Unsloth is a local UI for training and running Gemma 4, Qwen3.6, DeepSeek, Kimi, GLM and other models.
https://unsloth.ai/docs
agent
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fine-tuning
gemma
gemma3
gpt-oss
llama
llama3
llm
llms
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openai
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self-hosted
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tts
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unsloth
- Python 71.5%
- TypeScript 22.7%
- Shell 1.9%
- PowerShell 1.6%
- Rust 1.5%
- Other 0.7%
9b5c94df32* CLI: stop `unsloth connect` from leaking Studio credentials to unverified servers
`unsloth connect` (and `unsloth chat`) discovered a Studio base URL from
UNSLOTH_STUDIO_URL or the default localhost port after only an unauthenticated
/api/health probe, then sent credentials to it:
- keyless connect iterated every cached API key and sent each as a bearer token
to {base}/v1/models, so a malicious or port-preempting endpoint could harvest
all of them;
- with no cached key it self-issued a Studio JWT and POSTed it to
{base}/api/auth/api-keys;
- unsloth chat sent the same self-issued JWT to the discovered base.
The key cache was a flat, global list with no binding to a server identity, so a
key minted for one Studio could be replayed to any other.
Changes:
- Scope the agent key cache per base URL so a key is only ever replayed to the
exact server it was minted for. Pre-scoping flat caches are ignored rather
than replayed (at most one extra local mint on the next launch).
- Gate every automatic credential flow to loopback bases. A non-loopback
UNSLOTH_STUDIO_URL now requires an explicit --api-key and nothing is sent
automatically. SSH-tunnelled Studios that land on 127.0.0.1 keep working.
- Mint the API key locally against the Studio auth DB instead of POSTing a
self-issued JWT over the network, so no bearer token leaves the process on the
local path.
- Apply the same loopback gate to connect_studio_server (used by unsloth chat).
Fully closing same-host loopback-port preemption needs a signed /api/health
handshake so the client can verify the server identity before sending anything;
that is tracked as a server-side follow-up.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* CLI: verify Studio server identity before auto-sending credentials
Adds a challenge-response so `unsloth connect` and `unsloth chat` can confirm a
discovered loopback endpoint is really this install's Studio (not a process
that preempted the port) before sending it a cached or freshly minted
credential. This closes the same-host loopback-preemption gap left open by the
previous commit, which could only limit the blast radius.
Server:
- storage.get_or_create_identity_secret(): a dedicated server-wide secret in
app_secrets (kept separate from the per-user JWT secret), readable only by
the same OS user.
- storage.compute_identity_proof(nonce) = HMAC-SHA256(identity secret, nonce).
- GET /api/auth/identity?nonce=<base64url>: unauthenticated, returns the proof.
The nonce is opaque to the server and the proof reveals nothing about the
secret, so answering is safe.
Client:
- verify_studio_identity(base): sends a fresh 32-byte nonce, recomputes the
expected HMAC from the local same-user secret, and constant-time compares.
Fails closed on any error.
- connect._agent_api_key gates the loopback cached-key replay and the local
mint on it; connect_studio_server (used by unsloth chat) gates the
self-issued JWT on it.
A server that cannot read this install's secret (a different OS user, or a
remote/fake endpoint) cannot produce a matching proof, so the client refuses
and falls back to an explicit --api-key.
Tests: studio/backend/tests/test_identity.py (proof determinism, secret
persistence and caching, route response and nonce validation) and additions to
test_connect.py (the verify gate refuses when unverified, an explicit key skips
the check, and an end-to-end client plus server handshake against a stub HTTP
server).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* CLI: mint through the verified server instead of the local auth DB
CodeQL (py/clear-text-storage-sensitive-data) flagged the API key written to
the per-server cache and the agent config files once it was sourced from
storage.create_api_key(); the original HTTP-minted key did not trip the query.
Now that the identity handshake cryptographically confirms the loopback
responder really is this Studio before anything is sent, minting through the
server's /api/auth/api-keys endpoint with a self-issued JWT is safe again and
restores the original, CodeQL-clean data flow. The local-DB mint path is
removed.
The security properties are unchanged: discovery is still loopback-gated and
identity-verified, the key cache is still scoped per server, and a credential
reaches the server only after the handshake has proven its identity. The only
difference from the previous commit is that the self-issued JWT is sent to the
already-verified loopback server rather than the key being minted in-process.
Tests updated to mint through the fake server again.
