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")# pip install -U transformers accelerate # 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_19.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_19.safetensors
- Command line
-
hf download hf://biohub/ESMC-300M-sae-k64-codebook16384/layer_19.safetensors
-
curl -L -o layer_19.safetensors https://huggingface.co/biohub/ESMC-300M-sae-k64-codebook16384/resolve/main/layer_19.safetensors
126 MB
- Xet hash:
- 20bd0c0e6d5d93884057265cea6558735b9b95384e584a94312064b658a5744d
- Size of remote file:
- 126 MB
- SHA256:
- 0a6bb2cbc92f8d4339bd48a844530db4919509a48be9db5e005d79d547ab8fa4
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