 sh
Point the frontend build at a corporate npm mirror/proxy with UNSLOTH_NPM_REGISTRY (for the developer install behind a firewall that blocks registry.npmjs.org):
UNSLOTH_NPM_REGISTRY=https://artifactory.example.com/api/npm/npm/ ./install.sh --local
$env:UNSLOTH_NPM_REGISTRY='https://artifactory.example.com/api/npm/npm/'; .\install.ps1 --local
It is threaded as --registry into the Studio frontend npm/bun installs; the supply-chain locks (7-day min-release-age, exact version pins) stay in force.
Cap Studio's native CPU thread pools on high-core hosts: UNSLOTH_CPU_THREADS=8 unsloth studio -p 8888.
Uninstall
The recommended way to fully remove Unsloth Studio is the matching uninstall script for your OS. It stops any running servers, removes the install dir, the launcher data dir, the desktop shortcut, and any platform-specific entries (macOS .app bundle + Launch Services on Mac; Start Menu, HKCU\Software\Unsloth registry key and user PATH entries on Windows):
- MacOS, WSL, Linux:
curl -fsSL https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.sh | sh - Windows (PowerShell):
irm https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.ps1 | iex
If you only want to drop the install dir and keep the launcher/shortcut for a later reinstall, you can instead run rm -rf ~/.unsloth/studio (Mac/Linux/WSL) or Remove-Item -Recurse -Force "$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!