{"slug": "unsloth-run-and-train-local-llms", "title": "Unsloth: Run and Train Local LLMs", "summary": "Unsloth, an open-source AI startup, released Unsloth Studio (Beta), a platform that lets users run and train text, audio, embedding, and vision models locally on Windows, Linux, and macOS, with support for 500+ models, up to 2x faster training and 70% less VRAM usage, and MoE models up to 12x faster. The platform includes features like GGUF export, tool calling, code execution, API endpoints, and integration with agents like Claude Code and Codex. Unsloth claims to have fixed bugs in models like gpt-oss, Qwen3, Llama 4, Mistral, Gemma 1-3, and Phi-4 to improve accuracy.", "body_md": "[Features](#-features) •\n[News](#-unsloth-news) •\n[Quickstart](#-install) •\n[Notebooks](#-free-notebooks) •\n[Documentation](https://unsloth.ai/docs)\n\n```\ncurl -fsSL https://unsloth.ai/install.sh | sh\nirm https://unsloth.ai/install.ps1 | iex\n```\n\nUnsloth Studio (Beta) lets you run and train text, [audio](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning), [embedding](https://unsloth.ai/docs/new/embedding-finetuning), [vision](https://unsloth.ai/docs/basics/vision-fine-tuning) models on Windows, Linux and macOS.\n\n**Search + download + run models** including GGUF, LoRA adapters, safetensors**Export models**:[Save or export](https://unsloth.ai/docs/new/studio/export)models to GGUF, 16-bit safetensors and other formats.** Tool calling**: Support for[self-healing tool calling](https://unsloth.ai/docs/new/studio/chat#auto-healing-tool-calling)and web search: lets LLMs test code in Claude artifacts and sandbox environments[Code execution](https://unsloth.ai/docs/new/studio/chat#code-execution): Deploy and run local LLMs in Claude Code, Codex tools with Unsloth[API inference endpoint](https://unsloth.ai/docs/basics/api)[Auto set inference settings](https://unsloth.ai/docs/new/studio/chat#auto-parameter-tuning)and customize chat templates.- We work directly with teams behind\n[gpt-oss](https://docs.unsloth.ai/new/gpt-oss-how-to-run-and-fine-tune#unsloth-fixes-for-gpt-oss),[Qwen3](https://www.reddit.com/r/LocalLLaMA/comments/1kaodxu/qwen3_unsloth_dynamic_ggufs_128k_context_bug_fixes/),[Llama 4](https://github.com/ggml-org/llama.cpp/pull/12889),[Mistral](https://huggingface.co/mistralai/Mistral-Medium-3.5-128B/discussions/18),[Gemma 1-3](https://news.ycombinator.com/item?id=39671146), and[Phi-4](https://unsloth.ai/blog/phi4), where we’ve fixed bugs that improve model accuracy. - Chat with images, audio, PDFs, code, DOCX and more.\n[Connect API providers](https://unsloth.ai/docs/integrations/connections)(OpenAI, Anthropic) or servers (vLLM, Ollama). side by side with the same prompt.**Compare any two models****OpenAI/Anthropic-compatible APIs**: Serve local models through`/v1/chat/completions`\n\n,`/v1/responses`\n\nand`/v1/messages`\n\n.**Connect local models to agents**: Use`unsloth start`\n\nwith 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\n**MCP control endpoint** lets AI clients manage models, training, recipes and exports.\n\n- Train and RL\n**500+ models** up to**2x faster** with**70% less VRAM**; MoE up to** 12x faster**. - Train and run RL on\n[AMD GPUs](https://unsloth.ai/docs/basics/amd)across Windows, WSL and Linux. **Data Recipes**:[Auto-create datasets](https://unsloth.ai/docs/new/studio/data-recipe)from** PDF, CSV, DOCX**etc. Edit data in a visual-node workflow.uses[Reinforcement Learning](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide)**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.\n- Custom Triton and mathematical\n**kernels** built with PyTorch and Hugging Face. **Observability**: Monitor training live, track loss and GPU usage and customize graphs.