# Get Started

- [Unsloth Docs](/docs/get-started/readme.md): Unsloth is an open-source framework for running and training models.
- [Fine-tuning for Beginners](/docs/get-started/fine-tuning-for-beginners.md)
- [Unsloth Requirements](/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?](/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](/docs/get-started/unsloth-notebooks.md): Fine-tuning notebooks: Explore the Unsloth catalog.
- [Unsloth Model Catalog](/docs/get-started/unsloth-model-catalog.md)
- [Unsloth Installation](/docs/get-started/install.md): Learn to install Unsloth locally or online.
- [Install Unsloth via pip and uv](/docs/get-started/install/pip-install.md): To install Unsloth locally via Pip, follow the steps below:
- [Install Unsloth on MacOS](/docs/get-started/install/mac.md)
- [How to Fine-Tune LLMs on Windows with Unsloth (Step-by-Step Guide)](/docs/get-started/install/windows-installation.md): See how to install Unsloth on Windows to start fine-tuning LLMs locally.
- [Install Unsloth via Docker](/docs/get-started/install/docker.md): Install Unsloth using our official Docker container
- [Updating Unsloth](/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](/docs/get-started/install/amd.md): Learn how for fine-tune large language models (LLMs) on AMD GPUs with Unsloth.
- [Fine-tuning LLMs on Intel GPUs with Unsloth](/docs/get-started/install/intel.md): Learn how to train and fine-tune large language models on Intel GPUs.
- [Fine-tuning LLMs Guide](/docs/get-started/fine-tuning-llms-guide.md): Learn all the basics and best practices of fine-tuning. Beginner-friendly.
- [Datasets Guide](/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](/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?](/docs/get-started/fine-tuning-llms-guide/what-model-should-i-use.md)
- [Tutorial: How to Finetune Llama-3 and Use In Ollama](/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](/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](/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)](/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](/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](/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](/docs/get-started/reinforcement-learning-rl-guide/advanced-rl-documentation.md): Advanced documentation settings when using Unsloth with GRPO.
- [GSPO Reinforcement Learning](/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](/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](/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](/docs/get-started/reinforcement-learning-rl-guide/memory-efficient-rl.md)
- [Preference Optimization Training - DPO, ORPO & KTO](/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: