# Unsloth Docs Unsloth lets you run and train AI models on your own local hardware. Our docs will guide you through running & training your own model locally. Get started Our GitHub
Cover image

Google Gemma 4

Run and train Google's new Gemma 4 models!Gemma 4 landscape.pnggemma-4

Introducing Unsloth Studio

A new open, no-code web UI to train and run LLMs.landscape clip gemma.gifstudio
Cover image
Qwen3.5New Qwen3.5 Small & Medium LLMs are here!qwen35.pngqwen3.5
NVIDIA Nemotron 3Run the new 4B and 120B models by NVIDIA.nemotron 3 logo.pngnemotron-3
Faster MoE is here!Train MoE LLMs 12x faster with less VRAM.faster moe 1920.pngfaster-moe
Claude Code & CodexLearn to run local LLMs via Claude & OpenAI.claude code codex.pngclaude-code
Qwen3-Coder-NextRun & fine-tune the new 80B coding model.qwen3-coder-next logo.pngqwen3-coder-next
GLM-4.7-FlashRun & fine-tune 30B model for agentic coding.glm4.7flash.pngglm-4.7-flash
{% columns %} {% column width="50%" %} {% content-ref url="fine-tuning-llms-guide" %} [fine-tuning-llms-guide](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide) {% endcontent-ref %} {% content-ref url="unsloth-notebooks" %} [unsloth-notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks) {% endcontent-ref %} {% endcolumn %} {% column width="50%" %} {% content-ref url="unsloth-model-catalog" %} [unsloth-model-catalog](https://unsloth.ai/docs/get-started/unsloth-model-catalog) {% endcontent-ref %} {% content-ref url="../models/tutorials" %} [tutorials](https://unsloth.ai/docs/models/tutorials) {% endcontent-ref %} {% endcolumn %} {% endcolumns %} ### šŸ¦„ Why Unsloth? * We directly collab with teams behind [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://unsloth.ai/docs/models/tutorials/devstral-how-to-run-and-fine-tune), [Gemma 1-3](https://news.ycombinator.com/item?id=39671146) and [Phi-4](https://unsloth.ai/blog/phi4), where weā€™ve **fixed critical bugs** that greatly improved model accuracy. Andrej Karpathy for example has [praised our work](https://x.com/karpathy/status/1765473722985771335). * Unsloth streamlines local training, inference, data, and deployment * Unsloth supports inference and training for 500+ models: [vision](https://unsloth.ai/docs/basics/vision-fine-tuning), [TTS](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning), [embedding](https://unsloth.ai/docs/basics/embedding-finetuning), [RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) ### ā­ Features Unsloth lets you run and train models for 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) and more. Unsloth provides many key features for both inference and training: #### Inference * Search + download + run any model like GGUFs, LoRA adapters, safetensors. * [Self-healing tool calling](https://unsloth.ai/docs/new/studio/chat#auto-healing-tool-calling) / web search and call OpenAI-compatible APIs. * [Auto inference parameter](https://unsloth.ai/docs/new/studio/chat#auto-parameter-tuning) tuning and edit chat templates. * [Export or save](https://unsloth.ai/docs/new/studio/export) your model to GGUF, 16-bit safetensor etc. * [Compare outputs](https://unsloth.ai/docs/new/studio/chat#model-arena) with two different model side by side. #### Training * Train and [RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) 500+ models \~2x faster with \~70% less VRAM (no accuracy loss) * Supports full fine-tuning, pre-training, 4-bit, 16-bit and FP8 training. * [Auto-create datasets](https://unsloth.ai/docs/new/studio/data-recipe) from PDF, CSV, DOCX files. Edit data in a visual node workflow. * Observability: Monitor training live, track loss, GPU usage, customize graphs * Most efficient [**reinforcement learning**](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) library, using 80% less VRAM for GRPO, [FP8](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning) etc. * [Multi-GPU](https://unsloth.ai/docs/basics/multi-gpu-training-with-unsloth) works but a much better version is coming! ### Quickstart Unsloth supports MacOS, Linux, [Windows](https://unsloth.ai/docs/get-started/install/windows-installation), [NVIDIA](https://unsloth.ai/docs/get-started/install/pip-install), Intel and CPU setups. See: [unsloth-requirements](https://unsloth.ai/docs/get-started/fine-tuning-for-beginners/unsloth-requirements "mention"). Use the same command to update: #### **MacOS, Linux, WSL:** ```bash curl -fsSL https://unsloth.ai/install.sh | sh ``` #### **Windows PowerShell:** ```bash irm https://unsloth.ai/install.ps1 | iex ``` #### Docker Use our official **Docker image**: [`unsloth/unsloth`](https://hub.docker.com/r/unsloth/unsloth) which currently works for Windows, WSL and Linux. MacOS support coming soon. #### Launch Unsloth ```bash unsloth studio -H 0.0.0.0 -p 8888 ``` #### New Models
Cover image
Kimi K2.5kimi k25 logo.pngkimi-k2.5
MiniMax-M2.5minimax2.5 logo.pngminimax-m25
GLM-5glm 5 logo.pngglm-5
### What is Fine-tuning and RL? Why? [**Fine-tuning** an LLM](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide) customizes its behavior, enhances domain knowledge, and optimizes performance for specific tasks. By fine-tuning a pre-trained model (e.g. Llama-3.1-8B) on a dataset, you can: * **Update Knowledge**: Introduce new domain-specific information. * **Customize Behavior**: Adjust the modelā€™s tone, personality, or response style. * **Optimize for Tasks**: Improve accuracy and relevance for specific use cases. [**Reinforcement Learning (RL)**](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) is where an "agent" learns to make decisions by interacting with an environment and receiving **feedback** in the form of **rewards** or **penalties**. * **Action:** What the model generates (e.g. a sentence). * **Reward:** A signal indicating how good or bad the model's action was (e.g. did the response follow instructions? was it helpful?). * **Environment:** The scenario or task the model is working on (e.g. answering a userā€™s question). **Example fine-tuning or RL use-cases**: * Enables LLMs to predict if a headline impacts a company positively or negatively. * Can use historical customer interactions for more accurate and custom responses. * Fine-tune LLM on legal texts for contract analysis, case law research, and compliance. You can think of a fine-tuned model as a specialized agent designed to do specific tasks more effectively and efficiently. **Fine-tuning can replicate all of RAG's capabilities**, but not vice versa. {% columns %} {% column width="50%" %} {% content-ref url="fine-tuning-for-beginners/faq-+-is-fine-tuning-right-for-me" %} [faq-+-is-fine-tuning-right-for-me](https://unsloth.ai/docs/get-started/fine-tuning-for-beginners/faq-+-is-fine-tuning-right-for-me) {% endcontent-ref %} {% content-ref url="../basics/inference-and-deployment" %} [inference-and-deployment](https://unsloth.ai/docs/basics/inference-and-deployment) {% endcontent-ref %} {% endcolumn %} {% column width="50%" %} {% content-ref url="reinforcement-learning-rl-guide" %} [reinforcement-learning-rl-guide](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide) {% endcontent-ref %} {% content-ref url="../basics/unsloth-dynamic-2.0-ggufs" %} [unsloth-dynamic-2.0-ggufs](https://unsloth.ai/docs/basics/unsloth-dynamic-2.0-ggufs) {% endcontent-ref %} {% endcolumn %} {% endcolumns %}