[Multi-GPU](https://unsloth.ai/docs/basics/multi-gpu-training-with-unsloth)training is supported, with major improvements coming soon.\n\n[Unsloth Start](https://unsloth.ai/docs/integrations/unsloth-start) connects [Claude Code](https://unsloth.ai/docs/basics/claude-code), [Codex](https://unsloth.ai/docs/basics/codex) and other agents to local models with one command.\n\nStart Unsloth, load a model, open your project folder, then run:\n\n```\nunsloth start claude\n```\n\nReplace `claude`\n\nwith any supported agent:\n\n| Agent | Command |\n|---|---|\n| Claude Code | `unsloth start claude` |\n| OpenAI Codex | `unsloth start codex` |\n| Hermes Agent | `unsloth start hermes` |\n| OpenClaw | `unsloth start openclaw` |\n| OpenCode | `unsloth start opencode` |\n\nClaude Code, Codex and OpenCode can keep their current model and use Unsloth as a local subagent:\n\n```\nunsloth start claude --as-subagent --model unsloth/model-GGUF:quant\n```\n\nUnsloth can be used in two ways: through ** Unsloth Studio**, the web UI, or through\n\n**Unsloth Core**, the code-based version. Each has different requirements.\n\nUnsloth Studio (Beta) works on **Windows, Linux, WSL** and **macOS**.\n\n**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](https://unsloth.ai/docs/basics/amd).**Vulkan:** GGUF inference is supported on[compatible GPUs, including Intel GPUs](https://github.com/unslothai/unsloth/pull/5819). 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\n\n```\ncurl -fsSL https://unsloth.ai/install.sh | sh\n```\n\nUse the same command to update.\n\nTo force the Vulkan llama.cpp backend, set `UNSLOTH_FORCE_VULKAN=1`\n\n**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:\n\n```\nexport UNSLOTH_FORCE_VULKAN=1\ncurl -fsSL https://unsloth.ai/install.sh | sh\nirm https://unsloth.ai/install.ps1 | iex\n```\n\nUse the same command to update.\n\nTo force the Vulkan llama.cpp backend, set the environment variable before running the installer or updater:\n\n```\n$env:UNSLOTH_FORCE_VULKAN=1\nirm https://unsloth.ai/install.ps1 | iex\n```\n\nRe-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.\n\n```\nunsloth studio -p 8888\n```\n\nFor LAN or cloud access, add `-H 0.0.0.0`\n\n(raw port only; add `--cloudflare`\n\nfor a public URL). By default, Unsloth is accessible only locally.\n\nTo reach Unsloth over HTTPS, use `unsloth studio --secure`\n\n. Unsloth stays bound to localhost and is reached only through a free Cloudflare tunnel, which publishes it at a public `https://*.trycloudflare.com`\n\nURL (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).\n\nUse our [Docker image](https://hub.docker.com/r/unsloth/unsloth) `unsloth/unsloth`\n\ncontainer. Run:\n\n```\ndocker run -d -e JUPYTER_PASSWORD=\"mypassword\" \\\n  -p 8888:8888 -p 8000:8000 -p 2222:22 \\\n  -v $(pwd)/work:/workspace/work \\\n  --gpus all \\\n  unsloth/unsloth\n```\n\nTo see developer, nightly and uninstallation etc. instructions, see [advanced installation](#-advanced-installation).\n\n```\ncurl -LsSf https://astral.sh/uv/install.sh | sh\nuv venv unsloth_env --python 3.13\nsource unsloth_env/bin/activate\nuv pip install unsloth --torch-backend=auto\nwinget install -e --id Python.Python.3.13\nwinget install --id=astral-sh.uv  -e\nuv venv unsloth_env --python 3.13\n.\\unsloth_env\\Scripts\\activate\nuv pip install unsloth --torch-backend=auto\n```\n\nFor Windows, `pip install unsloth`\n\nworks only if you have PyTorch installed. Read our [Windows Guide](https://unsloth.ai/docs/get-started/install/windows-installation).\nYou can use the same Docker image as Unsloth Studio.