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-layer23-k128-codebook8192 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use biohub/ESMC-300M-sae-layer23-k128-codebook8192 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="biohub/ESMC-300M-sae-layer23-k128-codebook8192")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("biohub/ESMC-300M-sae-layer23-k128-codebook8192", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "available_layers": [ | |
| 23 | |
| ], | |
| "codebook_dim": 8192, | |
| "d_model": 960, | |
| "k": 128, | |
| "model_type": "esmc_sae", | |
| "transformers_version": "4.57.6" | |
| } | |