# Unsloth Documentation

## 🇺🇸 English

- [Unsloth Docs](https://unsloth.ai/docs/get-started/readme.md): Unsloth is an open-source framework for running and training models.
- [Fine-tuning for Beginners](https://unsloth.ai/docs/get-started/fine-tuning-for-beginners.md)
- [Unsloth Requirements](https://unsloth.ai/docs/get-started/fine-tuning-for-beginners/unsloth-requirements.md): Here are Unsloth's requirements including system and GPU VRAM requirements.
- [FAQ + Is Fine-tuning Right For Me?](https://unsloth.ai/docs/get-started/fine-tuning-for-beginners/faq-+-is-fine-tuning-right-for-me.md): If you're stuck on if fine-tuning is right for you, see here! Learn about fine-tuning misconceptions, how it compared to RAG and more:
- [Unsloth Notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks.md): Fine-tuning notebooks: Explore the Unsloth catalog.
- [Unsloth Model Catalog](https://unsloth.ai/docs/get-started/unsloth-model-catalog.md)
- [Unsloth Installation](https://unsloth.ai/docs/get-started/install.md): Learn to install Unsloth locally or online.
- [Install Unsloth via pip and uv](https://unsloth.ai/docs/get-started/install/pip-install.md): To install Unsloth locally via Pip, follow the steps below:
- [Install Unsloth on MacOS](https://unsloth.ai/docs/get-started/install/mac.md)
- [How to Fine-Tune LLMs on Windows with Unsloth (Step-by-Step Guide)](https://unsloth.ai/docs/get-started/install/windows-installation.md): See how to install Unsloth on Windows to start fine-tuning LLMs locally.
- [Install Unsloth via Docker](https://unsloth.ai/docs/get-started/install/docker.md): Install Unsloth using our official Docker container
- [Updating Unsloth](https://unsloth.ai/docs/get-started/install/updating.md): To update or use an old version of Unsloth, follow the steps below:
- [Fine-tuning LLMs on AMD GPUs with Unsloth Guide](https://unsloth.ai/docs/get-started/install/amd.md): Learn how to fine-tune large language models (LLMs) on AMD GPUs with Unsloth.
- [Fine-tuning LLMs on Intel GPUs with Unsloth](https://unsloth.ai/docs/get-started/install/intel.md): Learn how to train and fine-tune large language models on Intel GPUs.
- [Fine-tuning LLMs Guide](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide.md): Learn all the basics and best practices of fine-tuning. Beginner-friendly.
- [Datasets Guide](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide/datasets-guide.md): Learn how to create & prepare a dataset for fine-tuning.
- [LoRA fine-tuning Hyperparameters Guide](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide/lora-hyperparameters-guide.md): Learn step-by-step the best LLM fine-tuning settings - LoRA rank & alpha, epochs, batch size + gradient accumulation, QLoRA vs. LoRA, target modules, and more.
- [What Model Should I Use for Fine-tuning?](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide/what-model-should-i-use.md)
- [Tutorial: How to Finetune Llama-3 and Use In Ollama](https://unsloth.ai/docs/get-started/fine-tuning-llms-guide/tutorial-how-to-finetune-llama-3-and-use-in-ollama.md): Beginner's Guide for creating a customized personal assistant (like ChatGPT) to run locally on Ollama
- [Reinforcement Learning (RL) Guide](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide.md): Learn all about Reinforcement Learning (RL) and how to train your own DeepSeek-R1 reasoning model with Unsloth using GRPO. A complete guide from beginner to advanced.
- [Reinforcement Learning GRPO with 7x Longer Context](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/grpo-long-context.md): Learn how Unsloth enables ultra long context RL fine-tuning.
- [Vision Reinforcement Learning (VLM RL)](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/vision-reinforcement-learning-vlm-rl.md): Train Vision/multimodal models via GRPO and RL with Unsloth!
- [FP8 Reinforcement Learning](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/fp8-reinforcement-learning.md): Train reinforcement learning (RL) and GRPO in FP8 precision with Unsloth.
- [Tutorial: Train your own Reasoning model with GRPO](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/tutorial-train-your-own-reasoning-model-with-grpo.md): Beginner's Guide to transforming a model like Llama 3.1 (8B) into a reasoning model by using Unsloth and GRPO.
- [Advanced Reinforcement Learning Documentation](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation.md): Advanced documentation settings when using Unsloth with GRPO.
