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150b5ba25a* feat(studio): share the llama-server --parallel bounds as PARALLEL_MIN/MAX
The per-load parallel-slots field needs the same 1..64 range the CLI flag
validates, but models/inference.py cannot import run.py (run.py builds the
app that imports routes that import models). Promote the bounds into this
dependency-free module, which already owns the -np/--parallel semantics, and
record the deliberate mirrors that cannot import it (run.py, the unsloth CLI,
the web UI). The denylist entry stays: the first-class field is now the single
write path for the slot count, so a pass-through would still desync the
committed bookkeeping from llama-server.
* feat(studio): note the per-load override in the --parallel help text
--parallel is now the server-wide default that a per-load n_parallel (the
Studio Parallel Slots run setting) can override, not the definitive slot
count. Point at the new control so a user does not conclude a restart is the
only way to change slots, and record the shared PARALLEL_MIN/MAX mirror
alongside the existing CLI one.
* feat(studio): add n_parallel to LoadRequest and echo the slot counts
LoadRequest.n_parallel (optional, PARALLEL_MIN..PARALLEL_MAX) lets a load pick
its own llama-server --parallel count; omitted, the server-wide launch default
applies. ValidateModelRequest carries it too so the training-coexistence
estimate sizes the KV cache like the follow-up load rather than passing on a
smaller footprint.
LoadResponse and InferenceStatusResponse gain both requested_parallel_slots
(what the load was invoked with) and parallel_slots (what llama-server
actually runs after the fitter's slot reduction), so a client can tell an
honored request from a reduced one. Both are None where --parallel has no
meaning: non-GGUF loads and the diffusion runner.
* feat(studio): record the requested parallel-slot count on the backend
The auto GPU-memory fit may launch fewer slots than requested to keep the
model fully on GPU, so the committed effective count cannot answer "is the
live server what this request asked for?". Store the invoked count separately
(mirroring the _requested_n_ctx pattern) from the pre-reduction pending
kwargs, expose it as requested_parallel_slots, and have _already_in_target_state
compare requested-vs-requested: comparing against the effective count would
reload -- and re-reduce -- forever on an identical Apply.
The comparison sits in the non-diffusion branch, since the diffusion runner
ignores --parallel entirely. The requested value shares the effective count's
lifecycle, so every unload/kill path clears it and a stale count cannot
poison the next load's dedupe.
* feat(studio): honor a per-load parallel-slot count in /load and /validate
Resolve the slot count once per load -- the request field if set, else the
server-wide launch default -- and feed it to every consumer that must agree:
the training-coexistence guard, the llama-server load kwargs, and the reload
dedupe. Without the dedupe comparison a changed slot count would be swallowed
as already_loaded; it compares requested-vs-requested and skips the diffusion
runner, which ignores --parallel.
app.state.llama_parallel_slots is deliberately never written: it stays the
launch intent and the admission-queue fallback, so one load's override cannot
leak into later loads. /validate resolves the same way so its estimate cannot
undercount what the load then allocates.
Both /load returns and /status echo the counts through one helper, which
reports None for diffusion -- its load never commits a count, so echoing the
reset placeholder would fabricate an "invoked with 1 slot".
* feat(studio): accept nParallel in the chat-preset load config
ChatPresetLoadConfig is extra="forbid", so a preset carrying the new parallel
slots knob would 422 the whole settings sync without this field. Bounds come
from the shared PARALLEL_MIN/MAX rather than literals, so a future range
change cannot start rejecting presets the UI still allows.
* test(studio): cover the per-load parallel-slots knob
Pins the behaviors a regression would silently break: the requested-vs-effective
dedupe (comparing against the reduced count would reload forever), the diffusion
skip and its None echo, the requested count's reset lifecycle, and its commit
from the pre-reduction pending kwargs.
Also pins the three bounds mirrors that cannot import PARALLEL_MIN/MAX (run.py,
the unsloth CLI, the web UI) plus the preset model that can, so a range change
cannot leave one of them clamping or rejecting at the old limit.
* test(studio): refresh the --parallel denylist comments for the UI knob
The pinned rationale said the typer flag owns the slot count and pointed users
at a Studio restart. Parallel Slots / LoadRequest.n_parallel is now the other
managed writer, and the 1..64 guard is the shared PARALLEL_MIN/MAX -- a reader
following the old comments would conclude the UI control does not exist.
