Video by H2O.ai via YouTube
How to fine-tune domain-specific LLMs for tasks like text-to-SQL and multimodal QA using H2O Enterprise LLM Studio.
When prompt engineering alone is insufficient, fine-tuning a domain-specific model can reduce costs while improving accuracy. H2O Enterprise LLM Studio walks through the full instruction tuning process—leveraging LoRA adapters, built-in AutoML for hyperparameter optimization, and real-time training metrics like loss curves and validation perplexity. Models are evaluated for safety and quality, then exported directly to Hugging Face for distribution across the organization.
Technical Capabilities & Resources
➤ Multimodal Generative AI Tuning: Train models for domain-specific tasks including multi-modal causal language modeling and image/text classification.
🔗 https://docs.h2o.ai/h2o-enterprise-llm-studio/get-started/what-is-h2o-enterprise-llm-studio#use-cases
➤ Instruction Tuning & DPO Alignment: Fine-tune base models using labeled data, automated hyperparameter search, and preference optimization.
🔗 https://docs.h2o.ai/h2o-llmstudio/guide/experiments/supported-problem-types#dpo-modeling
➤ Augmentation for Fine-Tuning Datasets: Use LLM DataStudio to augment and prepare training data for downstream instruction tuning.
🔗 https://docs.h2o.ai/h2o-llm-data-studio/guide/augment/augmentation-datasets