Instructions to use ve-nk-at/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 ve-nk-at/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, "ve-nk-at/stack_exc_multilabel_base_class_head") - Transformers
How to use ve-nk-at/stack_exc_multilabel_base_class_head with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ve-nk-at/stack_exc_multilabel_base_class_head", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ve-nk-at/stack_exc_multilabel_base_class_head: direct link, hf CLI and curl.
- Browser
- Download file 5.91 kB
-
https://huggingface.co/ve-nk-at/stack_exc_multilabel_base_class_head/resolve/main/training_args.bin
- Command line
-
hf download hf://ve-nk-at/stack_exc_multilabel_base_class_head/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ve-nk-at/stack_exc_multilabel_base_class_head/resolve/main/training_args.bin
5.91 kB
- Xet hash:
- 0a576665bfb91a0c8fd71b4c6f74304347e09448b618a7d143bc9cdef17a363b
- Size of remote file:
- 5.91 kB
- SHA256:
- d4bb8e2ee3202964b91665e7e4f6b316ca421ef5dd78c1ca86c9b69263842455
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