- [GSPO Reinforcement Learning](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation/gspo-reinforcement-learning.md): Train with GSPO (Group Sequence Policy Optimization) RL in Unsloth.
- [RL Reward Hacking](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation/rl-reward-hacking.md): Learn what is Reward Hacking in Reinforcement Learning and how to counter it.
- [FP16 vs BF16 for RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation/fp16-vs-bf16-for-rl.md): Defeating the Training-Inference Mismatch via FP16 https://arxiv.org/pdf/2510.26788 shows how using float16 is better than bfloat16
- [Memory Efficient RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/memory-efficient-rl.md)
- [Preference Optimization Training - DPO, ORPO & KTO](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/preference-dpo-orpo-and-kto.md): Learn about preference alignment fine-tuning with DPO, GRPO, ORPO or KTO via Unsloth, follow the steps below:
- [Introducing Unsloth Studio](https://unsloth.ai/docs/new/studio.md): Run and train AI models locally with Unsloth Studio.
- [Get started with Unsloth Studio](https://unsloth.ai/docs/new/studio/start.md): A guide for getting started with the fine-tuning studio, data recipes, model exporting, and chat.
- [How to Run models with Unsloth Studio](https://unsloth.ai/docs/new/studio/chat.md): Run AI models, LLMs and GGUFs locally with Unsloth Studio.
- [Unsloth Studio Installation](https://unsloth.ai/docs/new/studio/install.md): Learn how to install Unsloth Studio on your local device.
- [Unsloth Data Recipes](https://unsloth.ai/docs/new/studio/data-recipe.md): Learn how to create, build and edit datasets with Unsloth Studio's Data Recipes.
- [Export models with Unsloth Studio](https://unsloth.ai/docs/new/studio/export.md): Learn how to export your safetensor or LoRA model files to GGUF or other formats.
- [Unsloth Updates](https://unsloth.ai/docs/new/changelog.md): Unsloth Changelog for our latest releases, improvements and fixes.
- [Qwen3.6 - How to Run Locally](https://unsloth.ai/docs/models/qwen3.6.md): Run the new Qwen3.6-27B and 35B-A3B models locally!
- [Gemma 4 - How to Run Locally](https://unsloth.ai/docs/models/gemma-4.md): Run Google’s new Gemma 4 models locally, including E2B, E4B, 26B A4B, and 31B.
- [Gemma 4 Fine-tuning Guide](https://unsloth.ai/docs/models/gemma-4/train.md): Train Gemma 4 by Google with Unsloth.
- [NVIDIA Nemotron 3 Nano Omni - How To Run Locally](https://unsloth.ai/docs/models/nemotron-3-nano-omni.md): Run & fine-tune Nemotron-3-Nano-Omni-30B-A3B locally on your device!
- [Kimi K2.6 - How to Run Locally](https://unsloth.ai/docs/models/kimi-k2.6.md): Step-by-step guide to running Kimi-K2.6 on your own local device.
- [Qwen3.5 - How to Run Locally](https://unsloth.ai/docs/models/qwen3.5.md): Run the new Qwen3.5 LLMs including Medium: Qwen3.5-35B-A3B, 27B, 122B-A10B, Small: Qwen3.5-0.8B, 2B, 4B, 9B and 397B-A17B on your local device!
- [Qwen3.5 Fine-tuning Guide](https://unsloth.ai/docs/models/qwen3.5/fine-tune.md): Learn how to fine-tune Qwen3.5 LLMs with Unsloth.
- [Qwen3.5 GGUF Benchmarks](https://unsloth.ai/docs/models/qwen3.5/gguf-benchmarks.md): See how Unsloth Dynamic GGUFs perform + analysis of perplexity, KL divergence & MXFP4.
- [Mistral 3.5 - How To Run Locally](https://unsloth.ai/docs/models/mistral-3.5.md): Guide for Mistral Mistral 3.5 models, to run or fine-tune locally on your device
- [GLM-5.1 - How to Run Locally](https://unsloth.ai/docs/models/glm-5.1.md): Run the new GLM-5.1 model by Z.ai on your own local device!
- [Qwen3-Coder-Next: How to Run Locally](https://unsloth.ai/docs/models/qwen3-coder-next.md): Guide to run Qwen3-Coder-Next locally on your device!
- [NVIDIA Nemotron 3 Nano - How To Run Guide](https://unsloth.ai/docs/models/nemotron-3.md): Run & fine-tune NVIDIA Nemotron 3 Nano locally on your device!
