Instructions to use Hugging-GK/stack_exc_multilabel_base_class_head with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Hugging-GK/stack_exc_multilabel_base_class_head with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("google/gemma-2-2b") model = PeftModel.from_pretrained(base_model, "Hugging-GK/stack_exc_multilabel_base_class_head") - Transformers
How to use Hugging-GK/stack_exc_multilabel_base_class_head with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Hugging-GK/stack_exc_multilabel_base_class_head", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 33081fb51eb90fef906bbb1b5b70c9c44fbc9e59f5bebb47945ea429a8649749
- Size of remote file:
- 665 MB
- SHA256:
- 1aea5392126810dfc84b61c5966397fd2ab8575f86954fa28e74b6ba77534071
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.