* feat(studio): note the per-load override in the CLI --parallel help
Both the plain-serve and `unsloth studio run` flags now describe a server-wide
default the Studio Parallel Slots run setting can override per load, matching
the backend help text.
* feat(studio): remember a per-model Parallel Slots override
nParallel joins the per-model config with the same null-means-follow-the-default
convention as the other knobs: null keeps the server-wide --parallel count, so
a blank control never pins a number and isDefaultConfig still deletes an
otherwise-untouched config instead of storing it.
The value is re-clamped to N_PARALLEL_MIN/MAX on every localStorage read and
write (the store is user-editable), and listing it in STORED_CONFIG_FIELDS
keeps it from being dropped as an unknown key. Legacy blobs predate the knob,
so their migration carries null. No schema-version bump: an additive optional
field, like the GPU fields before it.
* feat(studio): bridge nParallel between the per-model config and the store
The config->store, store->config and equality helpers all need the new field:
without the equality arm a slots-only edit reads as unchanged, so Apply is
dropped and the dirty state never lights up.
* feat(studio): track the parallel-slot override in the chat runtime store
nParallel holds the editable override and loadedNParallel the value the last
successful load sent, which the failed-switch rollback re-sends. Both are
per-model: they clear on unload and on a model switch, unlike the standing
preferences (GPU memory mode, speculative type) that survive one.
There is deliberately no backend-echo field for the control: the echo is the
resolved count, so adopting it would pin a blank "follow the server default"
input to an explicit number.
* feat(studio): type n_parallel and the slot-count echoes
The load request gains the optional per-load slot count, and both the load
response and the status payload gain requested_parallel_slots (invoked) and
parallel_slots (actually running after the fitter's reduction). Keys stay
snake_case: the payload is serialized as-is, with no case conversion.
* feat(studio): forward n_parallel to the validate preflight
validateModel builds its own body rather than forwarding the load payload, so
the slot count has to be listed explicitly. Slots scale the KV estimate, and
the preflight exists to refuse a load the training guard would then 409 -- an
unforwarded count would validate a smaller footprint than the load allocates.
* feat(studio): include nParallel in the active model's config
The sidebar assembles the active model's config from individually subscribed
store fields; an unsubscribed field would leave the form showing a stale value
after any external change.
* feat(studio): add the Parallel Slots control to the run settings
A numeric input in the GGUF advanced section, blank meaning "follow the server
default". It clamps on change like the Draft Tokens field rather than using
NumericValueInput, so there is no blur-draft to lose when the user types a
value and immediately clicks Load.
hasNonDefaultAdvanced counts it too, so a remembered override reopens the
advanced section instead of hiding the setting that is actually in effect.
* feat(studio): key the sidebar config form on nParallel too
The signature drives the remount that re-seeds the form; without the new field
an externally changed slot count would leave the sidebar showing the old one.
* feat(studio): send the Parallel Slots override on load
performLoad snapshots the slot count at click time (staged run-settings config
first, else the store) and sends it on both the validate preflight and the
load, so the two size the same footprint. A cross-model switch re-baselines it
like the other per-model knobs -- the previous model's count must not follow
onto the next one -- and the failed-switch rollback re-sends the previous
model's value so a rescue reload cannot silently drop to the server default.
The success path keeps the click-time value rather than the response echo: the
echo is the count the fitter resolved, so adopting it would turn a blank
"follow the server default" control into an explicit pin. Slots are GGUF-only,
so a transformers load sends and records null instead of a phantom override.
* feat(studio): carry the slot override through the compare-pane load
The compare pane builds its own load request, so it needs the field explicitly
or a pane with a remembered override would load at the server default. Its
validate preflight sends the same count, matching the comment above it that
promises validation is sized exactly as the load below.
GGUF-gated on both calls, and the store adopts the pane's own click-time value
rather than the resolved echo, mirroring the single-model path.
* feat(studio): honor the remembered slot override on startup auto-load
The auto-load path reads the per-model config and forwards every other
remembered knob, so a remembered Parallel Slots value was the one setting lost
on the "load last used model" path: llama-server came back at the server-wide
default with the control showing blank, and the first manual Apply afterwards
then forced a needless reload because the counts disagreed.
