Feature Extraction
Transformers
Safetensors
English
esm
pharmacore
sparse
drug-discovery
apple-silicon
protein-language-model
esm2
bioinformatics
computational-biology
pruning
efficient-inference
Eval Results (legacy)
Instructions to use stephenjun8192/esm2-35m-sparse50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stephenjun8192/esm2-35m-sparse50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="stephenjun8192/esm2-35m-sparse50")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("stephenjun8192/esm2-35m-sparse50") model = AutoModel.from_pretrained("stephenjun8192/esm2-35m-sparse50") - Notebooks
- Google Colab
- Kaggle