In the realm of AI, model optimization is a crucial step to tailor AI systems for specific use cases. However, the level of customization needed often correlates with the amount of training data required. Hereโs a brief overview of four key model optimization methods, illustrated with a graph: ๐โจ
๐ญ. ๐ฃ๐ฟ๐ผ๐บ๐ฝ๐ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด โ๏ธ
๐๐ฒ๐๐ฐ๐ฟ๐ถ๐ฝ๐๐ถ๐ผ๐ป: This method leverages zero-shot or few-shot learning to guide the AI model’s responses using well-crafted prompts.
๐๐
๐ฎ๐บ๐ฝ๐น๐ฒ: Asking a language model to write a poem with a prompt like “Write a poem about autumn leaves.”
๐ ๐ผ๐ฑ๐ฒ๐น๐: Both proprietary models like Gpt-4o & open source models like Llama 3.1
๐ง๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐ฅ๐ฒ๐พ๐๐ถ๐ฟ๐ฒ๐บ๐ฒ๐ป๐: Low
๐๐๐๐๐ผ๐บ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป ๐๐ฒ๐๐ฒ๐น: Low to Medium
๐ฎ. ๐ฅ๐ฒ๐๐ฟ๐ถ๐ฒ๐๐ฎ๐น ๐๐๐ด๐บ๐ฒ๐ป๐๐ฒ๐ฑ ๐๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป (๐๐ป-๐๐ผ๐ป๐๐ฒ๐ ๐ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด) ๐
๐๐ฒ๐๐ฐ๐ฟ๐ถ๐ฝ๐๐ถ๐ผ๐ป: Uses external databases to fetch relevant information, enhancing the model’s responses by providing additional context.
๐๐
๐ฎ๐บ๐ฝ๐น๐ฒ: A chatbot retrieving real-time data from Wikipedia to answer a user’s query about recent events.
๐ ๐ผ๐ฑ๐ฒ๐น๐: Both proprietary models like Gpt-4o & open source models like Llama 3.1
๐ง๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐ฅ๐ฒ๐พ๐๐ถ๐ฟ๐ฒ๐บ๐ฒ๐ป๐: Medium
๐๐๐๐๐ผ๐บ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป ๐๐ฒ๐๐ฒ๐น: High
๐ฏ. ๐ฃ๐ฎ๐ฟ๐ฎ๐บ๐ฒ๐๐ฒ๐ฟ ๐๐ณ๐ณ๐ถ๐ฐ๐ถ๐ฒ๐ป๐ ๐๐ถ๐ป๐ฒ ๐ง๐๐ป๐ถ๐ป๐ด (๐ฃ๐๐๐ง) ๐ ๏ธ
๐๐ฒ๐๐ฐ๐ฟ๐ถ๐ฝ๐๐ถ๐ผ๐ป: Adjusts the prompts given to the model without changing the underlying weights, using soft prompts that act as placeholders for the desired output. Techniques like LoRA, Soft Prompts
๐๐
๐ฎ๐บ๐ฝ๐น๐ฒ: Customizing a customer service bot to handle specific types of queries without retraining the entire model.
๐ ๐ผ๐ฑ๐ฒ๐น๐: Both proprietary models like Gpt-4o & open source models like Llama 3.1
๐ง๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐ฅ๐ฒ๐พ๐๐ถ๐ฟ๐ฒ๐บ๐ฒ๐ป๐: Medium to High
๐๐๐๐๐ผ๐บ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป ๐๐ฒ๐๐ฒ๐น: Medium to High
๐ฐ. ๐๐ป๐๐๐ฟ๐๐ฐ๐๐ถ๐ผ๐ป ๐๐ถ๐ป๐ฒ-๐๐๐ป๐ถ๐ป๐ด โ๏ธ
๐๐ฒ๐๐ฐ๐ฟ๐ถ๐ฝ๐๐ถ๐ผ๐ป: Involves adjusting the weights of the modelโs training parameters to better fit the specific use case.
๐๐
๐ฎ๐บ๐ฝ๐น๐ฒ: Retraining a language model on a large dataset of medical literature to create a specialized medical assistant.
๐ ๐ผ๐ฑ๐ฒ๐น๐: Only open source models like Llama 3.1
๐ง๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด ๐๐ฎ๐๐ฎ ๐ฅ๐ฒ๐พ๐๐ถ๐ฟ๐ฒ๐บ๐ฒ๐ป๐: High
๐๐๐๐๐ผ๐บ๐ถ๐๐ฎ๐๐ถ๐ผ๐ป ๐๐ฒ๐๐ฒ๐น: Very High
By understanding these methods, we can make informed decisions on how to optimize AI models effectively based on our specific needs and available training data. ๐ง๐ก