The more MonkeyPatch functions are used, the cheaper and faster they become through automatic model distillation. The library also handles model training, MLOps, and DataOps efforts to improve LLM capabilities. MonkeyPatch also provides a way to ensure predictable and consistent LLM execution, with automatic reductions in cost and latency through finetuning. It supports OpenAI models and plans to support Anthropic and popular open-source models in the future.
Key takeaways:
- MonkeyPatch is a tool that allows developers to easily integrate Language Learning Models (LLMs) into their Python applications, providing scalable and cost-effective solutions.
- The tool supports typed parameters and outputs, ensuring that the LLMs' outputs adhere to specific structures and rules, which makes the integration into existing workflows seamless and reliable.
- MonkeyPatch uses a concept called Test-Driven Alignment (TDA), which allows developers to align the behavior of a patched function with an expectation defined by a test, ensuring predictable and consistent LLM execution.
- As the number of data points increases, MonkeyPatch provides cost and latency benefits by using model distillation, a process that trains smaller, function-specific models to emulate the behavior of larger models, reducing computational cost and latency.