unsloth

Project Overview

Unsloth is an open-source library designed to accelerate large language model (LLM) fine-tuning. It significantly reduces VRAM usage and increases training speed, compatible with various LLM architectures.

Key Features

• Efficient fine-tuning for Llama, Mistral, Phi, Gemma, and more
• VRAM optimization using QLoRA and other techniques
• Speed optimization: 2-10x faster than standard fine-tuning
• 4-bit quantization support
• Seamless integration with Hugging Face ecosystem
• Custom dataset support

Target Audience

• AI researchers and developers
• Engineers needing to fine-tune LLMs
• Users wanting to experiment with LLMs at lower hardware costs

Quick Start

1. Install: pip install unsloth
2. Or use the official Colab notebooks
3. Load model and tokenizer
4. Apply LoRA configuration
5. Train and save the model

Core Advantages

• Low VRAM usage (4GB+ for 7B model fine-tuning)
• Fast training (2-10x speedup)
• High usability, compatible with Hugging Face
• Open-source and free with active community

System Requirements

• OS: Linux (preferred), Windows, macOS
• Architectures: x86_64 (CUDA), arm64 (inference only)
• Additional Dependencies: CUDA (training), PyTorch

Notes

• Training requires NVIDIA GPU and CUDA environment
• Check official documentation for specific model support