Large vs Small Language Models

Features

• Trained with large volumes of general text data
• Trained with focused text data
• Many billions of parameters
• Fewer parameters
• Comprehensive language generation capabilities in multiple contexts
• Focused language generation capabilities in specialized contexts
• Large size can impact performance and portability
• Fast and portable
• Time-consuming (and expensive) to fine-tune with your own training data
• Faster (and less expensive) to fine-tune with your own training data
• Examples include: OpenAI GPT 4, Mistral 7B, Meta Llama 3
• Examples include: Microsoft Phi 2, Microsoft Orca 2, OpenAI GPT Neo