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_21.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_21.safetensors
- Command line
-
hf download hf://biohub/ESMC-300M-sae-k64-codebook16384/layer_21.safetensors
-
curl -L -o layer_21.safetensors https://huggingface.co/biohub/ESMC-300M-sae-k64-codebook16384/resolve/main/layer_21.safetensors
126 MB
- Xet hash:
- 6263f8624bb8f0bd4c33049206f500245e846d8cc2b378e06d8f72029e53fb45
- Size of remote file:
- 126 MB
- SHA256:
- 37fe621de6abc8e73cfa5fd367de1ebf8d90a3b96315a591a6e529b5521b9536
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.