logit-lens

What problem does it solve?

Decodes intermediate layer activations into vocabulary predictions to reveal what the model "thinks" at each processing step, offering insight beyond the final output.

Core Features & Use Cases

• Decode per-layer predictions by applying final normalization and unembedding to hidden states to reveal evolving tokens.
• Visualize and compare layer-wise predictions to diagnose information flow and debugging tasks.
• Use for interpretability research, model auditing, and understanding recall dynamics across Transformer layers.

Quick Start

Prompt the model with a sample input and run a layer-wise trace to display the top token predictions at each layer.