Small Language Models vs Large Language Models
- Generative AI
This lesson explains the difference between large language models and small language models, and why a smaller model is often the better choice for simple, fast tasks like answering a basic question or guiding a user through a straightforward process. It walks through why small language models cost less to run, can operate on smaller devices, and often work offline, giving them a privacy advantage over larger models that need more infrastructure. It closes by introducing H2O's own small language models, Danube and Mississippi, and the tasks they are built for, including summarization, document processing, and optical character recognition.
Key takeaways
- How small language models differ from large language models in resource needs, cost, and the kinds of tasks each handles well
- Why running a small language model offline on a device offers a privacy advantage over sending data to a larger, hosted model
- What tasks H2O's Danube models are built for, including question answering, summarization, and data formatting
- How H2O's Mississippi models handle document processing and optical character recognition with fewer parameters than large models
- When a small language model is the better fit than a large one, for quick, targeted tasks rather than deep multi step analysis
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