Instructions to use jianqiang0213/mdeberta-v3-BR-binary-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jianqiang0213/mdeberta-v3-BR-binary-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("microsoft/mdeberta-v3-base") model = PeftModel.from_pretrained(base_model, "jianqiang0213/mdeberta-v3-BR-binary-lora") - Transformers
How to use jianqiang0213/mdeberta-v3-BR-binary-lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jianqiang0213/mdeberta-v3-BR-binary-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06ae568f399d0c9947daf3077a615bb214cf877f5149fa82bb02e2a19e3e4ff0
- Size of remote file:
- 5.91 kB
- SHA256:
- caff2db59800c6b170bd07f2270a76893fdf5e5b1bc7313e6eb1bc5ccf44de87
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.