Deploy an LLM with Guardrails in H2O
- Generative AI
- H2O MLOps
- H2O Cascade
This lesson walks through the full pipeline for building a custom large language model in H2O, starting with LLM Data Studio to generate question and answer pairs from a source document, then fine-tuning a model in H2O LLM Studio to write in a specific style. It shows how to deploy the tuned model as a REST endpoint through H2O MLOps and track experiments with tools like Neptune and Hugging Face. It closes with a side by side guardrails demo, comparing an unguarded model's off-brand answers to a guarded one that stays on topic and professional.
Key takeaways
- How LLM Data Studio turns a source PDF into thousands of question and answer pairs for fine-tuning
- How H2O LLM Studio fine-tunes an open-source model on a custom writing style using LoRA
- What H2O MLOps needs to deploy a fine-tuned model as a REST endpoint with GPU runtime settings
- Why guardrails change a model's tone, keeping responses on topic even with unusual persona prompts
- The demo compares guarded and unguarded responses side by side for several persona style prompts
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