Fine-Tuning Basics and a Live h2oGPT RAG Demo
- Generative AI
This lesson explains what fine-tuning actually changes in a large language model: not new facts, but tone, style, and how it stops or formats a response, illustrated with a before and after example on a base Falcon model. It then moves into a live demo of h2oGPT, H2O's open source chat tool, run both locally on a GPU server and through the hosted gpt.h2o.ai interface. The demo shows how uploading a document grounds the model's answers in current information and returns linked sources, instead of relying only on what the model memorized during training.
Key takeaways
- Why fine-tuning changes a model's tone, style, and stopping behavior rather than adding new facts to it
- How low-rank adaptation lets a large pretrained model be tuned on a small dataset in hours instead of months
- The difference between a base model's rambling response and a fine-tuned model's coherent, stop-aware response to the same prompt
- How uploading a document into h2oGPT grounds its answers in current information and returns linked sources
- What kind of training data structure, prompt and response pairs, is needed to fine-tune a model on a specific task
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