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osunlp/
SAE_DINOv3_ViT-B-16_IN1K

Name: SAE_DINOv3_ViT-B-16_IN1K
Availability: InStock

2511.17735

mit

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SAE for Meta's DINOv3 ViT-B/16 trained on ImageNet-1K Activations

Homepage: https://osu-nlp-group.github.io/saev
Code: https://github.com/OSU-NLP-Group/saev
Preprint: https://arxiv.org/abs/2511.17735
Demos: https://osu-nlp-group.github.io/saev#demos
Point of Contact: Sam Stevens

Checkpoints

Each checkpoint is a sparse autoencoder (SAE) trained on a different layer with a different sparsity level. Pick the checkpoint that matches your target layer and desired sparsity (L0).

Run ID	Layer	L0	MSE	Path
y7uk853s	6	2.2	2.8623	`layer_6/y7uk853s/sae.pt`
e4i667sz	6	5.1	2.6635	`layer_6/e4i667sz/sae.pt`
extgn8yv	6	19.5	2.5258	`layer_6/extgn8yv/sae.pt`
8hyzbyht	6	79.1	2.1732	`layer_6/8hyzbyht/sae.pt`
t2xcozei	6	277.5	2.1249	`layer_6/t2xcozei/sae.pt`
lppv40ws	6	725.8	1.9904	`layer_6/lppv40ws/sae.pt`
iyb7ec1w	7	0.1	7.7452	`layer_7/iyb7ec1w/sae.pt`
688bm8ht	7	4.4	6.3078	`layer_7/688bm8ht/sae.pt`
40j71aj2	7	8.7	6.0463	`layer_7/40j71aj2/sae.pt`
xucm378k	7	35.4	5.6832	`layer_7/xucm378k/sae.pt`
knz7yndg	7	132.6	5.0626	`layer_7/knz7yndg/sae.pt`
6yhupj05	7	598.6	4.7180	`layer_7/6yhupj05/sae.pt`
wgh9hgih	8	0.5	19.4129	`layer_8/wgh9hgih/sae.pt`
ttghd72n	8	7.1	16.0403	`layer_8/ttghd72n/sae.pt`
q6pg7hl9	8	12.2	15.1630	`layer_8/q6pg7hl9/sae.pt`
r3opp7dy	8	63.1	14.6620	`layer_8/r3opp7dy/sae.pt`
bk7iwhfu	8	215.1	13.1875	`layer_8/bk7iwhfu/sae.pt`
rc82kpln	8	690.9	12.5601	`layer_8/rc82kpln/sae.pt`
cozptrw2	9	1.1	45.2121	`layer_9/cozptrw2/sae.pt`
1aod3v62	9	10.7	39.1680	`layer_9/1aod3v62/sae.pt`
zvx4qkov	9	20.1	36.4623	`layer_9/zvx4qkov/sae.pt`
na2k2dyp	9	84.4	36.4057	`layer_9/na2k2dyp/sae.pt`
tqk8igwb	9	332.4	32.2874	`layer_9/tqk8igwb/sae.pt`
893a4vol	9	963.5	31.2656	`layer_9/893a4vol/sae.pt`
eqht2edc	10	8.7	132.2482	`layer_10/eqht2edc/sae.pt`
1hjlnu1s	10	21.5	115.6000	`layer_10/1hjlnu1s/sae.pt`
jpnwfh3w	10	47.5	108.0820	`layer_10/jpnwfh3w/sae.pt`
2we45xxf	10	341.3	99.9972	`layer_10/2we45xxf/sae.pt`
bv1h09se	10	454.0	98.0921	`layer_10/bv1h09se/sae.pt`
jjewtqwp	10	971.8	97.2687	`layer_10/jjewtqwp/sae.pt`
n1xwev0z	11	5.0	640.6468	`layer_11/n1xwev0z/sae.pt`
ef657fwa	11	6.5	621.5192	`layer_11/ef657fwa/sae.pt`
qoc1660r	11	89.9	560.8286	`layer_11/qoc1660r/sae.pt`
6crsj9gj	11	123.8	547.8894	`layer_11/6crsj9gj/sae.pt`
d4v8aruu	11	435.5	532.5820	`layer_11/d4v8aruu/sae.pt`
22p3bnt8	11	786.3	508.5309	`layer_11/22p3bnt8/sae.pt`

This metadata is also available in manifest.jsonl at the repo root for programmatic access.

Usage

python

from huggingface_hub import hf_hub_download

import saev.nn

path = hf_hub_download("osunlp/SAE_DINOv3_ViT-B-16_IN1K", "layer_11/22p3bnt8/sae.pt")
sae = saev.nn.load(path)

Inference Instructions

Follow the instructions here.