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h2oai/h2o-llmstudio
H2O LLM Studio - a framework and no-code GUI for fine-tuning LLMs. Documentation: https://docs.h2o.ai/h2o-llmstudio/
H2O LLM Studio is a no-code GUI and CLI framework for fine-tuning LLMs, built on top of HuggingFace Transformers. It targets ML practitioners who want a managed training workflow without writing training loops from scratch — dataset import, experiment tracking, eval metrics, and Hugging Face export in one place.
LoRA/QLoRA support with configurable rank, alpha, dropout, and target modules is well-thought-out and matches what you'd write manually. The shift from RLHF to DPO/IPO/KTO is the right call — RLHF is fragile and the preference optimization methods are more stable to train. DeepSpeed ZeRO integration for multi-GPU sharding is a real feature, not a checkbox — Stage 3 with offload options is configured per-experiment. The Wave-based GUI runs locally and doesn't phone home, which matters for teams with data privacy concerns.
The H2O Wave UI dependency is a significant coupling — Wave is a niche Python app framework with limited community support, and if H2O drops it you're stranded. The FedRAMP badge in the topics is marketing; the actual compliance scope is H2O's cloud offering, not this repo. Backwards compatibility is explicitly disclaimed ('pin the version'), which means upgrading between runs can silently break experiment reproducibility. The LLM-as-judge evaluation approach (using GPT to score outputs) has well-documented reliability problems that the docs don't address.