Instructions to use jianqiang0213/mdeberta-v3-SA-binary-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jianqiang0213/mdeberta-v3-SA-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-SA-binary-lora") - Transformers
How to use jianqiang0213/mdeberta-v3-SA-binary-lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jianqiang0213/mdeberta-v3-SA-binary-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0339c50567b124a22d7560454c71a77abb58e3e2e6527983687a585dc3d72416
- Size of remote file:
- 5.91 kB
- SHA256:
- 2f006727732a8896e413a1fd8e7e9c140353436bbe083b297bb0e96c6d695b12
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