* CLI: address review feedback on connect credential handling
- Reuse a saved per-server key before the loopback/identity gate. Keys are
scoped per base URL, so a key the user saved with --api-key for a remote or
SSH-tunnelled Studio (whose identity secret the local handshake can't match)
is replayed only to that exact server. The loopback + identity-handshake gate
now guards just auto-minting (self-issuing a JWT and creating a new key),
which is the path that needs a cryptographically verified local Studio. Fixes
keyless reuse being impossible for remote/tunnelled Studios the user had
saved a key for.
- connect_studio_server (unsloth chat / inference): when the user explicitly set
UNSLOTH_STUDIO_URL but the server can't be safely attached (non-loopback, or
identity unverifiable), fail with a clear message instead of silently loading
the model locally. Opportunistic discovery of the local default still falls
back to a local load.
- Harden cache parsing: tolerate a corrupt or hand-edited cache where a base
maps to a non-list (which would otherwise iterate a string into
single-character "keys"), and read the cache as UTF-8.
Tests updated and added: saved-key replay without the handshake for both local
and remote bases, keyless mint still refused when the loopback server is
unverified, and connect_studio_server erroring on an explicit remote while
falling back locally on default discovery.
* CLI: harden connect handshake against relay and gate cached minted keys
Addresses review feedback on the credential handshake:
- Refuse HTTP redirects on credential-bearing requests (the identity handshake,
/v1/models, key minting, and the chat HTTP backend). A process squatting the
discovered port could 302 /api/auth/identity to the real Studio and relay its
valid proof, or bounce a bearer-token request to another base, and urllib
follows redirects by default. A shared no-redirect opener now treats any 3xx
as an error.
- Give cached keys provenance. Keys the user supplied with --api-key are "saved"
and replay without the handshake (needed for remote or SSH-tunnelled Studios
whose secret the local handshake can't match). Keys we auto-mint are "minted"
and replay only after the identity handshake, so a port squatter can't collect
a previously minted localhost key just by answering the health check. New cache
shape: servers[base] = {"saved": [...], "minted": [...]}.
Known residual: a different-OS-user process that squats the port and can also
reach a genuine same-secret Studio elsewhere on loopback can still manually relay
the identity challenge. Fully closing that needs the proof bound to the server's
real listening port, or OS-level peer-credential checks; tracked as follow-up. A
same-user attacker is out of scope, since it can already read the 0600 key cache.
Tests: redirect rejection in the handshake, minted-cache requiring the handshake
while saved-cache bypasses it, and the existing suites updated for the new cache
shape. unsloth_cli (206) and test_identity.py (5) pass.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* CLI: keep urllib imports function-local in the no-redirect opener
The repo's import-hoist safety linter (scripts/verify_import_hoist.py) flags
hoisting urllib to module level because it re-points the 'urllib' name in the
pre-existing HttpChatBackend._request scope. Build the no-redirect opener lazily
with function-local urllib imports instead, matching this module's convention,
and restore the local 'import urllib.request' in _request and
verify_studio_identity.
* test(identity): skip route tests when routes.auth import chain is unavailable
The identity route tests build a TestClient from routes.auth, which pulls the
whole routes package (routes/__init__ -> inference -> llama_cpp, ...). In a
minimal test matrix without the heavy backend deps, or when another test in the
same process has already broken that import chain, importing it raised and the
two route tests hard-failed. Skip in that case instead: the proof crypto is
covered by the storage-level tests, and the full backend CI still exercises the
route. No behaviour change where the deps are present (5 passed in isolation).
* test(connect): make connect tests pass on native Windows
unsloth connect supports Windows: --no-launch prints PowerShell ($env:X =
"v" / Remove-Item Env:X) instead of POSIX (export/unset), and the launch
path bridges env into a Windows agent .exe over WSLENV. The tests hardcoded
the POSIX shell forms, so on a real windows-latest runner 12 of them failed on
the assertion string even though every command exited 0.
Add OS-aware assertion helpers (_assert_env_set / _assert_env_unset) that check
the right shell syntax for the host OS, and skip the two WSL-from-Linux shim
tests on native Windows (os.name is 'posix' inside WSL, so that path can't run
there). No change on Linux/macOS (57 passed); the connect command's behaviour
is untouched. Validated on a windows-latest staging runner.
