unslothai/unsloth

What it solves

Unsloth addresses the high computational demands of running and training large language models (LLMs). It provides a way to perform fine-tuning and inference with significantly reduced VRAM usage and increased speeds compared to standard methods.

How it works

The project offers two primary interfaces: Unsloth Studio, a web-based UI for running and training models, and Unsloth Core, a code-based version for developers. It utilizes custom Triton and mathematical kernels built with PyTorch and Hugging Face to optimize performance. It supports various quantization formats like GGUF and allows for efficient training techniques such as LoRA, QLoRA, and Reinforcement Learning (RL) with significantly lower memory requirements.

Who it’s for

• AI Developers looking for high-speed, low-memory fine-tuning and inference capabilities.
• Local LLM Enthusiasts wanting to run advanced models (text, vision, audio) on consumer hardware (NVIDIA, AMD, and Apple Silicon).
• Agent Developers needing to connect local models to frameworks like Claude Code or OpenAI Codex via compatible APIs.

Highlights

• High-Efficiency Training: Achieves up to 2x faster training and 70% less VRAM usage for standard models, and up to 12x faster for MoE models.
• Versatile Model Support: Supports text, audio, vision, and embedding models, including popular architectures like Llama, Mistral, Qwen, and Gemma.
• Multi-Modal Capabilities: Enables chatting with images, audio, PDFs, and code.
• Local-to-Agent Integration: Provides OpenAI/Anthropic-compatible APIs to connect local models to external agents.
• Hardware Flexibility: Works across Windows, Linux, macOS, and WSL, supporting NVIDIA, AMD, and Apple Silicon GPUs.