
Easily collect high-quality human data for AI and research

Released May 23
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Train and run open models locally with no-code UI
Unsloth is an open-source, no-code web UI for training, running and exporting open models in one unified local interface.

Unsloth is an open-source, no-code web UI for training, running and exporting open models locally on your own hardware.
Download Unsloth Studio for Mac or Windows, load a GGUF or Safetensors model, create datasets from documents using Data Recipes, train with no-code controls, compare models in Model Arena, and export to standard formats.
Unsloth provides a no-code web UI called Unsloth Studio that runs 100% offline on Mac and Windows. It supports loading GGUF and Safetensors models, tool-calling, web search, an OpenAI-compatible API, side-by-side model comparison (Model Arena), and uploading images, docs, audio, and code. For training, it offers auto-creation of datasets from PDF, CSV, and JSON files via Data Recipes, with real-time observability. Custom kernels enable optimized training for LoRA, FP8, FFT, PT, and 500+ models including text, vision, audio, and embeddings. Models can be exported to safetensors or GGUF for use with llama.cpp, vLLM, Ollama, and more. The open-source version is free on GitHub and Google Colab; Pro and Enterprise plans with faster training, lower memory usage, and multi-GPU support are available by contacting the team.
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