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

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データサイエンスとML

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

概要

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.

主な機能

Integration with Plotly for interactive prediction heatmaps
Remote execution capabilities for massive models like Llama-3.1-70B
5 GitHub stars
Support for batched prompt analysis and comparisons
Layer-wise vocabulary decoding and probability mapping
Internal hidden state visualization across the full sequence

ユースケース

Debugging model hallucinations by identifying where a prediction deviates
Mechanistic interpretability research to study information flow in transformers
Locating specific layers responsible for factual recall or task-specific logic

概要

主な機能

Integration with Plotly for interactive prediction heatmaps
Remote execution capabilities for massive models like Llama-3.1-70B
5 GitHub stars
Support for batched prompt analysis and comparisons
Layer-wise vocabulary decoding and probability mapping
Internal hidden state visualization across the full sequence

ユースケース

Debugging model hallucinations by identifying where a prediction deviates
Mechanistic interpretability research to study information flow in transformers
Locating specific layers responsible for factual recall or task-specific logic