Developer Frameworks & Infrastructure
Unsloth
Ultra-fast open-source LLM fine-tuning framework with 80% less VRAM
Open sourceRuns on your deviceEditors pick
GitHub stars
79.2K
79,182 stars
Community forks
7.4K
Open collaboration
License
Apache-2.0
Read the full license
Platforms
macOS - Windows - Linux
Also Web
Last activity
Nov 8
Repository push date
Best for
Low-resource local LLM fine-tuning
Fine-tune 8B and 70B open models on single GPUs using 80% less VRAM with QLoRA and LoRA.
Pricing checked Sep 2026
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About Unsloth
Unsloth is an open-source fine-tuning framework designed to make training custom language models fast and hardware-efficient. By rewriting PyTorch backpropagation kernels in Triton, Unsloth allows developers to fine-tune models like Llama 3, Mistral, and Qwen up to 5x faster while cutting VRAM requirements by 80%.
It enables fine-tuning 70B parameter models on single consumer GPUs or free Google Colab instances without compromising model accuracy.
- Delivers 2x to 5x faster training speeds with 80% lower VRAM usage
- Open-source core library with native support for Llama, Qwen, and Mistral
- One-click export to GGUF, vLLM, and Hugging Face Hub formats
- Multi-node multi-GPU scaling features require Unsloth Pro or Enterprise licenses
- Requires basic Python and PyTorch fine-tuning knowledge
git clone https://github.com/unslothai/unsloth.gitOpen source tool available on GitHub.
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