Instructions to use AmelieSchreiber/esm2_t30_150M_ptm_qlora_2100K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AmelieSchreiber/esm2_t30_150M_ptm_qlora_2100K with PEFT:
from peft import PeftModel from transformers import AutoModelForTokenClassification base_model = AutoModelForTokenClassification.from_pretrained("facebook/esm2_t30_150M_UR50D") model = PeftModel.from_pretrained(base_model, "AmelieSchreiber/esm2_t30_150M_ptm_qlora_2100K") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5e9aee86250175d53cf49c02bb18a8810925c7716e984a7ebf00f9d3cc2f277c
- Size of remote file:
- 1.97 MB
- SHA256:
- 3de72a937f0ccc95780f8d284deca1db8530886bfc0522f9699d5bce0fa5d044
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.