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-layer60-k512-codebook32768 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-6B-sae-layer60-k512-codebook32768 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="biohub/ESMC-6B-sae-layer60-k512-codebook32768")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMC-6B-sae-layer60-k512-codebook32768", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 753ff0513f6b8bd70ac80178ca8311e289d511f85f8d14d4501e0267037d9858
- Size of remote file:
- 671 MB
- SHA256:
- e6860becc8d9ede896a2654b6a24d4ce318d51656131b77955e9a50fef4e91ce
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