{"library":"torchtune","type":"library","category":null,"description":"torchtune is a PyTorch-native library designed for authoring, fine-tuning, and experimenting with Large Language Models (LLMs). It provides hackable training recipes for techniques like SFT, LoRA, QLoRA, FSDP, DPO, PPO, and QAT, supporting popular architectures such as Llama, Gemma, Mistral, Phi, and Qwen. While it offers a componentized design, memory efficiency, and strong integrations, active feature development for torchtune officially ceased in July 2025. The library will receive critical bug fixes and security patches through 2025, but no new features will be added, as the PyTorch team is developing a new product.","language":"python","status":"maintenance","version":"0.6.1","tags":["LLM","fine-tuning","PyTorch","AI","machine-learning","deep-learning","transformers","LoRA","QLoRA","FSDP","DPO"],"last_verified":"Sun May 24","install":[{"cmd":"pip install torch torchvision torchao","imports":["from torchtune.models.llama2 import lora_llama2_7b","from torchtune.data import Message","from torchtune.data import InputOutputToMessages","from torchtune.training import FullModelHFCheckpointer"]},{"cmd":"pip install torchtune","imports":[]}],"homepage":"https://pytorch.org/torchtune","github":"https://github.com/pytorch/torchtune","docs":"https://pytorch.org/torchtune/main/index.html","changelog":null,"pypi":"https://pypi.org/project/torchtune/","npm":null,"openapi_spec":null,"status_page":null,"smithery":null,"compatibility":{"summary":{"python_range":"3.10–3.9","success_rate":45,"avg_install_s":81.9,"avg_import_s":null,"wheel_type":"wheel"},"url":"https://checklist.day/v1/registry/torchtune/compatibility"}}