  As large language models (LLMs) become more capable, there is an urgent need
for interpretable and transparent tools. Current methods are difficult to
implement, and accessible tools to analyze model internals are lacking. To
bridge this gap, we present DeepDecipher - an API and interface for probing
neurons in transformer models' MLP layers. DeepDecipher makes the outputs of
advanced interpretability techniques for LLMs readily available. The
easy-to-use interface also makes inspecting these complex models more
intuitive. This paper outlines DeepDecipher's design and capabilities. We
demonstrate how to analyze neurons, compare models, and gain insights into
model behavior. For example, we contrast DeepDecipher's functionality with
similar tools like Neuroscope and OpenAI's Neuron Explainer. DeepDecipher
enables efficient, scalable analysis of LLMs. By granting access to
state-of-the-art interpretability methods, DeepDecipher makes LLMs more
transparent, trustworthy, and safe. Researchers, engineers, and developers can
quickly diagnose issues, audit systems, and advance the field.
