Flow – a compact data pipeline language that cuts LLM token usage by 33% (github.com)

A new compact data transformation language called Flow has been introduced, specifically designed to optimize performance for large language models (LLMs). Flow boasts a remarkable average token reduction of 33% when compared to traditional Python, making it significantly more efficient for data processing tasks. This improvement was validated through tests on fine-tuned models using consumer-grade GPUs, demonstrating that using Flow can cut down on unnecessary verbosity in code, which typically inflates token usage. The significance of Flow lies in its potential to enhance the efficiency of LLMs in data processing without sacrificing expressiveness. With a minimalist syntax that uses single-character operators for functions like filtering, mapping, and aggregating, Flow translates to clean, readable Python code while streamlining the token consumption required for model training and inference. It supports features such as batch processing, JSON parsing, and built-in type safety, enabling faster execution of data transformations. The architecture was built with accessibility in mind, allowing users with average consumer hardware to achieve substantial performance gains and explore the potential of purpose-built languages tailored for AI applications.