- [NVIDIA Nemotron-3-Super: How To Run Guide](https://unsloth.ai/docs/models/nemotron-3/nemotron-3-super.md): Run & fine-tune NVIDIA Nemotron-3-Super-120B-A12B locally on your device!
- [gpt-oss: How to Run Guide](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune.md): Run & fine-tune OpenAI's new open-source models!
- [gpt-oss Reinforcement Learning](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune/gpt-oss-reinforcement-learning.md)
- [Tutorial: How to Train gpt-oss with RL](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune/gpt-oss-reinforcement-learning/tutorial-how-to-train-gpt-oss-with-rl.md): Learn to train OpenAI gpt-oss with GRPO to autonomously beat 2048 locally or on Colab.
- [Tutorial: How to Fine-tune gpt-oss](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune/tutorial-how-to-fine-tune-gpt-oss.md): Learn step-by-step how to train OpenAI gpt-oss locally with Unsloth.
- [Long Context gpt-oss Training](https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune/long-context-gpt-oss-training.md)
- [Large language model (LLMs) Tutorials](https://unsloth.ai/docs/models/tutorials.md)
- [Qwen3 - How to Run & Fine-tune](https://unsloth.ai/docs/models/tutorials/qwen3-how-to-run-and-fine-tune.md): Learn to run & fine-tune Qwen3 locally with Unsloth + our Dynamic 2.0 quants
- [Qwen3-VL: How to Run Guide](https://unsloth.ai/docs/models/tutorials/qwen3-how-to-run-and-fine-tune/qwen3-vl-how-to-run-and-fine-tune.md): Learn to fine-tune and run Qwen3-VL locally with Unsloth.
- [Qwen3-2507: Run Locally Guide](https://unsloth.ai/docs/models/tutorials/qwen3-how-to-run-and-fine-tune/qwen3-2507.md): Run Qwen3-30B-A3B-2507 and 235B-A22B Thinking and Instruct versions locally on your device!
- [MiniMax-M2.7 - How to Run Locally](https://unsloth.ai/docs/models/tutorials/minimax-m27.md): Run MiniMax-M2.7 LLM locally on your own device!
- [GLM-5: How to Run Locally Guide](https://unsloth.ai/docs/models/tutorials/glm-5.md): Run the new GLM-5 model by Z.ai on your own local device!
- [Kimi K2.5: How to Run Locally Guide](https://unsloth.ai/docs/models/tutorials/kimi-k2.5.md): Guide on running Kimi-K2.5 on your own local device!
- [GLM-4.7-Flash: How To Run Locally](https://unsloth.ai/docs/models/tutorials/glm-4.7-flash.md): Run & fine-tune GLM-4.7-Flash locally on your device!
- [MiniMax-M2.5: How to Run Guide](https://unsloth.ai/docs/models/tutorials/minimax-m25.md): Run MiniMax-M2.5 locally on your own device!
- [Qwen3-Coder: How to Run Locally](https://unsloth.ai/docs/models/tutorials/qwen3-coder-how-to-run-locally.md): Run Qwen3-Coder-30B-A3B-Instruct and 480B-A35B locally with Unsloth Dynamic quants.
- [Gemma 3 - How to Run Guide](https://unsloth.ai/docs/models/tutorials/gemma-3-how-to-run-and-fine-tune.md): How to run Gemma 3 effectively with our GGUFs on llama.cpp, Ollama, Open WebUI and how to fine-tune with Unsloth!
- [Gemma 3n: How to Run & Fine-tune](https://unsloth.ai/docs/models/tutorials/gemma-3-how-to-run-and-fine-tune/gemma-3n-how-to-run-and-fine-tune.md): Run Google's new Gemma 3n locally with Dynamic GGUFs on llama.cpp, Ollama, Open WebUI and fine-tune with Unsloth!
- [DeepSeek-OCR 2: How to Run & Fine-tune Guide](https://unsloth.ai/docs/models/tutorials/deepseek-ocr-2.md): Guide on how to run and fine-tune DeepSeek-OCR-2 locally.
- [GLM-4.7: How to Run Locally Guide](https://unsloth.ai/docs/models/tutorials/glm-4.7.md): A guide on how to run Z.ai GLM-4.7 model on your own local device!
- [How to Run Qwen-Image-2512 Locally in ComfyUI](https://unsloth.ai/docs/models/tutorials/qwen-image-2512.md): Step-by-step tutorial for running Qwen-Image-2512 on your local device with ComfyUI.
