Fine-Tuning Principles for Large Language Models
- Generative AI
- H2O Cascade
This lesson continues the Large Language Models course with a deeper look at fine tuning, the process of adapting a pre-trained model to a specific task. It explains why fine tuning saves time and computing resources compared to training from scratch, and previews H2O LLM Studio as the no-code tool the course will use for hands on fine tuning. The lesson also covers how much data fine tuning typically needs, citing research such as the LIMA paper, which showed strong results from as few as 1,000 curated examples, and discusses how backbone size and specialization affect model choice.
Key takeaways
- Why fine tuning is faster and cheaper than training a model from scratch, since it reuses pre-trained knowledge
- How task specific data teaches a pre-trained model to specialize in things like sentiment analysis or summarization
- What backbones are: the foundational architecture and training data that underlying LLM variants are built from
- How the LIMA paper found that just 1,000 curated prompt and response examples produced results competitive with GPT-4
- The general guideline that 1,000 to 50,000 conversational samples, or up to 100,000 records, are usually enough for fine tuning
- Why generating synthetic data with an LLM can help when real data is limited by privacy or availability
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