INDEX

Explanations

words related to specific proper nouns, such as names of people or companies

oai_token-act-pair · gpt-3.5-turbo

references to specific companies, organizations, or entities

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

New Auto-Interp

Top Features by Cosine Similarity

Comparing With GPT2-SMALL @ 6-res-jb

Configuration

jbloom/GPT2-Small-SAEs-Reformatted/blocks.6.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.6.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

 Bethesda

-0.65

 Saban

-0.63

SOS

-0.63

atown

-0.58

 Clemson

-0.58

 rooted

-0.55

 Carnegie

-0.55

 Nielsen

-0.55

 yourselves

-0.54

 CARD

-0.54

POSITIVE LOGITS

eworks

0.99

anche

0.95

ifts

0.95

uctions

0.92

apes

0.92

utsch

0.90

Ħ¢

0.87

ymes

0.87

ifles

0.85

ippers

0.85

Activations Density 0.228%

words related to specific proper nouns, such as names of people or companies

references to specific companies, organizations, or entities

No Comments

No Known Activations