* feat(studio): seed the slot baseline from the status echo
Only the rollback baseline is seeded, never the editable control: the echo is
the resolved count, so adopting it would pin a blank "follow the server
default" input to a number. Without the seed, loadedNParallel stayed null
after a tab reload or a second tab adopting the running model, and a failed
switch then rolled the previous model back at the server default while every
other knob was restored.
* feat(studio): capture Parallel Slots in chat presets
The knob joins the preset load config end to end: captured from the store,
re-clamped when read back (persisted presets are untrusted input), applied on
switch, and summarized in the preset chip. Its default is null, so
coalesceDefaultLoadKnobs keeps a default-only preset empty rather than
persisting a no-op override.
* feat(studio): re-derive the preset state when Parallel Slots changes
Both preset memos snapshot the store through capturePresetLoadConfig, so
without the new dependency a slots-only edit left the unsaved-changes flag and
the load summary showing the previous value.
* test(studio): pin the Parallel Slots wiring end to end
Source-contract coverage for the hops a refactor can silently drop: the three
/load builders (interactive, compare pane, startup auto-load) and their
validate preflights, per-model persistence and clamping, the UI row, and the
status seed -- including the negative assertion that hydration seeds only the
rollback baseline, never the control, so the resolved echo cannot pin a blank
"server default" input.
* test(studio): pin nParallel in the preset load config
Covers capture, clamped read-back and apply on the frontend, plus the backend
field itself: ChatPresetLoadConfig is extra="forbid", so a missing or drifted
field 422s every settings sync that carries a preset.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fall back to one slot when llama-server lacks --kv-unified for PR #7447
Without --kv-unified an explicit --parallel N makes llama-server give each slot -c/N, so on a build without the flag choosing N slots silently shrinks every context window for a feature that build cannot serve. Clamp to one slot and log why, placed after the requested count is captured so the echo still reports it and before the KV estimates so the fit matches what actually launches.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Clear the slot control on load paths that never send it, and size the training guard for diffusion
Four review findings on the per-load Parallel Slots knob.
The editable nParallel control means "follow the server default" when null, so
any success path that does not send a slot count has to clear it. Three paths
kept a value staged for a different model:
- chat-adapter.ts, cached non-GGUF auto-load: the interactive and compare
builders already clear both fields for a non-GGUF response, this third one
did not. The field never renders for a non-GGUF target, so the stale count
was invisible and unclearable from the UI yet still persisted, and it flips
isDefaultConfig so a user with no overrides silently gets a stored entry.
- chat-adapter.ts, fresh-model fallback: its request omits n_parallel but its
success state resynced every other knob and left the slots alone, so a staged
edit survived against a server running the default and the next Apply
reloaded at a count that load never sent.
- apply-inference-status-to-store.ts: on a model change underneath the tab
every sibling knob adopts the new model's status, but nParallel updated only
its baseline, so the previous model's explicit count followed onto the new
model and saving or reloading there pinned it. Clear the control and keep
seeding the baseline for the rollback.
The training-coexistence guard sized a diffusion GGUF with the requested slot
count. _estimate_kv_cache_bytes scales the SWA cache with slots
(swa_limit = swa * slots + ubatch), but load_model hands a diffusion target to
_start_diffusion_server before the slot plumbing, so that runner is always
single-slot. At the new default of 4 this inflated the estimate and could 409 a
load that fits. An unclassified GGUF keeps the requested count.
Backend base KV depends on -c alone, not on --parallel, which is why only the
SWA term is affected: llama.cpp PR 14363 and discussion 4130.
Tests: three training-guard cases in test_parallel_slots_per_load.py and one
source contract in test_model_picker_contracts.py, each mutation-checked.
174 passed across the backend slot/admission/training suites, 56 across the
frontend contract suites.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Keep the slot control when re-adopting the running model, and never record slots for a diffusion load
Two follow-ups from the latest review round.
The first is a regression from
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| 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 • News • 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).
- Compare any two models side by side with the same prompt.
- OpenAI/Anthropic-compatible APIs: Serve local models through
/v1/chat/completions,/v1/responsesand/v1/messages. - Connect local models to agents: Use
unsloth startwith Claude Code, Codex, Hermes and more. - Web/PDF search can read PDF papers, manuals and other PDF results.
