H2O LLM Studio: Fine-Tune Language Models Without Code
- Generative AI
- H2O Cascade
This lesson walks through H2O LLM Studio, a no-code interface for fine-tuning large language models. It explains the difference between a foundation model and a fine-tuned model, then covers the LLM Studio pipeline: importing a dataset with instruction and answer columns, launching an experiment, and monitoring training and validation loss as it runs. It also shows how to compare multiple experiments and push a finished model to Hugging Face for deployment.
Key takeaways
- How LLM Studio's pipeline moves from importing a dataset through launching, monitoring, and comparing experiments
- Why a dataset for fine-tuning needs at least two columns, one for the instruction and one for the answer
- The difference between a pre-trained foundation model and a model fine-tuned on a smaller domain-specific dataset
- How to compare multiple experiments side by side to see which hyperparameter settings perform best
- The option to push a fine-tuned model to Hugging Face Hub for offline deployment outside LLM Studio
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