Feature Extraction
Transformers
English
torch
clip
vision
interpretability
sparse autoencoder
sae
mechanistic interpretability
Instructions to use Prisma-Multimodal/sparse-autoencoder-clip-b-32-sae-vanilla-x64-layer-9-hook_mlp_out-l1-8e-05 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Prisma-Multimodal/sparse-autoencoder-clip-b-32-sae-vanilla-x64-layer-9-hook_mlp_out-l1-8e-05 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Prisma-Multimodal/sparse-autoencoder-clip-b-32-sae-vanilla-x64-layer-9-hook_mlp_out-l1-8e-05")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Prisma-Multimodal/sparse-autoencoder-clip-b-32-sae-vanilla-x64-layer-9-hook_mlp_out-l1-8e-05", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c97f6815bc44029d37417061126c9cc48f66ab2f582454812bc14e1aace5dc0e
- Size of remote file:
- 302 MB
- SHA256:
- 18f0280cc390a280adb458b2b5b0b7837a4693de2c5a9dc852c529aeac7e0d21
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