* style(connect): tighten comments in the credential-leak fix
Condense the verbose explanatory comments and multi-line docstrings added by
this PR to one or two lines each, drop a few that just restated the code, and
keep the security rationale where it is load-bearing. Comment/whitespace only;
verified with unslothai/scripts comment_tools.py (check --strip-docstrings:
6/6 'code unchanged'). Tests unchanged: connect 57 passed, identity 5 passed.
* CLI/Studio: harden the identity handshake (review round)
Addresses the latest Codex/Gemini review of the handshake:
- Store the identity secret privately. sqlite3.connect created the auth DB
world-readable under a 022 umask, so another OS user could read app_secrets
and forge proofs, defeating the same-user assumption the handshake rests on.
The auth dir and DB are now restricted to owner-only (0700/0600); the JWT
secret and password hashes there get the same protection.
- Bind the proof to the server's listening port. The stateless HMAC(secret,
nonce) was relayable: a process squatting the discovered port could proxy the
challenge to the real Studio on another port and pass it back. The proof now
covers the port the server actually listens on (from the socket, never the
Host header) and the client checks it against the port it connected to, so a
relayed proof from a different port no longer matches. Closes the manual-relay
residual left after the redirect fix.
- Cap the identity response read (the server is still unverified at that point)
and serve the identity route from a sync def so its first-call SQLite read
runs in the threadpool instead of the event loop.
Tests: port-bound proof + relayed-proof rejection added; identity (5) and
unsloth_cli (58) suites pass. Verified end to end against a real backend
(auth DB owner-only, handshake + mint still succeed).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* CLI/Studio: bind the identity proof to the connection address, not just port
Follow-up to the port binding from the last review. Binding only to the port
left a cross-address relay: a squatter on a different loopback address but the
same port (for example localhost resolving to a squatter on ::1 while the real
Studio is on 127.0.0.1) could proxy the nonce to the real Studio and pass back
a proof that still matched, since both share the port.
The proof now covers the address and the port the connection landed on:
- Server: takes the address+port from request.scope, which uvicorn populates
from getsockname, so it is the real local address the client reached even
when Studio is bound to 0.0.0.0 (verified empirically), never the
client-controlled Host header.
- Client: resolves the base host to one concrete IP, talks to exactly that IP,
and binds the proof to (IP, port). A proof relayed from a Studio on a
different address or port was computed for that other endpoint and no longer
matches the one the client dialed.
Both sides normalise the address through ipaddress so equivalent forms compare
equal. Together with the private-secret and redirect fixes, this closes the
cross-user loopback relay an attacker can mount without reading the secret.
Tests: proof now bound to host+port; relayed-proof rejection retained; identity
(5) and unsloth_cli (58) suites pass. Verified end to end against a real backend
(handshake + mint still succeed).
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* CLI: pick the loopback address at discovery so localhost does not regress
find_studio_server now resolves a bare localhost base to its concrete loopback
addresses and returns the first that answers /api/health, IPv4 127.0.0.1 first
(where unsloth studio binds by default). The whole flow (health probe, identity
check, credential send) then targets that one address instead of racing
IPv4/IPv6 resolution, where localhost could resolve ::1-first and hide a Studio
bound to 127.0.0.1. A literal IP or remote name is unchanged.
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
---------
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 | ||
| .git-blame-ignore-revs | ||
| .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.
For a secure HTTPS link instead of a raw network port, use unsloth studio --secure. Studio stays bound to localhost and is served only through a free Cloudflare HTTPS tunnel (it fails closed if the tunnel can't start, so the raw port is never exposed).
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
$env:UNSLOTH_STUDIO_HOME='C:\path'; irm https://unsloth.ai/install.ps1 | iex
Cap Studio's native CPU thread pools on high-core hosts: UNSLOTH_CPU_THREADS=8 unsloth studio -p 8888.
Uninstall
The recommended way to fully remove Unsloth Studio is the matching uninstall script for your OS. It stops any running servers, removes the install dir, the launcher data dir, the desktop shortcut, and any platform-specific entries (macOS .app bundle + Launch Services on Mac; Start Menu, HKCU\Software\Unsloth registry key and user PATH entries on Windows):
- MacOS, WSL, Linux:
curl -fsSL https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.sh | sh - Windows (PowerShell):
irm https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.ps1 | iex
If you only want to drop the install dir and keep the launcher/shortcut for a later reinstall, you can instead run rm -rf ~/.unsloth/studio (Mac/Linux/WSL) or Remove-Item -Recurse -Force "$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!