- Python 71.5%
- TypeScript 22.7%
- Shell 1.9%
- PowerShell 1.6%
- Rust 1.5%
- Other 0.7%
ceef4123e6* Stop every running Unsloth server, and refuse to start a second on a taken port * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Check the fallback range, guard PID reuse, and keep writing studio.pid * Signal each server once when its PID is recorded in more than one file * Confirm a recorded PID is a Studio server before signalling it * Pin PID records to process start time and check every listener on a port * Keep every recorded start time per PID and accept in-process Studio servers * Match the blocking listener address and stop trusting unverifiable PID records * Never delete a PID record that cannot be verified * Detect our own server from our own records instead of a psutil listener scan * Match a pre-upgrade studio.pid to the blocked port before falling back * Never signal PID 0 or 1, and verify a per-port record before trusting it * Stop unverifiable records instead of skipping them, and record every bind address * Drop the command-line guess, fix Windows liveness, and free the PID record last * Studio: harden the per-port PID records against the cases that lose a server Follow-up on the per-port PID files. Each item below is a case where the new code either lost a server the old code could still stop, or stopped something that was not ours. All were reproduced against real Studio servers. studio/backend/run.py - Write the per-port record and the legacy studio.pid independently. They shared one try, so a studio root that could not take a new directory entry left the server recorded nowhere at all and unstoppable from the CLI; the old code still recorded it in studio.pid, which is an overwrite of an existing path and can still succeed. _remove_pid_file now also checks studio.pid when the per-port write failed. - Write the record through a temp file and os.replace. `stop` reads these concurrently and treats a truncated read as a corrupt record. - A failed Windows tasklist probe now means "alive", matching the CLI. Treating it as dead pruned a live server's record and let the next launch fall back past it, which is the orphan this work exists to fix. - Guard the unlink in _own_studio_on_port. Pruning is a courtesy and must not abort startup. - Extract _resolve_port so the requested-port abort is reachable from a test. Deleting that abort previously left the whole suite green. - Keep the plain fallback for api-only callers. The desktop app hardcodes 8888 and documents its reliance on the 8888-8908 range, and it reports a non-zero backend exit to the user as "Server stopped unexpectedly". It reads the bound port back from TAURI_PORT, as `studio run` does from app.state.server_port, so a fallback there is harmless and both servers are still recorded and stoppable. The interactive path prints the requested port, so it still aborts. - isdigit() is not enough to gate int(): a superscript two passes it and the ValueError escaped into every caller of _read_pid_record. unsloth_cli/commands/studio.py - An untimed record no longer cancels a timed one for the same PID. Every current server writes both a timed per-port record and an untimed studio.pid, so the start-time check was inert exactly where it mattered, and after a crash plus a PID reuse `stop` sent SIGTERM to whatever unrelated process had inherited the PID. - Distinguish an unreadable record from an invalid one. A root-owned record, or one caught mid-write, still belongs to a live server, and deleting it stranded that server. - Route every PID-file removal through _unlink_quietly. One undeletable record raised PermissionError and left the remaining live servers running. - Same isdigit()/int() guard as the backend. Tests - The requested-port abort, the recorded bind address, and the api-only fallback are now covered; all three previously survived deletion. - tests/studio/test_studio_pid_file_contract.py pins run.py's filename scheme to the CLI's glob and keeps studio.pid parseable by an older CLI. It lives under tests/studio because unsloth_cli/tests is not run by any workflow. - test_cli_studio_stop_windows.py now checks _signal_stop as well as stop. The kill moved into _signal_stop, so the os.kill(pid, 0) guard passed vacuously. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: let a caller that follows the port keep the fallback, and never take studio.pid from a live server Two problems with keying the own-server abort on api_only. `unsloth studio run` is not the bare-banner path: it stores `app = run_server(...)` and reads `app.state.server_port` back, then uses it for the health wait, the model load and the printed base URL. Gating on api_only aborted it, so starting a second model while the first was up stopped working, where before it landed on the next port and printed the right URL. Replace the proxy with an explicit abort_if_own_studio, defaulting to the old api_only behaviour so the exec'd `run.py` path is unchanged, and have `studio run` opt out. The api_only exemption also reopened the orphan from the other side. _write_pid_file overwrote studio.pid unconditionally, and a pre-upgrade server is recorded there and nowhere else, so an exempt launch falling back past one erased its only record. Take the file over only when it is free, already ours, or held by a dead PID. Also resync _pid_is_studio_backend with the CLI copy: an untimed record next to a timed one carried no information but cancelled the start-time check, which is what let a reused PID be treated as ours. Tests: 51 backend, 26 CLI, 9 under tests/studio. Real Studio servers still abort the bare same-port relaunch, still fall back past a foreign listener, and one `unsloth studio stop` still stops every server in all five scenarios. * Studio: hand over the legacy PID pointer, and fail stop on unreadable records Two follow-ups from review of the previous commit. Only one backend owns studio.pid at a time. When that server exited it deleted the file, so an older CLI, which reads nothing else, could no longer stop a sibling that was still serving. _remove_pid_file now hands the pointer to a live sibling instead of dropping it. _pid_file_entries skipped records it could not read, for instance one written by a server started under sudo. When that was the only record, stop printed "No running Unsloth server found" and exited 0 while the server kept serving. Unreadable records are now reported and make stop exit 1, so a partial stop is never mistaken for a complete one. * [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> Co-authored-by: Daniel Han <unslothai@gmail.com> |
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| studio | ||
| tests | ||
| unsloth | ||
| unsloth_cli | ||
| .gitattributes | ||
| .gitignore | ||
| .pre-commit-ci.yaml | ||
| .pre-commit-config.yaml | ||
| _changelog_build.py | ||
| build.sh | ||
| CHANGELOG.md | ||
| cli.py | ||
| CODE_OF_CONDUCT.md | ||
| CONTRIBUTING.md | ||
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| install.ps1 | ||
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| 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!