Instructions to use gplsi/Constructive_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gplsi/Constructive_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="gplsi/Constructive_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("gplsi/Constructive_model") model = AutoModelForSequenceClassification.from_pretrained("gplsi/Constructive_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from gplsi/Constructive_model: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/gplsi/Constructive_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://gplsi/Constructive_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/gplsi/Constructive_model/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 7f5035cf89e00843bbf0c2d1062897decf61b0fda9bc07414f6a582656cfd0ae
- Size of remote file:
- 499 MB
- SHA256:
- 8a38d1e12af12487709eb15dec78bcc6548ed41541690be10749c09155397287
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