Transfer Learning and Adaptation in LLM Architecture
- Generative AI
- H2O Cascade
This lesson walks through transfer learning, where a model pretrained on a large dataset is fine tuned on a smaller labeled dataset for a new task instead of training from scratch, and domain adaptation, which addresses the gap between a source domain and a new target domain. It uses a robot recognizing objects in an unfamiliar place to explain feature based, instance based, and model based adaptation in plain terms. The lesson then connects these ideas to fine tuning large language models with H2O LLM Studio, which adjusts a pretrained model toward a specific writing style, personality, or domain.
Key takeaways
- How transfer learning starts from a pretrained model instead of training a new one from scratch
- Why domain adaptation aligns a model's learned representations with a new target environment
- What feature based, instance based, and model based adaptation each change about how a model learns
- How H2O LLM Studio fine tunes a pretrained model to match a specific writing style or domain
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