Does it support data visualization?

Yes, the skill provides implementation patterns for creating interactive Plotly heatmaps that visualize layer-wise predictions across the entire input sequence.

How does Logit Lens help with model debugging?

It allows you to pinpoint the exact layer where a model switches from a correct to an incorrect prediction, helping you understand if a failure is due to early information processing or late-stage convergence.

What is the Logit Lens technique?

Logit Lens is a method for inspecting the intermediate hidden states of a transformer by passing them through the final layer norm and unembedding head to see what the model 'thinks' at that specific layer.

Can I run this on large models without a local GPU?

Yes, the skill includes patterns for remote execution, allowing you to run complex interpretability tasks on large models like Llama-3.1-70B using the NDIF API.

Which models can I use with this skill?

This skill utilizes the nnsight library, which supports a wide range of architectures including GPT-2, Llama 3, and most Hugging Face transformer models.

Logit Lens Interpretability

Name: Logit Lens Interpretability
Author: ndif-team

byndif-team

•

Ciencia de Datos y ML

Decodes intermediate transformer layer activations into human-readable vocabulary predictions to visualize model processing.

The Logit Lens skill enables developers and researchers to peer into the 'black box' of LLMs by mapping hidden states from intermediate layers back into the vocabulary space. By applying the final layer norm and unembedding head throughout the network, this skill reveals how predictions evolve from generic tokens in early layers to specific, factual outputs in later stages. It is an essential tool for mechanistic interpretability, debugging unexpected model behaviors, and understanding the precise layer where a model retrieves specific information or makes a logical leap during its forward pass.

Características Principales

01Integration with Plotly for interactive prediction heatmaps

02Remote execution capabilities for massive models like Llama-3.1-70B

035 GitHub stars

04Support for batched prompt analysis and comparisons

05Layer-wise vocabulary decoding and probability mapping

06Internal hidden state visualization across the full sequence

Casos de Uso

01Debugging model hallucinations by identifying where a prediction deviates

02Mechanistic interpretability research to study information flow in transformers

03Locating specific layers responsible for factual recall or task-specific logic

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add ndif-team/skills logit-lens

For use in Claude.ai and ChatGPT

Download Skill