Feature Extraction
Transformers
English
esmc_sae
biology
esm
protein
sparse-autoencoder
interpretability
protein-embeddings
protein-language-model
unsupervised-learning
Instructions to use biohub/ESMC-6B-sae-k64-codebook16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-6B-sae-k64-codebook16384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="biohub/ESMC-6B-sae-k64-codebook16384")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMC-6B-sae-k64-codebook16384", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2b19e78dbc4f3074e7af658456fc684311a8c9a3180b449ca841881501e4fcf7
- Size of remote file:
- 336 MB
- SHA256:
- 62a29c72a0cdf4506978ce756a40f8c9551c1640db44c03d6c6414a01608bedb
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