\n\nFor RTX 50x, B200, 6000 GPUs: `uv pip install unsloth --torch-backend=auto`\n\n. Read our guides for: [Blackwell](https://unsloth.ai/docs/blog/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth) and [DGX Spark](https://unsloth.ai/docs/blog/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth).\n\nTo install Unsloth on **AMD** and **Intel** GPUs, follow our [AMD Guide](https://unsloth.ai/docs/basics/amd) and [Intel Guide](https://unsloth.ai/docs/get-started/install/intel).\n\nTrain for free with our notebooks. You can use our new [free Unsloth Studio notebook](https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb) to run and train models for free in a web UI.\nRead our [guide](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide). Add dataset, run, then deploy your trained model.\n\n| Model | Free Notebooks | Performance | Memory use |\n|---|---|---|---|\nGemma 4 (E2B) |\n|\n\n**Qwen3.5 (4B)**▶️ Start for free** gpt-oss (20B)**▶️ Start for free** Qwen3.5 GSPO**▶️ Start for free** gpt-oss (20B): GRPO**▶️ Start for free** Qwen3: Advanced GRPO**▶️ Start for free** embeddinggemma (300M)**▶️ Start for free** Mistral Ministral 3 (3B)**▶️ Start for free** Llama 3.1 (8B) Alpaca**▶️ Start for free** Llama 3.2 Conversational**▶️ Start for free** Orpheus-TTS (3B)**▶️ Start for free- See all our notebooks for:\n[Kaggle](https://github.com/unslothai/notebooks?tab=readme-ov-file#-kaggle-notebooks),[GRPO](https://unsloth.ai/docs/get-started/unsloth-notebooks#grpo-reasoning-rl-notebooks),[TTS](https://unsloth.ai/docs/get-started/unsloth-notebooks#text-to-speech-tts-notebooks),[embedding](https://unsloth.ai/docs/new/embedding-finetuning)&[Vision](https://unsloth.ai/docs/get-started/unsloth-notebooks#vision-multimodal-notebooks) - See\n[all our models](https://unsloth.ai/docs/get-started/unsloth-model-catalog)and[all our notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks) - See detailed documentation for Unsloth\n[here](https://unsloth.ai/docs)\n\n**AMD training**: Train, run RL, chat and deploy on AMD GPUs across Windows, WSL and Linux.[Guide](https://unsloth.ai/docs/basics/amd)** GGUF hardware controls**: Choose GPU/layer placement, offload MoE experts and use multi-GPU or Tensor Parallelism.[#6414](https://github.com/unslothai/unsloth/pull/6414)**Local models for any agent**: Use`unsloth start`\n\nwith Claude Code, Codex, Hermes, OpenCode, OpenClaw and more through Unsloth's OpenAI- and Anthropic-compatible APIs.[Guide](https://unsloth.ai/docs/basics/api)**MCP control endpoint**: Let compatible clients manage models, training, recipes, checkpoints and exports.[#7191](https://github.com/unslothai/unsloth/pull/7191)**Local inference reliability**: Resume long chats faster, recover stalled downloads and reuse existing GGUF files.[#7204](https://github.com/unslothai/unsloth/pull/7204)•[#6858](https://github.com/unslothai/unsloth/pull/6858)•[#7209](https://github.com/unslothai/unsloth/pull/7209)**New models**:[Qwen-AgentWorld](https://huggingface.co/unsloth/Qwen-AgentWorld-35B-A3B-GGUF),[Ornith](https://huggingface.co/unsloth/models?search=ornith),[Kimi K2.7 Code](https://unsloth.ai/docs/models/kimi-k2.7-code)and[MiniMax M3](https://unsloth.ai/docs/models/minimax-m3)**GLM-5.2**: Run Z.ai's 744B-parameter, 1M-context open model locally with Unsloth Dynamic GGUFs.[Guide](https://unsloth.ai/docs/models/glm-5.2)**DeepSeek-V4**: Run DeepSeek-V4-Flash locally with corrected multi-turn and tool-calling behavior.[Guide](https://unsloth.ai/docs/models/deepseek-v4)**DiffusionGemma**: Run and fine-tune Google's diffusion language model with 1.8x faster inference in Unsloth Studio.[Guide](https://unsloth.ai/docs/models/diffusiongemma)**Qwen3.6**: Run and train Qwen3.6 with MTP for 1.4-2.2x faster inference and NVFP4 quants for supported GPUs.