- GGUF hardware controls: Choose GPUs/layers, offload MoE experts, use multi-GPU or Tensor Parallelism.
- The opt-in MCP control endpoint lets AI clients manage models, training, recipes and exports.
Training
- Train and RL 500+ models up to 2x faster with 70% less VRAM; MoE up to 12x faster.
- Train and run RL on AMD GPUs across Windows, WSL and Linux.
- Data Recipes: Auto-create datasets from PDF, CSV, DOCX etc. Edit data in a visual-node workflow.
- Reinforcement Learning uses 80% less VRAM for GRPO, FP8 and vision RL, with 7x longer contexts.
- Long-context training: 3x faster, 30% less VRAM and 500K+ context.
- Supports LoRA/QLoRA, full fine-tuning, RL, pretraining, 4-bit, 16-bit and FP8.
- Custom Triton and mathematical kernels built with PyTorch and Hugging Face.
- Observability: Monitor training live, track loss and GPU usage and customize graphs.
- Multi-GPU training is supported, with major improvements coming soon.
🚀 Unsloth Start
Unsloth Start connects Claude Code, Codex and other agents to local models with one command.
Start Unsloth, load a model, open your project folder, then run:
unsloth start claude
Replace claude with any supported agent:
| Agent | Command |
|---|---|
| Claude Code | unsloth start claude |
| OpenAI Codex | unsloth start codex |
| Hermes Agent | unsloth start hermes |
| OpenClaw | unsloth start openclaw |
| OpenCode | unsloth start opencode |
| Pi Coding Agent | unsloth start pi |
Claude Code, Codex, OpenCode and Pi can keep their current model and use Unsloth as a local subagent:
unsloth start claude --as-subagent --model unsloth/model-GGUF:quant
📥 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: Training, RL, chat and deployment work on Windows, WSL and Linux. Read the AMD guide.
- Vulkan: GGUF inference is supported on compatible GPUs, including Intel GPUs. Vulkan accelerates GGUF inference only; training still requires a supported PyTorch or MLX backend.
- 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.
To force the Vulkan llama.cpp backend, set UNSLOTH_FORCE_VULKAN=1 before installing or updating. The setting selects the llama.cpp binary bundle, so setting it only when launching Studio cannot replace an existing CPU bundle:
export UNSLOTH_FORCE_VULKAN=1
curl -fsSL https://unsloth.ai/install.sh | sh
Windows:
irm https://unsloth.ai/install.ps1 | iex
Use the same command to update.
To force the Vulkan llama.cpp backend, set the environment variable before running the installer or updater:
$env:UNSLOTH_FORCE_VULKAN=1
irm https://unsloth.ai/install.ps1 | iex
Re-running the current installer replaces a previously selected CPU bundle when the backend differs. A separate Vulkan SDK is not required; the GPU driver must provide a working Vulkan runtime.
Launch
unsloth studio -p 8888
For LAN or cloud access, add -H 0.0.0.0 (raw port only; add --cloudflare for a public URL). By default, Unsloth is accessible only locally.
To reach Unsloth over HTTPS, use unsloth studio --secure. Unsloth 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 Unsloth 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
- AMD training: Train, run RL, chat and deploy on AMD GPUs across Windows, WSL and Linux. Guide
- GGUF hardware controls: Choose GPU/layer placement, offload MoE experts and use multi-GPU or Tensor Parallelism. #6414
- Local models for any agent: Use
unsloth startwith Claude Code, Codex, Hermes, OpenCode, OpenClaw, Pi and more through Unsloth's OpenAI- and Anthropic-compatible APIs. Guide - MCP control endpoint: Let compatible clients manage models, training, recipes, checkpoints and exports. #7191
- Local inference reliability: Resume long chats faster, recover stalled downloads and reuse existing GGUF files. #7204 • #6858 • #7209
- New models: Qwen-AgentWorld, Ornith, Kimi K2.7 Code and MiniMax M3
- GLM-5.2: Run Z.ai's 744B-parameter, 1M-context open model locally with Unsloth Dynamic GGUFs. Guide
- DeepSeek-V4: Run DeepSeek-V4-Flash locally with corrected multi-turn and tool-calling behavior. Guide
- DiffusionGemma: Run and fine-tune Google's diffusion language model with 1.8x faster inference in Unsloth Studio. Guide
- Qwen3.6: Run and train Qwen3.6 with MTP for 1.4-2.2x faster inference and NVFP4 quants for supported GPUs. Guide
- Gemma 4: Run and train Gemma 4 text, image and audio models with QAT, MTP, GGUF and MLX support. Guide
- MCP servers: Connect local models to files, apps, databases and external tools through Model Context Protocol. Guide
- Connections: Mix local models with API providers (OpenAI, Anthropic) or servers (vLLM, Ollama) in the same interface. Guide
- Introducing Unsloth Studio: our new web UI for running and training LLMs. Blog
- 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. Unsloth 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 (subject to your firewall). It does not create a public internet URL; add--cloudflareto also publish an internet-reachablehttps://*.trycloudflare.comlink even behind a firewall. Only use this on a network you trust.
