- Python 71.5%
- TypeScript 22.7%
- Shell 1.9%
- PowerShell 1.6%
- Rust 1.5%
- Other 0.7%
65587c2be7* Studio: Data settings tab, uploaded files manager, quant pinning, image preview fix Settings - New Data tab in the settings sidebar, under Connections. Chat data management (archived chats, confirm before deleting, exports, import, clear all) moved there from the Chat tab. - New Archive all chats action with confirmation. Archives every chat in Recents and Projects; compare pairs count as one chat. - New Uploaded files manager listing RAG documents (chats, projects, knowledge bases) and chat message attachments with location, size and date. Files can be opened in a new tab or deleted. Deleting a chat attachment keeps the message text. Backend - GET /api/rag/documents lists all uploaded RAG documents with file size plus KB and project names. - GET /api/chat/attachments lists chat message attachments; per attachment file and delete endpoints included. Model selector - Downloaded GGUF quants can be pinned from the quant row (next to the settings and delete actions). Pinned quants show at the top of On Device under a Pinned heading as model name plus a grey quant chip and load directly with one click. Non GGUF cached repos pin as a whole. - Toned down the green of the downloaded label. Fix - Clicking an image attachment in chat now opens the preview overlay. The tooltip trigger wrapper called preventDefault before composed handlers ran, which made Radix DialogTrigger skip opening. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: image previews and file type chips in uploaded files list Image attachments now show a small thumbnail (lazy loaded from the stored bytes, object URL revoked on unmount) and every row shows a grey uppercase type chip derived from the extension or content type. Non image rows keep a file icon. Name cell floors its width and clips overflow so narrow dialogs stay aligned. * Harden attachment serving, add tests, and polish pinned rows and previews - Strict base64 decoding for attachment files: corrupt payloads now return 422 instead of silently serving empty or garbled bytes; whitespace, missing padding, the URL-safe alphabet, and RFC 2397 percent-encoded data URLs are all handled - New backend test suite covering attachment listing, size accounting, malformed rows, deletion semantics, and every file-serving edge case - Pinned quant rows show a Loaded tag when that exact quant is active, and reveal unpin, settings, and delete actions on hover - Uploaded files dialog is wider and chat locations link straight to the thread the attachment belongs to - Chat image preview is now a chrome-free lightbox: dimmed backdrop, rounded image, corner close button, click outside to dismiss - File opens go through a synchronous window.open so Safari and Firefox popup blockers do not eat them * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Uploaded files: click a file to jump to its chat, square thumbs, new Data icon - Clicking a file row (thumbnail or name) now goes straight to the chat it belongs to; files without a chat open directly as before - File thumbnails pin a small 7px radius: the theme scales rounded-md up to a near circle at this size - Settings Data tab now uses the database-setting icon * Uploaded files is now a Data tab subpage instead of a popup - Manage swaps the tab body for an inline Uploaded files page with a back header, matching the rest of settings navigation - Size column header and values are left aligned like the other columns - Column widths tightened so the table fits the settings panel * Lightbox polish and Data tab row order - Image preview close button is transparent until hovered - Preview image no longer rounds its corners - Import chats now sits below Clear all chats in the Data tab * Data tab: export chats as fine-tuning data and open them in Recipes - New Fine-tuning section in Settings > Data converts every chat into a JSONL dataset in the OpenAI messages format, one conversation per line with string-only system/user/assistant turns - The Train tab detects this file as chatml natively: no column mapping and no standardization pass, and it works with train on completions since every assistant turn sits behind the chat template response marker - Consecutive same-role turns merge, trailing turns without an assistant reply drop, and reasoning, tool calls, and images are excluded so chat templates format the data cleanly - Open in Recipes stages the JSONL as a local seed upload, creates a new Data Recipe with the seed block preconfigured, and jumps to the editor * Data tab: load chats straight into the Train tab, row moved to the top - New Load in Train tab button uploads the fine-tuning JSONL through the training dataset endpoint, selects it in the training config store, and opens the Train tab with the dataset loaded and format-checked - Use chats as training data now sits at the very top of the Data tab - The Chats subheading is gone; chat rows flow directly under it * Address review findings on the uploads manager and quant pins - Deleting the last attachment stores '[]' instead of NULL: a NULL reads back as a missing field and triggers the legacy IndexedDB backfill, which resurrected the deleted attachment on the next chat load - The attachment file endpoint now serves audio: adapter parts store {data, format} raw base64 and compare chats store a bare base64 string; media type comes from the attachment contentType or the format - Compare-chat uploads live in message content parts, not attachments; the uploads list now includes those blobs via synthetic content-part ids that the same get and delete routes resolve - Deleting a quant from the expanded repo row also unpins it so a pinned row cannot try to load a file that no longer exists - Thumbnails in the uploads list fetch their blob only once the row is visible, so a long screenshot history does not download everything - Nine new backend tests cover audio serving, content-part listing, serving, deletion, and the empty-list delete behavior * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Data tab: single action dropdown with format choices for chat