Feature Extraction
Transformers
English
esmc_sae
biology
esm
protein
sparse-autoencoder
interpretability
protein-embeddings
protein-language-model
unsupervised-learning
Instructions to use biohub/ESMC-6B-sae-k64-codebook16384 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-6B-sae-k64-codebook16384 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="biohub/ESMC-6B-sae-k64-codebook16384")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMC-6B-sae-k64-codebook16384", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e2647a405fbeb2073d80f873e45d9dc11f788547e95822d3ced97f90d6f58eb
- Size of remote file:
- 336 MB
- SHA256:
- f55fae5859b4ba400db228a1a73327c9310d201713d78619d0b5d0b4cb0880eb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.