- Python 71.5%
- TypeScript 22.7%
- Shell 1.9%
- PowerShell 1.6%
- Rust 1.5%
- Other 0.7%
7f0910fcc6* Add Agents settings tab for unsloth start Adds a Settings > Agents tab documenting the `unsloth start` command: quickstart, supported agents with click-to-copy commands, model selection, common options, remote Studio setup, argument pass-through, and a dry-run preview. Agent CLIs found on PATH are badged as installed. Also removes the "New" badge from the System and Chat tabs. * Use official brand logos for agents, invert Ollama and OpenRouter in dark mode Claude Code and OpenAI Codex now use the Anthropic and OpenAI logos from the provider-logos registry; agents without an official asset keep the monogram tile. Also inverts the Ollama and OpenRouter logos in dark mode so their monochrome marks stay visible. * Title Agents tab "Agents (unsloth start)" and move it below Connections The in-tab header now reads "Agents (unsloth start)" while the sidebar label stays "Agents". Reorders the tab to sit below Connections. * Address review: guard PATH detection, fix copy timeout, OS-aware remote snippet - Only probe agent PATH in the desktop app on a loopback backend, so Installed badges are not driven by a remote server's environment. - Show the "none found" note only when detection actually ran and returned empty, not when the call failed. - Share one copy hook that resets its timeout on rapid clicks and clears it on unmount. - Render the Remote Studio snippet with PowerShell syntax on Windows. - Note that --no-launch can still load a model when --model is set. - Drop unused quickstart translation keys. * Add interactive Agents command builder * Add local subagent command guidance * Add official coding agent icons * Use client OS for remote commands, fix copy a11y and model wording (#7303) - Pick the remote snippet shell from the client platform, not the server deviceType - Single-line the model examples so they paste in POSIX, PowerShell and cmd - Split the pass-through block into independent one-command copies - Derive detection visibility instead of clearing state in the effect - Announce copy success to assistive tech - Correct the quickstart/model copy: bare start uses the loaded model * Shell-quote the model, forward the HF token, and fix the quant placeholder - Quote the --model value in the generated and subagent commands so a local path with spaces or metacharacters stays a single argument (client-OS aware) - Pass the saved Hugging Face token to listGgufVariants so gated repos resolve - Show 'No separate quantization' instead of a stuck 'Loading quantizations...' when a model has no variants; clear the failure once a later request succeeds * Fix Agents command discovery and routing * Unsloth start improvements: download progress, server reuse, and safe model switching (#7313) * Improve unsloth start runtime lifecycle * Remove speculative Gemma prompt override * Polish model download progress output * Refine unsloth start status output * Clarify unsloth readiness banner * Clarify model reuse and switching output * Queue model switches behind active inference * Tighten unsloth start model switching * Reduce model switch bookkeeping * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix Studio re-exec compatibility * Recheck sidecar reservation after inference drain * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Pass start marker through child environment * Fix key redaction, switch-waiter ordering, and stop/messaging gaps for PR #7313 - Redact minted sk-unsloth keys from the startup-failure log tail: the early key marker lands in the server log before the model load finishes, so a load-phase crash printed a live key to the terminal - Deregister a finished switch waiter before releasing the swap gate so a swap on another event loop cannot count it as still queued and unload the model the finished request is about to generate against - Warn on same-repo quant switches: an explicit variant replaces the resident weights for every attached session, but the repo ids match so no switch warning was printed - Note the agent exit code when it is nonzero so the server keep-alive message does not read as a successful session - Use taskkill /T in unsloth studio stop so llama-server children stop too * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Tighten comments in start, studio, and inference changes --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Daniel Han <danielhanchen@gmail.com> * Unsloth start: add local subagents for Claude Code, Codex, OpenCode and Pi (#7326) Bring the local-subagent support onto main. The original change (#7316) merged into the stacked pr/daniel-unsloth-start-audit branch rather than main, and #7313 reached main via squash, so these files never landed on main. Adds --as-subagent for claude, codex, opencode and pi: the parent agent keeps its own cloud model while a locally served GGUF is registered as a delegated subagent, using ephemeral per-session config that never touches the user's real agent config. * Fix Agents builder defaults and flag validation * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix Agents variant and provider fallbacks * Fix local model and Pi subagent edge cases * Agents tab: flag the Codex row when the loaded model is not GGUF * Agents tab: target the active Studio server, wrap narrow rows, index the tab's search terms * Agents tab: build copied commands from the browser-reachable Studio and show the key placeholder * Preserve cache load ids and path variants in built commands for PR #7312 A GGUF outside the active Hugging Face cache only loads by its snapshot path, so keep that load_id for --model while still listing the row by repo id. Path based models carry their quant in --gguf-variant rather than a ":variant" suffix, and the active selection now keeps the variant inference status reports for them. * Agents tab: index the intro for agent-name searches and keep long commands inside the panel * List GGUF variants from the cache the command loads from for PR #7312 A snapshot outside the active Hugging Face cache was offering the remote variant list, so a quant absent from that snapshot could be selected and the generated command would fail to load it. * Agents tab: omit --api-key so the CLI can replay a saved key for the base * Agents tab: label the indexed heading rows and fall back to the active desktop API base * Agents tab: name every supported agent in the indexed intro for PR #7303 * Send the cached GGUF load path and fix the agents tab search targets for PR #7312 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Tighten the agents tab comments for PR #7303 * Build the agents tab example commands from the active Studio base for PR #7303 * Keep the resident model on its active cache load for PR #7312 * Tighten the agents tab and cached GGUF comments for PR #7312 * Take the agent command shell from the Studio host for PR #7303 * Stop emitting snapshot paths as --model and keep unsloth start searchable for PR #7312 * Pick the command shell from where the CLI runs for PR #7303 * Match a path load by its advertised id and follow the resident model for PR #7312 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Keep an explicit quantization and retire superseded native-grant labels for PR #7312 * Scope the remembered quant, stop following unloaded models and keep local GGUF paths for PR #7312 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Stop shadowing the path classifier, match snapshot ordering and sequence status polls for PR #7312 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Release stale native-grant picks, keep local GGUF identities and index snapshot aliases for PR #7312 * Index inactive-cache snapshots, widen local GGUF detection and clear retired quants for PR #7312 * Classify cached repos by snapshot, merge repo ids case-insensitively and keep loose GGUFs variantless for PR #7312 * Fix snapshot alias, partial split and mmproj-only handling for PR #7312 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Trust scanned model_format and drop incomplete snapshot ids for PR #7312 * Exclude mmproj and partial downloads, keep path case and drop duplicate scan for PR #7312 * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Restrict revision aliases and require complete snapshot variants for PR #7312 * Index revisions individually and hide partial variants for PR #7312 --------- Co-authored-by: shimmyshimmer <107991372+shimmyshimmer@users.noreply.github.com> Co-authored-by: Daniel Han <danielhanchen@gmail.com> Co-authored-by: oobabooga <oobabooga4@gmail.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> |
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| .pre-commit-ci.yaml | ||
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| build.sh | ||
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| 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.
- 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
- 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.
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!