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mindchain
/
t5gemma2-sae-all-layers

Transformers
sae
sparse-autoencoder
t5gemma
t5gemma2
mechanistic-interpretability
activation-steering
steering
neuronpedia
gemma-scope
sae-lens
llm-interpretability
explainable-ai
xai
model-steering
feature-engineering
representation-learning
Model card Files Files and versions
xet
Community

Instructions to use mindchain/t5gemma2-sae-all-layers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use mindchain/t5gemma2-sae-all-layers with Transformers:

    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("mindchain/t5gemma2-sae-all-layers", dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
t5gemma2-sae-all-layers
756 MB
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  • 1 contributor
History: 51 commits
mindchain's picture
mindchain
Upload README.md with huggingface_hub
62a771b verified 6 months ago
  • decoder
    Upload decoder/sae_decoder_17.pt with huggingface_hub 6 months ago
  • encoder
    Upload encoder/sae_encoder_17.pt with huggingface_hub 6 months ago
  • .gitattributes
    1.52 kB
    initial commit 6 months ago
  • README.md
    5.44 kB
    Upload README.md with huggingface_hub 6 months ago
  • card.json
    1.46 kB
    Upload card.json with huggingface_hub 6 months ago
  • colab.ipynb
    7.25 kB
    Upload colab.ipynb with huggingface_hub 6 months ago
  • t5gemma2-sae-all-layers.ipynb
    7.25 kB
    Upload t5gemma2-sae-all-layers.ipynb with huggingface_hub 6 months ago
  • t5gemma_sae_colab.ipynb
    7.25 kB
    Upload t5gemma_sae_colab.ipynb with huggingface_hub 6 months ago