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osunlp/
SAE_DINOv3_ViT-L-16_IN1K

Name: SAE_DINOv3_ViT-L-16_IN1K
Availability: InStock

2511.17735

mit

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SAE for Meta's DINOv3 ViT-L/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
jsqj2arm	13	2.8	4.9303	`layer_13/jsqj2arm/sae.pt`
kk60aru4	13	6.3	4.6254	`layer_13/kk60aru4/sae.pt`
y8q60ohz	13	23.3	4.3962	`layer_13/y8q60ohz/sae.pt`
ag8agm56	13	90.7	3.7613	`layer_13/ag8agm56/sae.pt`
220r8j1q	13	247.5	3.6310	`layer_13/220r8j1q/sae.pt`
fkl5sxba	13	900.1	3.4952	`layer_13/fkl5sxba/sae.pt`
fi7qafny	15	1.2	12.0470	`layer_15/fi7qafny/sae.pt`
txmrh5nd	15	5.0	10.8379	`layer_15/txmrh5nd/sae.pt`
aq8vvjub	15	8.8	10.3427	`layer_15/aq8vvjub/sae.pt`
xfgouwrz	15	38.9	9.7837	`layer_15/xfgouwrz/sae.pt`
rsmrpkly	15	92.4	9.0052	`layer_15/rsmrpkly/sae.pt`
e9oeml82	15	151.3	8.8066	`layer_15/e9oeml82/sae.pt`
di427rrs	17	1.3	27.7408	`layer_17/di427rrs/sae.pt`
edx9q34f	17	5.8	24.9920	`layer_17/edx9q34f/sae.pt`
n7pv6rkj	17	11.8	23.5505	`layer_17/n7pv6rkj/sae.pt`
4rhpmk3f	17	42.6	22.8549	`layer_17/4rhpmk3f/sae.pt`
jqx6qdxv	17	141.7	20.8065	`layer_17/jqx6qdxv/sae.pt`
vkdu21ck	17	507.3	20.0385	`layer_17/vkdu21ck/sae.pt`
y6osup5x	19	3.0	75.8186	`layer_19/y6osup5x/sae.pt`
aa30r3nm	19	8.1	70.1213	`layer_19/aa30r3nm/sae.pt`
0tj48gqd	19	22.2	64.6201	`layer_19/0tj48gqd/sae.pt`
7dr58kwn	19	284.6	59.5570	`layer_19/7dr58kwn/sae.pt`
2uqtzyv6	19	418.0	57.5434	`layer_19/2uqtzyv6/sae.pt`
s96104bm	19	975.5	55.9320	`layer_19/s96104bm/sae.pt`
qcyausyf	21	2.2	215.4446	`layer_21/qcyausyf/sae.pt`
x7py290w	21	7.6	207.6037	`layer_21/x7py290w/sae.pt`
v4pyroov	21	28.1	186.8892	`layer_21/v4pyroov/sae.pt`
t1ip1brk	21	41.6	182.1836	`layer_21/t1ip1brk/sae.pt`
pz4up9fd	21	401.4	175.3216	`layer_21/pz4up9fd/sae.pt`
y8vhxwya	21	773.8	165.5911	`layer_21/y8vhxwya/sae.pt`
lnleoyf6	23	0.0	1833.7889	`layer_23/lnleoyf6/sae.pt`
t1vh0qy1	23	3.3	1657.9833	`layer_23/t1vh0qy1/sae.pt`
fxcpfysr	23	9.4	1503.3433	`layer_23/fxcpfysr/sae.pt`
kd2pd8rs	23	83.0	1446.8923	`layer_23/kd2pd8rs/sae.pt`
1qynjykb	23	214.8	1347.6047	`layer_23/1qynjykb/sae.pt`
9fn4l6rf	23	1574.5	1256.6656	`layer_23/9fn4l6rf/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-L-16_IN1K", "layer_23/9fn4l6rf/sae.pt")
sae = saev.nn.load(path)

Inference Instructions

Follow the instructions here.