While this isn't the first blog about DSPy, I've noticed recent updates to the DSPy documentation and GitHub repository, including a new optimization method called BootstrapFinetune.
This isn't my first blog post on DSPy—I've written several before. However, I've noticed some recent updates to DSPy, and I'd rather not consult the documentation every time I want to build programs. So, I plan to jot down some basic DSPy concepts in this post. Additionally, I intend to use this document as external knowledge for GPT or Claude.
This post builds upon my previous blog of GPT-4o-mini's performance on MMLU Pro using BootstrapFewShotWithRandomSearch and BootstrapFewShotWithOptuna. In this continuation, I will examine the newly introduced optimizers, MIPRO and MIPROV2, to assess their optimization capabilities and determine the potential performance enhancements they may bring to GPT-4o-mini.
DSPy is an optimization framework that enhances prompts and responses from models like GPT-4o-mini. It showcases the magic of the framework and demonstrates how to use its powerful optimizers to improve the cost-effective model. The MMLU-Pro dataset is an advanced dataset with complex questions and increased answer choices. The evaluation metric is defined to check if the model's responses match the true answers.
DSPy is a framework developed by Stanford. It is used for programming to automatically optimize prompts and weights in Large Language Models (LLMs). DSPy can enhance the reliability of any model, whether it's GPT-4, LLaMA3 or Mistral, for any task you require.
