Instructions to use jianqiang0213/mdeberta-v3-MX-binary-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jianqiang0213/mdeberta-v3-MX-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-MX-binary-lora") - Transformers
How to use jianqiang0213/mdeberta-v3-MX-binary-lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jianqiang0213/mdeberta-v3-MX-binary-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 68813ecfd9478e3d76fe967e292251d9a2deb8ba854f3b5a56538cbd533702d8
- Size of remote file:
- 2.37 MB
- SHA256:
- 31cfb1f0c7084dc08635c77632d60cfe7bf760743deabaf0a7b31c81a3c98037
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