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unslothai/unsloth

🔥 Hot★ 64,553PythonAudience · developerComplexity · 3/5ActiveLicenseSetup · hard

TLDR

Speed up and shrink the memory footprint of fine-tuning large AI language models on your own GPU by up to 2x faster and 70% less VRAM, with no accuracy loss.

Mindmap

mindmap
  root((Unsloth))
    What it does
      Fine-tune models faster
      Use less GPU memory
      Support 500+ models
    How it works
      Custom kernel optimizations
      Low-level math speedups
      Quantized training option
    Ways to use it
      Studio web interface
      Core Python scripts
    Use cases
      Domain-specific chatbots
      Custom model training
      Budget-friendly fine-tuning
    Tech stack
      Python
      NVIDIA GPUs
      macOS and AMD support

Things people build with this

USE CASE 1

Fine-tune Llama or Mistral models on your own data to create a domain-specific chatbot without expensive cloud GPUs.

USE CASE 2

Train a language model to adopt a specific writing style or tone by fine-tuning on examples of that style.

USE CASE 3

Reduce GPU memory requirements so you can train larger models on consumer-grade hardware like a single RTX 4090.

USE CASE 4

Experiment with reinforcement learning or quantized training methods to optimize model behavior and size.

Tech stack

PythonNVIDIA CUDAPyTorchTransformers

Getting it running

Difficulty · hard Time to first run · 1h+

Requires NVIDIA GPU with CUDA support and careful PyTorch/CUDA version alignment; building optimized kernels may be needed.

Use freely for any purpose, including commercial use, as long as you keep the copyright notice and license text.

In plain English

Unsloth is a tool for running and fine-tuning large AI language models on your own computer, with a focus on making this dramatically faster and less demanding on memory. Fine-tuning means taking an already-trained AI model and training it further on your own data so it behaves differently, for example, teaching a general-purpose language model to answer questions in a specific style or domain. The problem Unsloth addresses is that fine-tuning large models typically requires enormous amounts of GPU memory (VRAM) and takes a long time, pricing out anyone without expensive hardware. Unsloth achieves its efficiency gains through custom low-level code optimizations called kernels, which are tuned routines that make the mathematical operations inside neural network training run faster. According to the README it can make training up to 2x faster while using up to 70% less VRAM compared to standard approaches, with no loss in accuracy. It supports over 500 different open-source models including Llama, Gemma, Qwen, DeepSeek, Mistral, and others. There are two ways to use it: Unsloth Studio is a web-based graphical interface you run locally where you can download models, chat with them, and train them through a visual interface; Unsloth Core is the code-based version for more advanced users who want to write training scripts in Python. It supports various training methods including standard fine-tuning, reinforcement learning, and quantized training (reducing model precision to save memory). It runs on NVIDIA GPUs primarily, with macOS and AMD support growing. The tech stack is Python, installable via pip or a one-line shell script.

Copy-paste prompts

Prompt 1
Show me how to use Unsloth to fine-tune a Llama 2 model on my custom dataset with Python code.
Prompt 2
What are the memory and speed improvements I can expect when using Unsloth versus standard PyTorch fine-tuning?
Prompt 3
How do I set up Unsloth Studio to download a model, chat with it, and train it through the web interface?
Prompt 4
Can I use Unsloth to do reinforcement learning training on a language model, and what's the setup process?
Prompt 5
What GPU hardware do I need to fine-tune a 70B parameter model using Unsloth with quantization?
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.