H2O LLM Studio

Fine-tuning large language models without writing code is the core pitch of H2O LLM Studio, a GUI framework from H2O.ai built on the H2O Wave web stack. Users import datasets, configure experiments through a browser interface, and train with LoRA, DoRA, rank-stabilized LoRA, 8-bit low-memory mode, or full-parameter updates, with DPO, IPO, and KTO preference optimization having replaced the earlier RLHF implementation. The tool tracks experiments with loss curves and validation metrics, supports chatting with a model right after training, and exports finished checkpoints directly to the Hugging Face Hub in transformers format. It runs on Ubuntu 16.04 or newer with at least one recent NVIDIA GPU, with 24 GB or more of GPU memory recommended for larger models, and installs via make setup with Python 3.10 or as a published Docker image. The code is Apache-2.0 licensed with no paid tier, so commercial use is unrestricted. Data scientists who want reproducible fine-tuning runs without hand-writing training loops are the main audience, and the project holds around 5,000 GitHub stars.