[Guide](https://unsloth.ai/docs/models/qwen3.6)**Gemma 4**: Run and train Gemma 4 text, image and audio models with QAT, MTP, GGUF and MLX support.[Guide](https://unsloth.ai/docs/models/gemma-4)**MCP servers**: Connect local models to files, apps, databases and external tools through Model Context Protocol.[Guide](https://unsloth.ai/docs/basics/mcp)**Connections**: Mix local models with API providers (OpenAI, Anthropic) or servers (vLLM, Ollama) in the same interface.[Guide](https://unsloth.ai/docs/integrations/connections)**Introducing Unsloth Studio**: our new web UI for running and training LLMs.[Blog](https://unsloth.ai/docs/new/studio)- Train\n**MoE LLMs 12x faster** with 35% less VRAM - DeepSeek, GLM, Qwen and gpt-oss.[Blog](https://unsloth.ai/docs/new/faster-moe) **Embedding models**: Unsloth now supports ~1.8-3.3x faster embedding fine-tuning.[Blog](https://unsloth.ai/docs/new/embedding-finetuning)•[Notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks#embedding-models)- New\n**7x longer context RL** vs. all other setups, via our new batching algorithms.[Blog](https://unsloth.ai/docs/new/grpo-long-context) - New RoPE & MLP\n**Triton Kernels**&** Padding Free + Packing**: 3x faster training & 30% less VRAM.[Blog](https://unsloth.ai/docs/new/3x-faster-training-packing) **500K Context**: Training a 20B model with >500K context is now possible on an 80GB GPU.[Blog](https://unsloth.ai/docs/blog/500k-context-length-fine-tuning)**FP8 & Vision RL**: You can now do FP8 & VLM GRPO on consumer GPUs.[FP8 Blog](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning)•[Vision RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/vision-reinforcement-learning-vlm-rl)\n\nThe below advanced instructions are for Unsloth Studio. For Unsloth Core advanced installation, [view our docs](https://unsloth.ai/docs/get-started/install/pip-install#advanced-pip-installation).\n\nThe developer install builds from the `main`\n\nbranch, which is the latest (nightly) source.\n\n```\ngit clone https://github.com/unslothai/unsloth\ncd unsloth\n./install.sh --local\nunsloth studio -p 8888\n```\n\nTo install into an isolated location (its own virtual env, `auth/`\n\n, `studio.db`\n\n, cache and llama.cpp build), set `UNSLOTH_STUDIO_HOME`\n\nand pass it again at launch:\n\n```\nUNSLOTH_STUDIO_HOME=\"$PWD/.studio\" ./install.sh --local\nUNSLOTH_STUDIO_HOME=\"$PWD/.studio\" unsloth studio -p 8888\n```\n\nThen to update :\n\n```\ncd unsloth && git pull\n./install.sh --local\nunsloth studio -p 8888\n```\n\nThe developer install builds from the `main`\n\nbranch, which is the latest (nightly) source.\n\n```\ngit clone https://github.com/unslothai/unsloth.git\ncd unsloth\nSet-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass\n.\\install.ps1 --local\nunsloth studio -p 8888\n```\n\nTo install into an isolated location (its own virtual env, `auth/`\n\n, `studio.db`\n\n, cache and llama.cpp build), set `UNSLOTH_STUDIO_HOME`\n\nand pass it again at launch:\n\n```\n$env:UNSLOTH_STUDIO_HOME=\"$PWD\\.studio\"; .\\install.ps1 --local\n$env:UNSLOTH_STUDIO_HOME=\"$PWD\\.studio\"; unsloth studio -p 8888\n```\n\nThen to update :\n\n```\ncd unsloth; git pull\n.\\install.ps1 --local\nunsloth studio -p 8888\n```\n\nBy default `unsloth studio`\n\nbinds to `127.0.0.1`\n\n(this machine only). To reach it from another device, pick one of:\n\n`--secure`\n\n(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.\n\n```\nunsloth studio --secure -p 8888\n```\n\n`-H 0.0.0.0`\n\n: 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`--cloudflare`\n\nto also publish an internet-reachable`https://*.trycloudflare.com`\n\nlink even behind a firewall. Only use this on a network you trust.