- [Run Qwen-Image-2512 in stable-diffusion.cpp Tutorial](https://unsloth.ai/docs/models/tutorials/qwen-image-2512/stable-diffusion.cpp.md): Tutorial for using Qwen-Image-2512 in stable-diffusion.cpp.
- [Devstral 2 - How to Run Guide](https://unsloth.ai/docs/models/tutorials/devstral-2.md): Guide for local running Mistral Devstral 2 models: 123B-Instruct-2512 and Small-2-24B-Instruct-2512.
- [Ministral 3 - How to Run Guide](https://unsloth.ai/docs/models/tutorials/ministral-3.md): Guide for Mistral Ministral 3 models, to run or fine-tune locally on your device
- [DeepSeek-OCR: How to Run & Fine-tune](https://unsloth.ai/docs/models/tutorials/deepseek-ocr-how-to-run-and-fine-tune.md): Guide on how to run and fine-tune DeepSeek-OCR locally.
- [Kimi K2 Thinking: Run Locally Guide](https://unsloth.ai/docs/models/tutorials/kimi-k2-thinking-how-to-run-locally.md): Guide on running Kimi-K2-Thinking and Kimi-K2 on your own local device!
- [GLM-4.6: Run Locally Guide](https://unsloth.ai/docs/models/tutorials/glm-4.6-how-to-run-locally.md): A guide on how to run Z.ai GLM-4.6 and GLM-4.6V-Flash model on your own local device!
- [Qwen3-Next: Run Locally Guide](https://unsloth.ai/docs/models/tutorials/qwen3-next.md): Run Qwen3-Next-80B-A3B-Instruct and Thinking versions locally on your device!
- [FunctionGemma: How to Run & Fine-tune](https://unsloth.ai/docs/models/tutorials/functiongemma.md): Learn how to run and fine-tune FunctionGemma locally on your device and phone.
- [DeepSeek-V3.1: How to Run Locally](https://unsloth.ai/docs/models/tutorials/deepseek-v3.1-how-to-run-locally.md): A guide on how to run DeepSeek-V3.1 and Terminus on your own local device!
- [DeepSeek-R1-0528: How to Run Locally](https://unsloth.ai/docs/models/tutorials/deepseek-r1-0528-how-to-run-locally.md): A guide on how to run DeepSeek-R1-0528 including Qwen3 on your own local device!
- [Liquid LFM2.5: How To Run & Fine-tune](https://unsloth.ai/docs/models/tutorials/lfm2.5.md): Run and fine-tune LFM2.5 Instruct and Vision locally on your device!
- [Magistral: How to Run & Fine-tune](https://unsloth.ai/docs/models/tutorials/magistral-how-to-run-and-fine-tune.md): Meet Magistral - Mistral's new reasoning models.
- [IBM Granite 4.0](https://unsloth.ai/docs/models/tutorials/ibm-granite-4.0.md): How to run IBM Granite-4.0 with Unsloth GGUFs on llama.cpp, Ollama and how to fine-tune!
- [Llama 4: How to Run & Fine-tune](https://unsloth.ai/docs/models/tutorials/llama-4-how-to-run-and-fine-tune.md): How to run Llama 4 locally using our dynamic GGUFs which recovers accuracy compared to standard quantization.
- [Grok 2](https://unsloth.ai/docs/models/tutorials/grok-2.md): Run xAI's Grok 2 model locally!
- [Devstral: How to Run & Fine-tune](https://unsloth.ai/docs/models/tutorials/devstral-how-to-run-and-fine-tune.md): Run and fine-tune Mistral Devstral 1.1, including Small-2507 and 2505.
- [How to Run Local LLMs with Docker: Step-by-Step Guide](https://unsloth.ai/docs/models/tutorials/how-to-run-llms-with-docker.md): Learn how to run Large Language Models (LLMs) with Docker & Unsloth on your local device.
- [DeepSeek-V3-0324: How to Run Locally](https://unsloth.ai/docs/models/tutorials/deepseek-v3-0324-how-to-run-locally.md): How to run DeepSeek-V3-0324 locally using our dynamic quants which recovers accuracy
- [DeepSeek-R1: How to Run Locally](https://unsloth.ai/docs/models/tutorials/deepseek-r1-how-to-run-locally.md): A guide on how you can run our 1.58-bit Dynamic Quants for DeepSeek-R1 using llama.cpp.
