H2O LLM Studio: Fine-Tune LLMs Without Writing Code

Fine-tuning LLMs typically means writing training scripts, managing GPU memory, configuring hyperparameters, and debugging CUDA errors. H2O LLM Studio replaces all that with a browser-based GUI.

Point, click, fine-tune.

What It Does

H2O LLM Studio is a no-code framework for fine-tuning large language models. Upload your data, pick a base model, adjust settings through sliders and dropdowns, and start training.

Key features:

• No coding required — Everything through a web GUI
• LoRA support — Low-Rank Adaptation for efficient fine-tuning
• 8-bit training — Run on consumer GPUs with lower memory
• RLHF support — Reinforcement learning from human feedback
• Experiment tracking — Compare runs, visualize metrics

Getting Started

# Clone and setup
git clone https://github.com/h2oai/h2o-llmstudio.git
cd h2o-llmstudio
make setup

# Launch GUI
make wave

Then open http://localhost:10101 in your browser.

The Workflow

1. Import data — Upload CSV/JSONL with your training examples
2. Configure experiment — Select base model, training params
3. Train — Monitor progress in real-time
4. Evaluate — Test on held-out data
5. Export — Download weights or push to Hugging Face

Why This Matters

Fine-tuning was the domain of ML engineers with GPU clusters. H2O LLM Studio democratizes it:

• Researchers can iterate on domain-specific models without engineering support
• Product teams can prototype fine-tuned models before committing to infrastructure
• Students can learn fine-tuning concepts hands-on

The LoRA + 8-bit combination means you can fine-tune a 7B model on a single RTX 3090.

My Take

I’m usually skeptical of “no-code ML” tools—they often hide complexity that matters. But H2O LLM Studio hits the right balance. It exposes the important hyperparameters while handling the boilerplate.

For quick experiments and prototypes, this beats writing training loops. For production, you’ll probably still want more control.

Links:

• GitHub
• Documentation