INDEX

Explanations

proper nouns related to movies, locations, and events

oai_token-act-pair · gpt-3.5-turbo

titles and terms related to films and aviation incidents

oai_token-act-pair · gpt-4o-mini Triggered by @bot

New Auto-Interp

Top Features by Cosine Similarity

Comparing With GPT2-SMALL @ 2-res-jb

Configuration

jbloom/GPT2-Small-SAEs-Reformatted/blocks.2.hook_resid_pre

Prompts (Dashboard)

24,576 prompts, 128 tokens each

Dataset (Dashboard)

Skylion007/openwebtext

Features

24,576

Data Type

torch.float32

Hook Point

blocks.2.hook_resid_pre

Architecture

standard

Context Size

128

Dataset

Skylion007/openwebtext

Hook Point Layer

Activation Function

relu

Embeds

PlotsExplanationShow Test FieldDefault Test Text

IFrame

Link

Not in Any Lists

No Comments

Negative Logits

pez

-0.78

ICAN

-0.76

merce

-0.74

izabeth

-0.74

elaide

-0.73

aturally

-0.73

itably

-0.72

 digestive

-0.71

 hosp

-0.70

livion

-0.70

POSITIVE LOGITS

 Fighters

1.14

 Runner

1.07

 Zone

0.99

 Scenes

0.96

 Games

0.95

 Cities

0.93

 Breaker

0.92

 Blade

0.92

 Against

0.91

 Theory

0.91

Activations Density 0.087%

proper nouns related to movies, locations, and events

titles and terms related to films and aviation incidents

No Comments

No Known Activations