Instructions to use AliSaadatV/LoRA_esm2_t33_650M_UR50D-finetunedv2-BINDING with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AliSaadatV/LoRA_esm2_t33_650M_UR50D-finetunedv2-BINDING with PEFT:
from peft import PeftModel from transformers import AutoModelForTokenClassification base_model = AutoModelForTokenClassification.from_pretrained("facebook/esm2_t33_650M_UR50D") model = PeftModel.from_pretrained(base_model, "AliSaadatV/LoRA_esm2_t33_650M_UR50D-finetunedv2-BINDING") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from AliSaadatV/LoRA_esm2_t33_650M_UR50D-finetunedv2-BINDING: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/AliSaadatV/LoRA_esm2_t33_650M_UR50D-finetunedv2-BINDING/resolve/main/training_args.bin
- Command line
-
hf download hf://AliSaadatV/LoRA_esm2_t33_650M_UR50D-finetunedv2-BINDING/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/AliSaadatV/LoRA_esm2_t33_650M_UR50D-finetunedv2-BINDING/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- e824d14b7faf9c4fa5945a591c1eea79a5321918242223d933decda87fe297c8
- Size of remote file:
- 5.37 kB
- SHA256:
- 9e848acd13bdfe280a11fcb3053bcbcb36b879a44b24a761ef3c519210a63e83
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.