Platypus: Quick, Cheap, and Powerful Refinement of LLMs

Fine-tuning, merging, and dataset curation are interesting ways to derive domain or task-specific results from Large Language Models (LLMs). One model refinement technique to consider is the Platypus approach, introduced in the recent whitepaper, Platypus: Quick, Cheap, and Powerful Refinement of LLMs.

Background

Imagine the world of Large Language Models (LLMs) as a garage filled with various models of cars. These models represent the vast knowledge and capabilities of LLMs. Platypus, introduced by researchers at Boston University, is like a team of expert engineers and designers who take these existing car models and customize them to create high-performance vehicles.

The challenge is to retain the integrity of the existing car models while enhancing specific features like speed, efficiency, and adaptability. Platypus does this by meticulously selecting, tuning, and combining parts, like fine-tuning and merging LoRA modules.

By adopting the Platypus method, organizations can leverage the benefits of LLMs in a more efficient and targeted way, like having a versatile sports car tuned for any type of race.

Real World Example

Consider the world of competitive AI benchmarking. Platypus models topped the HuggingFace’s Open LLM Leaderboard, showcasing their superior performance.

“After introducing Platypus, our models achieved first place, outperforming other competitive models in the industry” — HuggingFace Open LLM Leaderboard

For the Technically Curious

The whitepaper is 17 pages long, so I loaded it into a “ChatGPT for PDF” service to save reading time. You can ask questions, get a summary, etc., of the whitepaper at https://askyourpdf.com/conversations/d/b5282f1c-52d7-4e72-ac9a-0704cb9a708c.

For the Experimenters

Check out the Platypus GitHub Repository for code samples, integration guides, and more.