Unsloth

// Overview

Unsloth is a library for fine-tuning and running large language models with lower memory use and shorter training times. It focuses on LoRA, QLoRA, and other parameter-efficient methods, along with full fine-tuning, reinforcement learning, and 4-bit, 16-bit, and FP8 training. According to the project, it can train and run RL on 500+ models up to 2x faster while using up to 70% less VRAM, with no accuracy loss.

It comes in two forms. Unsloth Studio is a web UI that runs on Windows, Linux, WSL, and macOS, letting you search, download, run, and train text, vision, audio, and embedding models, and export them to formats like GGUF and 16-bit safetensors. Unsloth Core is the code-based version installed with pip or uv, aimed at people who want to script training and inference directly.

It fits the fine-tuning frameworks category by lowering the hardware bar for training. With its custom Triton kernels and memory savings, you can fine-tune many open models on a single consumer GPU instead of a multi-GPU cluster.

// What it does

• Trains and runs RL on 500+ models up to 2x faster with up to 70% less VRAM, per the project
• Supports LoRA, QLoRA, full fine-tuning, RL, pretraining, and 4-bit, 16-bit, and FP8 training
• Custom Triton and math kernels, including collaborations with PyTorch and Hugging Face
• Unsloth Studio web UI to search, download, run, train, and export models on Windows, Linux, WSL, and macOS
• Exports trained models to GGUF, 16-bit safetensors, and other formats
• Reinforcement Learning support using up to 80% less VRAM for methods like GRPO

// Getting started

You can use Unsloth either through the Studio web UI or as a code-based install. The commands below come straight from the README.

curl -fsSL https://unsloth.ai/install.sh | sh

irm https://unsloth.ai/install.ps1 | iex

unsloth studio -p 8888

uv venv unsloth_env --python 3.13
source unsloth_env/bin/activate
uv pip install unsloth --torch-backend=auto

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Fine-tune an open LLM with LoRA or QLoRA on a single consumer GPU when VRAM is limited
• Run reinforcement learning (for example GRPO) on a model with reduced memory use
• Train and export a custom model to GGUF or safetensors for local inference
• Search, download, and run text, vision, audio, or embedding models locally through the Studio web UI

// Unsloth in the news

1. 2026-08-19MAJORUnsloth Dynamic 3.0 — GGUF quants keep 10% more accuracy per gigabyte
2. 2026-08-11MAJORUnsloth Desktop — run and train local AI models without writing code
3. 2026-07-20MAJORUnsloth v0.1.50-beta — AMD GPU support lands across Windows, WSL, and Linux
4. 2026-06-08MAJOROpenEnv for Agentic RL Moves to Multi-Org Governance — Meta-PyTorch, NVIDIA, Hugging Face, Unsloth, Modal, Prime Intellect, Mercor, Fleet AI, and Reflection Form 9-Member Steering Committee With PyTorch Foundation, vLLM, SkyRL, Lightning AI, Scale AI, and Stanford as Adopters
5. 2026-04-23MAJORUnsloth Studio v0.1.37 — New UI Redesign, Qwen3.6 Support, Preserve Thinking Mode

From the AI/TLDR release feed — every item is source-verified when it ships.

// How Unsloth compares

Unsloth alongside other open-source fine-tuning frameworks tools AI/TLDR tracks, ranked by GitHub stars.