Feature Extraction
Transformers
English
esmc_sae
biology
esm
protein
sparse-autoencoder
interpretability
protein-embeddings
protein-language-model
unsupervised-learning
Instructions to use biohub/ESMC-300M-sae-layer23-k32-codebook65536 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-300M-sae-layer23-k32-codebook65536 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="biohub/ESMC-300M-sae-layer23-k32-codebook65536")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMC-300M-sae-layer23-k32-codebook65536", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- config.json +10 -0
- layer_23.safetensors +3 -0
config.json
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{
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"available_layers": [
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23
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],
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"codebook_dim": 65536,
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"d_model": 960,
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"k": 32,
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"model_type": "esmc_sae",
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"transformers_version": "4.57.6"
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}
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layer_23.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:bbb659942548da2e945849bee2b9580545557688e9e2b29df525bad5f22db1a4
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size 503845000
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