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 optimizer.pt from ve-nk-at/stack_exc_multilabel_base_class_head: direct link, hf CLI and curl.
- Browser
- Download file 1.33 GB
-
https://huggingface.co/ve-nk-at/stack_exc_multilabel_base_class_head/resolve/main/optimizer.pt
- Command line
-
hf download hf://ve-nk-at/stack_exc_multilabel_base_class_head/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/ve-nk-at/stack_exc_multilabel_base_class_head/resolve/main/optimizer.pt
1.33 GB
- Xet hash:
- 5d3e53a595638cfa7ada28cd702b515f05234e68bcf1d57009e104d426d36097
- Size of remote file:
- 1.33 GB
- SHA256:
- a8f596b6105c75cc2ec9f59068bd674b80f36b95967e6aad2bb78f6fe906d794
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