RevLLM is a Python library and a Streamlit webapp for exploring internal workings of Large Language Models (LLMs).

RevLLM builds on top the nanoGPT implementation of the GPT-2 model family developed by Andrej Karpathy and adheres to its spirit of simplicity and transparency. We restrict the dependencies to a bare minimum and strive for clean and simple code that can be easily understood and reused.

The RevLLM library implements various methods for analyzing the internal data-flow of transformer decoder-type models. In particular, RevLLM,

• shows how transformer models map a prompt sentence to a sequence of tokens (integers).

• explores the token embedding space with statistical methods.

• implements various input prompt importance methods to identify prompt tokens that the model pays most attention to.

• implements logit lens interpretability methods to shed light on the flow of information through the transformer block sequence.

To facilitate the ease of use and provide a hassle-free experimentation experience, we accompany the library with an interactive Streamlit app and provide a web interface to access the library functionality. The app automatically downloads and instantiates a chosen model from the GPT-2 family using the Huggingface model repository, and exposes the RevLLM library methods through a convenient interface.

https://revllm.dev/

Project sponsored by the German Federal Ministry of Education and Research

(Förderkennzeichen: 01IS23S42)