PEFT
PyTorch
English
esm
esm2
ESM-2
protein language model
LoRA
Low Rank Adaptation
biology
CAFA-5
protein function prediction
Instructions to use AmelieSchreiber/esm2_t6_8M_UR50D_cafa5_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AmelieSchreiber/esm2_t6_8M_UR50D_cafa5_lora with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("AmelieSchreiber/cafa_5_protein_function_prediction") model = PeftModel.from_pretrained(base_model, "AmelieSchreiber/esm2_t6_8M_UR50D_cafa5_lora") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e8368dfb68f9f56f355052dd94b953c3e7cafbaefd1c8e4164a3b89711993ee1
- Size of remote file:
- 38.9 MB
- SHA256:
- a484f225509f2031dd395d6eefe5a93ffb67e012071e11386fff64d7d205f539
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.