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_24.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_24.safetensors
- Command line
-
hf download hf://biohub/ESMC-300M-sae-k64-codebook16384/layer_24.safetensors
-
curl -L -o layer_24.safetensors https://huggingface.co/biohub/ESMC-300M-sae-k64-codebook16384/resolve/main/layer_24.safetensors
126 MB
- Xet hash:
- 58f8de9f22fb1a1d38bf91f1af31840d00f5f29ac4bf0d8266b72f10ceae27ea
- Size of remote file:
- 126 MB
- SHA256:
- e4fe3c89470b9334282db5cd51f5cc0e5ec150e460557ae070069d58334ed3a8
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