Fine-tuning LLMs has become much easier because of open-source tools. You no longer need to build the full training stack from scratch. Whether you want low-VRAM training, LoRA, QLoRA, RLHF, DPO, multi-GPU scaling, or a simple UI, there is likely a library that fits your workflow. Here are the best open-source libraries worth knowing for fine-tuning LLMs locally. From faster speeds to reduced load, all of them have something to offer. Table of contents 1. Unsloth 2. LLaMA-Factory 3. DeepSpeed 4. PEFT 5. Axolotl 6. TRL 7. torchtune 8. LitGPT 9. SWIFT 10. AutoTrain Advanced Which One Should You Use? Frequently Asked Questions 1. Unsloth Unsloth is built for fast and memory-efficient LLM fine-tuning. It is useful when you want to train models locally, on Colab, Kaggle, or on consumer GPUs. The project says it can train and run hundreds of models faster while using less VRAM. Best for: Fast local fine-tuning, low-VRAM setups, Hugging Face models, and quick experiments.…