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-k64-codebook16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-300M-sae-k64-codebook16384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="biohub/ESMC-300M-sae-k64-codebook16384")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMC-300M-sae-k64-codebook16384", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download layer_17.safetensors from biohub/ESMC-300M-sae-k64-codebook16384: direct link, hf CLI and curl.
- Browser
- Download file 126 MB
-
https://huggingface.co/biohub/ESMC-300M-sae-k64-codebook16384/resolve/main/layer_17.safetensors
- Command line
-
hf download hf://biohub/ESMC-300M-sae-k64-codebook16384/layer_17.safetensors
-
curl -L -o layer_17.safetensors https://huggingface.co/biohub/ESMC-300M-sae-k64-codebook16384/resolve/main/layer_17.safetensors
126 MB
- Xet hash:
- f0f3b996a1ba0ac82bc72291fc0964b4723a6c95c9f7a43c011c920b03bb32e0
- Size of remote file:
- 126 MB
- SHA256:
- 317710a0b365904e42b942469d559609b2ea766168da63d19d155fa8fcd31312
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