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Joseph Bloom · Open Source Sparse Autoencoders for all Residual Stream Layers of GPT2-Small
GPT2-Small
Residual Stream
9-RES-JB
14949
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MODEL
9-res-jb
Source/SAE
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Explanations
time periods represented in a specific format
oai_token-act-pair · gpt-3.5-turbo
No Scores
references to dates and names related to statistics or historical events
oai_token-act-pair · gpt-4o-mini
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New Auto-Interp
AutoInterp Type
claude-4-5-haiku
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Top Features by Cosine Similarity
Comparing With
GPT2-SMALL @ 9-res-jb
Configuration
jbloom/GPT2-Small-SAEs-Reformatted/blocks.9.hook_resid_pre
How To Load
Prompts (Dashboard)
24,576 prompts, 128 tokens each
Dataset (Dashboard)
Skylion007/openwebtext
Features
24,576
Data Type
torch.float32
Hook Point
blocks.9.hook_resid_pre
Architecture
standard
Context Size
128
Dataset
Skylion007/openwebtext
Hook Point Layer
9
Activation Function
relu
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https://www.neuronpedia.org/gpt2-small/9-res-jb/14949?embed=true&embedexplanation=true&embedplots=true&embedsteer=true&embedactivations=true&embedlink=true&embedtest=true
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Negative Logits
Bray
-0.97
Shank
-0.89
Bir
-0.81
arth
-0.78
Brah
-0.75
Hem
-0.72
roth
-0.72
Bai
-0.72
lash
-0.70
Hound
-0.69
POSITIVE LOGITS
1998
1.28
1999
1.24
98
1.20
1998
1.20
98
1.18
1997
1.16
1999
1.15
2000
1.13
1996
1.08
97
1.07
Act
ivations
Density 0.192%
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