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
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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1b40df8f0654229477fde93178840a04cef30b4d41e6bed7a2e81f89890a2782
- Size of remote file:
- 21 MB
- SHA256:
- bb6a3b5300774a5e0ac1bd639fd2a3edafff20309ece2ae8e8a739bb0c57b75b
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