training data - The three fine-tune buttons collapse into one dropdown plus a run button; pick Load in Train tab, Open in Recipes, or Export JSONL, then click the arrow to run it - The dropdown's Format section adds ShareGPT and Alpaca alongside the default OpenAI messages format, ticked like a checklist; all three shapes are auto-detected by the Train tab's format check - Alpaca is single-turn, so each user to assistant pair becomes its own record with the system prompt and earlier turns carried in the input column - Shorter description on the training data row - Uploaded files rows show the size under the file name instead of a separate column, matching the tighter layout * Polish the training data action control - Run button is a true circle (icon-sm plus rounded-full) with a heavier arrow stroke - Dropdown trigger uses the shared standard chevron and a fixed width so switching actions no longer resizes the control * Shorten the training data row description * Use the standard chevron for the run button and enlarge the ticks - Run button uses the shared standard right chevron so it matches the dropdown chevron instead of the hugeicons arrow - Dropdown ticks bumped up a size for legibility * Reword the training data row description * Shorten Data Recipes to Recipes in the training data description * List Export JSONL first and rename the default format to Chat Completions * Handle legacy string content in fine-tune exports and gate Train on chat-only hosts - messageToPlainText now accepts plain-string message content, the shape legacy and imported histories store, so those conversations export instead of being skipped as having no exchange - The Load in Train tab action is disabled on chat-only hosts the same way the sidebar gates Train; the default action falls back to Export JSONL there so the run button never uploads a dataset that /studio would immediately redirect away from * Narrow the training data action dropdown slightly * Drop the format picker from the training data dropdown Chat Completions (OpenAI messages) is the only export format we ship, so the ShareGPT and Alpaca options and the Format section are removed. The export always uses the OpenAI messages shape. * Address the second round of review findings Security - Chat attachment data URLs no longer echo their embedded media type: anything that is not a plain raster image serves as octet-stream, so imported text/html or SVG payloads cannot render under the app origin - Uploaded .html/.htm RAG documents serve as text/plain for the same reason; the preview sheet only uses the file URL for PDFs Uploads manager - Remote image URLs in imported chats are no longer listed as stored uploads (nothing to serve, and delete would strip the chat reference); the delete guard mirrors the same data:-only rule - Deleting a content-part upload refetches the list since the remaining parts re-index, keeping sibling row ids current - Deleting a project document from the Data tab invalidates the project sources cache like the sources panel does - Data-tab deletions now patch the loaded thread's in-memory copy via a small event, so a later repo sync cannot write the attachment back Fine-tune export - Branch siblings from retries stay out of the exported conversation; only the selected chain converts (full exports still keep everything) - Assistant turns before the first user turn drop, preserving leading system prompts, so no unconditioned assistant targets are emitted Four new backend tests cover the media type clamp and remote-URL rows; two existing tests updated for the clamped types * Fix uploaded file lifecycle and model state * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Make archived chats a Data settings subpage * Studio: fix attachment route tests and pinned quant edge cases - test_chat_attachments: drop asyncio.run around the synchronous /attachments routes (list/get/delete are plain def, so asyncio.run raised 'a coroutine was expected' and failed the Repo tests CI job). - test_chat_attachments: align compare-chat content-part assertions with the stable content-hash id scheme (content-part-sha256-...) instead of the removed array-index ids; resolve ids from the listing. - pickers: pass disabled={deleteDisabled} to the pinned-quant delete action so a quant cannot be deleted mid model-load, matching the expanded variant rows. - pickers: build the pinned-quant existence set from the query-unfiltered cached GGUF repos (format filter still applied) so a pinned quant stays findable when the search term matches only its quant name. * Fix Studio review regressions * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Guard fine-tune export content blocks * Add Export button for archived chats Adds an Export action to the Archived chats view in Settings > Data that downloads only the archived chats as a JSON backup (their threads, messages and projects). The button sits in the archived header row and appears only when archived chats exist. * Refactor archived export into pure, testable units Split the archived-chats export into a dependency-free filter (archived-chat-export.ts) and a shared JSON download helper (download-json.ts). Skip the download when nothing is archived so a stray call never drops an empty file. No behavior change to the button. --------- Co-authored-by: shimmyshimmer <shimmyshimmer@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Etherll <61019402+Etherll@users.noreply.github.com> Co-authored-by: Unsloth <michaelhan@Michaels-MacBook-Pro.local> Co-authored-by: Lee Jackson <130007945+Imagineer99@users.noreply.github.com> |
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| .pre-commit-ci.yaml | ||
| .pre-commit-config.yaml | ||
| build.sh | ||
| cli.py | ||
| CODE_OF_CONDUCT.md | ||
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| 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. Unsloth 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 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
- 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. 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.
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!