- [DeepSeek-R1 Dynamic 1.58-bit](https://unsloth.ai/docs/models/tutorials/deepseek-r1-how-to-run-locally/deepseek-r1-dynamic-1.58-bit.md): See performance comparison tables for Unsloth's Dynamic GGUF Quants vs Standard IMatrix Quants.
- [Phi-4 Reasoning: How to Run & Fine-tune](https://unsloth.ai/docs/models/tutorials/phi-4-reasoning-how-to-run-and-fine-tune.md): Learn to run & fine-tune Phi-4 reasoning models locally with Unsloth + our Dynamic 2.0 quants
- [QwQ-32B: How to Run effectively](https://unsloth.ai/docs/models/tutorials/qwq-32b-how-to-run-effectively.md): How to run QwQ-32B effectively with our bug fixes and without endless generations + GGUFs.
- [Inference & Deployment](https://unsloth.ai/docs/basics/inference-and-deployment.md): Learn how to save your finetuned model so you can run it in your favorite inference engine.
- [Saving to GGUF](https://unsloth.ai/docs/basics/inference-and-deployment/saving-to-gguf.md): Saving models to 16bit for GGUF so you can use it for Ollama, Jan AI, Open WebUI and more!
- [Speculative Decoding](https://unsloth.ai/docs/basics/inference-and-deployment/saving-to-gguf/speculative-decoding.md): Speculative Decoding with llama-server, llama.cpp, vLLM and more for 2x faster inference
- [vLLM Deployment & Inference Guide](https://unsloth.ai/docs/basics/inference-and-deployment/vllm-guide.md): Guide on saving and deploying LLMs to vLLM for serving LLMs in production
- [vLLM Engine Arguments](https://unsloth.ai/docs/basics/inference-and-deployment/vllm-guide/vllm-engine-arguments.md)
- [LoRA Hot Swapping Guide](https://unsloth.ai/docs/basics/inference-and-deployment/vllm-guide/lora-hot-swapping-guide.md)
- [Saving to Ollama](https://unsloth.ai/docs/basics/inference-and-deployment/saving-to-ollama.md)
- [Deploying models to LM Studio](https://unsloth.ai/docs/basics/inference-and-deployment/lm-studio.md): Saving models to GGUF so you can run and deploy them to LM Studio
- [How to install LM Studio CLI in Linux Terminal](https://unsloth.ai/docs/basics/inference-and-deployment/lm-studio/how-to-install-lm-studio-cli-in-linux-terminal.md): LM Studio CLI installation guide without a UI in a terminal instance.
- [SGLang Deployment & Inference Guide](https://unsloth.ai/docs/basics/inference-and-deployment/sglang-guide.md): Guide on saving and deploying LLMs to SGLang for serving LLMs in production
- [llama-server & OpenAI endpoint Deployment Guide](https://unsloth.ai/docs/basics/inference-and-deployment/llama-server-and-openai-endpoint.md): Deploying via llama-server with an OpenAI compatible endpoint
- [How to Run and Deploy LLMs on your iOS or Android Phone](https://unsloth.ai/docs/basics/inference-and-deployment/deploy-llms-phone.md): Tutorial for fine-tuning your own LLM and deploying it on your Android or iPhone with ExecuTorch.
- [Troubleshooting Inference](https://unsloth.ai/docs/basics/inference-and-deployment/troubleshooting-inference.md): If you're experiencing issues when running or saving your model.
- [How to Run Local LLMs with Claude Code](https://unsloth.ai/docs/basics/claude-code.md): Guide to use open models with Claude Code on your local device.
- [How to Run Local LLMs with OpenAI Codex](https://unsloth.ai/docs/basics/codex.md): Use open models with OpenAI Codex on your device locally.
- [Multi-GPU Fine-tuning with Unsloth](https://unsloth.ai/docs/basics/multi-gpu-training-with-unsloth.md): Learn how to fine-tune LLMs on multiple GPUs and parallelism with Unsloth.
- [Multi-GPU Fine-tuning with Distributed Data Parallel (DDP)](https://unsloth.ai/docs/basics/multi-gpu-training-with-unsloth/ddp.md): Learn how to use the Unsloth CLI to train on multiple GPUs with Distributed Data Parallel (DDP)!
- [Fine-tuning Embedding Models with Unsloth Guide](https://unsloth.ai/docs/basics/embedding-finetuning.md): Learn how to easily fine-tune embedding models with Unsloth.
- [Fine-tune MoE Models 12x Faster with Unsloth](https://unsloth.ai/docs/basics/faster-moe.md): Train MoE LLMs locally using Unsloth Guide.
