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:
- 2cd307d059ac71f9d81bf72c36e3cb361df4d098468a4b60f89e1ac9f82435a2
- Size of remote file:
- 21 MB
- SHA256:
- 420112aa910d0af1046aff7e1e8308755011e82a838595f49652800c681e7b10
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