# Get Started - [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. - [AMD AI Reinforcement Learning Hackathon with Unsloth](https://unsloth.ai/docs/get-started/install/amd/amd-hackathon.md): ​​Learn hands-on techniques for ​Reinforcement Learning for AI models with Unsloth from Daniel Han, the creator of 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. - [Conda Install](https://unsloth.ai/docs/get-started/install/conda-install.md): To install Unsloth locally on Conda, follow the steps below: - [How to Fine-tune LLMs in VS Code with Unsloth & Colab GPUs](https://unsloth.ai/docs/get-started/install/vs-code.md): Guide to fine-tuning models directly in Visual Studio Code via Unsloth and Google Colab. - [Google Colab](https://unsloth.ai/docs/get-started/install/google-colab.md): To install and run Unsloth on Google Colab, follow the steps below: - [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: - [Training AI Agents with RL](https://unsloth.ai/docs/get-started/reinforcement-learning-rl-guide/training-ai-agents-with-rl.md): Learn how to train AI agents for real-world tasks using Reinforcement Learning (RL). --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://unsloth.ai/docs/get-started.md?ask= ``` 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.