unsloth studio -H 0.0.0.0 -p 8888
The Cloudflare tunnel is off by default: -H 0.0.0.0 exposes the raw port only, not a public internet URL. Pair the wildcard bind with --cloudflare (unsloth studio -H 0.0.0.0 --cloudflare) to also publish a public https://*.trycloudflare.com link, or prefer --secure (above), which keeps the raw port private. --cloudflare has no effect on a loopback bind.
On a wildcard bind Unsloth works out the address to share by asking ifconfig.me for the public IP, then asks check-host.net whether that port is reachable so it can tell you if a firewall is in the way. Both contact a third party. Set UNSLOTH_STUDIO_DISABLE_PUBLIC_CHECK=1 to skip them; the banner then shows the LAN address and no reachability line.
The first time Unsloth is published on a public URL (--secure or --cloudflare) with the auto-generated admin password still in place, it asks for a new admin password in the terminal (masked input with confirmation) before the public link goes up. Without an attached terminal it warns instead and keeps the bootstrap deadline: Unsloth shuts down after UNSLOTH_STUDIO_BOOTSTRAP_TIMEOUT (default 1 hour) unless the password is changed in the web UI.
For headless setups that cannot answer that prompt, set the initial admin password non-interactively with --password (only takes effect when no password is set yet; if one already exists it is a hard error, so rotate later with unsloth studio reset-password):
unsloth studio --secure --password 'your-strong-password' # visible in `ps`/history
UNSLOTH_STUDIO_PASSWORD='your-strong-password' unsloth studio --secure # via env var
printf '%s\n' 'your-strong-password' | unsloth studio --secure --password - # via stdin
A literal --password VALUE is visible in the process list and shell history, so prefer the UNSLOTH_STUDIO_PASSWORD env var or --password - (stdin) for automation. This applies to any launch (public or a headless -H 0.0.0.0 bind), and the password is set in the parent before the server binds, so it never reaches a re-executed child process.
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 Unsloth.
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
Skip the post-install prompt that starts Unsloth (useful for automated installs):
curl -fsSL https://unsloth.ai/install.sh | UNSLOTH_SKIP_AUTOSTART=1 sh
$env:UNSLOTH_SKIP_AUTOSTART=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
$env:UNSLOTH_NPM_REGISTRY='https://artifactory.example.com/api/npm/npm/'; .\install.ps1 --local
It is threaded as --registry into the Unsloth frontend npm/bun installs; the supply-chain locks (7-day min-release-age, exact version pins) stay in force.
Cap Unsloth's native CPU thread pools on high-core hosts: UNSLOTH_CPU_THREADS=8 unsloth studio -p 8888.
Uninstall
The recommended way to fully remove Unsloth Studio is the matching uninstall script for your OS. It stops any running servers, removes the install dir, the launcher data dir, the desktop shortcut, and any platform-specific entries (macOS .app bundle + Launch Services on Mac; Start Menu, HKCU\Software\Unsloth registry key and user PATH entries on Windows):
- MacOS, WSL, Linux:
curl -fsSL https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.sh | sh - Windows (PowerShell):
irm https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.ps1 | iex
If you only want to drop the install dir and keep the launcher/shortcut for a later reinstall, you can instead run rm -rf ~/.unsloth/studio (Mac/Linux/WSL) or Remove-Item -Recurse -Force "$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!