- [Text-to-Speech (TTS) Fine-tuning Guide](https://unsloth.ai/docs/basics/text-to-speech-tts-fine-tuning.md): Learn how to to fine-tune TTS & STT voice models with Unsloth.
- [Unsloth Dynamic 2.0 GGUFs](https://unsloth.ai/docs/basics/unsloth-dynamic-2.0-ggufs.md): A big new upgrade to our Dynamic Quants!
- [Unsloth Dynamic GGUFs on Aider Polyglot](https://unsloth.ai/docs/basics/unsloth-dynamic-2.0-ggufs/unsloth-dynamic-ggufs-on-aider-polyglot.md): Performance of Unsloth Dynamic GGUFs on Aider Polyglot Benchmarks
- [Tool Calling Guide for Local LLMs](https://unsloth.ai/docs/basics/tool-calling-guide-for-local-llms.md)
- [Vision Fine-tuning](https://unsloth.ai/docs/basics/vision-fine-tuning.md): Learn how to fine-tune vision/multimodal LLMs with Unsloth
- [Troubleshooting & FAQs](https://unsloth.ai/docs/basics/troubleshooting-and-faqs.md): Tips to solve issues, and frequently asked questions.
- [Hugging Face Hub, XET debugging](https://unsloth.ai/docs/basics/troubleshooting-and-faqs/hugging-face-hub-xet-debugging.md): Debugging, troubleshooting stalled, stuck downloads and slow downloads
- [Chat Templates](https://unsloth.ai/docs/basics/chat-templates.md): Learn the fundamentals and customization options of chat templates, including Conversational, ChatML, ShareGPT, Alpaca formats, and more!
- [Unsloth Environment Flags](https://unsloth.ai/docs/basics/unsloth-environment-flags.md): Advanced flags which might be useful if you see breaking finetunes, or you want to turn stuff off.
- [Continued Pretraining](https://unsloth.ai/docs/basics/continued-pretraining.md): AKA as Continued Finetuning. Unsloth allows you to continually pretrain so a model can learn a new language.
- [Finetuning from Last Checkpoint](https://unsloth.ai/docs/basics/finetuning-from-last-checkpoint.md): Checkpointing allows you to save your finetuning progress so you can pause it and then continue.
- [Unsloth Benchmarks](https://unsloth.ai/docs/basics/unsloth-benchmarks.md): Unsloth recorded benchmarks on NVIDIA GPUs.
- [3x Faster LLM Training with Unsloth Kernels + Packing](https://unsloth.ai/docs/blog/3x-faster-training-packing.md): Learn how Unsloth increases training throughput and eliminates padding waste for fine-tuning.
- [500K Context Length Fine-tuning](https://unsloth.ai/docs/blog/500k-context-length-fine-tuning.md): Learn how to enable >500K token context window fine-tuning with Unsloth.
- [Quantization-Aware Training (QAT)](https://unsloth.ai/docs/blog/quantization-aware-training-qat.md): Quantize models to 4-bit with Unsloth and PyTorch to recover accuracy.
- [Fine-Tuning LLMs on NVIDIA DGX Station with Unsloth](https://unsloth.ai/docs/blog/dgx-station.md): NVIDIA DGX Station tutorial on how to fine-tune with notebooks from Unsloth.
- [How to Fine-tune LLMs with Unsloth & Docker](https://unsloth.ai/docs/blog/how-to-fine-tune-llms-with-unsloth-and-docker.md): Learn how to fine-tune LLMs or do Reinforcement Learning (RL) with Unsloth's Docker image.
- [Fine-tuning LLMs with NVIDIA DGX Spark and Unsloth](https://unsloth.ai/docs/blog/fine-tuning-llms-with-nvidia-dgx-spark-and-unsloth.md): Tutorial on how to fine-tune and do reinforcement learning (RL) with OpenAI gpt-oss on NVIDIA DGX Spark.
- [Fine-tuning LLMs with Blackwell, RTX 50 series & Unsloth](https://unsloth.ai/docs/blog/fine-tuning-llms-with-blackwell-rtx-50-series-and-unsloth.md): Learn how to fine-tune LLMs on NVIDIA's Blackwell RTX 50 series and B200 GPUs with our step-by-step guide.


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# Agent Instructions: Querying This Documentation

If you need additional information, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on a page URL with the `ask` query parameter:

```
GET https://unsloth.ai/docs/get-started/readme.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.