Instructions to use AmelieSchreiber/esm2_t12_35M_ptm_qlora_2100K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AmelieSchreiber/esm2_t12_35M_ptm_qlora_2100K with PEFT:
from peft import PeftModel from transformers import AutoModelForTokenClassification base_model = AutoModelForTokenClassification.from_pretrained("facebook/esm2_t12_35M_UR50D") model = PeftModel.from_pretrained(base_model, "AmelieSchreiber/esm2_t12_35M_ptm_qlora_2100K") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4ef6c6ab7be8aadc945671f8857ae2b9ae879344db76858e69bbb0d192352598
- Size of remote file:
- 4.16 kB
- SHA256:
- 93307a60032c8f7e795cecb4fb452cb73258770b6df20af8bae5ea2e6ecd2adb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.