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-k64-codebook131072 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-6B-sae-layer60-k64-codebook131072 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="biohub/ESMC-6B-sae-layer60-k64-codebook131072")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMC-6B-sae-layer60-k64-codebook131072", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 51ec498ce5ab293de497c8f8967af9752e3926ef29750c415c97abd777a08faf
- Size of remote file:
- 2.69 GB
- SHA256:
- ac561ba5ec667bbf915fae9df87b937257a9b324e253f5b9172676878260d49f
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