---

name: h2o-llmstudio description: "Use H2O LLM Studio for no-code and CLI LLM fine-tuning, dataset/config preparation, training diagnostics, evaluation, prompting, export, and the Wave GUI runtime." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

H2O LLM Studio

Use this repo skill when a task involves H2O LLM Studio as a package or runtime: preparing datasets/configs, launching the Wave GUI, running or debugging fine-tuning experiments, understanding model/evaluation behavior, or prompting/exporting a trained experiment.

Before you act

1. Read repo provenance if the user is working in a checkout and you need to decide whether this skill matches the current source version.
2. Read runtime setup before recommending install, app, Docker, CUDA, DeepSpeed, or working-directory commands.
3. Run or adapt scripts/check_environment.py for safe import/backend/runtime-asset diagnostics before starting services, downloads, or training.
4. Use cross-cutting troubleshooting for Python version, package layout, CUDA/NVIDIA, DeepSpeed, keyring, data/model download, and credential failures.

Route by task

User task	Read
Start or debug the no-code Wave GUI, Docker app, remote proxy, app workdir, settings, keyring, or browser/server lifecycle	app-and-ui
Build or validate YAML configs, choose problem types, map dataset columns, inspect data schemas, or round-trip config files	configuration-and-data
Launch or diagnose train.py, CPU/GPU tiny smokes, multi-GPU/DeepSpeed, mixed precision, LoRA, output artifacts, or experiment status	training-and-experiments
Understand model wrappers, losses, metrics, prediction files, plots, AI judge metrics, or generation vs forward-pass evaluation behavior	modeling-and-evaluation
Prompt a trained experiment, preflight saved artifacts, change generation parameters, publish to Hugging Face Hub, or hand off to h2oGPT	export-and-prompt

Install and quick verification

From a user's own H2O LLM Studio runtime root, use the repo-supported Python 3.10 setup path before app or training work:

make setup
python scripts/check_environment.py --runtime-root . --check-config-assets

When make setup is not appropriate, create an isolated Python 3.10 environment and install the same locked/runtime dependency set described in runtime setup; do not modify a user-owned environment without approval.

Fast runtime facts

• Package metadata expects Python 3.10 and a GPU-oriented PyTorch/Transformers stack.
• The documented runtime is usually a source-layout H2O LLM Studio runtime root or Docker image, not a tiny import-only library. Commands that refer to llm_studio/... should be run from the user's own runtime root or translated to an equivalent module/wrapper command.
• Production fine-tuning is NVIDIA-GPU oriented. CPU-style unit/integration configs can validate mechanics, but they do not prove real large-model throughput or DeepSpeed/multi-GPU behavior.
• The app and config layers use runtime assets such as prompts/, model_cards/, static/, and pyproject.toml; importing llm_studio alone is not enough to prove a working app/training root.
• Optional or external services include Hugging Face Hub/model downloads, Kaggle/HF datasets, OpenAI-compatible judge metrics, W&B logging, S3/Azure/H2O Drive data connectors, Docker/NVIDIA container runtime, and browser UI tests.

Safe first checks

python scripts/check_environment.py --runtime-root . --check-cuda --check-config-assets
python sub-skills/configuration-and-data/scripts/inspect_config.py --help
python sub-skills/training-and-experiments/scripts/check_training_environment.py --help
python sub-skills/export-and-prompt/scripts/check_experiment_artifacts.py --help

These helpers diagnose environment/config/artifact readiness. They do not download models, start Wave, run training, call OpenAI, publish to Hugging Face, or mutate experiment outputs unless an option explicitly says so.

When to stop and ask

Ask before running long training, downloading datasets/models, starting a public-facing Wave server, using API/HF/W&B/cloud credentials, publishing to Hugging Face Hub, modifying a user-owned Python environment, changing Docker/NVIDIA host setup, or overwriting an experiment output directory.