SAEV
Collection
SAEs for vision models like CLIP or DINOv2 • 7 items • Updated • 5
SAE for Meta's DINOv3 ViT-S/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 |
|---|---|---|---|---|
| 3zih0tpa | 6 | 0.6 | 1.4566 | layer_6/3zih0tpa/sae.pt |
| f5qog0he | 6 | 4.9 | 1.1679 | layer_6/f5qog0he/sae.pt |
| u5aqv7t7 | 6 | 19.4 | 1.0534 | layer_6/u5aqv7t7/sae.pt |
| usiwpodi | 6 | 45.3 | 0.9598 | layer_6/usiwpodi/sae.pt |
| nbqbvh45 | 6 | 193.2 | 0.8631 | layer_6/nbqbvh45/sae.pt |
| sfpco1tn | 6 | 431.9 | 0.8223 | layer_6/sfpco1tn/sae.pt |
| l9stkmwt | 7 | 0.7 | 2.8639 | layer_7/l9stkmwt/sae.pt |
| tbfdr3cc | 7 | 4.6 | 2.3978 | layer_7/tbfdr3cc/sae.pt |
| zaxl9nqu | 7 | 9.3 | 2.2369 | layer_7/zaxl9nqu/sae.pt |
| r2g7cj5v | 7 | 26.9 | 2.1348 | layer_7/r2g7cj5v/sae.pt |
| d5ej9yuh | 7 | 102.6 | 1.8223 | layer_7/d5ej9yuh/sae.pt |
| 36538viq | 7 | 322.3 | 1.7001 | layer_7/36538viq/sae.pt |
| p0z7t1ci | 8 | 1.9 | 5.3336 | layer_8/p0z7t1ci/sae.pt |
| 125xh1t9 | 8 | 5.2 | 4.9238 | layer_8/125xh1t9/sae.pt |
| qwf07reo | 8 | 12.3 | 4.4735 | layer_8/qwf07reo/sae.pt |
| tzwgex0i | 8 | 48.4 | 4.2854 | layer_8/tzwgex0i/sae.pt |
| 5gjy7lwi | 8 | 159.8 | 3.7245 | layer_8/5gjy7lwi/sae.pt |
| bvsb2257 | 8 | 352.9 | 3.5116 | layer_8/bvsb2257/sae.pt |
| qt0fmmxm | 9 | 7.4 | 11.2642 | layer_9/qt0fmmxm/sae.pt |
| 1o4uc5bf | 9 | 17.5 | 10.1970 | layer_9/1o4uc5bf/sae.pt |
| z1qvy51u | 9 | 84.1 | 10.1741 | layer_9/z1qvy51u/sae.pt |
| 1ihxsv0i | 9 | 167.0 | 8.9344 | layer_9/1ihxsv0i/sae.pt |
| euoj6wv0 | 9 | 229.1 | 8.7690 | layer_9/euoj6wv0/sae.pt |
| ickedctl | 9 | 381.9 | 8.4021 | layer_9/ickedctl/sae.pt |
| chn5wi3x | 10 | 1.8 | 28.7944 | layer_10/chn5wi3x/sae.pt |
| 219r3phu | 10 | 11.0 | 25.1447 | layer_10/219r3phu/sae.pt |
| knglrhzb | 10 | 23.6 | 23.1152 | layer_10/knglrhzb/sae.pt |
| jt45lucm | 10 | 29.7 | 22.6514 | layer_10/jt45lucm/sae.pt |
| 6hrok1al | 10 | 234.3 | 20.5268 | layer_10/6hrok1al/sae.pt |
| g4dexqq1 | 10 | 387.2 | 19.6115 | layer_10/g4dexqq1/sae.pt |
| jgu19fzx | 11 | 4.3 | 279.2303 | layer_11/jgu19fzx/sae.pt |
| 8yd05vxi | 11 | 6.9 | 263.3258 | layer_11/8yd05vxi/sae.pt |
| utmjp20e | 11 | 36.2 | 253.5636 | layer_11/utmjp20e/sae.pt |
| gc6iqrf2 | 11 | 95.1 | 240.0467 | layer_11/gc6iqrf2/sae.pt |
| 5ewxrjg4 | 11 | 130.8 | 222.1000 | layer_11/5ewxrjg4/sae.pt |
| 36ztscy4 | 11 | 420.3 | 206.9659 | layer_11/36ztscy4/sae.pt |
This metadata is also available in manifest.jsonl at the repo root for programmatic access.
Usage
from huggingface_hub import hf_hub_download
import saev.nn
path = hf_hub_download("osunlp/SAE_DINOv3_ViT-S-16_IN1K", "layer_11/36ztscy4/sae.pt")
sae = saev.nn.load(path)
Inference Instructions
Follow the instructions here.
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