\n\n```\nunsloth studio -H 0.0.0.0 -p 8888\n```\n\nThe Cloudflare tunnel is **off by default**: `-H 0.0.0.0`\n\nexposes the raw port only, not a public internet URL. Pair the wildcard bind with `--cloudflare`\n\n(`unsloth studio -H 0.0.0.0 --cloudflare`\n\n) to also publish a public `https://*.trycloudflare.com`\n\nlink, or prefer `--secure`\n\n(above), which keeps the raw port private. `--cloudflare`\n\nhas no effect on a loopback bind.\n\nOn a wildcard bind Unsloth works out the address to share by asking `ifconfig.me`\n\nfor the public IP, then asks `check-host.net`\n\nwhether 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`\n\nto skip them; the banner then shows the LAN address and no reachability line.\n\nThe first time Unsloth is published on a public URL (`--secure`\n\nor `--cloudflare`\n\n) 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`\n\n(default 1 hour) unless the password is changed in the web UI.\n\nFor headless setups that cannot answer that prompt, set the initial admin password non-interactively with `--password`\n\n(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`\n\n):\n\n```\nunsloth studio --secure --password 'your-strong-password'        # visible in `ps`/history\nUNSLOTH_STUDIO_PASSWORD='your-strong-password' unsloth studio --secure   # via env var\nprintf '%s\\n' 'your-strong-password' | unsloth studio --secure --password -   # via stdin\n```\n\nA literal `--password VALUE`\n\nis visible in the process list and shell history, so prefer the `UNSLOTH_STUDIO_PASSWORD`\n\nenv var or `--password -`\n\n(stdin) for automation. This applies to any launch (public or a headless `-H 0.0.0.0`\n\nbind), and the password is set in the parent before the server binds, so it never reaches a re-executed child process.\n\nServer-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`\n\nwhen exposing Unsloth.\n\nInstaller 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`\n\n; on Windows set it with `$env:`\n\nbefore piping to `iex`\n\n.\n\nSkip PyTorch (GGUF-only mode):\n\n```\ncurl -fsSL https://unsloth.ai/install.sh | UNSLOTH_NO_TORCH=1 sh\n$env:UNSLOTH_NO_TORCH=1; irm https://unsloth.ai/install.ps1 | iex\n```\n\nSkip the post-install prompt that starts Unsloth (useful for automated installs):\n\n```\ncurl -fsSL https://unsloth.ai/install.sh | UNSLOTH_SKIP_AUTOSTART=1 sh\n$env:UNSLOTH_SKIP_AUTOSTART=1; irm https://unsloth.ai/install.ps1 | iex\n```\n\nPin the Python version:\n\n```\ncurl -fsSL https://unsloth.ai/install.sh | UNSLOTH_PYTHON=3.12 sh\n$env:UNSLOTH_PYTHON='3.12'; irm https://unsloth.ai/install.ps1 | iex\n```\n\nInstall to a custom location with `UNSLOTH_STUDIO_HOME`\n\n:\n\n```\ncurl -fsSL https://unsloth.ai/install.sh | UNSLOTH_STUDIO_HOME=/abs/path sh\n$env:UNSLOTH_STUDIO_HOME='C:\\path'; irm https://unsloth.ai/install.ps1 | iex\n```\n\nOn macOS, the installer defaults to the system certificate store (`UV_SYSTEM_CERTS=1`\n\n) so uv trusts the CAs in your Keychain, needed behind TLS-inspecting proxies (Cisco Umbrella, Zscaler, etc.). Opt out with:\n\n```\ncurl -fsSL https://unsloth.ai/install.sh | UV_SYSTEM_CERTS=0 sh\n```\n\nPoint the frontend build at a corporate npm mirror/proxy with `UNSLOTH_NPM_REGISTRY`\n\n(for the developer install behind a firewall that blocks `registry.npmjs.org`\n\n):\n\n```\nUNSLOTH_NPM_REGISTRY=https://artifactory.example.com/api/npm/npm/ ./install.sh --local\n$env:UNSLOTH_NPM_REGISTRY='https://artifactory.example.com/api/npm/npm/'; .\\install.ps1 --local\n```\n\nIt is threaded as `--registry`\n\ninto the Unsloth frontend `npm`\n\n/`bun`\n\ninstalls; the supply-chain locks (7-day `min-release-age`\n\n, exact version pins) stay in force.