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:
- 4ae2248034e538048221bb1c9cc160373b759b1fc869de3d85553c4c0fcc2eec
- Size of remote file:
- 21 MB
- SHA256:
- 113629d9c9452ef47b5a02d3487f8c2c7adc350fbe9f8a0503eb6077e2f7e159
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