\n\nCap Unsloth's native CPU thread pools on high-core hosts: `UNSLOTH_CPU_THREADS=8 unsloth studio -p 8888`\n\n.\n\nThe recommended way to fully remove Unsloth Studio is the matching uninstall script for your OS. It stops any running servers, removes the install dir, the launcher data dir, the desktop shortcut, and any platform-specific entries (macOS `.app`\n\nbundle + Launch Services on Mac; Start Menu, `HKCU\\Software\\Unsloth`\n\nregistry key and user `PATH`\n\nentries on Windows):\n\n-\n**MacOS, WSL, Linux:**`curl -fsSL https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.sh | sh`\n\n-\n**Windows (PowerShell):**`irm https://raw.githubusercontent.com/unslothai/unsloth/main/scripts/uninstall.ps1 | iex`\n\nIf you only want to drop the install dir and keep the launcher/shortcut for a later reinstall, you can instead run `rm -rf ~/.unsloth/studio`\n\n(Mac/Linux/WSL) or `Remove-Item -Recurse -Force \"$HOME\\.unsloth\\studio\"`\n\n(Windows). The model cache at `~/.cache/huggingface`\n\nis not touched by any of these.\n\nFor more info, [see our docs](https://unsloth.ai/docs/new/studio/install#uninstall).\n\nYou 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:\n\n-\n**MacOS, Linux, WSL:**`~/.cache/huggingface/hub/`\n\n-\n**Windows:**`%USERPROFILE%\\.cache\\huggingface\\hub\\`\n\n| Type | Links |\n|---|---|\nDiscord |\n\n[Join Discord server](https://discord.com/invite/unsloth)**r/unsloth Reddit**[Join Reddit community](https://reddit.com/r/unsloth)** Documentation & Wiki**[Read Our Docs](https://unsloth.ai/docs)** Twitter (aka X)**[Follow us on X](https://twitter.com/unslothai)** Our Models**[Unsloth Catalog](https://unsloth.ai/docs/get-started/unsloth-model-catalog)** Blog**[Read our Blogs](https://unsloth.ai/blog)You can cite the Unsloth repo as follows:\n\n```\n@software{unsloth,\n  author = {Daniel Han, Michael Han and Unsloth team},\n  title = {Unsloth},\n  url = {https://github.com/unslothai/unsloth},\n  year = {2023}\n}\n```\n\nIf you trained a model with 🦥Unsloth, you can use this cool sticker!\n\nUnsloth 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\n\n**.**\n\n[AGPL-3.0](https://github.com/unslothai/unsloth?tab=AGPL-3.0-2-ov-file)This structure helps support ongoing Unsloth development while keeping the project open source and enabling the broader ecosystem to continue growing.\n\n- The\n[llama.cpp library](https://github.com/ggml-org/llama.cpp)that lets users run and save models with Unsloth - The Hugging Face team and their libraries:\n[transformers](https://github.com/huggingface/transformers)and[TRL](https://github.com/huggingface/trl) - The Pytorch and\n[Torch AO](https://github.com/unslothai/unsloth/pull/3391)team for their contributions - NVIDIA for their\n[NeMo DataDesigner](https://github.com/NVIDIA-NeMo/DataDesigner)library and their contributions - And of course for every single person who has contributed or has used Unsloth!", "url": "https://wpnews.pro/news/unsloth-run-and-train-local-llms", "canonical_source": "https://github.com/unslothai/unsloth", "published_at": "2026-08-03 23:54:50+00:00", "updated_at": "2026-08-04 00:22:38.454171+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-tools", "ai-infrastructure"], "entities": ["Unsloth", "Unsloth Studio", "Claude Code", "Codex", "OpenAI", "Anthropic", "Hugging Face", "PyTorch"], "alternates": {"html": "https://wpnews.pro/news/unsloth-run-and-train-local-llms", "markdown": "https://wpnews.pro/news/unsloth-run-and-train-local-llms.md", "text": "https://wpnews.pro/news/unsloth-run-and-train-local-llms.txt", "jsonld": "https://wpnews.pro/news/unsloth-run-and-train-local-llms.jsonld"}}